Demand-Driven Operational Design for Shared Mobility with Ride-pooling Options

University Transportation Centers Program2020PDFTech ReportZhao, DongfangGuo, XiaotongLi, XiaopengSamaranayake, SamithaUniversity of South Florida. School of Civil and Environmental EngineeringCornell University. Center for Transportation, Environment, and Community HealthUnited States. Department of Transportation. University Transportation Centers (UTC) ProgramUnited States. Department of Transportation. Office of the Assistant Secretary for Research and TechnologyCornell University. Center for Transportation, Environment, and Community Health. (CTECH)United StatesCase studiesMachine learningRidesourcingShared mobilityTravel demandElectric vehiclesGravityRidesharingQ learningride poolinggravity modelcolumn generation69A355174711910/1/2018 \u2013 04/13/2020This project aims to develop a demand-driven approach for shared mobility operations with machine learning and math programming methods. The objective of this approach is to incorporate economic, environment and equity impacts over an entire operational cycle. Both ride-hailing systems (e.g. Lyft) and ride-pooling systems (e.g. UberPool) will be investigated. The developed models are tested with real-world taxi data including detailed trajectories of vehicles and their loading states at all times. The authors proposed a deep Q learning model to optimize the system performance. In the model, the authors train a Q network and operate the system with real-time demands. Services include serving travel demands, rebalance, charging in charging stations. Case study in New York City (NYC) is explored and the results are analyzed in the case study section. Further, the authors propose a mathematical programming approach for solving the vehicle routing problem for a high-capacity ride-pooling system. This approach attempts to extend the existing Request-Trip-Vehicle-graph based method that used heuristic methods to generate vehicle routes into one that use customized column generation to generate candidate optimal vehicle routes more efficiently.125