Infrastructure Adaptation Planning for Autonomous Vehicles

Chen, Zhibin; Yin, Yafeng · 2016 · ROSA P / Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE)

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Summary

This report addresses the need for infrastructure adaptation planning to facilitate the deployment of autonomous vehicles (AVs) within heterogeneous traffic streams containing both conventional vehicles (CVs) and AVs. The authors argue that government agencies must proactively dedicate specific lanes and zones to AVs to improve network throughput and promote technology adoption. The study focuses on two primary applications: optimizing the time-dependent deployment of AV lanes and designing optimal AV zones where AVs are centrally controlled to achieve system-optimal flow. To address the first objective, the authors develop a bi-level mathematical model. The lower level employs a multi-class network equilibrium model to describe route choices for CVs and AVs, while the upper level optimizes the location, timing, and quantity of AV lane deployments to minimize total social cost. This framework incorporates an endogenous AV diffusion model, which forecasts market penetration based on the net benefits (e.g., reduced travel time and safety improvements) derived from the infrastructure changes. The problem is solved using an active-set algorithm. For the second objective, the authors propose a "mixed routing equilibrium" model to capture scenarios where AVs follow user-optimal routing outside designated zones but adhere to system-optimal routing within them. The optimal design of these zones is formulated as a mixed-integer bi-level programming problem and solved using a simulated annealing algorithm. Numerical examples based on a South Florida transportation network demonstrate the efficacy of these models. The results indicate that optimized, progressive deployment of AV lanes significantly reduces social costs and accelerates AV market penetration compared to static or non-optimized plans. Sensitivity analyses reveal that factors such as the ratio of AV-lane capacity to regular-lane capacity, unsafety factors associated with CVs, and the value of time for AV users critically influence adoption rates. Furthermore, the AV zone design examples show that while dedicated zones can reduce travel times for AVs and improve overall system performance, they may increase travel costs for some CVs, necessitating a careful tradeoff in infrastructure design. The significance of this work lies in providing a rigorous mathematical framework for transportation planners to integrate AV infrastructure into existing networks. By modeling the feedback loop between infrastructure deployment and market adoption, the study offers actionable strategies for minimizing social costs and maximizing the benefits of AV technology. The introduction of the mixed routing equilibrium model also contributes to the theoretical understanding of network equilibrium in environments where different vehicle types operate under distinct routing principles.

Key finding

Dedicated AV lanes and zones optimized through bi-level programming models can minimize social costs and accelerate autonomous vehicle market penetration by leveraging reduced travel times and safety benefits.

Methodology

modeling

Provenance

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Topics

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• acceptance adoption