When do autonomous vehicles solve or exacerbate different urban mobility problems?

Abstract

Background: The introduction of autonomous vehicles (AVs) could fundamentally transform urban transportation, but their system-level effects on cities remain poorly understood. Previous research has focused primarily on individual adoption decisions or specific impacts like congestion, without capturing the complex interactions between adoption patterns, modal shifts, and transportation system performance.

Goal: This study investigates how autonomous vehicles might affect urban mobility problems, considering both modal shifts and induced demand, and examines which policies could effectively mitigate potential negative impacts while preserving benefits.

Method: An agent-based model combined with mesoscopic traffic simulation was developed to simulate travel behavior in Rotterdam, Netherlands. The model integrates empirical data on population distribution, travel patterns, and network characteristics with a mode choice framework accounting for heterogeneous time valuations. A full-factorial analysis explored 144 scenarios varying AV costs, perceived time value, space efficiency, and induced demand. Eight representative scenarios were then tested against nine policy combinations including congestion pricing and speed reductions.

Results: AV adoption patterns appear to depend more strongly on space efficiency than cost or comfort advantages. A critical threshold around a density factor of 0.5 (compared to conventional vehicles) emerged - below this threshold, high AV adoption can maintain system performance, while above it, increased adoption tends to degrade network performance regardless of other characteristics. The model also revealed that AVs compete more directly with sustainable transport modes than with private cars, potentially undermining urban sustainability goals. Traditional policy interventions showed limited effectiveness across different scenarios, with localized restrictions proving particularly inadequate for managing system-level impacts.

Conclusions: Autonomous vehicles may represent neither an inherent solution nor an inevitable problem for urban mobility. Their impact appears likely to depend on the interaction between their operating characteristics, adoption patterns, and policy frameworks. The significant variations between potential futures - ranging from improved mobility to system strain - emphasize the importance of proactive policy consideration in AV development. Results suggest that cities should focus on ensuring space-efficient AV operations rather than just regulating costs or access, while developing more dynamic and comprehensive policy frameworks to manage the transition.