Synergistic Interactions of Dynamic Ridesharing and Battery Electric Vehicles Land Use, Transit, and Auto Pricing Policies

It is widely recognized that new vehicle and fuel technology is necessary, but not sufficient, to meet deep greenhouse gas (GHG) reductions goals for both the U.S. and the state of California. Demand management strategies (such as land use, transit, and auto pricing) are also needed to reduce passenger vehicle miles traveled (VMT) and related GHG emissions. In this study, the authors explore how demand management strategies may be combined with new vehicle technology (battery electric vehicles or BEVs) and services (dynamic ridesharing) to enhance VMT and GHG reductions. Owning a BEV or using a dynamic ridesharing service may be more feasible when distances to destinations are made shorter and alternative modes of travel are provided by demand management strategies. To examine potential markets, we use the San Francisco Bay Area activity based travel demand model to simulate business-as-usual, transit oriented development, and auto pricing policies with and without high, medium, and low dynamic ridesharing participation rates and BEV daily driving distance ranges. The results of this study suggest that dynamic ridesharing has the potential to significantly reduce VMT and related GHG emissions, which may be greater than land use and transit policies typically included in Sustainable Community Strategies (under California Senate Bill 375), if travelers are willing pay with both time and money to use the dynamic ridesharing system. However, in general, large synergistic effects between ridesharing and transit oriented development or auto pricing policies were not found in this study. The results of the BEV simulations suggest that TODs may increase the market for BEVs by less than 1% in the Bay Area and that auto pricing policies may increase the market by as much as 7%. However, it is possible that larger changes are possible over time in faster growing regions where development is currently at low density levels (for example, the Central Valley in California). The VMT Fee scenarios show larger increases in the potential market for BEV (as much as 7%). Future research should explore the factors associated with higher dynamic ridesharing and BEV use including individual attributes, characteristics of tours and trips, and time and cost benefits. In addition, the travel effects of dynamic ridesharing systems should be simulated explicitly, including auto ownership, mode choice, destination, and extra VMT to pick up a passenger

Modeling and Evaluation of a Ridesharing Matching System from Multi-Stakeholders\u27 Perspective

With increasing travel demand and mobility service quality expectations, demand responsive innovative services continue to emerge. Ridesharing is an established, yet evolving, mobility option that can provide more customized, reliable shared service without any new investment in the transportation infrastructure. To maximize the benefits of ridesharing service, efficient matching and distribution of riders among available drivers can provide a reliable mobility option under most operating conditions. Service efficiency of ridesharing depends on the system performance (e.g., trip travel time, trip delay, trip distance, detour distance, and trip satisfaction) acceptable to diverse mobility stakeholders (e.g., riders, drivers, ridesharing operators, and transportation agencies). This research modeled the performance of a ridesharing service system considering four objectives: (i) minimization of system-wide passengers’ waiting time, (ii) minimization of system-wide vehicle miles travelled (VMT), (iii) minimization of system-wide detour distance, and (iv) maximization of system-wide drivers’ profit. Tradeoff evaluation of objectives revealed that system-wide VMT minimization objective performed best with least sacrifices on the other three objectives from their respective best performance level based on set of routes generated in this study. On the other hand, system-wide drivers’ profit maximization objective provided highest monetary incentives for drivers and riders in terms of maximizing profit and saving travel cost respectively. System-wide minimization of detour distance was found to be least flexible in providing shared rides. The findings of this research provide useful insights on ridesharing system modeling and performance evaluation, and can be used in developing and implementing ridesharing service considering multiple stakeholders’ concerns

Design and operation of efficient and budget-balanced shared-use mobility systems

Final ReportTraffic congestion has become a serious issue around the globe, partly owing to single-occupancy commuter trips. Ridesharing can present a suitable alternative for serving commuter trips. However, there are several important obstacles that impede ridesharing systems from becoming a viable mode of transportation, including the lack of a guarantee for a ride back home as well as the difficulty of obtaining a critical mass of participants. This paper addresses these obstacles by introducing a Traveler Incentive Program (TIP) to promote community-based ridesharing with a ride-back home guarantee among commuters. The TIP program allocates incentives to (1) directly subsidize a select set of ridesharing rides, and (2) encourage a few, carefully selected set of travelers to change their travel behavior (i.e., departure or arrival times). We formulate the underlying ride-matching problem as a budget-constrained min-cost flow problem, and present a Lagrangian Relaxation-based algorithm with a worst-case optimality bound to solve large-scale instances of this problem in polynomial time. We further propose a polynomial-time budget-balanced version of the problem. Numerical experiments suggest that allocating subsidies to change travel behavior is significantly more beneficial than directly subsidizing rides. Furthermore, using a flat tax rate as low as 1\% can double the system's social welfare in the budget-balanced variant of the incentive program.http://deepblue.lib.umich.edu/bitstream/2027.42/167380/1/Design and operation of efficient and budget-balanced shared-use mobility systems.pdfDescription of Design and operation of efficient and budget-balanced shared-use mobility systems.pdf : Final Repor

Efficiency, Fairness, and Stability in Non-Commercial Peer-to-Peer Ridesharing

Unlike commercial ridesharing, non-commercial peer-to-peer (P2P) ridesharing has been subject to limited research -- although it can promote viable solutions in non-urban communities. This paper focuses on the core problem in P2P ridesharing: the matching of riders and drivers. We elevate users' preferences as a first-order concern and introduce novel notions of fairness and stability in P2P ridesharing. We propose algorithms for efficient matching while considering user-centric factors, including users' preferred departure time, fairness, and stability. Results suggest that fair and stable solutions can be obtained in reasonable computational times and can improve baseline outcomes based on system-wide efficiency exclusively