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1309. A Queueing-based Throughput Analysis of the Mixed Traffic with Autonomous Vehicles

Invited abstract in session MA-55: Transportation Network Modelling and Optimization I, stream Transportation.

Monday, 8:30-10:00
Room: S02 (building: 101)

Authors (first author is the speaker)

1. Xiangdong Chen
Civil and Environment Engineering, National University of Singapore
2. Hao Guan
CEE, National University of Singapore
3. Qiang Meng
Civil and Environment Engineering, National University of Singapore

Abstract

In recent years, the automotive industry and transportation systems have seen a remarkable surge in the development of autonomous vehicles (AVs), driven by advancements in automation and vehicle-to-everything (V2X) communication technologies. In the foreseeable future, when AVs come into our society, it is evident that semi-AVs will continue to play a predominant role in the market due to safety concerns and cost considerations. This study aims to provide models to derive and analyze the throughput and travel time of the transitional mixed traffic with semi- and fully-AVs. To accurately capture the dynamics of different platooning behaviors of fully- and semi-AVs and their impact on travel efficiency, we employ a Markov chain model to derive headway distributions under varying traffic densities, and integrate the Markov chain with a state-dependent queueing model to characterize the mixed traffic flow. The derived mean headways are then utilized to compute the service rate of a state-dependent queueing system, enabling the calculation of long-term throughput and mean travel time through a highway segment. This study reinforces the research on transitional stages of mixed traffic with semi-AVs, revealing the potential for semi-AVs to enhance traffic efficiency. Moreover, it provides valuable insights for the development of management and control strategies in the transitional mixed traffic stages, guiding future designs and implementations.

Keywords

Status: accepted

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