Travel behavior and system dynamics in a simple gamified automated multimodal network

Mor Collins, Shelly Etzioni and Eran Ben-Elia

Transportation Research Part A: Policy and Practice, 2024, vol. 183, issue C

Abstract: Automated Vehicles (AVs) are poised to disrupt travel patterns and the sustainability of transportation networks. Conventional methods for studying these changes, such as stated preference surveys and agent-based simulations, have limitations. Serious games offer a promising alternative, providing a controlled and engaging environment for investigating travel behavior. In our study, 200 participants, grouped into sessions of 10, engaged in a competitive serious game simulating 50 daily choices of travel mode and departure time across three automated options. Two scenarios were examined: one with recurring congestion and another with nonrecurring congestion. Automated transit had fixed schedules, while private and shared rides could adapt to a congested bottleneck. Results revealed that ridesharing dominated, reaching 60% mode share under recurring congestion, displacing transit, and a comparative equilibrium emerged between shared and private rides. In the nonrecurring congestion scenario, ridesharing dropped to 37%, and a comparable multimodal equilibrium developed. Participants rarely achieved the optimal score, attaining a maximum of 88% of its potential. This study highlights a policy paradox: unregulated AV traffic can reduce transit use, exacerbate recurring congestion, yet necessitate increased transit investment to address nonrecurring congestion, confirming the Downs-Thomson paradox. Creating appealing mass transit alternatives is imperative to ensure efficiency and sustainability in the era of automated mobility.

Keywords: Shared Automated Vehicles; Ridesharing; Reinforced Learning; User Equilibrium; System Optimum; Serious Game (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.tra.2024.104060

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