Reinforcement-Learning-Based Cooperative Adaptive Cruise Control of Buses in the Lincoln Tunnel Corridor With Time-Varying Topology

Gao, Weinan; Gao, Jingqin; Ozbay, Kaan; Jiang, Zhong-Ping; Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART)

doi:10.1109/TITS.2019.2895285

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Reinforcement-Learning-Based Cooperative Adaptive Cruise Control of Buses in the Lincoln Tunnel Corridor With Time-Varying Topology

2019-02-20
By Gao, Weinan ; Gao, Jingqin ; Ozbay, Kaan ; ...

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Creators:

Gao, Weinan ; Gao, Jingqin ; Ozbay, Kaan ; Jiang, Zhong-Ping

Gao, Weinan ; Gao, Jingqin ; Ozbay, Kaan ; Jiang, Zhong-Ping Less -
Corporate Creators:

Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART)
Corporate Contributors:

University Transportation Centers Program (U.S.)
Subject/TRT Terms:

[+]

Advanced Vehicle Control Systems Autonomous Intelligent Cruise Control Autonomous Vehicles Connected And Autonomous Vehicles Connected Vehicles Cooperative Adaptive Cruise Control Intelligent Transportation Systems Learning Reinforcement Learning Time-varying Topology Topology
DOI:

http://doi.org/10.1109/TITS.2019.2895285
Resource Type:

Manuscript
Geographical Coverage:

United States ; New Jersey ; New York

United States ; New Jersey ; New York Less -
Edition:

Final manuscript prior to publication in IEEE
Corporate Publisher:

Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART)
Abstract:

The exclusive bus lane (XBL) is one of the most popular bus transit systems in US. The Lincoln Tunnel utilizes an XBL through the tunnel in the AM peak period. This paper proposes a novel data-driven cooperative adaptive cruise control (CACC) algorithm that aims to minimize a cost function for connected and autonomous buses along the XBL. Different from existing model-based CACC algorithms, the proposed approach employs the idea of reinforcement learning (RL), which does not rely on accurate knowledge of bus dynamics. Considering a time-varying topology where each autonomous vehicle can only receive information from preceding vehicles that are within its communication range, a distributed controller is learned real-time by online headway, velocity, and acceleration data collected from system trajectories. The convergence of the proposed algorithm and the stability of the closed-loop system are rigorously analyzed. The effectiveness of the proposed approach is demonstrated using a well-calibrated Paramics microscopic traffic simulation model of the XBL corridor. Simulation results show that the travel times in the autonomous version of the XBL are close to the present day travel times even when the bus volume is increased by 30%.
Content Notes:

This work has been partly supported by the NYU’s C2SMART Center and the U.S. National Science Foundation grant ECCS-1501044. W. Gao, J. Gao, K. Ozbay and Z. Jiang, "Reinforcement-Learning-Based Cooperative Adaptive Cruise Control of Buses in the Lincoln Tunnel Corridor with Time-Varying Topology," in IEEE Transactions on Intelligent Transportation Systems. http://doi.org/10.1109/TITS.2019.2895285
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University Transportation Centers US Transportation Collection
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urn:sha256:a8c479335027964b424e66adb3297fbd695286ac7b26157d5c9ab9a6a3f90a86
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https://rosap.ntl.bts.gov/view/dot/40269/dot_40269_DS1.pdf
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Reinforcement-Learning-Based Cooperative Adaptive Cruise Control of Buses in the Lincoln Tunnel Corridor With Time-Varying Topology

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