United States Department of Transportation
i
Synergistic interactions of dynamic ridesharing and battery electric vehicles land use, transit, and auto pricing policies.
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2015-10-01
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Abstract: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.
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