(Created page with "{{Incubator}} Category:Incubator topics Several agencies have already begun to integrate connected and autonomous vehicles into their travel forecasting process. All are...")
(No difference)

Revision as of 21:45, 8 November 2017

This page is open for editing because it is part of the Incubator. Have something to add? Please register so you can contribute. Have an option you would like to share? Please click on the 'Talk' button to enter the dialogue. The TF Resource Volunteers appreciate your feedback and interest.

Several agencies have already begun to integrate connected and autonomous vehicles into their travel forecasting process. All are "works in progress," such that the descriptions below are likely to evolve over the next year in response to changing analytical needs, knowledge is gained about social and behavioral responses to CAVs, and best practices emerge.

Ontario (Canada)

The ability to explicitly represent CAV demand and impacts was recently added to Ontario's provincial model. The modeling system, known as the Transport and Regional Economic Simulation of Ontario (TRESO), is a microsimulation-based modeling system that integrates local and long-distance resident, visitors, and commercial vehicle travel models with a space-time traffic assignment operating at two levels of network resolution. The specific enhancements relevant to modeling connected and autonomous vehicles include:

  • Vehicles are added to the synthetic population (household and persons) based upon user-specified rules of CAV5 adoption by market segment. The possibilities include conventional and autonomous vehicles by SAE automation level that are either privately owned or shared. The latter are intentionally vaguely defined to enable travelers to choose the service or mode with highest utility during mode choice. The markets can be segmented by income, household structure, area type, or other household or person attributes.
  • CAVs and mobility services (e.g., Lyft, Uber) have been added to the mode choice model, both as top-level choices as well as access and egress modes for various transit submodes.
  • A ride-pairing module has been added to match inter-household trips by user-defined criteria and market shares to reflect potentially increased ride-sharing in both CAV and traditional vehicles. The matches are often made for similar travelers moving between the same origin and destination within a given time slice, which can vary by origin and/or destination region or accessibility levels at the origin or destination.
  • The capacities in traffic assignment are adjusted based upon the degree of market penetration by CAVs implied during vehicle synthesis. The adjustments are based upon a methodology advanced by Levin & Boyles (2015) and traffic flow and vehicular considerations shared by Mahmassani (2016).