Indirect reinforcement learning for incident-responsive ramp control

被引：6

作者：

Lu, Chao ^{[1
]}

Chen, Haibo ^{[1
]}

Grant-Muller, Susan ^{[1
]}

机构：

[1] Univ Leeds, ITS, Leeds LS2 9JT, W Yorkshire, England

来源：

TRANSPORTATION: CAN WE DO MORE WITH LESS RESOURCES? - 16TH MEETING OF THE EURO WORKING GROUP ON TRANSPORTATION - PORTO 2013 | 2014年 / 111卷

关键词：

reinforcement learning; ramp metering; incident; cell transmission model; CAPACITY;

D O I：

10.1016/j.sbspro.2014.01.146

中图分类号：

U [交通运输];

学科分类号：

08 ; 0823 ;

摘要：

A centralised strategy named indirect reinforcement learning ramp controller (IRLRC) has been developed in this paper to deal with ramp control problems for the congested traffic caused by incidents. IRLRC is developed on the basis of Dyna-Q architecture, under which a modified asymmetric cell transmission model (ACTM) and the standard Q-learning algorithm are combined together. The simulation-based test shows that compared with the no controlled situation, IRLRC can reduce the total travel time up to 24%, which outperforms the direct reinforcement learning (DRL) method with a reduction of 18% after the same number of iterations. (C) 2013 The Authors. Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Scientific Committee

引用

页码：1112 / 1122

页数：11

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