Preferences of urban rail users for first- and last-mile autonomous vehicles: Price and service elasticities of demand in a multimodal environment
place - urban, place - asia, mode - rail, mode - bike, mode - bus, mode - car, mode - pedestrian, mode - demand responsive transit, ridership - demand, ridership - mode choice, planning - surveys
Autonomous vehicle, Transit access, Stated choice, Elasticity of demand, On-demand service, Urban rail
Integrating autonomous vehicles (AVs) into transit networks is a critical problem for metropolitan areas worldwide. This study investigates the preferences of urban rail/rapid transit users for first- and last-mile AV services, which are not well understood. First, it develops an access mode choice model incorporating on-demand transit access enabled by AVs. Second, the model is estimated with stated choice data obtained from a survey of 2,300 residents living within 1–5 km of their nearest rail stations in the Tokyo metropolitan area, in which choice tasks are based on recent access to home-end stations. Finally, estimated models are used to compute elasticities of demand for access modes with respect to AV cost and wait time. The results show that price sensitivities of demand for AV services lie within a reasonable range of transit fare elasticities. AVs have diverse substitution patterns with existing modes. Riding in AV services is more likely to be a substitute for riding on buses/in slower modes of transit for all travel purposes, and for driving cars, particularly for leisure trips, but less likely for cycling and walking, particularly for work trips. The AV wait time elasticities might vary according to the waiting environment and conditions. The results also suggest that AVs may particularly benefit those who currently have restrictions in accessing transit.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Abe, R. (2021). Preferences of urban rail users for first- and last-mile autonomous vehicles: Price and service elasticities of demand in a multimodal environment. Transportation Research Part C: Emerging Technologies, Vol. 126, 103105.