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

Mobility on Demand in the United States

The growth of shared mobility services and enabling technologies, such as smartphone apps, is contributing to the commodification and aggregation of transportation services. This chapter reviews terms and definitions related to Mobility on Demand (MOD) and Mobility as a Service (MaaS), the mobility marketplace, stakeholders, and enablers. This chapter also reviews the U.S. Department of Transportation’s MOD Sandbox Program, including common opportunities and challenges, partnerships, and case studies for employing on-demand mobility pilots and programs. The chapter concludes with a discussion of vehicle automation and on-demand mobility including pilot projects and the potential transformative impacts of shared automated vehicles on parking, land use, and the built environment

Enhancing Capacity and Managing Demand to Increase Short-Term Throughput on the San Francisco-Oakland Bay Bridge

While there are many proposals for fixing congestion between San Francisco and Oakland in California by adding a new bridge or tube, these solutions will take decades to implement even though a solution is needed now. This thesis assesses sixteen different strategies for reducing congestion in the short-term in the four categories of improving transit, promoting carpooling, implementing intelligent transportation systems practices, and incentivizing alternatives to using the Bay Bridge. Top priorities include HOV improvements on the West Grand Avenue and Powell Street onramps, altering WestCAT Lynx and BART transit services, partnering with rideshare apps to increase transit station accessibility (last mile problem), partnering with vanpool/minibus apps, promoting carpooling and implementing a citizen report system for carpool violators, shifting corporate cultures away from requiring employees to drive and drive alone, and lastly, altering land-use planning practices. To reach this conclusion, an inventory of current proposals and relevant research was compiled. Ridership and capacity data for the various modes of transportation across the bay were assessed for shortfalls and opportunities. Through this research and its resultant conclusions, focus can be placed on the best strategies to pursue in the near-term, while the Bay Area waits on a second bridge or tube in the long-term

Chapter 3 - Mobility on demand (MOD) and mobility as a service (MaaS): early understanding of shared mobility impacts and public transit partnerships

Technology is changing the way we move and reshaping cities and society. Shared and on-demand mobility represent notable transportation shifts in the 21st century. In recent years, mobility on demand (MOD)—where consumers access mobility, goods, and services on-demand by dispatching shared modes, courier services, public transport, and other innovative strategies—has grown rapidly due to technological advancements; changing consumer preferences; and a range of economic, environmental, and social factors. New attitudes toward sharing, MOD, and mobility as a service (MaaS) are changing traveler behavior and creating new opportunities and challenges for public transportation. This chapter discusses similarities and differences between the evolving concepts of MaaS and MOD. Next, it characterizes the range of existing public transit and MOD service models and enabling partnerships. The chapter also explores emerging trends impacting public transportation. While vehicle automation could result in greater public transit competition in the future, it could also foster new opportunities for transit enhancements (e.g., microtransit services, first- and last-mile connections, reduced operating costs). The chapter concludes with a discussion of how MOD/MaaS partnerships and automation could enable the public transit industry to reinvent itself, making it more attractive and competitive with private vehicle ownership and use

Proactive empty vehicle rebalancing for Demand Responsive Transport services

Worldwide, ridesharing business is steadily growing and has started to receive attention also by public transport operators. With future fleets of Autonomous Vehicles, new business models connecting schedule-based public transport and feeder fleets might become a feasible transport mode. However, such fleets require a good management to warrant a high level of service. One of the key aspects of this is proactive vehicle rebalancing based on the expected demand for trips. In this paper we model vehicle rebalancing as the Dynamic Transportation Problem. Results suggest that waiting times can be cut by around 30 % without increasing the overall vehicle miles travelled for a feeder fleet in rural Switzerland

A Better Match for Drivers and Riders: Reinforcement Learning at Lyft

To better match drivers to riders in our ridesharing application, we revised Lyft's core matching algorithm. We use a novel online reinforcement learning approach that estimates the future earnings of drivers in real time and use this information to find more efficient matches. This change was the first documented implementation of a ridesharing matching algorithm that can learn and improve in real time. We evaluated the new approach during weeks of switchback experimentation in most Lyft markets, and estimated how it benefited drivers, riders, and the platform. In particular, it enabled our drivers to serve millions of additional riders each year, leading to more than $30 million per year in incremental revenue. Lyft rolled out the algorithm globally in 2021