The Impact of Shared Mobility Options on Travel Demand

Abstract

Newly available shared mobility options are having a large impact on travel. Car- and bike-sharing and ride-hailing have become increasingly viable and attractive travel modes since they have been app-based and able to link riders and vehicles in real time and space. This project aimed to provide much needed information on how app-based shared mobility options are affecting travel behavior, and specifically how they are changing the parameters leading to mode choice and mode share. We used three available secondary data sets to explore whether shared mobility options substitute for or complement traditional modes. The first set of data came from the 2017 Puget Sound regional Household Travel survey. We found that car-sharing and ride-hailing substituted for household vehicle trips. Yet they induced more travel, which could add to traffic congestion but could also improve access to activities. Substitution effects with transit and biking, and additional walking, differed by day of week and commute status, suggesting that future research focus on the temporal and purpose characteristics of trips by shared mobility. The second set of data came from the Washington State Commute Trip Reduction (CTR) program. We found that in the immediate, CTR instruments used to collect data on commute trips could add questions about shared mobility options. In the long run, CTR employer and employee surveys could be redesigned to facilitate the evaluation of employers’ TDM efforts. Also, deploying apps to support the commute trip could yield invaluable and timely information for transportation policy and research. The third set of data addressed “shared micro-mobility,” an increasingly popular form of shared mobility that includes bicycles and scooters. Companies that offer this service have dispersed hundreds or even thousands of bicycles and scooters across individual cities for customers to use. A few companies provide real-time location data for their bikes and scooters via the Internet. We created a computer program to continuously “scrape” and archive such data. A technical description of the online database system was provided. A pilot-study served to analyze one year of data and to create trip generation models.