Project: #357 Modeling the impact of dynamic tolling in large-scale regional networks: a case study for DVRPC Progress Report - Reporting Period Ending: Sept. 30, 2021 Principal Investigator: Sean Qian Status: Active Start Date: July 1, 2021 End Date: June 30, 2022 Research Type: Advanced Grant Type: Research Grant Program: FAST Act - Mobility National (2016 - 2022) Grant Cycle: 2021 Mobility UTC Progress Report (Last Updated: Sept. 23, 2021, 9:48 p.m.) % Project Completed to Date: 20 % Grant Award Expended: 10 % Match Expended & Document: 10 USDOT Requirements Accomplishments In this research project, we aim to develop a large-scale multi-class network modeling and simulation framework, that holistically models the spatio-temporal behaviors of private cars, ride-hailing cars, freight trucks, respectively. The result includes the prediction of travel time, travel delay, vehicle-mile-traveled and emissions for each of those vehicle classes, travel modes, either at road and intersection level or averaged at traffic zone level by time of day. Potential tolling strategies, such as locations and pricing, can be evaluated and deployed, with the consideration of both system mobility and social equity. Task 1: Identify various data sources for travel behavior modeling in regional networks (100% done) Task 2: Establishing a multi-class dynamic network model for the Philadelphia Metropolitan Region considering tolling (20% done), to be 75% done by Dec 2021. This project has provided inter-disciplinary training for one phd student. Impacts This project is a continuation of the research from the Mobility21 projects “Data-driven Network Models for Analyzing Multi-modal Transportation Systems” in FY 2018, and “Mesoscopic car-truck flow modeling and simulation: theory and applications” in FY 2019, both led by PI Qian. It further extends the data-driven multi-modal mesoscopic network modeling and simulation framework on passenger and freight transportation developed in the two previous projects to include the tolling facilities, particularly arbitrary toll locations and costs in general. This project incorporates tolling into the multi-class simulation framework, and offers fundamental knowledge on how to turn multi-source data to decision making on tolling. The results are expected to be commercialized by PI Qian's spinnoff firm TraffiQure LLC. Other This research leads to a new modeling framework for data-driven multi-modal mesoscopic network flow and an extended simulation framework on passenger and freight transportation developed in the two previous projects to include the tolling facilities, particularly arbitrary toll locations and costs in general. This project incorporates tolling into the multi-class simulation framework Outcomes New Partners n/a Issues n/a