1-6: Predicting Travel and Congestion in a Post-Pandemic America
|Principal Investigator||Mark Burris, Ph.D.|
|Final Report (DOI)||Available Soon|
|Policy Brief||Available Soon|
|RIP||View RIP entry|
COVID-19 has affected travel behavior in many ways. Changes include a predictable reduction in overall travel, and even larger reductions in transit travel, carpooling, and travel on toll facilities. What happens to travel and congestion once we return to a ‘new normal’? There is concern that transit ridership will not fully rebound causing additional congestion in dense urban areas that rely on ransit and escalate equity issues for underserved sectors and populations. Also toll road and managed lane traffic may not rebound causing both inefficient use of that road space and revenue shortfalls. This project will examine recent trends and attempt to predict travel in a ‘new normal’ (post-pandemic) environment utilizing behavioral economics, psychology, big data, and transportation expertise. Phase 1 focuses on identifying the changes in travel and the potential recovery rates for the travel modes while Phase 2 integrates these changes in planning models to predict future traffic projections using real world scenarios. Phase 1 includes a series of behavioral economics laboratory-based experiments, involving the latest methodologies such as tracking eye movements and using sensors to track brain activity as respondents make travel choices in an experimental environment that will be based on realistic travel situations. These studies will be combined with detailed traffic and transit data from several urban areas along with expert opinions from transit operators and toll road authorities to predict a ‘new normal’. In Phase 2, researchers will use available data from large-scale origin-destination data sets along with findings from Phase 1 to better predict travel, congestion, farebox revenues and toll revenues in a ‘new normal’ along with impacts on travelers of different income levels and ethnicities.