<?xml version="1.0" encoding="UTF-8"?><metadata>
<Esri>
<CreaDate>20191023</CreaDate>
<CreaTime>14051600</CreaTime>
<ArcGISFormat>1.0</ArcGISFormat>
<SyncOnce>TRUE</SyncOnce>
<ModDate>20211203</ModDate>
<ModTime>102004</ModTime>
</Esri>
<dataIdInfo>
<idCitation>
<resTitle>GenerateServiceAreas</resTitle>
<date>
<createDate>20191023</createDate>
</date>
</idCitation>
<idAbs>
<para>Creates network service areas around facilities. A
network service area is a region that encompasses all streets that
can be accessed within a given distance or travel time from one or
more facilities. For instance, the 10-minute service area for a
facility includes all the streets that can be reached within 10
minutes from that facility.</para>
<para> Service areas are commonly used to visualize and measure
accessibility. For example, a three-minute drive-time polygon
around a grocery store can determine which residents can
reach the store within three minutes and are thus more likely to
shop there.</para>
</idAbs>
<descKeys KeyTypCd="005">
<keyTyp>
<keyTyp>005</keyTyp>
</keyTyp>
<keyword/>
</descKeys>
<searchKeys>
<keyword>Drivetime polygons</keyword>
<keyword>Drivetime areas</keyword>
<keyword>Service areas</keyword>
<keyword>Trade areas</keyword>
<keyword>Network buffer</keyword>
<keyword>Market areas</keyword>
<keyword>Sales territories</keyword>
</searchKeys>
<idCredit>Esri and its data vendors.</idCredit>
<resConst>
<Consts>
<useLimit>
<p>
This geoprocessing tool is available for users with an <a href="http://www.arcgis.com/features/plans/pricing.html"> ArcGIS Online organizational subscription</a>
or an <a href="https://developers.arcgis.com/pricing/">ArcGIS Developer account</a>
. To access this tool, you'll need to sign in with an account that is a member of an organizational subscription or a developer account. Each successful tool execution incurs <a href="http://links.esri.com/network-analysis-service-credits">service credits.</a>
</p>
<p>
If you don't have an account, you can sign up for a <a href="http://goto.arcgisonline.com/features/trial">free trial of ArcGIS</a>
or a <a href="http://goto.arcgisonline.com/developers/signup">free ArcGIS Developer account.</a>
</p>
</useLimit>
</Consts>
</resConst>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX">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</Data>
</Thumbnail>
</Binary>
</dataIdInfo>
<distInfo>
<distributor>
<distorFormat>
<formatName>ArcToolbox Tool</formatName>
</distorFormat>
</distributor>
</distInfo>
<mdDateSt>20191023</mdDateSt>
<mdContact>
<rpOrgName>Environmental Systems Research Institute, Inc. (Esri)</rpOrgName>
<rpCntInfo>
<cntAddress>
<delPoint>380 New York Street</delPoint>
<city>Redlands</city>
<adminArea>California</adminArea>
<postCode>92373-8100</postCode>
<eMailAdd>info@esri.com</eMailAdd>
<country>United States</country>
</cntAddress>
<cntPhone>
<voiceNum>909-793-2853</voiceNum>
<faxNum>909-793-5953</faxNum>
</cntPhone>
<cntOnlineRes>
<linkage>http://www.esri.com</linkage>
</cntOnlineRes>
</rpCntInfo>
<role>
<RoleCd>007</RoleCd>
</role>
</mdContact>
<tool displayname="GenerateServiceAreas" name="GenerateServiceAreas" softwarerestriction="none" toolboxalias="NetworkAnalysis">
<summary>
<para>Creates network service areas around facilities. A
network service area is a region that encompasses all streets that
can be accessed within a given distance or travel time from one or
more facilities. For instance, the 10-minute service area for a
facility includes all the streets that can be reached within 10
minutes from that facility.</para>
<para> Service areas are commonly used to visualize and measure
accessibility. For example, a three-minute drive-time polygon
around a grocery store can determine which residents can
reach the store within three minutes and are thus more likely to
shop there.</para>
</summary>
<alink_name>
GenerateServiceAreas
_naservice</alink_name>
<parameters>
<param datatype="Feature Set" direction="Input" displayname="Facilities" expression="Facilities" name="Facilities" sync="true" type="Required">
<pythonReference>
<para>The input locations around which service areas are generated. </para>
<para> You can load up to 1,000 facilities.</para>
<para> The facilities feature set has an associated attribute
table. The fields in the attribute table are described below.</para>
<para>ObjectID</para>
<para>The system-managed ID field.</para>
<para>Name</para>
<para>The name of the facility. If the name is not specified, a name
is automatically generated at solve time.</para>
<para>All fields from the input facilities are included in the output polygons when the Polygons for Multiple Facilities
parameter is set to Overlapping or Not Overlapping. The ObjectID field on the input facilities is transferred to the FacilityOID field on the output polygons. </para>
<para>Breaks</para>
<para>Specify the extent of service area to be calculated on a per facility basis. </para>
<para>This attribute allows you to specify a different service area break value for each facility. For example, with two facilities, you can generate 5- and 10-minute service area polygons for one facility and 6-, 9-, and 12-minute polygons for the other facility.</para>
<para>Separate multiple break values with a space, and specify the numeric values using the dot character as your decimal separator, even if the locale of your computer defines a different decimal separator. For example, the value 5.5 10 15.5 specifies three break values around a facility. </para>
<para>AdditionalTime</para>
<para>The amount of time spent at the facility, which reduces the extent of the service area calculated for the given facility. The default value is 0.</para>
<para>For example, when calculating service areas that represent fire station response times, AdditionalTime can store the turnout time, which is the time it takes a crew to put on the appropriate protective equipment and exit the fire station, for each fire station. Assume Fire Station 1 has a turnout time of 1 minute and Fire Station 2 has a turnout time of 3 minutes. If a 5-minute service area is calculated for both fire stations, the actual service area for Fire Station 1 is 4 minutes (since 1 of the 5 minutes is required as turnout time). Similarly, Fire Station 2 has a service area of only 2 minutes from the fire station. </para>
<para>AdditionalDistance</para>
<para>The extra distance traveled to reach the facility before the service is calculated. This attribute reduces the extent of the service area calculated for the given facility. The default value is 0.</para>
<para>Generally, the location of a facility, such as a store location, isn't exactly on the streets; it is set back somewhat from the road. This attribute value can be used to model the distance between the actual facility location and its location on the street, if it is important to include that distance when calculating the service areas for the facility. </para>
<para>AdditionalCost</para>
<para>The extra cost spent at the facility, which reduces the extent of the service area calculated for the given facility. The default value is 0. </para>
<para>Use this attribute value when the travel mode for the analysis uses an impedance attribute that is neither time based nor distance based The units for the attribute values are interpreted to be in unknown units. </para>
<para>CurbApproach</para>
<para>Specifies the direction a vehicle may arrive at and depart
from the facility. The field value is specified as one of the
following integers (use the numeric code, not the name in parentheses): </para>
<para>
<bulletList>
<bullet_item> 0 (Either side of vehicle)—The vehicle can approach and depart the facility in either direction, so a U-turn is allowed at the facility. This setting can be chosen if it is possible and practical for a vehicle to turn around at the facility. This decision may depend on the width of the road and the amount of traffic or whether the facility has a parking lot where vehicles can pull in and turn around.</bullet_item>
<bullet_item> 1 (Right side of vehicle)—When the vehicle approaches and departs the facility, the curb must be on the right side of the vehicle. A U-turn is prohibited. This is typically used for vehicles such as buses that must arrive with the bus stop on the right-hand side.</bullet_item>
<bullet_item> 2 (Left side of vehicle)—When the vehicle approaches and departs
the facility, the curb must be on the left side of the vehicle. A
U-turn is prohibited. This is typically used for vehicles such as buses that must arrive with the bus stop on the left-hand side.</bullet_item>
<bullet_item>3 (No U-Turn)—When
the vehicle approaches the facility, the curb can be on either side
of the vehicle; however, the vehicle must depart without turning
around.</bullet_item>
</bulletList>
</para>
<para>The CurbApproach property is designed to work with both types of national driving standards: right-hand traffic (United States) and left-hand traffic (United Kingdom). First, consider a facility on the left side of a vehicle. It is always on the left side regardless of whether the vehicle travels on the left or right half of the road. What may change with national driving standards is your decision to approach a facility from one of two directions; that is, so it ends up on the right or left side of the vehicle. For example, if you want to arrive at a facility and not have a lane of traffic between the vehicle and the facility, you would choose 1 (Right side of vehicle) in the United States and 2 (Left side of vehicle) in the United Kingdom.</para>
<para>Bearing</para>
<para>The direction in which a point is moving. The units are degrees and are measured clockwise from true north. This field is used in conjunction with the BearingTol field. </para>
<para>Bearing data is usually sent automatically from a mobile device equipped with a GPS receiver. Try to include bearing data if you are loading an input location that is moving, such as a pedestrian or a vehicle. </para>
<para>Using this field tends to prevent adding locations to the wrong edges, which can occur when a vehicle is near an intersection or an overpass for example. Bearing also helps the tool determine on which side of the street the point is. </para>
<para>For more information, see Bearing and BearingTol in the ArcGIS help system. </para>
<para>BearingTol</para>
<para>The bearing tolerance value creates a range of acceptable bearing values when locating moving points on an edge using the Bearing field. If the value from the Bearing field is within the range of acceptable values that are generated from the bearing tolerance on an edge, the point can be added as a network location there; otherwise, the closest point on the next-nearest edge is evaluated. </para>
<para>The units are in degrees, and the default value is 30. Values must be greater than 0 and less than 180. A value of 30 means that when ArcGIS Network Analyst extension attempts to add a network location on an edge, a range of acceptable bearing values is generated 15 degrees to either side of the edge (left and right) and in both digitized directions of the edge. </para>
<para>For more information, see Bearing and BearingTol in the ArcGIS help system. </para>
<para>NavLatency</para>
<para>This field is only used in the solve process if Bearing and BearingTol also have values; however, entering a NavLatency value is optional, even when values are present in Bearing and BearingTol. NavLatency indicates how much time is expected to elapse from the moment GPS information is sent from a moving vehicle to a server and the moment the processed route is received by the vehicle's navigation device. </para>
<para>The time units of NavLatency are the same as the units specified by the timeUnits property of the analysis object.</para>
</pythonReference>
<dialogReference>
<para>The input locations around which service areas are generated. </para>
<para> You can load up to 1,000 facilities.</para>
<para> The facilities feature set has an associated attribute
table. The fields in the attribute table are described below.</para>
<para>ObjectID</para>
<para>The system-managed ID field.</para>
<para>Name</para>
<para>The name of the facility. If the name is not specified, a name
is automatically generated at solve time.</para>
<para>All fields from the input facilities are included in the output polygons when the Polygons for Multiple Facilities
parameter is set to Overlapping or Not Overlapping. The ObjectID field on the input facilities is transferred to the FacilityOID field on the output polygons. </para>
<para>Breaks</para>
<para>Specify the extent of service area to be calculated on a per facility basis. </para>
<para>This attribute allows you to specify a different service area break value for each facility. For example, with two facilities, you can generate 5- and 10-minute service area polygons for one facility and 6-, 9-, and 12-minute polygons for the other facility.</para>
<para>Separate multiple break values with a space, and specify the numeric values using the dot character as your decimal separator, even if the locale of your computer defines a different decimal separator. For example, the value 5.5 10 15.5 specifies three break values around a facility. </para>
<para>AdditionalTime</para>
<para>The amount of time spent at the facility, which reduces the extent of the service area calculated for the given facility. The default value is 0.</para>
<para>For example, when calculating service areas that represent fire station response times, AdditionalTime can store the turnout time, which is the time it takes a crew to put on the appropriate protective equipment and exit the fire station, for each fire station. Assume Fire Station 1 has a turnout time of 1 minute and Fire Station 2 has a turnout time of 3 minutes. If a 5-minute service area is calculated for both fire stations, the actual service area for Fire Station 1 is 4 minutes (since 1 of the 5 minutes is required as turnout time). Similarly, Fire Station 2 has a service area of only 2 minutes from the fire station. </para>
<para>AdditionalDistance</para>
<para>The extra distance traveled to reach the facility before the service is calculated. This attribute reduces the extent of the service area calculated for the given facility. The default value is 0.</para>
<para>Generally, the location of a facility, such as a store location, isn't exactly on the streets; it is set back somewhat from the road. This attribute value can be used to model the distance between the actual facility location and its location on the street, if it is important to include that distance when calculating the service areas for the facility. </para>
<para>AdditionalCost</para>
<para>The extra cost spent at the facility, which reduces the extent of the service area calculated for the given facility. The default value is 0. </para>
<para>Use this attribute value when the travel mode for the analysis uses an impedance attribute that is neither time based nor distance based The units for the attribute values are interpreted to be in unknown units. </para>
<para>CurbApproach</para>
<para>Specifies the direction a vehicle may arrive at and depart
from the facility. The field value is specified as one of the
following integers (use the numeric code, not the name in parentheses): </para>
<para>
<bulletList>
<bullet_item> 0 (Either side of vehicle)—The vehicle can approach and depart the facility in either direction, so a U-turn is allowed at the facility. This setting can be chosen if it is possible and practical for a vehicle to turn around at the facility. This decision may depend on the width of the road and the amount of traffic or whether the facility has a parking lot where vehicles can pull in and turn around.</bullet_item>
<bullet_item> 1 (Right side of vehicle)—When the vehicle approaches and departs the facility, the curb must be on the right side of the vehicle. A U-turn is prohibited. This is typically used for vehicles such as buses that must arrive with the bus stop on the right-hand side.</bullet_item>
<bullet_item> 2 (Left side of vehicle)—When the vehicle approaches and departs
the facility, the curb must be on the left side of the vehicle. A
U-turn is prohibited. This is typically used for vehicles such as buses that must arrive with the bus stop on the left-hand side.</bullet_item>
<bullet_item>3 (No U-Turn)—When
the vehicle approaches the facility, the curb can be on either side
of the vehicle; however, the vehicle must depart without turning
around.</bullet_item>
</bulletList>
</para>
<para>The CurbApproach property is designed to work with both types of national driving standards: right-hand traffic (United States) and left-hand traffic (United Kingdom). First, consider a facility on the left side of a vehicle. It is always on the left side regardless of whether the vehicle travels on the left or right half of the road. What may change with national driving standards is your decision to approach a facility from one of two directions; that is, so it ends up on the right or left side of the vehicle. For example, if you want to arrive at a facility and not have a lane of traffic between the vehicle and the facility, you would choose 1 (Right side of vehicle) in the United States and 2 (Left side of vehicle) in the United Kingdom.</para>
<para>Bearing</para>
<para>The direction in which a point is moving. The units are degrees and are measured clockwise from true north. This field is used in conjunction with the BearingTol field. </para>
<para>Bearing data is usually sent automatically from a mobile device equipped with a GPS receiver. Try to include bearing data if you are loading an input location that is moving, such as a pedestrian or a vehicle. </para>
<para>Using this field tends to prevent adding locations to the wrong edges, which can occur when a vehicle is near an intersection or an overpass for example. Bearing also helps the tool determine on which side of the street the point is. </para>
<para>For more information, see Bearing and BearingTol in the ArcGIS help system. </para>
<para>BearingTol</para>
<para>The bearing tolerance value creates a range of acceptable bearing values when locating moving points on an edge using the Bearing field. If the value from the Bearing field is within the range of acceptable values that are generated from the bearing tolerance on an edge, the point can be added as a network location there; otherwise, the closest point on the next-nearest edge is evaluated. </para>
<para>The units are in degrees, and the default value is 30. Values must be greater than 0 and less than 180. A value of 30 means that when ArcGIS Network Analyst extension attempts to add a network location on an edge, a range of acceptable bearing values is generated 15 degrees to either side of the edge (left and right) and in both digitized directions of the edge. </para>
<para>For more information, see Bearing and BearingTol in the ArcGIS help system. </para>
<para>NavLatency</para>
<para>This field is only used in the solve process if Bearing and BearingTol also have values; however, entering a NavLatency value is optional, even when values are present in Bearing and BearingTol. NavLatency indicates how much time is expected to elapse from the moment GPS information is sent from a moving vehicle to a server and the moment the processed route is received by the vehicle's navigation device. </para>
<para>The time units of NavLatency are the same as the units specified by the timeUnits property of the analysis object.</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Break Values" expression="Break_Values" name="Break_Values" sync="true" type="Required">
<pythonReference>
<para> Specifies the size and number of service area polygons to
generate for each facility. The units are determined by the Break
Units value.</para>
<para> Multiple polygon breaks can be set to create concentric
service areas per facility. For instance, to find 2-, 3-, and 5-mile service areas for each facility, type 2 3 5, separating the
values with a space, and set Break Units to Miles. There is no limit to the number of break values you specify. </para>
<para>The size of the maximum break value can't exceed the equivalent of 300 minutes or 300 miles (482.80 kilometers). When generating detailed polygons, the maximum service-area size is limited to 15 minutes and 15 miles (24.14 kilometers).</para>
</pythonReference>
<dialogReference>
<para> Specifies the size and number of service area polygons to
generate for each facility. The units are determined by the Break
Units value.</para>
<para> Multiple polygon breaks can be set to create concentric
service areas per facility. For instance, to find 2-, 3-, and 5-mile service areas for each facility, type 2 3 5, separating the
values with a space, and set Break Units to Miles. There is no limit to the number of break values you specify. </para>
<para>The size of the maximum break value can't exceed the equivalent of 300 minutes or 300 miles (482.80 kilometers). When generating detailed polygons, the maximum service-area size is limited to 15 minutes and 15 miles (24.14 kilometers).</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Break Units" expression="Minutes | Meters | Kilometers | Feet | Yards | Miles | NauticalMiles | Seconds | Hours | Days | Other" name="Break_Units" sync="true" type="Required">
<pythonReference>
<para> The unit for the Break Values parameter.
</para>
<para>The units you choose for this parameter determine whether the tool will create service areas by measuring driving distance or driving time. Choose a time unit to measure driving time. To measure driving distance, choose a distance unit. Your choice also determines in which units the tool will report total driving time or distance in the results. </para>
<para>
The choices are: <bulletList>
<bullet_item>Meters</bullet_item>
<bullet_item>Kilometers</bullet_item>
<bullet_item>Feet</bullet_item>
<bullet_item>Yards</bullet_item>
<bullet_item>Miles</bullet_item>
<bullet_item>NauticalMiles</bullet_item>
<bullet_item>Seconds</bullet_item>
<bullet_item>Minutes</bullet_item>
<bullet_item>Hours</bullet_item>
<bullet_item>Days</bullet_item>
</bulletList>
</para>
</pythonReference>
<dialogReference>
<para> The unit for the Break Values parameter.
</para>
<para>The units you choose for this parameter determine whether the tool will create service areas by measuring driving distance or driving time. Choose a time unit to measure driving time. To measure driving distance, choose a distance unit. Your choice also determines in which units the tool will report total driving time or distance in the results. </para>
<para>
The choices are: <bulletList>
<bullet_item>Meters</bullet_item>
<bullet_item>Kilometers</bullet_item>
<bullet_item>Feet</bullet_item>
<bullet_item>Yards</bullet_item>
<bullet_item>Miles</bullet_item>
<bullet_item>NauticalMiles</bullet_item>
<bullet_item>Seconds</bullet_item>
<bullet_item>Minutes</bullet_item>
<bullet_item>Hours</bullet_item>
<bullet_item>Days</bullet_item>
</bulletList>
</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Analysis Region" expression="{routing_1}" name="Analysis_Region" sync="true" type="Optional">
<pythonReference>
<para>The region in which to perform the analysis. If a value is not specified for this parameter, the tool
will automatically calculate the region name based on the location
of the input points. Setting the name of the region is required only if the automatic detection of the region name is not accurate for your inputs.</para>
<para>
To specify a region, use one of
the following values:
<bulletList>
<bullet_item> Europe</bullet_item>
<bullet_item> Japan</bullet_item>
<bullet_item>Korea</bullet_item>
<bullet_item> MiddleEastAndAfrica</bullet_item>
<bullet_item> NorthAmerica</bullet_item>
<bullet_item> SouthAmerica</bullet_item>
<bullet_item>SouthAsia</bullet_item>
<bullet_item>Thailand</bullet_item>
</bulletList>
</para>
<para>
The following region names are no longer supported and will be removed in future releases. If you specify one of the deprecated region names, the tool automatically assigns a supported region name for your region.
<bulletList>
<bullet_item>Greece redirects to Europe</bullet_item>
<bullet_item>India redirects to SouthAsia</bullet_item>
<bullet_item>Oceania redirects to SouthAsia</bullet_item>
<bullet_item>SouthEastAsia redirects to SouthAsia</bullet_item>
<bullet_item>Taiwan redirects to SouthAsia</bullet_item>
</bulletList>
</para>
</pythonReference>
<dialogReference>
<para>The region in which to perform the analysis. If a value is not specified for this parameter, the tool
will automatically calculate the region name based on the location
of the input points. Setting the name of the region is required only if the automatic detection of the region name is not accurate for your inputs.</para>
<para>
To specify a region, use one of
the following values:
<bulletList>
<bullet_item> Europe</bullet_item>
<bullet_item> Japan</bullet_item>
<bullet_item>Korea</bullet_item>
<bullet_item> MiddleEastAndAfrica</bullet_item>
<bullet_item> NorthAmerica</bullet_item>
<bullet_item> SouthAmerica</bullet_item>
<bullet_item>SouthAsia</bullet_item>
<bullet_item>Thailand</bullet_item>
</bulletList>
</para>
<para>
The following region names are no longer supported and will be removed in future releases. If you specify one of the deprecated region names, the tool automatically assigns a supported region name for your region.
<bulletList>
<bullet_item>Greece redirects to Europe</bullet_item>
<bullet_item>India redirects to SouthAsia</bullet_item>
<bullet_item>Oceania redirects to SouthAsia</bullet_item>
<bullet_item>SouthEastAsia redirects to SouthAsia</bullet_item>
<bullet_item>Taiwan redirects to SouthAsia</bullet_item>
</bulletList>
</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Travel Direction" expression="{Away From Facility | Towards Facility}" name="Travel_Direction" sync="true" type="Optional">
<pythonReference>
<para> Specifies whether the direction of travel used to
generate the service area polygons is toward or away from the
facilities.
</para>
<para>
<bulletList>
<bullet_item> Away From Facility—The service area is generated in the
direction away from the facilities.</bullet_item>
<bullet_item> Towards Facility—The service area is created in the
direction towards the facilities.</bullet_item>
</bulletList>
</para>
<para> The direction of travel may change the shape of the
polygons because impedances on opposite sides of streets may differ
or one-way restrictions may exist, such as one-way streets. The
direction you should choose depends on the nature of your service
area analysis. The service area for a pizza delivery store, for
example, should be created away from the facility, whereas the
service area of a hospital should be created toward the
facility.</para>
</pythonReference>
<dialogReference>
<para> Specifies whether the direction of travel used to
generate the service area polygons is toward or away from the
facilities.
</para>
<para>
<bulletList>
<bullet_item> Away From Facility—The service area is generated in the
direction away from the facilities.</bullet_item>
<bullet_item> Towards Facility—The service area is created in the
direction towards the facilities.</bullet_item>
</bulletList>
</para>
<para> The direction of travel may change the shape of the
polygons because impedances on opposite sides of streets may differ
or one-way restrictions may exist, such as one-way streets. The
direction you should choose depends on the nature of your service
area analysis. The service area for a pizza delivery store, for
example, should be created away from the facility, whereas the
service area of a hospital should be created toward the
facility.</para>
</dialogReference>
</param>
<param datatype="Date" direction="Input" displayname="Time of Day" expression="{Time_of_Day}" name="Time_of_Day" sync="true" type="Optional">
<pythonReference>
<para> The time to depart from or arrive at the facilities. The
interpretation of this value depends on whether travel is toward or
away from the facilities.
</para>
<para>
<bulletList>
<bullet_item> It represents the departure time if Travel Direction is
set to Away from Facility.</bullet_item>
<bullet_item> It represents the arrival time if Travel Direction is set
to Toward Facility.</bullet_item>
</bulletList>
</para>
<para>You can use the Time Zone for Time of Day parameter to specify whether this time and date refers to UTC or the time zone in which the facility is located.</para>
<para> Repeatedly solving the same analysis, but using different
Time of Day values, allows you to see how a facility's reach
changes over time. For instance, the five-minute service area
around a fire station may start out large in the early morning,
diminish during the morning rush hour, grow in the late morning,
and so on, throughout the day.</para>
</pythonReference>
<dialogReference>
<para> The time to depart from or arrive at the facilities. The
interpretation of this value depends on whether travel is toward or
away from the facilities.
</para>
<para>
<bulletList>
<bullet_item> It represents the departure time if Travel Direction is
set to Away from Facility.</bullet_item>
<bullet_item> It represents the arrival time if Travel Direction is set
to Toward Facility.</bullet_item>
</bulletList>
</para>
<para>You can use the Time Zone for Time of Day parameter to specify whether this time and date refers to UTC or the time zone in which the facility is located.</para>
<para> Repeatedly solving the same analysis, but using different
Time of Day values, allows you to see how a facility's reach
changes over time. For instance, the five-minute service area
around a fire station may start out large in the early morning,
diminish during the morning rush hour, grow in the late morning,
and so on, throughout the day.</para>
</dialogReference>
</param>
<param datatype="Boolean" direction="Input" displayname="Use Hierarchy" expression="{Use_Hierarchy}" name="Use_Hierarchy" sync="true" type="Optional">
<pythonReference>
<para>Specify whether hierarchy should be used when finding the best
route between the facility and the incident.
</para>
<para>
<bulletList>
<bullet_item>Checked (True)—Use the hierarchy attribute for the analysis. Using a hierarchy results in the solver preferring higher-order edges to lower-order edges. Hierarchical solves are faster, and they can be used to simulate the preference of a driver who chooses to travel on freeways over local roads when possible—even if that means a longer trip.</bullet_item>
<bullet_item> Unchecked (False)—Do not use the hierarchy attribute for the analysis. Not using a hierarchy yields an accurate service area measured along all edges of the network dataset regardless of hierarchy level.</bullet_item>
</bulletList>
</para>
<para>Regardless of whether the Use Hierarchy parameter is checked (True), hierarchy is always used when the largest break value exceeds 240 minutes or 240 miles (386.24 kilometers).</para>
</pythonReference>
<dialogReference>
<para>Specify whether hierarchy should be used when finding the best
route between the facility and the incident.
</para>
<para>
<bulletList>
<bullet_item>Checked (True)—Use the hierarchy attribute for the analysis. Using a hierarchy results in the solver preferring higher-order edges to lower-order edges. Hierarchical solves are faster, and they can be used to simulate the preference of a driver who chooses to travel on freeways over local roads when possible—even if that means a longer trip.</bullet_item>
<bullet_item> Unchecked (False)—Do not use the hierarchy attribute for the analysis. Not using a hierarchy yields an accurate service area measured along all edges of the network dataset regardless of hierarchy level.</bullet_item>
</bulletList>
</para>
<para>Regardless of whether the Use Hierarchy parameter is checked (True), hierarchy is always used when the largest break value exceeds 240 minutes or 240 miles (386.24 kilometers).</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="UTurn at Junctions" expression="{Allowed | Not Allowed | Allowed Only at Dead Ends | Allowed Only at Intersections and Dead Ends}" name="UTurn_at_Junctions" sync="true" type="Optional">
<pythonReference>
<para>Use this parameter to restrict or permit the service area to make U-turns at junctions. In order to understand the parameter values, consider for a moment the following terminology: a junction is a point where a street segment ends and potentially connects to one or more other segments; a pseudo-junction is a point where exactly two streets connect to one another; an intersection is a point where three or more streets connect; and a dead-end is where one street segment ends without connecting to another. Given this information, the parameter can have the following values: </para>
<para>
<bulletList>
<bullet_item> Allowed—U-turns are permitted everywhere. Allowing
U-turns implies that the vehicle can turn around at any junction and
double back on the same street. This is the default value.</bullet_item>
<bullet_item> Not Allowed—U-turns are prohibited at all junctions: pseudo-junctions, intersections, and dead-ends.</bullet_item>
<bullet_item> Allowed only at Dead Ends—U-turns are prohibited at all
junctions, except those that have only one connected street feature (a dead
end).</bullet_item>
<bullet_item> Allowed only at Intersections and Dead Ends—U-turns are prohibited at
pseudo-junctions where exactly two adjacent streets meet, but U-turns are permitted
at intersections and dead ends. This prevents turning around in the middle of the road where one length of road happened to be digitized as two street features.</bullet_item>
</bulletList>
</para>
</pythonReference>
<dialogReference>
<para>Use this parameter to restrict or permit the service area to make U-turns at junctions. In order to understand the parameter values, consider for a moment the following terminology: a junction is a point where a street segment ends and potentially connects to one or more other segments; a pseudo-junction is a point where exactly two streets connect to one another; an intersection is a point where three or more streets connect; and a dead-end is where one street segment ends without connecting to another. Given this information, the parameter can have the following values: </para>
<para>
<bulletList>
<bullet_item> Allowed—U-turns are permitted everywhere. Allowing
U-turns implies that the vehicle can turn around at any junction and
double back on the same street. This is the default value.</bullet_item>
<bullet_item> Not Allowed—U-turns are prohibited at all junctions: pseudo-junctions, intersections, and dead-ends.</bullet_item>
<bullet_item> Allowed only at Dead Ends—U-turns are prohibited at all
junctions, except those that have only one connected street feature (a dead
end).</bullet_item>
<bullet_item> Allowed only at Intersections and Dead Ends—U-turns are prohibited at
pseudo-junctions where exactly two adjacent streets meet, but U-turns are permitted
at intersections and dead ends. This prevents turning around in the middle of the road where one length of road happened to be digitized as two street features.</bullet_item>
</bulletList>
</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Geometry at Overlaps" expression="{Overlapping | Not Overlapping | Merge by Break Value}" name="Polygons_for_Multiple_Facilities" sync="true" type="Optional">
<pythonReference>
<para> Choose how service area polygons are generated when
multiple facilities are present in the analysis.
</para>
<para>
<bulletList>
<bullet_item> Overlapping—Creates individual polygons for each facility.
The polygons can overlap each other. This is the default value.</bullet_item>
<bullet_item> Not Overlapping—Creates individual polygons such that a
polygon from one facility cannot overlap polygons from other
facilities; furthermore, any portion of the network can only be
covered by the service area of the nearest facility.</bullet_item>
<bullet_item> Merge by Break Value—Creates and joins the polygons of
different facilities that have the same break value.</bullet_item>
</bulletList>
</para>
<para>When using Overlapping or Not Overlapping, all fields from the input facilities are included in the output polygons, with the exception that values from the input ObjectID field are transferred to the FacilityOID field of the output polygons. The FacilityOID field is null when merging by break value, and the input fields are not included in the output.</para>
</pythonReference>
<dialogReference>
<para> Choose how service area polygons are generated when
multiple facilities are present in the analysis.
</para>
<para>
<bulletList>
<bullet_item> Overlapping—Creates individual polygons for each facility.
The polygons can overlap each other. This is the default value.</bullet_item>
<bullet_item> Not Overlapping—Creates individual polygons such that a
polygon from one facility cannot overlap polygons from other
facilities; furthermore, any portion of the network can only be
covered by the service area of the nearest facility.</bullet_item>
<bullet_item> Merge by Break Value—Creates and joins the polygons of
different facilities that have the same break value.</bullet_item>
</bulletList>
</para>
<para>When using Overlapping or Not Overlapping, all fields from the input facilities are included in the output polygons, with the exception that values from the input ObjectID field are transferred to the FacilityOID field of the output polygons. The FacilityOID field is null when merging by break value, and the input fields are not included in the output.</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Geomtery at Cutoffs" expression="{Rings | Disks}" name="Polygon_Overlap_Type" sync="true" type="Optional">
<pythonReference>
<para> Specifies the option to create concentric service area
polygons as disks or rings. This option is applicable only when
multiple break values are specified for the facilities.
</para>
<para>
<bulletList>
<bullet_item> Rings—The polygons representing larger breaks exclude the polygons of smaller breaks.
This creates polygons going between consecutive breaks. Use this
option if you want to find the area from one break to another. For
instance, if you create 5- and 10-minute service areas, then the
10-minute service area polygon will exclude the area under the
5-minute service area polygon. This is the default value.</bullet_item>
<bullet_item> Disks—Creates polygons going from the facility to the
break. For instance, if you create 5- and 10-minute service areas,
then the 10-minute service area polygon will include the area under
the 5-minute service area polygon.</bullet_item>
</bulletList>
</para>
</pythonReference>
<dialogReference>
<para> Specifies the option to create concentric service area
polygons as disks or rings. This option is applicable only when
multiple break values are specified for the facilities.
</para>
<para>
<bulletList>
<bullet_item> Rings—The polygons representing larger breaks exclude the polygons of smaller breaks.
This creates polygons going between consecutive breaks. Use this
option if you want to find the area from one break to another. For
instance, if you create 5- and 10-minute service areas, then the
10-minute service area polygon will exclude the area under the
5-minute service area polygon. This is the default value.</bullet_item>
<bullet_item> Disks—Creates polygons going from the facility to the
break. For instance, if you create 5- and 10-minute service areas,
then the 10-minute service area polygon will include the area under
the 5-minute service area polygon.</bullet_item>
</bulletList>
</para>
</dialogReference>
</param>
<param datatype="Boolean" direction="Input" displayname="Detailed Polygons" expression="{Detailed_Polygons}" name="Detailed_Polygons" sync="true" type="Optional">
<pythonReference>
<para>Use of this parameter is no longer recommended. If you want to generate detailed polygons, set the Polygon Detail parameter value to High.</para>
<para> Specifies the option to create detailed or generalized
polygons.
</para>
<para>
<bulletList>
<bullet_item> Unchecked (False)—Creates generalized polygons, which are
generated quickly and are fairly accurate. This is the
default.</bullet_item>
<bullet_item> Checked (True)—Creates detailed polygons, which
accurately model the service area lines and may contain islands of
unreached areas. This option is much slower than generating
generalized polygons. This option isn't supported when using
hierarchy.</bullet_item>
</bulletList>
</para>
<para>The tool supports generating detailed polygons only if the largest
value specified in the Break Values parameter is less than or equal to 15
minutes or 15 miles (24.14 kilometers).</para>
</pythonReference>
<dialogReference>
<para>Use of this parameter is no longer recommended. If you want to generate detailed polygons, set the Polygon Detail parameter value to High.</para>
<para> Specifies the option to create detailed or generalized
polygons.
</para>
<para>
<bulletList>
<bullet_item> Unchecked (False)—Creates generalized polygons, which are
generated quickly and are fairly accurate. This is the
default.</bullet_item>
<bullet_item> Checked (True)—Creates detailed polygons, which
accurately model the service area lines and may contain islands of
unreached areas. This option is much slower than generating
generalized polygons. This option isn't supported when using
hierarchy.</bullet_item>
</bulletList>
</para>
<para>The tool supports generating detailed polygons only if the largest
value specified in the Break Values parameter is less than or equal to 15
minutes or 15 miles (24.14 kilometers).</para>
</dialogReference>
</param>
<param datatype="Linear Unit" direction="Input" displayname="Polygon Trim Distance" expression="{Polygon_Trim_Distance}" name="Polygon_Trim_Distance" sync="true" type="Optional">
<pythonReference>
<para> Specifies the distance within which the service area
polygons are trimmed. This is useful when finding service areas in
places that have a sparse street network and you don't want the
service area to cover large areas where there are no street
features.</para>
<para> The default value is 100 meters. No value or a value of 0 for this parameter
specifies that the service area polygons should not be trimmed. This
parameter value is ignored when using hierarchy.</para>
</pythonReference>
<dialogReference>
<para> Specifies the distance within which the service area
polygons are trimmed. This is useful when finding service areas in
places that have a sparse street network and you don't want the
service area to cover large areas where there are no street
features.</para>
<para> The default value is 100 meters. No value or a value of 0 for this parameter
specifies that the service area polygons should not be trimmed. This
parameter value is ignored when using hierarchy.</para>
</dialogReference>
</param>
<param datatype="Linear Unit" direction="Input" displayname="Polygon Simplification Tolerance" expression="{Polygon_Simplification_Tolerance}" name="Polygon_Simplification_Tolerance" sync="true" type="Optional">
<pythonReference>
<para> Specify by how much you want to simplify the polygon
geometry.</para>
<para> Simplification maintains critical vertices of a
polygon to define its essential shape and removes other vertices. The
simplification distance you specify is the maximum offset
the simplified polygon boundaries can deviate from the original polygon boundaries.
Simplifying a polygon reduces the number of vertices and tends to
reduce drawing times.</para>
</pythonReference>
<dialogReference>
<para> Specify by how much you want to simplify the polygon
geometry.</para>
<para> Simplification maintains critical vertices of a
polygon to define its essential shape and removes other vertices. The
simplification distance you specify is the maximum offset
the simplified polygon boundaries can deviate from the original polygon boundaries.
Simplifying a polygon reduces the number of vertices and tends to
reduce drawing times.</para>
</dialogReference>
</param>
<param datatype="Feature Set" direction="Input" displayname="Point Barriers" expression="{Point_Barriers}" name="Point_Barriers" sync="true" type="Optional">
<pythonReference>
<para>One or more points that will act as temporary
restrictions or represent additional time or distance that may be
required to travel on the underlying streets. For example, a point
barrier can be used to represent a fallen tree along a street or
time delay spent at a railroad crossing.</para>
<para> The tool imposes a limit of 250 points that can be added
as barriers.</para>
<para>When specifying point barriers, you can set properties for each, such as its name or barrier type, using the following attributes:</para>
<para>
Name</para>
<para> The name of the barrier.</para>
<para> BarrierType </para>
<para>Specifies whether the point barrier restricts travel
completely or adds time or distance when it is crossed. The value
for this attribute is specified as one of the following
integers (use the numeric code, not the name in parentheses):</para>
<para>
<bulletList>
<bullet_item> 0 (Restriction)—Prohibits travel through the barrier. The barrier
is referred to as a restriction point barrier since it acts as a
restriction.</bullet_item>
<bullet_item> 2 (Added Cost)—Traveling through the barrier increases the travel
time or distance by the amount specified in the
Additional_Time, Additional_Distance, or Additional_Cost field. This barrier type is
referred to as an added-cost point barrier.</bullet_item>
</bulletList>
</para>
<para> Additional_Time </para>
<para>The added travel time when the
barrier is traversed. This field is applicable only for added-cost
barriers and only if the travel mode used for the analysis uses an impedance attribute that is time based. </para>
<para>This field
value must be greater than or equal to zero, and its units are the same as those specified in the
Measurement Units parameter.</para>
<para> Additional_Distance</para>
<para>The added distance when the
barrier is traversed. This field is applicable only for added-cost
barriers and only if the travel mode used for the analysis uses an impedance attribute that is distance based. </para>
<para>The field value
must be greater than or equal to zero, and its units are the same as those specified in the
Measurement Units parameter.</para>
<para>Additional_Cost</para>
<para>The added cost when the
barrier is traversed. This field is applicable only for added-cost
barriers and only if the travel mode used for the analysis uses an impedance attribute that is neither time based nor distance based. </para>
<para>FullEdge</para>
<para>Specifies how the restriction point barriers are applied to the edge elements during the analysis. The field value is specified as one of the following integers (use the numeric code, not the name in parentheses): </para>
<para>
<bulletList>
<bullet_item>0 (False)—Permits travel on the edge up to the barrier but not through it. This is the default value.</bullet_item>
<bullet_item>1 (True)—Restricts travel anywhere on the associated edge.</bullet_item>
</bulletList>
</para>
<para> CurbApproach</para>
<para>Specifies the direction of traffic that is affected by the barrier. The field value is specified as one of the following integers (use the numeric code, not the name in parentheses): </para>
<para>
<bulletList>
<bullet_item>0 (Either side of vehicle)—The barrier affects travel over the edge in both directions.</bullet_item>
<bullet_item>1 (Right side of vehicle)—Vehicles are only affected if the barrier is on their right side during the approach. Vehicles that traverse the same edge but approach the barrier on their left side are not affected by the barrier. </bullet_item>
<bullet_item>2 (Left side of vehicle)—Vehicles are only affected if the barrier is on their left side during the approach. Vehicles that traverse the same edge but approach the barrier on their right side are not affected by the barrier. </bullet_item>
</bulletList>
</para>
<para>Because junctions are points and don't have a side, barriers on junctions affect all vehicles regardless of the curb approach. </para>
<para>The CurbApproach attribute is designed to work with both types of national driving standards: right-hand traffic (United States) and left-hand traffic (United Kingdom). First, consider a facility on the left side of a vehicle. It is always on the left side regardless of whether the vehicle travels on the left or right half of the road. What may change with national driving standards is your decision to approach a facility from one of two directions, that is, so it ends up on the right or left side of the vehicle. For example, if you want to arrive at a facility and not have a lane of traffic between the vehicle and the facility, you would choose 1 (Right side of vehicle) in the United States and 2 (Left side of vehicle) in the United Kingdom.</para>
<para>Bearing</para>
<para>The direction in which a point is moving. The units are degrees and are measured clockwise from true north. This field is used in conjunction with the BearingTol field. </para>
<para>Bearing data is usually sent automatically from a mobile device equipped with a GPS receiver. Try to include bearing data if you are loading an input location that is moving, such as a pedestrian or a vehicle. </para>
<para>Using this field tends to prevent adding locations to the wrong edges, which can occur when a vehicle is near an intersection or an overpass for example. Bearing also helps the tool determine on which side of the street the point is. </para>
<para>For more information, see Bearing and BearingTol in the ArcGIS help system. </para>
<para>BearingTol</para>
<para>The bearing tolerance value creates a range of acceptable bearing values when locating moving points on an edge using the Bearing field. If the value from the Bearing field is within the range of acceptable values that are generated from the bearing tolerance on an edge, the point can be added as a network location there; otherwise, the closest point on the next-nearest edge is evaluated. </para>
<para>The units are in degrees, and the default value is 30. Values must be greater than 0 and less than 180. A value of 30 means that when ArcGIS Network Analyst extension attempts to add a network location on an edge, a range of acceptable bearing values is generated 15 degrees to either side of the edge (left and right) and in both digitized directions of the edge. </para>
<para>For more information, see Bearing and BearingTol in the ArcGIS help system. </para>
<para>NavLatency</para>
<para>This field is only used in the solve process if Bearing and BearingTol also have values; however, entering a NavLatency value is optional, even when values are present in Bearing and BearingTol. NavLatency indicates how much time is expected to elapse from the moment GPS information is sent from a moving vehicle to a server and the moment the processed route is received by the vehicle's navigation device. </para>
<para>The time units of NavLatency are the same as the units specified by the timeUnits property of the analysis object.</para>
</pythonReference>
<dialogReference>
<para>One or more points that will act as temporary
restrictions or represent additional time or distance that may be
required to travel on the underlying streets. For example, a point
barrier can be used to represent a fallen tree along a street or
time delay spent at a railroad crossing.</para>
<para> The tool imposes a limit of 250 points that can be added
as barriers.</para>
<para>When specifying point barriers, you can set properties for each, such as its name or barrier type, using the following attributes:</para>
<para>
Name</para>
<para> The name of the barrier.</para>
<para> BarrierType </para>
<para>Specifies whether the point barrier restricts travel
completely or adds time or distance when it is crossed. The value
for this attribute is specified as one of the following
integers (use the numeric code, not the name in parentheses):</para>
<para>
<bulletList>
<bullet_item> 0 (Restriction)—Prohibits travel through the barrier. The barrier
is referred to as a restriction point barrier since it acts as a
restriction.</bullet_item>
<bullet_item> 2 (Added Cost)—Traveling through the barrier increases the travel
time or distance by the amount specified in the
Additional_Time, Additional_Distance, or Additional_Cost field. This barrier type is
referred to as an added-cost point barrier.</bullet_item>
</bulletList>
</para>
<para> Additional_Time </para>
<para>The added travel time when the
barrier is traversed. This field is applicable only for added-cost
barriers and only if the travel mode used for the analysis uses an impedance attribute that is time based. </para>
<para>This field
value must be greater than or equal to zero, and its units are the same as those specified in the
Measurement Units parameter.</para>
<para> Additional_Distance</para>
<para>The added distance when the
barrier is traversed. This field is applicable only for added-cost
barriers and only if the travel mode used for the analysis uses an impedance attribute that is distance based. </para>
<para>The field value
must be greater than or equal to zero, and its units are the same as those specified in the
Measurement Units parameter.</para>
<para>Additional_Cost</para>
<para>The added cost when the
barrier is traversed. This field is applicable only for added-cost
barriers and only if the travel mode used for the analysis uses an impedance attribute that is neither time based nor distance based. </para>
<para>FullEdge</para>
<para>Specifies how the restriction point barriers are applied to the edge elements during the analysis. The field value is specified as one of the following integers (use the numeric code, not the name in parentheses): </para>
<para>
<bulletList>
<bullet_item>0 (False)—Permits travel on the edge up to the barrier but not through it. This is the default value.</bullet_item>
<bullet_item>1 (True)—Restricts travel anywhere on the associated edge.</bullet_item>
</bulletList>
</para>
<para> CurbApproach</para>
<para>Specifies the direction of traffic that is affected by the barrier. The field value is specified as one of the following integers (use the numeric code, not the name in parentheses): </para>
<para>
<bulletList>
<bullet_item>0 (Either side of vehicle)—The barrier affects travel over the edge in both directions.</bullet_item>
<bullet_item>1 (Right side of vehicle)—Vehicles are only affected if the barrier is on their right side during the approach. Vehicles that traverse the same edge but approach the barrier on their left side are not affected by the barrier. </bullet_item>
<bullet_item>2 (Left side of vehicle)—Vehicles are only affected if the barrier is on their left side during the approach. Vehicles that traverse the same edge but approach the barrier on their right side are not affected by the barrier. </bullet_item>
</bulletList>
</para>
<para>Because junctions are points and don't have a side, barriers on junctions affect all vehicles regardless of the curb approach. </para>
<para>The CurbApproach attribute is designed to work with both types of national driving standards: right-hand traffic (United States) and left-hand traffic (United Kingdom). First, consider a facility on the left side of a vehicle. It is always on the left side regardless of whether the vehicle travels on the left or right half of the road. What may change with national driving standards is your decision to approach a facility from one of two directions, that is, so it ends up on the right or left side of the vehicle. For example, if you want to arrive at a facility and not have a lane of traffic between the vehicle and the facility, you would choose 1 (Right side of vehicle) in the United States and 2 (Left side of vehicle) in the United Kingdom.</para>
<para>Bearing</para>
<para>The direction in which a point is moving. The units are degrees and are measured clockwise from true north. This field is used in conjunction with the BearingTol field. </para>
<para>Bearing data is usually sent automatically from a mobile device equipped with a GPS receiver. Try to include bearing data if you are loading an input location that is moving, such as a pedestrian or a vehicle. </para>
<para>Using this field tends to prevent adding locations to the wrong edges, which can occur when a vehicle is near an intersection or an overpass for example. Bearing also helps the tool determine on which side of the street the point is. </para>
<para>For more information, see Bearing and BearingTol in the ArcGIS help system. </para>
<para>BearingTol</para>
<para>The bearing tolerance value creates a range of acceptable bearing values when locating moving points on an edge using the Bearing field. If the value from the Bearing field is within the range of acceptable values that are generated from the bearing tolerance on an edge, the point can be added as a network location there; otherwise, the closest point on the next-nearest edge is evaluated. </para>
<para>The units are in degrees, and the default value is 30. Values must be greater than 0 and less than 180. A value of 30 means that when ArcGIS Network Analyst extension attempts to add a network location on an edge, a range of acceptable bearing values is generated 15 degrees to either side of the edge (left and right) and in both digitized directions of the edge. </para>
<para>For more information, see Bearing and BearingTol in the ArcGIS help system. </para>
<para>NavLatency</para>
<para>This field is only used in the solve process if Bearing and BearingTol also have values; however, entering a NavLatency value is optional, even when values are present in Bearing and BearingTol. NavLatency indicates how much time is expected to elapse from the moment GPS information is sent from a moving vehicle to a server and the moment the processed route is received by the vehicle's navigation device. </para>
<para>The time units of NavLatency are the same as the units specified by the timeUnits property of the analysis object.</para>
</dialogReference>
</param>
<param datatype="Feature Set" direction="Input" displayname="Line Barriers" expression="{Line_Barriers}" name="Line_Barriers" sync="true" type="Optional">
<pythonReference>
<para>One or more lines that prohibit travel anywhere
the lines intersect the streets. For example, a parade or protest
that blocks traffic across several street segments can be modeled
with a line barrier. A line barrier can also quickly fence off
several roads from being traversed, thereby channeling possible
routes away from undesirable parts of the street
network.</para>
<para> The tool imposes a limit on the number of streets you can
restrict using the Line Barriers parameter. While there is no limit to
the number of lines you can specify as line barriers, the combined
number of streets intersected by all the lines cannot exceed
500.</para>
<para>When specifying the line barriers, you can set name and barrier type properties for each using the following attributes:</para>
<para>
Name</para>
<para> The name of the barrier.</para>
</pythonReference>
<dialogReference>
<para>One or more lines that prohibit travel anywhere
the lines intersect the streets. For example, a parade or protest
that blocks traffic across several street segments can be modeled
with a line barrier. A line barrier can also quickly fence off
several roads from being traversed, thereby channeling possible
routes away from undesirable parts of the street
network.</para>
<para> The tool imposes a limit on the number of streets you can
restrict using the Line Barriers parameter. While there is no limit to
the number of lines you can specify as line barriers, the combined
number of streets intersected by all the lines cannot exceed
500.</para>
<para>When specifying the line barriers, you can set name and barrier type properties for each using the following attributes:</para>
<para>
Name</para>
<para> The name of the barrier.</para>
</dialogReference>
</param>
<param datatype="Feature Set" direction="Input" displayname="Polygon Barriers" expression="{Polygon_Barriers}" name="Polygon_Barriers" sync="true" type="Optional">
<pythonReference>
<para>The polygons that either completely restrict travel or
proportionately scale the time or distance required to travel on
the streets intersected by the polygons.</para>
<para> The service imposes a limit on the number of streets you
can restrict using the Polygon Barriers parameter. While there is
no limit to the number of polygons you can specify as polygon
barriers, the combined number of streets intersected by all the
polygons cannot exceed 2,000.</para>
<para>When specifying the polygon barriers, you can set properties for each, such as its name or barrier type, using the following attributes:</para>
<para>
Name</para>
<para> The name of the barrier.</para>
<para> BarrierType</para>
<para> Specifies whether the barrier restricts travel completely
or scales the cost (such as time or distance) for traveling through it. The field
value is specified as one of the following integers (use the numeric code, not the name in parentheses):</para>
<para>
<bulletList>
<bullet_item> 0 (Restriction)—Prohibits traveling through any part of the barrier.
The barrier is referred to as a restriction polygon barrier since it
prohibits traveling on streets intersected by the barrier. One use
of this type of barrier is to model floods covering areas of the
street that make traveling on those streets impossible.</bullet_item>
<bullet_item> 1 (Scaled Cost)—Scales the cost (such as travel time or distance) required to travel the
underlying streets by a factor specified using the ScaledTimeFactor
or ScaledDistanceFactor field. If the streets are partially
covered by the barrier, the travel time or distance is apportioned
and then scaled. For example, a factor of 0.25 means that travel
on underlying streets is expected to be four times faster than
normal. A factor of 3.0 means it is expected to take three
times longer than normal to travel on underlying streets. This
barrier type is referred to as a scaled-cost polygon barrier. It
can be used to model storms that reduce travel speeds in specific
regions.</bullet_item>
</bulletList>
</para>
<para>ScaledTimeFactor</para>
<para> This is the factor by which the travel time of the streets
intersected by the barrier is multiplied. The field value must be greater than zero. </para>
<para>This field is applicable only for scaled-cost
barriers and only if the travel mode used for the analysis uses an impedance attribute that is time based. </para>
<para>ScaledDistanceFactor</para>
<para> This is the factor by which the distance of the streets
intersected by the barrier is multiplied. The field value must be greater than zero.</para>
<para>This field is applicable only for scaled-cost
barriers and only if the travel mode used for the analysis uses an impedance attribute that is distance based. </para>
<para>ScaledCostFactor</para>
<para> This is the factor by which the cost of the streets
intersected by the barrier is multiplied. The field value must be greater than zero. </para>
<para>This field is applicable only for scaled-cost
barriers and only if the travel mode used for the analysis uses an impedance attribute that is neither time based nor distance based. </para>
</pythonReference>
<dialogReference>
<para>The polygons that either completely restrict travel or
proportionately scale the time or distance required to travel on
the streets intersected by the polygons.</para>
<para> The service imposes a limit on the number of streets you
can restrict using the Polygon Barriers parameter. While there is
no limit to the number of polygons you can specify as polygon
barriers, the combined number of streets intersected by all the
polygons cannot exceed 2,000.</para>
<para>When specifying the polygon barriers, you can set properties for each, such as its name or barrier type, using the following attributes:</para>
<para>
Name</para>
<para> The name of the barrier.</para>
<para> BarrierType</para>
<para> Specifies whether the barrier restricts travel completely
or scales the cost (such as time or distance) for traveling through it. The field
value is specified as one of the following integers (use the numeric code, not the name in parentheses):</para>
<para>
<bulletList>
<bullet_item> 0 (Restriction)—Prohibits traveling through any part of the barrier.
The barrier is referred to as a restriction polygon barrier since it
prohibits traveling on streets intersected by the barrier. One use
of this type of barrier is to model floods covering areas of the
street that make traveling on those streets impossible.</bullet_item>
<bullet_item> 1 (Scaled Cost)—Scales the cost (such as travel time or distance) required to travel the
underlying streets by a factor specified using the ScaledTimeFactor
or ScaledDistanceFactor field. If the streets are partially
covered by the barrier, the travel time or distance is apportioned
and then scaled. For example, a factor of 0.25 means that travel
on underlying streets is expected to be four times faster than
normal. A factor of 3.0 means it is expected to take three
times longer than normal to travel on underlying streets. This
barrier type is referred to as a scaled-cost polygon barrier. It
can be used to model storms that reduce travel speeds in specific
regions.</bullet_item>
</bulletList>
</para>
<para>ScaledTimeFactor</para>
<para> This is the factor by which the travel time of the streets
intersected by the barrier is multiplied. The field value must be greater than zero. </para>
<para>This field is applicable only for scaled-cost
barriers and only if the travel mode used for the analysis uses an impedance attribute that is time based. </para>
<para>ScaledDistanceFactor</para>
<para> This is the factor by which the distance of the streets
intersected by the barrier is multiplied. The field value must be greater than zero.</para>
<para>This field is applicable only for scaled-cost
barriers and only if the travel mode used for the analysis uses an impedance attribute that is distance based. </para>
<para>ScaledCostFactor</para>
<para> This is the factor by which the cost of the streets
intersected by the barrier is multiplied. The field value must be greater than zero. </para>
<para>This field is applicable only for scaled-cost
barriers and only if the travel mode used for the analysis uses an impedance attribute that is neither time based nor distance based. </para>
</dialogReference>
</param>
<param datatype="Multiple Value" direction="Input" displayname="Restrictions" expression="{Caution Light | Flashing Light | Gate | No Right on Red (not in use) | No Straight Ahead (not in use) | Oneway | Red Light | Speed Hump | Stop Sign}" name="Restrictions" sync="true" type="Optional">
<pythonReference>
<para> Specify which travel restrictions should be honored by the tool
when determining the service areas. </para>
<para>A restriction represents a driving
preference or requirement. In most cases, restrictions cause roads
to be prohibited. For instance, using the Avoid Toll Roads restriction will result in a route that will include toll roads only when it is required to travel on toll roads to visit an incident or a facility. Height Restriction makes it possible to route around any clearances that are lower than the height of your vehicle. If you are carrying corrosive materials on your vehicle, using the Any Hazmat Prohibited restriction prevents hauling the materials along roads where it is marked illegal to do so. </para>
<para>The values you provide for this parameter are ignored unless Travel Mode is set to Custom.</para>
<para>
<para>Some restrictions require an additional value to be
specified for their use. This value must be associated
with the restriction name and a specific parameter intended to work
with the restriction. You can identify such restrictions if their
names appear in the AttributeName column in the Attribute
Parameter Values parameter. The ParameterValue field should be
specified in the Attribute Parameter Values parameter for the
restriction to be correctly used when finding traversable roads.</para>
</para>
<para>
<para>Some restrictions are supported only in certain countries; their availability is stated by region in the list below. Of the restrictions that have limited availability within a region, you can determine whether the restriction is available in a particular country by reviewing the table in the Country List section of Data coverage for network analysis services web page. If a country has a value of Yes in the Logistics Attribute column, the restriction with select availability in the region is supported in that country. If you specify restriction names that are not available in the country where your incidents are located, the service ignores the invalid restrictions. The service also ignores restrictions when the Restriction Usage attribute parameter value is between 0 and 1 (see the Attribute Parameter Value parameter). It prohibits all restrictions when the Restriction Usage parameter value is greater than 0.</para>
</para>
<para>
The tool supports the following restrictions:
<bulletList>
<bullet_item>
<para>Any Hazmat Prohibited—The results will not include roads
where transporting any kind of hazardous material is
prohibited. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Avoid Carpool Roads—The results will avoid roads that are
designated exclusively for car pool (high-occupancy)
vehicles. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Express Lanes—The results will avoid roads designated
as express lanes. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Ferries—The results will avoid ferries. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Gates—The results will avoid roads where there are
gates, such as keyed access or guard-controlled
entryways.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Limited Access Roads—The results will avoid roads
that are limited-access highways.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Private Roads—The results will avoid roads that are
not publicly owned and maintained.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Roads Unsuitable for Pedestrians—The results will avoid roads that are
unsuitable for pedestrians.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Stairways—The results will avoid all stairways on a pedestrian-suitable route.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Toll Roads—The results will avoid all toll
roads for automobiles.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Toll Roads for Trucks—The results will avoid all toll
roads for trucks.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Truck Restricted Roads—The results will avoid roads where trucks are not allowed, except when making deliveries.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para> Avoid Unpaved Roads—The results will avoid roads that are
not paved (for example, dirt, gravel, and so on). </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Axle Count Restriction—The results will not include roads
where trucks with the specified number of axles are prohibited. The
number of axles can be specified using the Number of Axles
restriction parameter.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Driving a Bus—The results will not include roads where
buses are prohibited. Using this restriction will also ensure that
the results will honor one-way streets. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Driving a Delivery Vehicle—The results will not include
roads where delivery vehicles are prohibited. Using this restriction
will also ensure that the results will honor one-way
streets. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Driving a Taxi—The results will not include roads where
taxis are prohibited. Using this restriction will also ensure that
the results will honor one-way streets. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Driving a Truck—The results will not include roads where
trucks are prohibited. Using this restriction will also ensure that
the results will honor one-way streets. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Driving an Automobile—The results will not include roads
where automobiles are prohibited. Using this restriction will also
ensure that the results will honor one-way streets. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Driving an Emergency Vehicle—The results will not include
roads where emergency vehicles are prohibited. Using this
restriction will also ensure that the results will honor one-way
streets.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Height Restriction—The results will not include roads
where the vehicle height exceeds the maximum allowed height for the
road. The vehicle height can be specified using the Vehicle Height
(meters) restriction parameter. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Kingpin to Rear Axle Length Restriction—The results will
not include roads where the vehicle length exceeds the maximum
allowed kingpin to rear axle for all trucks on the road. The length
between the vehicle kingpin and the rear axle can be specified
using the Vehicle Kingpin to Rear Axle Length (meters) restriction
parameter. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Length Restriction—The results will not include roads
where the vehicle length exceeds the maximum allowed length for the
road. The vehicle length can be specified using the Vehicle Length
(meters) restriction parameter. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Preferred for Pedestrians—The results will use preferred routes suitable for pedestrian navigation. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Riding a Motorcycle—The results will not include roads
where motorcycles are prohibited. Using this restriction will also
ensure that the results will honor one-way streets.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Roads Under Construction Prohibited—The results will not
include roads that are under construction.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Semi or Tractor with One or More Trailers Prohibited—The
results will not include roads where semis or tractors with one or
more trailers are prohibited. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Single Axle Vehicles Prohibited—The results will not
include roads where vehicles with single axles are
prohibited.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Tandem Axle Vehicles Prohibited—The results will not
include roads where vehicles with tandem axles are
prohibited.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Through Traffic Prohibited—The results will not include
roads where through traffic (non local) is prohibited.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Truck with Trailers Restriction—The results will not
include roads where trucks with the specified number of trailers on
the truck are prohibited. The number of trailers on the truck can
be specified using the Number of Trailers on Truck restriction
parameter.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Use Preferred Hazmat Routes—The results will prefer roads
that are designated for transporting any kind of hazardous
materials. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Use Preferred Truck Routes—The results will prefer roads
that are designated as truck routes, such as the roads that are
part of the national network as specified by the National Surface
Transportation Assistance Act in the United States, or roads that
are designated as truck routes by the state or province, or roads
that are preferred by the truckers when driving in an
area.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Walking—The results will not include roads where
pedestrians are prohibited.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Weight Restriction—The results will not include roads
where the vehicle weight exceeds the maximum allowed weight for the
road. The vehicle weight can be specified using the Vehicle Weight
(kilograms) restriction parameter.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Weight per Axle Restriction—The results will not include
roads where the vehicle weight per axle exceeds the maximum allowed
weight per axle for the road. The vehicle weight per axle can be
specified using the Vehicle Weight per Axle (kilograms) restriction
parameter.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Width Restriction—The results will not include roads where
the vehicle width exceeds the maximum allowed width for the road.
The vehicle width can be specified using the Vehicle Width (meters)
restriction parameter.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
</bulletList>
<para>The Driving a Delivery Vehicle restriction attribute is no longer available. The service will ignore this restriction since it is invalid. To achieve similar results, use the Driving a Truck restriction attribute along with the Avoid Truck Restricted Roads restriction attribute.</para>
</para>
</pythonReference>
<dialogReference>
<para> Specify which travel restrictions should be honored by the tool
when determining the service areas. </para>
<para>A restriction represents a driving
preference or requirement. In most cases, restrictions cause roads
to be prohibited. For instance, using the Avoid Toll Roads restriction will result in a route that will include toll roads only when it is required to travel on toll roads to visit an incident or a facility. Height Restriction makes it possible to route around any clearances that are lower than the height of your vehicle. If you are carrying corrosive materials on your vehicle, using the Any Hazmat Prohibited restriction prevents hauling the materials along roads where it is marked illegal to do so. </para>
<para>The values you provide for this parameter are ignored unless Travel Mode is set to Custom.</para>
<para>
<para>Some restrictions require an additional value to be
specified for their use. This value must be associated
with the restriction name and a specific parameter intended to work
with the restriction. You can identify such restrictions if their
names appear in the AttributeName column in the Attribute
Parameter Values parameter. The ParameterValue field should be
specified in the Attribute Parameter Values parameter for the
restriction to be correctly used when finding traversable roads.</para>
</para>
<para>
<para>Some restrictions are supported only in certain countries; their availability is stated by region in the list below. Of the restrictions that have limited availability within a region, you can determine whether the restriction is available in a particular country by reviewing the table in the Country List section of Data coverage for network analysis services web page. If a country has a value of Yes in the Logistics Attribute column, the restriction with select availability in the region is supported in that country. If you specify restriction names that are not available in the country where your incidents are located, the service ignores the invalid restrictions. The service also ignores restrictions when the Restriction Usage attribute parameter value is between 0 and 1 (see the Attribute Parameter Value parameter). It prohibits all restrictions when the Restriction Usage parameter value is greater than 0.</para>
</para>
<para>
The tool supports the following restrictions:
<bulletList>
<bullet_item>
<para>Any Hazmat Prohibited—The results will not include roads
where transporting any kind of hazardous material is
prohibited. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Avoid Carpool Roads—The results will avoid roads that are
designated exclusively for car pool (high-occupancy)
vehicles. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Express Lanes—The results will avoid roads designated
as express lanes. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Ferries—The results will avoid ferries. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Gates—The results will avoid roads where there are
gates, such as keyed access or guard-controlled
entryways.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Limited Access Roads—The results will avoid roads
that are limited-access highways.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Private Roads—The results will avoid roads that are
not publicly owned and maintained.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Roads Unsuitable for Pedestrians—The results will avoid roads that are
unsuitable for pedestrians.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Stairways—The results will avoid all stairways on a pedestrian-suitable route.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Toll Roads—The results will avoid all toll
roads for automobiles.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Toll Roads for Trucks—The results will avoid all toll
roads for trucks.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Avoid Truck Restricted Roads—The results will avoid roads where trucks are not allowed, except when making deliveries.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para> Avoid Unpaved Roads—The results will avoid roads that are
not paved (for example, dirt, gravel, and so on). </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Axle Count Restriction—The results will not include roads
where trucks with the specified number of axles are prohibited. The
number of axles can be specified using the Number of Axles
restriction parameter.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Driving a Bus—The results will not include roads where
buses are prohibited. Using this restriction will also ensure that
the results will honor one-way streets. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Driving a Delivery Vehicle—The results will not include
roads where delivery vehicles are prohibited. Using this restriction
will also ensure that the results will honor one-way
streets. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Driving a Taxi—The results will not include roads where
taxis are prohibited. Using this restriction will also ensure that
the results will honor one-way streets. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Driving a Truck—The results will not include roads where
trucks are prohibited. Using this restriction will also ensure that
the results will honor one-way streets. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Driving an Automobile—The results will not include roads
where automobiles are prohibited. Using this restriction will also
ensure that the results will honor one-way streets. </para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Driving an Emergency Vehicle—The results will not include
roads where emergency vehicles are prohibited. Using this
restriction will also ensure that the results will honor one-way
streets.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Height Restriction—The results will not include roads
where the vehicle height exceeds the maximum allowed height for the
road. The vehicle height can be specified using the Vehicle Height
(meters) restriction parameter. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Kingpin to Rear Axle Length Restriction—The results will
not include roads where the vehicle length exceeds the maximum
allowed kingpin to rear axle for all trucks on the road. The length
between the vehicle kingpin and the rear axle can be specified
using the Vehicle Kingpin to Rear Axle Length (meters) restriction
parameter. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Length Restriction—The results will not include roads
where the vehicle length exceeds the maximum allowed length for the
road. The vehicle length can be specified using the Vehicle Length
(meters) restriction parameter. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Preferred for Pedestrians—The results will use preferred routes suitable for pedestrian navigation. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Riding a Motorcycle—The results will not include roads
where motorcycles are prohibited. Using this restriction will also
ensure that the results will honor one-way streets.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Roads Under Construction Prohibited—The results will not
include roads that are under construction.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Semi or Tractor with One or More Trailers Prohibited—The
results will not include roads where semis or tractors with one or
more trailers are prohibited. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Single Axle Vehicles Prohibited—The results will not
include roads where vehicles with single axles are
prohibited.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Tandem Axle Vehicles Prohibited—The results will not
include roads where vehicles with tandem axles are
prohibited.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Through Traffic Prohibited—The results will not include
roads where through traffic (non local) is prohibited.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Truck with Trailers Restriction—The results will not
include roads where trucks with the specified number of trailers on
the truck are prohibited. The number of trailers on the truck can
be specified using the Number of Trailers on Truck restriction
parameter.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Use Preferred Hazmat Routes—The results will prefer roads
that are designated for transporting any kind of hazardous
materials. </para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Use Preferred Truck Routes—The results will prefer roads
that are designated as truck routes, such as the roads that are
part of the national network as specified by the National Surface
Transportation Assistance Act in the United States, or roads that
are designated as truck routes by the state or province, or roads
that are preferred by the truckers when driving in an
area.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Walking—The results will not include roads where
pedestrians are prohibited.</para>
<para>Availability: All countries</para>
</bullet_item>
<bullet_item>
<para>Weight Restriction—The results will not include roads
where the vehicle weight exceeds the maximum allowed weight for the
road. The vehicle weight can be specified using the Vehicle Weight
(kilograms) restriction parameter.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Weight per Axle Restriction—The results will not include
roads where the vehicle weight per axle exceeds the maximum allowed
weight per axle for the road. The vehicle weight per axle can be
specified using the Vehicle Weight per Axle (kilograms) restriction
parameter.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
<bullet_item>
<para>Width Restriction—The results will not include roads where
the vehicle width exceeds the maximum allowed width for the road.
The vehicle width can be specified using the Vehicle Width (meters)
restriction parameter.</para>
<para>Availability: Select countries in North America and Europe</para>
</bullet_item>
</bulletList>
<para>The Driving a Delivery Vehicle restriction attribute is no longer available. The service will ignore this restriction since it is invalid. To achieve similar results, use the Driving a Truck restriction attribute along with the Avoid Truck Restricted Roads restriction attribute.</para>
</para>
</dialogReference>
</param>
<param datatype="Record Set" direction="Input" displayname="Attribute Parameter Values" expression="{Attribute_Parameter_Values}" name="Attribute_Parameter_Values" sync="true" type="Optional">
<pythonReference>
<para> Specifies additional values required by some restrictions, such as the weight of a vehicle for Weight Restriction. You can also use the attribute parameter to specify whether any restriction prohibits, avoids, or prefers
travel on roads that use the restriction. If the restriction is
meant to avoid or prefer roads, you can further specify the degree
to which they are avoided or preferred using this
parameter. For example, you can choose to never use toll roads, avoid them as much as possible, or even highly prefer them.</para>
<para>The values you provide for this parameter are ignored unless Travel Mode is set to Custom.</para>
<para>
If you specify the Attribute Parameter Values parameter from a
feature class, the field names on the feature class must match the fields as follows:
<bulletList>
<bullet_item>AttributeName—Lists the name of the restriction.</bullet_item>
<bullet_item>ParameterName—Lists the name of the parameter associated with the
restriction. A restriction can have one or more ParameterName field
values based on its intended use.</bullet_item>
<bullet_item>ParameterValue—The value for ParameterName used by the tool
when evaluating the restriction.</bullet_item>
</bulletList>
</para>
<para> The Attribute Parameter Values parameter is dependent on the
Restrictions parameter. The ParameterValue field is applicable only
if the restriction name is specified as the value for the
Restrictions parameter.</para>
<para>
In Attribute Parameter Values, each
restriction (listed as AttributeName) has a ParameterName field
value, Restriction Usage, that specifies whether the restriction
prohibits, avoids, or prefers travel on the roads associated with
the restriction as well as the degree to which the roads are avoided or
preferred. The Restriction Usage ParameterName can be assigned any of
the following string values or their equivalent numeric values
listed in the parentheses:
<bulletList>
<bullet_item> PROHIBITED (-1)—Travel on the roads using the restriction is completely
prohibited.</bullet_item>
<bullet_item> AVOID_HIGH (5)—It
is highly unlikely the tool will include in the route the roads
that are associated with the restriction.</bullet_item>
<bullet_item> AVOID_MEDIUM (2)—It
is unlikely the tool will include in the route the roads that are
associated with the restriction.</bullet_item>
<bullet_item> AVOID_LOW (1.3)—It
is somewhat unlikely the tool will include in the route the roads
that are associated with the restriction.</bullet_item>
<bullet_item> PREFER_LOW (0.8)—It
is somewhat likely the tool will include in the route the roads
that are associated with the restriction.</bullet_item>
<bullet_item> PREFER_MEDIUM (0.5)—It is likely the tool will include in the route the roads that
are associated with the restriction.</bullet_item>
<bullet_item> PREFER_HIGH (0.2)—It is highly likely the tool will include in the route the roads
that are associated with the restriction.</bullet_item>
</bulletList>
</para>
<para> In most cases, you can use the default value, PROHIBITED,
for the Restriction Usage if the restriction is dependent on a
vehicle characteristic such as vehicle height. However, in some
cases, the Restriction Usage value depends on your routing
preferences. For example, the Avoid Toll Roads restriction has the
default value of AVOID_MEDIUM for the Restriction Usage attribute.
This means that when the restriction is used, the tool will try to
route around toll roads when it can. AVOID_MEDIUM also indicates
how important it is to avoid toll roads when finding the best
route; it has a medium priority. Choosing AVOID_LOW puts lower
importance on avoiding tolls; choosing AVOID_HIGH instead gives it a higher importance and thus makes it more acceptable for
the service to generate longer routes to avoid tolls. Choosing
PROHIBITED entirely disallows travel on toll roads, making it
impossible for a route to travel on any portion of a toll road.
Keep in mind that avoiding or prohibiting toll roads, and thus
avoiding toll payments, is the objective for some. In contrast,
others prefer to drive on toll roads, because avoiding traffic is
more valuable to them than the money spent on tolls. In the latter
case, choose PREFER_LOW, PREFER_MEDIUM, or PREFER_HIGH as
the value for Restriction Usage. The higher the preference, the
farther the tool will go out of its way to travel on the roads
associated with the restriction.</para>
</pythonReference>
<dialogReference>
<para> Specifies additional values required by some restrictions, such as the weight of a vehicle for Weight Restriction. You can also use the attribute parameter to specify whether any restriction prohibits, avoids, or prefers
travel on roads that use the restriction. If the restriction is
meant to avoid or prefer roads, you can further specify the degree
to which they are avoided or preferred using this
parameter. For example, you can choose to never use toll roads, avoid them as much as possible, or even highly prefer them.</para>
<para>The values you provide for this parameter are ignored unless Travel Mode is set to Custom.</para>
<para>
If you specify the Attribute Parameter Values parameter from a
feature class, the field names on the feature class must match the fields as follows:
<bulletList>
<bullet_item>AttributeName—Lists the name of the restriction.</bullet_item>
<bullet_item>ParameterName—Lists the name of the parameter associated with the
restriction. A restriction can have one or more ParameterName field
values based on its intended use.</bullet_item>
<bullet_item>ParameterValue—The value for ParameterName used by the tool
when evaluating the restriction.</bullet_item>
</bulletList>
</para>
<para> The Attribute Parameter Values parameter is dependent on the
Restrictions parameter. The ParameterValue field is applicable only
if the restriction name is specified as the value for the
Restrictions parameter.</para>
<para>
In Attribute Parameter Values, each
restriction (listed as AttributeName) has a ParameterName field
value, Restriction Usage, that specifies whether the restriction
prohibits, avoids, or prefers travel on the roads associated with
the restriction as well as the degree to which the roads are avoided or
preferred. The Restriction Usage ParameterName can be assigned any of
the following string values or their equivalent numeric values
listed in the parentheses:
<bulletList>
<bullet_item> PROHIBITED (-1)—Travel on the roads using the restriction is completely
prohibited.</bullet_item>
<bullet_item> AVOID_HIGH (5)—It
is highly unlikely the tool will include in the route the roads
that are associated with the restriction.</bullet_item>
<bullet_item> AVOID_MEDIUM (2)—It
is unlikely the tool will include in the route the roads that are
associated with the restriction.</bullet_item>
<bullet_item> AVOID_LOW (1.3)—It
is somewhat unlikely the tool will include in the route the roads
that are associated with the restriction.</bullet_item>
<bullet_item> PREFER_LOW (0.8)—It
is somewhat likely the tool will include in the route the roads
that are associated with the restriction.</bullet_item>
<bullet_item> PREFER_MEDIUM (0.5)—It is likely the tool will include in the route the roads that
are associated with the restriction.</bullet_item>
<bullet_item> PREFER_HIGH (0.2)—It is highly likely the tool will include in the route the roads
that are associated with the restriction.</bullet_item>
</bulletList>
</para>
<para> In most cases, you can use the default value, PROHIBITED,
for the Restriction Usage if the restriction is dependent on a
vehicle characteristic such as vehicle height. However, in some
cases, the Restriction Usage value depends on your routing
preferences. For example, the Avoid Toll Roads restriction has the
default value of AVOID_MEDIUM for the Restriction Usage attribute.
This means that when the restriction is used, the tool will try to
route around toll roads when it can. AVOID_MEDIUM also indicates
how important it is to avoid toll roads when finding the best
route; it has a medium priority. Choosing AVOID_LOW puts lower
importance on avoiding tolls; choosing AVOID_HIGH instead gives it a higher importance and thus makes it more acceptable for
the service to generate longer routes to avoid tolls. Choosing
PROHIBITED entirely disallows travel on toll roads, making it
impossible for a route to travel on any portion of a toll road.
Keep in mind that avoiding or prohibiting toll roads, and thus
avoiding toll payments, is the objective for some. In contrast,
others prefer to drive on toll roads, because avoiding traffic is
more valuable to them than the money spent on tolls. In the latter
case, choose PREFER_LOW, PREFER_MEDIUM, or PREFER_HIGH as
the value for Restriction Usage. The higher the preference, the
farther the tool will go out of its way to travel on the roads
associated with the restriction.</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Time Zone for Time of Day" expression="{Geographically Local | UTC}" name="Time_Zone_for_Time_of_Day" sync="true" type="Optional">
<pythonReference>
<para>
Specifies the time zone or zones of the Time of Day parameter.
</para>
<para>
<bulletList>
<bullet_item>
Geographically Local—The Time of Day parameter refers to the time zone or zones in which the facilities are located. Therefore, the start or end times of the service areas are staggered by time zone. Setting Time of Day to 9:00 a.m., choosing geographically local for Time Zone for Time of Day, and solving causes service areas to be generated for 9:00 a.m. Eastern Time for any facilities in the Eastern Time Zone, 9:00 a.m. Central Time for facilities in the Central Time Zone, 9:00 a.m. Mountain Time for facilities in the Mountain Time Zone, and so on, for facilities in different time zones.
<para>If stores in a chain that span the U.S. open at 9:00 a.m. local time, this parameter value could be chosen to find market territories at opening time for all stores in one solve. First, the stores in the Eastern Time Zone open and a polygon is generated, then an hour later stores open in Central Time, and so on. Nine o'clock is always in local time but staggered in real time.</para>
</bullet_item>
<bullet_item>
UTC—The Time of Day parameter refers to Coordinated Universal Time (UTC). Therefore, all facilities are reached or departed from simultaneously, regardless of the time zone each is in.
<para>Setting Time of Day to 2:00 p.m., choosing UTC, then solving causes service areas to be generated for 9:00 a.m. Eastern Standard Time for any facilities in the Eastern Time Zone, 8:00 a.m. Central Standard Time for facilities in the Central Time Zone, 7:00 a.m. Mountain Standard Time for facilities in the Mountain Time Zone, and so on, for facilities in different time zones.</para>
<para>The scenario above assumes standard time. During daylight saving time, the Eastern, Central, and Mountain Times would each be one hour ahead (that is, 10:00, 9:00, and 8:00 a.m., respectively).</para>
<para>One of the cases in which the UTC option is useful is to visualize emergency-response coverage for a jurisdiction that is split into two time zones. The emergency vehicles are loaded as facilities. Time of Day is set to now in UTC. (You need to determine what the current time and date are in terms of UTC to correctly use this option.) Other properties are set and the analysis is solved. Even though a time-zone boundary divides the vehicles, the results show areas that can be reached given current traffic conditions. This same process can be used for other times as well, not just for now.</para>
</bullet_item>
</bulletList>
</para>
<para>Irrespective of the Time Zone for Time of Day setting, all facilities must be in the same time zone
when
Time of Day has a nonnull value and Polygons for Multiple Facilities is set to create merged or nonoverlapping polygons.</para>
</pythonReference>
<dialogReference>
<para>
Specifies the time zone or zones of the Time of Day parameter.
</para>
<para>
<bulletList>
<bullet_item>
Geographically Local—The Time of Day parameter refers to the time zone or zones in which the facilities are located. Therefore, the start or end times of the service areas are staggered by time zone. Setting Time of Day to 9:00 a.m., choosing geographically local for Time Zone for Time of Day, and solving causes service areas to be generated for 9:00 a.m. Eastern Time for any facilities in the Eastern Time Zone, 9:00 a.m. Central Time for facilities in the Central Time Zone, 9:00 a.m. Mountain Time for facilities in the Mountain Time Zone, and so on, for facilities in different time zones.
<para>If stores in a chain that span the U.S. open at 9:00 a.m. local time, this parameter value could be chosen to find market territories at opening time for all stores in one solve. First, the stores in the Eastern Time Zone open and a polygon is generated, then an hour later stores open in Central Time, and so on. Nine o'clock is always in local time but staggered in real time.</para>
</bullet_item>
<bullet_item>
UTC—The Time of Day parameter refers to Coordinated Universal Time (UTC). Therefore, all facilities are reached or departed from simultaneously, regardless of the time zone each is in.
<para>Setting Time of Day to 2:00 p.m., choosing UTC, then solving causes service areas to be generated for 9:00 a.m. Eastern Standard Time for any facilities in the Eastern Time Zone, 8:00 a.m. Central Standard Time for facilities in the Central Time Zone, 7:00 a.m. Mountain Standard Time for facilities in the Mountain Time Zone, and so on, for facilities in different time zones.</para>
<para>The scenario above assumes standard time. During daylight saving time, the Eastern, Central, and Mountain Times would each be one hour ahead (that is, 10:00, 9:00, and 8:00 a.m., respectively).</para>
<para>One of the cases in which the UTC option is useful is to visualize emergency-response coverage for a jurisdiction that is split into two time zones. The emergency vehicles are loaded as facilities. Time of Day is set to now in UTC. (You need to determine what the current time and date are in terms of UTC to correctly use this option.) Other properties are set and the analysis is solved. Even though a time-zone boundary divides the vehicles, the results show areas that can be reached given current traffic conditions. This same process can be used for other times as well, not just for now.</para>
</bullet_item>
</bulletList>
</para>
<para>Irrespective of the Time Zone for Time of Day setting, all facilities must be in the same time zone
when
Time of Day has a nonnull value and Polygons for Multiple Facilities is set to create merged or nonoverlapping polygons.</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Travel Mode" expression="{Travel_Mode}" name="Travel_Mode" sync="true" type="Optional">
<pythonReference>
<para>The mode of transportation to model in the analysis. Travel modes are managed in ArcGIS Online and can be configured by the administrator of your organization to reflect your organization's workflows. You need to specify the name of a travel mode that is supported by your organization. </para>
<para>To get a list of supported travel mode names, use the same GIS server connection you used to access this tool, and run the GetTravelModes tool in the Utilities toolbox. The GetTravelModes tool adds a table, Supported Travel Modes, to the application. Any value in the Travel Mode Name field from the Supported Travel Modes table can be specified as input. You can also specify the value from the Travel Mode Settings field as input. This reduces the tool execution time because the tool does not have to find the settings based on the travel mode name. </para>
<para>The default value, Custom, allows you to configure your own travel mode using the custom travel mode parameters (UTurn at Junctions, Use Hierarchy, Restrictions, Attribute Parameter Values, and Impedance). The default values of the custom travel mode parameters model traveling by car. You may want to choose Custom and set the custom travel mode parameters listed above to model a pedestrian with a fast walking speed or a truck with a given height, weight, and cargo of certain hazardous materials. You can try different settings to get the analysis results you want. Once you have identified the analysis settings, work with your organization's administrator and save these settings as part of a new or existing travel mode so that everyone in your organization can run the analysis with the same settings. </para>
<para>When you choose Custom, the values you set for the custom travel mode parameters are included in the analysis. Specifying another travel mode, as defined by your organization, causes any values you set for the custom travel mode parameters to be ignored; the tool overrides them with values from your specified travel mode.</para>
</pythonReference>
<dialogReference>
<para>The mode of transportation to model in the analysis. Travel modes are managed in ArcGIS Online and can be configured by the administrator of your organization to reflect your organization's workflows. You need to specify the name of a travel mode that is supported by your organization. </para>
<para>To get a list of supported travel mode names, use the same GIS server connection you used to access this tool, and run the GetTravelModes tool in the Utilities toolbox. The GetTravelModes tool adds a table, Supported Travel Modes, to the application. Any value in the Travel Mode Name field from the Supported Travel Modes table can be specified as input. You can also specify the value from the Travel Mode Settings field as input. This reduces the tool execution time because the tool does not have to find the settings based on the travel mode name. </para>
<para>The default value, Custom, allows you to configure your own travel mode using the custom travel mode parameters (UTurn at Junctions, Use Hierarchy, Restrictions, Attribute Parameter Values, and Impedance). The default values of the custom travel mode parameters model traveling by car. You may want to choose Custom and set the custom travel mode parameters listed above to model a pedestrian with a fast walking speed or a truck with a given height, weight, and cargo of certain hazardous materials. You can try different settings to get the analysis results you want. Once you have identified the analysis settings, work with your organization's administrator and save these settings as part of a new or existing travel mode so that everyone in your organization can run the analysis with the same settings. </para>
<para>When you choose Custom, the values you set for the custom travel mode parameters are included in the analysis. Specifying another travel mode, as defined by your organization, causes any values you set for the custom travel mode parameters to be ignored; the tool overrides them with values from your specified travel mode.</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Impedance" expression="{Drive Time | Truck Time | Walk Time | Travel Distance | Time | Length}" name="Impedance" sync="true" type="Optional">
<pythonReference>
<para>
Specifies the impedance, which is a value that represents the effort or cost of traveling along road segments or on other parts of the transportation network. </para>
<para>Travel time is an impedance; a car may take 1 minute to travel a mile along an empty road. Travel times can vary by travel mode—a pedestrian may take more than 20 minutes to walk the same mile, so it is important to choose the right impedance for the travel mode you are modeling. </para>
<para>Travel distance can also be an impedance; the length of a road in kilometers can be thought of as impedance. Travel distance in this sense is the same for all modes—a kilometer for a pedestrian is also a kilometer for a car. (What may change is the pathways on which the different modes are allowed to travel, which affects distance between points, and this is modeled by travel mode settings.)</para>
<para>The value you provide for this parameter is ignored unless Travel Mode is set to Custom, which is the default value.</para>
<para>
Choose from the following impedance values:
<bulletList>
<bullet_item>TravelTime—Historical and live traffic data are used. This option is good for modeling the time it takes automobiles to travel along roads at a specific time of the day using live traffic speed data where available. When using TravelTime, you can optionally set the TravelTime::Vehicle Maximum Speed (km/h) attribute parameter to specify the physical limitation of the speed the vehicle is capable of traveling.</bullet_item>
<bullet_item>Minutes—Live traffic data is not used, but historical average speeds for automobiles data is used.</bullet_item>
<bullet_item>TruckTravelTime—Historical and live traffic data are used, but the speed is capped at the posted truck speed limit. This is good for modeling the time it takes for the trucks to travel along roads at a specific time. When using TruckTravelTime, you can optionally set the TruckTravelTime::Vehicle Maximum Speed (km/h) attribute parameter to specify the physical limitation of the speed the truck is capable of traveling.</bullet_item>
<bullet_item>TruckMinutes—Live traffic data is not used, but the smaller of the historical average speeds for automobiles and the posted speed limits for trucks are used.</bullet_item>
<bullet_item>WalkTime—The default is a speed of 5 km/hr on all roads and paths, but this can be configured through the WalkTime::Walking Speed (km/h) attribute parameter.</bullet_item>
<bullet_item>Miles—Length measurements along roads are stored in miles and can be used for performing analysis based on shortest distance.</bullet_item>
<bullet_item>Kilometers—Length measurements along roads are stored in kilometers and can be used for performing analysis based on shortest distance.</bullet_item>
<bullet_item>TimeAt1KPH—The default is a speed of 1 km/hr on all roads and paths. The speed cannot be changed using any attribute parameter.</bullet_item>
</bulletList>
</para>
<para>If you choose a time-based impedance, such as TravelTime, TruckTravelTime, Minutes, TruckMinutes, or WalkTime, the Break Units parameter must be set to a time-based value; if you choose a distance-based impedance such as Miles, Kilometers, Break Units must be distance-based.</para>
<para>Drive Time, Truck Time, Walk Time, and Travel Distance impedance values are no longer supported and will be removed in a future release. If you use one of these values, the tool uses the value of the Time Impedance parameter for time-based values or the Distance Impedance parameter for distance-based values.</para>
</pythonReference>
<dialogReference>
<para>
Specifies the impedance, which is a value that represents the effort or cost of traveling along road segments or on other parts of the transportation network. </para>
<para>Travel time is an impedance; a car may take 1 minute to travel a mile along an empty road. Travel times can vary by travel mode—a pedestrian may take more than 20 minutes to walk the same mile, so it is important to choose the right impedance for the travel mode you are modeling. </para>
<para>Travel distance can also be an impedance; the length of a road in kilometers can be thought of as impedance. Travel distance in this sense is the same for all modes—a kilometer for a pedestrian is also a kilometer for a car. (What may change is the pathways on which the different modes are allowed to travel, which affects distance between points, and this is modeled by travel mode settings.)</para>
<para>The value you provide for this parameter is ignored unless Travel Mode is set to Custom, which is the default value.</para>
<para>
Choose from the following impedance values:
<bulletList>
<bullet_item>TravelTime—Historical and live traffic data are used. This option is good for modeling the time it takes automobiles to travel along roads at a specific time of the day using live traffic speed data where available. When using TravelTime, you can optionally set the TravelTime::Vehicle Maximum Speed (km/h) attribute parameter to specify the physical limitation of the speed the vehicle is capable of traveling.</bullet_item>
<bullet_item>Minutes—Live traffic data is not used, but historical average speeds for automobiles data is used.</bullet_item>
<bullet_item>TruckTravelTime—Historical and live traffic data are used, but the speed is capped at the posted truck speed limit. This is good for modeling the time it takes for the trucks to travel along roads at a specific time. When using TruckTravelTime, you can optionally set the TruckTravelTime::Vehicle Maximum Speed (km/h) attribute parameter to specify the physical limitation of the speed the truck is capable of traveling.</bullet_item>
<bullet_item>TruckMinutes—Live traffic data is not used, but the smaller of the historical average speeds for automobiles and the posted speed limits for trucks are used.</bullet_item>
<bullet_item>WalkTime—The default is a speed of 5 km/hr on all roads and paths, but this can be configured through the WalkTime::Walking Speed (km/h) attribute parameter.</bullet_item>
<bullet_item>Miles—Length measurements along roads are stored in miles and can be used for performing analysis based on shortest distance.</bullet_item>
<bullet_item>Kilometers—Length measurements along roads are stored in kilometers and can be used for performing analysis based on shortest distance.</bullet_item>
<bullet_item>TimeAt1KPH—The default is a speed of 1 km/hr on all roads and paths. The speed cannot be changed using any attribute parameter.</bullet_item>
</bulletList>
</para>
<para>If you choose a time-based impedance, such as TravelTime, TruckTravelTime, Minutes, TruckMinutes, or WalkTime, the Break Units parameter must be set to a time-based value; if you choose a distance-based impedance such as Miles, Kilometers, Break Units must be distance-based.</para>
<para>Drive Time, Truck Time, Walk Time, and Travel Distance impedance values are no longer supported and will be removed in a future release. If you use one of these values, the tool uses the value of the Time Impedance parameter for time-based values or the Distance Impedance parameter for distance-based values.</para>
</dialogReference>
</param>
<param datatype="Boolean" direction="Input" displayname="Save Output Network Analysis Layer" expression="{Save_Output_Network_Analysis_Layer}" name="Save_Output_Network_Analysis_Layer" sync="true" type="Optional">
<pythonReference>
<para>
Specifies whether the tool will save the analysis settings as a network analysis layer file. You cannot directly work with this file even when you open the file in an ArcGIS Desktop application such as ArcMap. It is meant to be sent to Esri Technical Support to diagnose the quality of results returned from the tool.
</para>
<para>
<bulletList>
<bullet_item>Checked (True in Python)—The output will be saved as a network analysis layer file. The file will be downloaded to a temporary directory on your machine. In ArcGIS Pro, the location of the downloaded file can be determined by viewing the value for the Output Network Analysis Layer parameter in the entry corresponding to the tool execution in the geoprocessing history of your project. In ArcMap, the location of the file can be determined by accessing the Copy Location option in the shortcut menu on the Output Network Analysis Layer parameter in the entry corresponding to the tool execution in the Geoprocessing Results window. </bullet_item>
<bullet_item>Unchecked (False in Python)—The output will not be saved as a network analysis layer file. This is the default.</bullet_item>
</bulletList>
</para>
</pythonReference>
<dialogReference>
<para>
Specifies whether the tool will save the analysis settings as a network analysis layer file. You cannot directly work with this file even when you open the file in an ArcGIS Desktop application such as ArcMap. It is meant to be sent to Esri Technical Support to diagnose the quality of results returned from the tool.
</para>
<para>
<bulletList>
<bullet_item>Checked (True in Python)—The output will be saved as a network analysis layer file. The file will be downloaded to a temporary directory on your machine. In ArcGIS Pro, the location of the downloaded file can be determined by viewing the value for the Output Network Analysis Layer parameter in the entry corresponding to the tool execution in the geoprocessing history of your project. In ArcMap, the location of the file can be determined by accessing the Copy Location option in the shortcut menu on the Output Network Analysis Layer parameter in the entry corresponding to the tool execution in the Geoprocessing Results window. </bullet_item>
<bullet_item>Unchecked (False in Python)—The output will not be saved as a network analysis layer file. This is the default.</bullet_item>
</bulletList>
</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Overrides" expression="{Overrides}" name="Overrides" sync="true" type="Optional">
<pythonReference>
<para>Additional settings that can influence the behavior of the solver when finding solutions for the network analysis problems.
</para>
<para> The value for this parameter must be specified in JavaScript Object Notation (JSON). For example, a valid value is of the following form: {"overrideSetting1" : "value1", "overrideSetting2" : "value2"}. The override setting name is always enclosed in double quotation marks. The values can be a number, Boolean, or a string.</para>
<para> The default value for this parameter is no
value, which indicates not to override any solver
settings.</para>
<para> Overrides are advanced settings that should be
used only after careful analysis of the results obtained before and
after applying the settings. A list of supported override settings
for each solver and their acceptable values can be obtained by
contacting Esri Technical Support.</para>
</pythonReference>
<dialogReference>
<para>Additional settings that can influence the behavior of the solver when finding solutions for the network analysis problems.
</para>
<para> The value for this parameter must be specified in JavaScript Object Notation (JSON). For example, a valid value is of the following form: {"overrideSetting1" : "value1", "overrideSetting2" : "value2"}. The override setting name is always enclosed in double quotation marks. The values can be a number, Boolean, or a string.</para>
<para> The default value for this parameter is no
value, which indicates not to override any solver
settings.</para>
<para> Overrides are advanced settings that should be
used only after careful analysis of the results obtained before and
after applying the settings. A list of supported override settings
for each solver and their acceptable values can be obtained by
contacting Esri Technical Support.</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Time Impedance" expression="{Time}" name="Time_Impedance" sync="true" type="Optional">
<pythonReference>
<para>The time-based impedance, which is a value that represents the travel time along road segments or on other parts of the transportation network.</para>
<para>If the impedance for the travel mode, as specified using the Impedance parameter,</para>
is time based, the value for Time Impedance and Impedance parameters must be identical. Otherwise, the service will return an error.
</pythonReference>
<dialogReference>
<para>The time-based impedance, which is a value that represents the travel time along road segments or on other parts of the transportation network.</para>
<para>If the impedance for the travel mode, as specified using the Impedance parameter,</para>
is time based, the value for Time Impedance and Impedance parameters must be identical. Otherwise, the service will return an error.
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Distance Impedance" expression="{Length}" name="Distance_Impedance" sync="true" type="Optional">
<pythonReference>
<para>The distance-based impedance, which is a value that represents the travel distance along road segments or on other parts of the transportation network.</para>
<para>If the impedance for the travel mode, as specified using the Impedance parameter,</para>
is distance based, the value for Distance Impedance and Impedance parameters must be identical. Otherwise, the service will return an error.
</pythonReference>
<dialogReference>
<para>The distance-based impedance, which is a value that represents the travel distance along road segments or on other parts of the transportation network.</para>
<para>If the impedance for the travel mode, as specified using the Impedance parameter,</para>
is distance based, the value for Distance Impedance and Impedance parameters must be identical. Otherwise, the service will return an error.
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Polygon Detail" expression="{Standard | Generalized | High}" name="Polygon_Detail" sync="true" type="Optional">
<pythonReference>
<para>
Specifies the level of detail for the output polygons.
</para>
<para>
<bulletList>
<bullet_item>Standard—Creates polygons with a standard level of detail. This is the default. Standard polygons are generated quickly and are fairly accurate, but quality deteriorates somewhat as you move toward the borders of the service area polygons. </bullet_item>
<bullet_item>Generalized—Creates generalized polygons using the hierarchy present in the network data source in order to produce results quickly. Generalized polygons are inferior in quality as compared to standard or high precision polygons. </bullet_item>
<bullet_item>High—Creates polygons with the highest level of details. Holes within the polygon may exist; they represent islands of network elements, such as streets, that couldn't be reached without exceeding the cutoff impedance or due to travel restrictions This option should be used for applications in which very precise results are important. </bullet_item>
</bulletList>
</para>
<para>If your analysis covers an urban area with a grid-like street network, the difference between generalized and standard polygons will be minimal. However, for mountain and rural roads, the standard and detailed polygons may present significantly more accurate results than generalized polygons.</para>
<para>The tool supports generating high precision polygons only if the largest
value specified in the Break Values parameter is less than or equal to 15
minutes or 15 miles (24.14 kilometers).</para>
</pythonReference>
<dialogReference>
<para>
Specifies the level of detail for the output polygons.
</para>
<para>
<bulletList>
<bullet_item>Standard—Creates polygons with a standard level of detail. This is the default. Standard polygons are generated quickly and are fairly accurate, but quality deteriorates somewhat as you move toward the borders of the service area polygons. </bullet_item>
<bullet_item>Generalized—Creates generalized polygons using the hierarchy present in the network data source in order to produce results quickly. Generalized polygons are inferior in quality as compared to standard or high precision polygons. </bullet_item>
<bullet_item>High—Creates polygons with the highest level of details. Holes within the polygon may exist; they represent islands of network elements, such as streets, that couldn't be reached without exceeding the cutoff impedance or due to travel restrictions This option should be used for applications in which very precise results are important. </bullet_item>
</bulletList>
</para>
<para>If your analysis covers an urban area with a grid-like street network, the difference between generalized and standard polygons will be minimal. However, for mountain and rural roads, the standard and detailed polygons may present significantly more accurate results than generalized polygons.</para>
<para>The tool supports generating high precision polygons only if the largest
value specified in the Break Values parameter is less than or equal to 15
minutes or 15 miles (24.14 kilometers).</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Output Type" expression="{Polygons | Lines | Polygons and lines}" name="Output_Type" sync="true" type="Optional">
<pythonReference>
<para>
Specifies the type of output to be generated. Service area output can be line features representing the roads reachable before the cutoffs are exceeded or the polygon features encompassing these lines (representing the reachable area)
</para>
<para>
<bulletList>
<bullet_item>Polygons—The service area output will contain polygons only. This is the default.</bullet_item>
<bullet_item>Lines—The service area output will contain lines only.</bullet_item>
<bullet_item>Polygons and lines—The service area output will contain both polygons and lines.</bullet_item>
</bulletList>
</para>
</pythonReference>
<dialogReference>
<para>
Specifies the type of output to be generated. Service area output can be line features representing the roads reachable before the cutoffs are exceeded or the polygon features encompassing these lines (representing the reachable area)
</para>
<para>
<bulletList>
<bullet_item>Polygons—The service area output will contain polygons only. This is the default.</bullet_item>
<bullet_item>Lines—The service area output will contain lines only.</bullet_item>
<bullet_item>Polygons and lines—The service area output will contain both polygons and lines.</bullet_item>
</bulletList>
</para>
</dialogReference>
</param>
<param datatype="String" direction="Input" displayname="Output Format" expression="{Feature Set | JSON File | GeoJSON File}" name="Output_Format" sync="true" type="Optional">
<pythonReference>
<para>
Specifies the format in which the output features will be created. </para>
<para>
Choose from the following options:
<bulletList>
<bullet_item>Feature Set—The output features will be returned as feature classes and tables. This is the default. </bullet_item>
<bullet_item>JSON File—The output features will be returned as a compressed file containing the JSON representation of the outputs. When this option is specified, the output is a single file (with a .zip extension) that contains one or more JSON files (with a .json extension) for each of the outputs created by the service. </bullet_item>
<bullet_item>GeoJSON File—The output features will be returned as a compressed file containing the GeoJSON representation of the outputs. When this option is specified, the output is a single file (with a .zip extension) that contains one or more GeoJSON files (with a .geojson extension) for each of the outputs created by the service.</bullet_item>
</bulletList>
</para>
<para>When a file based output format, such as JSON File or GeoJSON File, is specified, no outputs will be added to the display because the application, such as ArcMap or ArcGIS Pro, cannot draw the contents of the result file. Instead, the result file is downloaded to a temporary directory on your machine. In ArcGIS Pro, the location of the downloaded file can be determined by viewing the value for the Output Result File parameter in the entry corresponding to the tool execution in the geoprocessing history of your project. In ArcMap, the location of the file can be determined by accessing the Copy Location option in the shortcut menu on the Output Result File parameter in the entry corresponding to the tool execution in the Geoprocessing Results window. </para>
</pythonReference>
<dialogReference>
<para>
Specifies the format in which the output features will be created. </para>
<para>
Choose from the following options:
<bulletList>
<bullet_item>Feature Set—The output features will be returned as feature classes and tables. This is the default. </bullet_item>
<bullet_item>JSON File—The output features will be returned as a compressed file containing the JSON representation of the outputs. When this option is specified, the output is a single file (with a .zip extension) that contains one or more JSON files (with a .json extension) for each of the outputs created by the service. </bullet_item>
<bullet_item>GeoJSON File—The output features will be returned as a compressed file containing the GeoJSON representation of the outputs. When this option is specified, the output is a single file (with a .zip extension) that contains one or more GeoJSON files (with a .geojson extension) for each of the outputs created by the service.</bullet_item>
</bulletList>
</para>
<para>When a file based output format, such as JSON File or GeoJSON File, is specified, no outputs will be added to the display because the application, such as ArcMap or ArcGIS Pro, cannot draw the contents of the result file. Instead, the result file is downloaded to a temporary directory on your machine. In ArcGIS Pro, the location of the downloaded file can be determined by viewing the value for the Output Result File parameter in the entry corresponding to the tool execution in the geoprocessing history of your project. In ArcMap, the location of the file can be determined by accessing the Copy Location option in the shortcut menu on the Output Result File parameter in the entry corresponding to the tool execution in the Geoprocessing Results window. </para>
</dialogReference>
</param>
<param datatype="Multiple Value" direction="Input" displayname="Exclude Sources from Polygon Generation" expression="{cad_road_centerlines_bin}" name="Exclude_Sources_from_Polygon_Generation" sync="true" type="Optional">
<pythonReference>
<para>You can exclude certain network dataset edge sources when generating service area polygons. Polygons will not be generated around the excluded sources, even though they are traversed in the analysis.</para>
<para>Excluding a network source from service area polygons does not prevent those sources from being traversed. Excluding sources from service area polygons only influences the shape of the service area polygons. To prevent traversal of a given network source, you must create an appropriate restriction when defining your network dataset.</para>
<para>This is useful if you have some network sources that you don't want to be included in the polygon generation because they create less accurate polygons or are inconsequential for the service area analysis. For example, while creating a walk-time service area in a multimodal network that includes streets and metro lines, you should choose to exclude the metro lines from polygon generation. Although the traveler can use the metro lines, they cannot stop partway along a metro line and enter a nearby building. Instead, they must travel the full length of the metro line, exit the metro system at a station, then use the streets to walk to the building. It would be inaccurate to generate a polygon feature around a metro line.</para>
</pythonReference>
<dialogReference>
<para>You can exclude certain network dataset edge sources when generating service area polygons. Polygons will not be generated around the excluded sources, even though they are traversed in the analysis.</para>
<para>Excluding a network source from service area polygons does not prevent those sources from being traversed. Excluding sources from service area polygons only influences the shape of the service area polygons. To prevent traversal of a given network source, you must create an appropriate restriction when defining your network dataset.</para>
<para>This is useful if you have some network sources that you don't want to be included in the polygon generation because they create less accurate polygons or are inconsequential for the service area analysis. For example, while creating a walk-time service area in a multimodal network that includes streets and metro lines, you should choose to exclude the metro lines from polygon generation. Although the traveler can use the metro lines, they cannot stop partway along a metro line and enter a nearby building. Instead, they must travel the full length of the metro line, exit the metro system at a station, then use the streets to walk to the building. It would be inaccurate to generate a polygon feature around a metro line.</para>
</dialogReference>
</param>
<param datatype="Multiple Value" direction="Input" displayname="Accumulate Attributes" expression="{Time | Length}" name="Accumulate_Attributes" sync="true" type="Optional">
<pythonReference>
<para>
A list of cost attributes to be accumulated during analysis. These accumulated attributes are for reference only; the solver only uses the cost attribute used by your designated travel mode when solving the analysis.
</para>
<para>For each cost attribute that is accumulated, a Total_[Cost Attribute Name]_[Units] field is populated in the outputs created from the tool.</para>
</pythonReference>
<dialogReference>
<para>
A list of cost attributes to be accumulated during analysis. These accumulated attributes are for reference only; the solver only uses the cost attribute used by your designated travel mode when solving the analysis.
</para>
<para>For each cost attribute that is accumulated, a Total_[Cost Attribute Name]_[Units] field is populated in the outputs created from the tool.</para>
</dialogReference>
</param>
<param datatype="Boolean" direction="Input" displayname="Ignore Network Location Fields" expression="{Ignore_Network_Location_Fields}" name="Ignore_Network_Location_Fields" sync="true" type="Optional">
<pythonReference>
<para>
Specifies whether the network location fields will be considered when locating inputs such as stops or facilities on the network.
</para>
<para>
<bulletList>
<bullet_item>Checked (True in Python)—Network location fields will not be considered when locating the inputs on the network. Instead, the inputs will always be located by performing a spatial search. This is the default value.</bullet_item>
<bullet_item>Unchecked (False in Python)—Network location fields will be considered when locating the inputs on the network.</bullet_item>
</bulletList>
</para>
</pythonReference>
<dialogReference>
<para>
Specifies whether the network location fields will be considered when locating inputs such as stops or facilities on the network.
</para>
<para>
<bulletList>
<bullet_item>Checked (True in Python)—Network location fields will not be considered when locating the inputs on the network. Instead, the inputs will always be located by performing a spatial search. This is the default value.</bullet_item>
<bullet_item>Unchecked (False in Python)—Network location fields will be considered when locating the inputs on the network.</bullet_item>
</bulletList>
</para>
</dialogReference>
</param>
</parameters>
<returnvalues/>
<environments/>
<usage>
<bullet_item>
<para> Tools in the Ready To Use toolbox
areArcGIS Online geoprocessing services that use ArcGIS Online's hosted data and analysis
capabilities.</para>
</bullet_item>
<bullet_item>
<para> The tool creates drive-time areas if the value for the Break Units parameter is set to a time unit.
Similarly, the tool creates drive-distance areas if the Break Units are distance units.</para>
</bullet_item>
<bullet_item>
<para> You need to specify at least one facility to successfully execute the tool. You can load up to 1,000 facilities. </para>
</bullet_item>
<bullet_item>
<para>You can add up to 250 point barriers. You can add any number of line or polygon barriers, but line barriers cannot intersect more than 500 street features, and polygon barriers can't intersect more than 2,000 features.</para>
</bullet_item>
<bullet_item>
<para>Regardless of whether the Use Hierarchy parameter is checked (True), hierarchy is always used when the largest break value exceeds 240 minutes or 240 miles (386.24 kilometers). When the output service areas aren't overlapping and generalized, this limit is reduced to 15 minutes and 15 miles (24.14 kilometers).</para>
</bullet_item>
<bullet_item>
<para>If the distance between an input point and its nearest traversable street is greater than 12.42 miles (20 kilometers), the point is excluded from the analysis.</para>
</bullet_item>
<bullet_item>
<para> Travel times cannot exceed 9 hours (540 minutes) when walking or 5 hours (300 minutes) for all other travel modes.</para>
</bullet_item>
<bullet_item>
<para>Travel distances cannot exceed 27 miles (43.45 kilometers) when walking or 300 miles (482.80 kilometers) for all other travel modes.</para>
</bullet_item>
<bullet_item>
<para>When walking, the maximum travel time when generating detailed polygons cannot exceed 5 hours (300 minutes). For all other travel modes, the maximum travel time cannot exceed 15 minutes.</para>
</bullet_item>
<bullet_item>
<para>For all travel modes including walking, the maximum travel distance when generating detailed polygons cannot exceed 15 miles (24.14 kilometers). </para>
</bullet_item>
<bullet_item>
<para>When walking, the maximum travel time when generating service area lines cannot exceed 5 hours (300 minutes). For all other travel modes, the maximum travel time cannot exceed 15 minutes.</para>
</bullet_item>
<bullet_item>
<para>For all travel modes including walking, the maximum travel distance when generating service area lines cannot exceed 15 miles (24.14 kilometers).</para>
</bullet_item>
<bullet_item>
<para>Polygon trim distance cannot exceed 500 meters.</para>
</bullet_item>
<bullet_item>
<para> Using this service consumes credits. For more information, see Service Credits Overview.</para>
</bullet_item>
</usage>
<scriptExamples>
<scriptExample>
<title>GenerateServiceAreas example (stand-alone script)</title>
<para>The following Python script demonstrates how to use the GenerateServiceAreas tool in a script.</para>
<code xml:space="preserve">"""This example shows how to generate 5, 10, 15 minute drive time areas around facilities."""
import sys
import time
import arcpy
# Change the username and password applicable to your own ArcGIS Online account
username = "&lt;your user name&gt;"
password = "&lt;your password&gt;"
sa_service = "https://logistics.arcgis.com/arcgis/services;World/ServiceAreas;{0};{1}".format(username, password)
# Add the geoprocessing service as a toolbox.
# Check https://pro.arcgis.com/en/pro-app/arcpy/functions/importtoolbox.htm for
# other ways in which you can specify credentials to connect to a geoprocessing service.
arcpy.ImportToolbox(sa_service)
# Set the variables to call the tool
facilities = "C:/data/Inputs.gdb/Stores"
output_service_areas = "C:/data/Results.gdb/StoreServiceAreas"
# Call the tool
result = arcpy.GenerateServiceAreas_ServiceAreas(facilities, "5 10 15", "Minutes")
arcpy.AddMessage("Running the analysis with result ID: {}".format(result.resultID))
# Check the status of the result object every 1 second until it has a
# value of 4 (succeeded) or greater
while result.status &lt; 4:
time.sleep(1)
# print any warning or error messages returned from the tool
result_severity = result.maxSeverity
if result_severity == 2:
arcpy.AddError("An error occured when running the tool")
arcpy.AddError(result.getMessages(2))
sys.exit(2)
elif result_severity == 1:
arcpy.AddWarning("Warnings were returned when running the tool")
arcpy.AddWarning(result.getMessages(1))
# Store the output drive time polygons to a geodatabase
result.getOutput(0).save(output_service_areas)
</code>
</scriptExample>
</scriptExamples>
<shortdesc>
This service creates service areas around input points. Service areas include drive-time areas and are similar to buffers but are measured along roads instead of straight lines. </shortdesc>
<arcToolboxHelpPath>withheld</arcToolboxHelpPath>
</tool>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX">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</Data>
</Thumbnail>
</Binary>
</metadata>
