Impact of Automated Mobility-On-Demand on Weekly Activity Patterns: A Study of Singapore. Issue 6 (June 2022)

• Record Type: Journal Article
• Authors: Hermawan, Karina; Seshadri, Ravi; Sakai, Takanori; Zegras, P. Christopher; Ben-Akiva, Moshe
• Is Part Of: Transportation research record. Volume 2676:Issue 6(2022)
• Page Start: 340
• Page End: 352
• Publication Date: 2022-06
• Subjects: data and data science -- mode choice data -- planning and analysis -- choice models -- demand estimation -- forecasts/forecasting -- mode choice -- models/modeling -- ridership estimation modeling -- travel demand modeling -- cell phone data -- traveler behavior and values -- behavior analysis -- behavioral process -- behaviors -- pattern; Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/03611981221074376
• Languages: English
• ISSNs: 0361-1981

Abstract

The use of on-demand ride services has continued to grow rapidly in recent years. At some point, given current technologies of automation, it is plausible that these rides will be driverless, termed automated mobility-on-demand (AMoD). This research examines how eager people are to adopt AMoD ride services and whether they will change their travel behaviors and activity patterns when these services are available. We use data from the first ever activity-based stated preferences (SP) survey and estimate an ordered logit model to answer these questions. We demonstrate the capability of the unique SP survey data in capturing preferences toward an emerging transportation mode by considering the utilities of week-level activities and trips as functions of activity duration, scheduling preferences, travel disutility, and sociodemographic variables. Our key findings suggest that people do display a propensity to use the new AMoD services, but this propensity falls as more travel is undertaken with AMoD. Moreover, those who are likely to use AMoD tend to be car-less, young, and frequent users of ride-hailing services. They would typically use AMoD to perform additional leisure, personal, and meal activities, which may increase travel and travel costs. The model and results have important policy implications and applications.