The investigators of the project would like to acknowledge support from the National Institute for Transportation and Communities (NITC) under grant number (881) and the Oregon Department of Transportation under grant number SPR-788.
Autonomous vehicles -- United States, Traffic Engineering, Transportation
Performance-based planning helps local and state decision makers to understand the potential impacts of policy decisions, supporting cost-effective investments and policy choices that can help achieve policy goals. In addition, it can enable monitoring of progress and facilitate needed adjustments, help them communicate to the public, and assist them with meeting federal regulations and the intent of MAP21. The Regional Strategic Planning Model (RSPM) is a performance-based planning tool first developed by Oregon State DOT (as GreenSTEP) and later adapted for use by other states in the form of the Federal Highway Administration (FHWA) Emissions Reduction Policy Analysis Tool (EERPAT) and the underlying basis of the SHRP2 C16 Smart Growth Area Planning software (SmartGAP).
As the popularity of the RSPM tool grows and application cases expand, there is recognition that a deeper understanding is needed to determine how mode choices and mode share may be impacted by policy and investment decisions and how these mode choices further influence performance outcomes of the transportation system. This is particularly important when the tool is applied in a broader base of planning and decision-making processes to truly understand what may be the best decisions for the entire multi-modal and inter-modal transportation system.
ODOT is sponsoring a first phase research project led by this research team to incorporate broad stroke multi-modal travel choices in the RSPM tool. This proposed project hopes to leverage the ODOT and NITC funding to further study, along with existing modes, emerging travel modes, including car sharing, bike sharing, and autonomous vehicles, with stated preference (SP) experiments, and incorporate these new options into the RSPM tool. These modes have been rapidly gaining popularity worldwide, which will have long-term implications for car ownership decisions, fleet characteristics, travel patterns, and further system-wide performance outcomes. By incorporating these modes in the mode choice module, this project will make the RSPM tool sensitive to policies and investment targeted to shift mode share and enable it to evaluate futures in which these modes may become the mainstream, besides contributing to the emerging body of research that aims to better understanding these modes.
Wang, Liming, Joseph Broach, and Huajie Yang. Modeling for New Modes: Autonomous Vehicles & Shared Rides. Project Brief, NITC-RR-881. Portland, OR: Transportation Research and Education Center (TREC), 2018.