Reinforcement Learning Control for On-Ramp Metering Based on Traffic Simulation

Wang, Xingju; Liu, Bohan; Niu, Xueqin; Miyagi, Toshihiko

doi:10.1061/41064(358)378

Cited by 5 publications

(1 citation statement)

References 11 publications

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“…Iwata, Tsubota, and Kawashima have proposed the ramp metering technique using the predicted value by a traffic simulator [4]. Reinforcement learning ramp metering based on traffic simulation model with desired speed was proposed by Wang et al [5]. The aim of this study is to propose reinforcement learning ramp metering without complete information.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning Ramp Metering without Complete Information

Wang

Gao

2012

Journal of Control Science and Engineering

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This paper develops a model of reinforcement learning ramp metering (RLRM) without complete information, which is applied to alleviate traffic congestions on ramps. RLRM consists of prediction tools depending on traffic flow simulation and optimal choice model based on reinforcement learning theories. Moreover, it is also a dynamic process with abilities of automaticity, memory and performance feedback. Numerical cases are given in this study to demonstrate RLRM such as calculating outflow rate, density, average speed, and travel time compared to no control and fixed-time control. Results indicate that the greater is the inflow, the more is the effect. In addition, the stability of RLRM is better than fixed-time control.

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Section: Introductionmentioning

confidence: 99%