Knowledge Commons of Institute of Automation,CAS
Deep Imitation Learning for Traffic Signal Control and Operations Based on Graph Convolutional Neural Networks | |
Li Xiaoshuang1,2![]() ![]() ![]() ![]() ![]() | |
2020 | |
会议名称 | 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) |
会议日期 | 2020-9 |
会议地点 | Rhodes, Greece |
出版者 | IEEE |
摘要 | Traffic signal control plays an essential role in the Intelligent Transportation Systems (ITS). Due to the intrinsic uncertainty and the significant increase in travel demand, in many cases, a traffic system still has to rely on human engineers to cope with the complicated and challenging traffic control and operation problem, which cannot be handled well by the traditional methods alone. Thus, imitating the good working experience of engineers to solve traffic signal control problems remains a practical, smart, and cost effective approach. In this paper, we construct a modelling framework to imitate how engineers cope with complex scenarios through learning from the historical record of manipulations by traffic operators. To extract spatial-temporal traffic demand features of the entire road network, a specially designed mask and a graph convolutional neural network (GCNN) are employed in this framework. The simulation experiments results showed that, compared with the original deployed control scheme, our method reduced the average waiting time, average time loss of vehicles, and vehicle throughput by 6.6%, 7.2%, and 6.85%, respectively. |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48765 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Zhu Fenghua |
作者单位 | 1.State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 3.Harbin University Of Science And Technology, Harbin, 150080, China. 4.Enjoyor Co., Ltd. Hangzhou 310030, China. |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Li Xiaoshuang,Guo Zhongzheng,Dai Xingyuan,et al. Deep Imitation Learning for Traffic Signal Control and Operations Based on Graph Convolutional Neural Networks[C]:IEEE,2020. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Li et al_2020_Deep I(314KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 |
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