A Randomized Encouragement Experiment to Measure the Causal Effects of Shared Mobility Services on Travel Behavior and Car Ownership
Xiao Wen, University of WashingtonShow Abstract
Andisheh Ranjbari, University of Washington
Yanbo Ge, National Renewable Energy Laboratory (NREL)
Michiko Namazu, University of British Columbia
Don MacKenzie, University of Washington
Shared mobility services including car-sharing and ride-hailing have been previously reported to reduce car ownership and greenhouse gas emissions and to change travel behaviors. However, most prior studies have suffered from selection bias (i.e. differences in underlying behavior among shared mobility adopters versus non-adopters) and/or have relied on mobility service adopters to construct their own counterfactual scenarios (i.e. how they would have behaved if services were not available). To strengthen causal inferences, this study employs a randomized encouragement experimental design in a before/after survey to identify the causal effects of joining car-sharing (ReachNow) or ride-hailing (Lyft) services on daily trip rates and car ownership. The questionnaires are sent out to students, faculty, and staff at the University of Washington in Seattle, Washington in two waves, one and half years apart. Between the two waves, a subset of respondents who were not already car-sharing or ride-hailing users were randomly selected to receive incentives to begin using one or both of those services (The usage level of incentives is reported for each service). The car-share membership and ride-hailing usage of respondents were compared between the two waves of surveys, and two statistical methods (instrumental variable and difference-in-difference) were used to identify if using shared mobility services affected people’s travel behavior, but the analysis results did not show statistically significant impacts on people’s trip rates nor vehicle ownership as a result of using car-sharing or ride-hailing services.
Taxis, Apps, and Transit: How the Flow of Information May Redistribute Transport Supply to Meet Demand
Adam Davidson, City University of New York (CUNY)Show Abstract
Transport data that fuels smartphone-apps has progressively become a tool to help people achieve mobility by adding legibility, usability and reliability to the transport ecosystem. This paper’s goal is to understand the impact of data and information on transportation supply by evaluating the spatial distribution of New York City’s for-hire-vehicle (FHV) market. By using increasingly robust data about vehicle assets, transport providers have found new ways to help match supply and demand. Here, data has two purposes: 1) to inform the traveling public of their supply options; and, if needed 2) to spatially match asset supply with user demand. This interaction has the possibility to shift supply or demand as users experience more options or operators seek under-served markets. Improved data reporting requirements coincided with new FHV-market entrants to form a natural experiment that reveals changes in transport supply. By comparing the spatial distribution of FHV’s in 2015 to a 2012 control, we see that supply increased in thinner markets in ways that are more complex than just adding supply to the street-hail system. This paper compares the spatial distribution of trip origins between the population of street-hail taxis, Uber, and Uber booked through a mobility-aggregator called Transit App to the 2012 control. It finds that as more segments of data & information are utilized to visualize or arrange supply, supply becomes more distributed relative to public transit service and the city core. Utilization of data & information appears key in helping supply to spatially distribute towards thinner demand.