Difference between revisions of "3.2 All Streets Network Preparation"

Latest revision as of 19:43, 25 August 2020

The NERPM-AB (activity-based) model uses microzones as the fundamental spatial unit for predicting travel demand. In order to support this more detailed geography, DaySim is designed to take advantage of more accurate distances for short distance on-street trips than are available from the TAZ-to-TAZ road network skims. These more accurate shortest-path node-to-node distances are calculated from an “All Streets Network” which is a network that (as the name indicates) that consists of all the streets in the model region. A typical model network would only consist of freeways, expressways, and major and minor arterials. This coarser network cannot accurately represent the accessibility between small geographies such as microzones.

These short distance microzones-level impedances are most important for non-motorized trips such as bike and walk trips. Thus, facilities such as freeways and expressways, freeway ramps, and other facilities that are not available to non-motorized modes are excluded from the all-streets based network pathbuilding. In addition, short driveways (if present) are also typically dropped from the all-streets network in order to reduce the computation burden. The modified all streets network is then used to generate node-to-node shortest path distance as described in the next section.

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 [[Category:3.0 Model Design]]
-The NERPM activity-based model uses parcels as the fundamental spatial unit for predicting travel demand.  In order to support this more detailed geography, DaySim is designed to take advantage of more accurate distances for short distance on-street trips than are available from the TAZ-to-TAZ road network skims. These more accurate shortest-path node-to-node distances are calculated from an “All Streets Network” which is a network that (as the name indicates) that consists of all the streets in the model region. A typical model network would only consist of freeways, expressways, and major and minor arterials.  This coarser network cannot accurately represent the accessibility between small geographies such as parcels.
+The NERPM-AB (activity-based) model uses microzones as the fundamental spatial unit for predicting travel demand.  In order to support this more detailed geography, DaySim is designed to take advantage of more accurate distances for short distance on-street trips than are available from the TAZ-to-TAZ road network skims. These more accurate shortest-path node-to-node distances are calculated from an “All Streets Network” which is a network that (as the name indicates) that consists of all the streets in the model region. A typical model network would only consist of freeways, expressways, and major and minor arterials.  This coarser network cannot accurately represent the accessibility between small geographies such as microzones.
-These short distance parcel-level impedances are most important for non-motorized trips such as bike and walk trips.  Thus, facilities such as freeways and expressways, freeway ramps, and other facilities that are not available to non-motorized modes are excluded from the all-streets based network pathbuilding.  In addition, short driveways (if present) are also typically dropped from the all-streets network in order to reduce the computation burden.  The modified all streets network is then used to generate node-to-node shortest path distance as described in the next section.
+These short distance microzones-level impedances are most important for non-motorized trips such as bike and walk trips.  Thus, facilities such as freeways and expressways, freeway ramps, and other facilities that are not available to non-motorized modes are excluded from the all-streets based network pathbuilding.  In addition, short driveways (if present) are also typically dropped from the all-streets network in order to reduce the computation burden.  The modified all streets network is then used to generate node-to-node shortest path distance as described in the next section.