Promoting Autonomous Vehicles Using Travel Demand and Lane Management Strategies

Seilabi, Sania E; Tabesh, Mahmood; Davatgari, Amir; Miralinaghi, Mohammad; Labi, Samuel · 2020 · ROSA P / Frontiers in Built Environment

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Summary

This paper addresses the challenge of urban congestion during the transition period toward widespread autonomous vehicle (AV) adoption, characterized by mixed fleets of AVs and human-driven vehicles (HDVs). The authors identify a critical gap in existing literature: while AV-dedicated lanes can significantly increase road capacity and reduce total travel time, their implementation often creates social inequity. Specifically, reallocating road capacity to AVs may disproportionately increase travel times for HDV users, who are likely to be lower-income individuals in the early stages of the transition, while benefiting higher-income early AV adopters. To mitigate this, the study proposes a framework integrating connectivity-enabled travel demand management with lane management strategies. The methodology centers on a bi-level optimization model termed Travel Demand and Lane Management in the AV Transition Era (TLMAV). The upper level represents the metropolitan transportation authority, which seeks to minimize total system travel time by determining optimal credit allocations and charging schemes for a Tradable Credit Scheme (TCS). This optimization is subject to Pareto optimality constraints and specific equity constraints that limit the increase in travel costs for HDV users. The lower level models traveler behavior, where users choose routes and lane types to minimize their individual travel costs, comprising both time and credit consumption. The model assumes a network with both AV-dedicated and general-purpose lanes, with AVs capable of using both while HDVs are restricted to general-purpose lanes. The authors demonstrate the existence and uniqueness of the equilibrium solution and solve the NP-hard problem using a relaxation method. Numerical experiments were conducted on an eight-node, 14-link network over a 10-period transition horizon, with AV market penetration increasing from 10% to higher levels. The study compared four scenarios: no intervention, AV-dedicated lanes only, AV lanes with TCS but no equity constraints, and the full TLMAV model with equity constraints. Results showed that AV-dedicated lanes alone reduced total travel time significantly but increased average travel times for HDV users in early periods. Adding a TCS further reduced total travel time but imposed higher credit costs on HDV users without addressing equity. The TLMAV model successfully balanced efficiency and equity; while it resulted in slightly higher total travel time than the unconstrained TCS case, it kept HDV travel costs within acceptable thresholds defined by the equity constraints. The model also demonstrated that equity thresholds could be gradually relaxed over time to motivate the long-term shift toward AV adoption. The significance of this work lies in providing a practical mechanism for transportation authorities to manage the AV transition while maintaining social equity. By leveraging the automation and connectivity features of AVs to implement real-time tradable credit schemes, agencies can offset the negative impacts of lane reallocation on HDV users. The findings suggest that integrating economic instruments like TCS with infrastructure planning is essential to ensure that congestion mitigation strategies do not exacerbate socioeconomic disparities, thereby facilitating a smoother and more equitable transition to autonomous mobility.

Key finding

The TLMAV framework reduces total system travel time while maintaining human-driven vehicle travel costs within acceptable equity thresholds through a tradable credit scheme.

Methodology

modeling

Provenance

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