Difference between revisions of "3.12 Assignment"
(Created page with "Category:3.0 Model Design") |
|||
| Line 1: | Line 1: | ||
[[Category:3.0 Model Design]] | [[Category:3.0 Model Design]] | ||
| + | |||
| + | <h2>Highway</h2> | ||
| + | |||
| + | The ‘Highway Assignment’ application performs the final vehicle assignment by time period step using Cube’s HIGHWAY program on the unloaded networks (again by time period) prepared earlier. | ||
| + | |||
| + | Application Name = Highway Assignment | ||
| + | Application Purpose = Assigns daily vehicle demand onto the regional planning network; Assigns four (4) period and daily vehicle demand onto the regional planning network (AM, MD, PM, NT, Daily) | ||
| + | Primary Input = Regional planning highway network and vehicle trip tables (*.mat) | ||
| + | Primary Output = Final Daily, AM, MD, PM and NT model period loaded highway networks (*.net) containing link volumes and congested link travel times (speeds) | ||
| + | |||
| + | The purpose of the trip assignment model is to load vehicle trips onto the representation of the transportation highway network. This process results in traffic estimates on individual links in the network that ultimately attempt to simulate general vehicular travel throughout the planning region. | ||
| + | |||
| + | The highway vehicle trips are loaded separately by occupancy class. The assignment classes are required in order to prohibit single-occupancy vehicles and truck from utilizing the high-occupancy vehicle only lanes on the highways and interstates. The assignment employs a user equilibrium method that is an iterative process to achieve a convergent solution where no travelers on the roadway network can improve travel-times by shifting routes. Throughout each of these iterations, Cube Voyager computes network-link flows, which incorporate link-capacity restraint effects and flow-dependent travel-times. At the end of assignment, validation reports are generated by comparing the modeled volumes to observed traffic count on network links. Finally, the loaded networks are skimmed to produce skim LOS variables to be used in the next global iteration of the model. | ||
| + | |||
| + | <h2>Transit<h2> | ||
| + | |||
| + | Transit assignment is the process of loading the transit trips onto the transit paths. There are only two time periods (Peak and Off-peak) for transit assignment as opposed to four in highway assignment. Peak (PK) represents the combination of the AM peak and PM peak transit trips produced by Daysim, while Off-peak (OP) represents the combination of the midday and evening transit trips. All the transit trips that are assigned are generated from DaySim output. Transit assignment for 2 sub-modes (Bus and project mode) and three access modes (Walk, PNR, and KNR) is performed by ‘Transit Assignment’ application using Cube’s PUBLIC TRANSPORT program on the transit networks prepared in the earlier steps. | ||
| + | |||
| + | Application Name = Transit Assignment | ||
| + | Application Purpose = Assigns transit vehicle trip matrices onto the regional transit network; Assigns two (2) periods of transit demand onto the regional planning transit network, peak (PK) and off-peak (OP) which summed together represent the daily transit assignment results. | ||
| + | Primary Input = Regional planning transit network and transit trip tables (*.mat) estimated from the DaySim activity-based model | ||
| + | Primary Output = Daily (peak + off-peak) transit route-level ridership and boardings/alightings by stop and station | ||
| + | |||
| + | The PUBLIC TRANSPORT assignment process generates an output DBF file which contains a summary of boardings and alightings by route as well as station-level summaries. | ||
| + | |||
| + | DaySim produces a transit trip database including the origin TAZ, destination TAZ, transit sub-mode, time of day and the number of transit trips. The transit trip database is then converted into a Cube Voyager transit trip matrix with the following tables below. | ||
| + | |||
| + | #Walk-to-bus and BRT | ||
| + | #Walk-to-project and premium | ||
| + | #PNR-to-bus and BRT | ||
| + | #PNR-to-project and premium | ||
| + | #KNR-to-bus and BRT | ||
| + | #KNR –to-project and premium | ||
| + | #CBDKNR-to-bus and BRT | ||
| + | #CBDKNR-to-project and premium | ||
| + | #Fringe PNR | ||
Revision as of 21:17, 17 October 2016
Highway
The ‘Highway Assignment’ application performs the final vehicle assignment by time period step using Cube’s HIGHWAY program on the unloaded networks (again by time period) prepared earlier.
Application Name = Highway Assignment Application Purpose = Assigns daily vehicle demand onto the regional planning network; Assigns four (4) period and daily vehicle demand onto the regional planning network (AM, MD, PM, NT, Daily) Primary Input = Regional planning highway network and vehicle trip tables (*.mat) Primary Output = Final Daily, AM, MD, PM and NT model period loaded highway networks (*.net) containing link volumes and congested link travel times (speeds)
The purpose of the trip assignment model is to load vehicle trips onto the representation of the transportation highway network. This process results in traffic estimates on individual links in the network that ultimately attempt to simulate general vehicular travel throughout the planning region.
The highway vehicle trips are loaded separately by occupancy class. The assignment classes are required in order to prohibit single-occupancy vehicles and truck from utilizing the high-occupancy vehicle only lanes on the highways and interstates. The assignment employs a user equilibrium method that is an iterative process to achieve a convergent solution where no travelers on the roadway network can improve travel-times by shifting routes. Throughout each of these iterations, Cube Voyager computes network-link flows, which incorporate link-capacity restraint effects and flow-dependent travel-times. At the end of assignment, validation reports are generated by comparing the modeled volumes to observed traffic count on network links. Finally, the loaded networks are skimmed to produce skim LOS variables to be used in the next global iteration of the model.
