Long short-term memory model for traffic congestion prediction with online open data
Chen, Yuanyuan1,2; Lv, Yisheng1; Li, Zhenjiang1; Wang, Fei-Yue1,3
2016
Conference Name2016 IEEE 19th International Conference on Intelligent Transportation Systems
Conference Date1-4 Nov. 2016
Conference PlaceRio de Janeiro, Brazil
AbstractTraffic congestion in metropolitan areas has become more and more serious. Over the past decades, many academic and industrial efforts have been made to alleviate this problem, among which providing accurate, timely and predictive traffic conditions is a promising approach. Nowadays, online open data have rich traffic related information. Typical such resources include official websites of traffic management and operations, web-based map services (like Google map), weather forecasting websites, and local events (sport games, music concerts, etc.) websites. In this paper, online open data are discussed to provide traffic related information. Traffic conditions collected from web based map services are used to demonstrate the feasibility. The stacked long short-term memory model, a kind of deep architecture, is used to learn and predict the patterns of traffic conditions. Experimental results show that the proposed model for traffic condition prediction has superior performance over multilayer perceptron model, decision tree model and support vector machine model.
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20168
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
Affiliation1.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Qingdao Academy of Intelligent Industries
Recommended Citation
GB/T 7714
Yuan-yuan Chen, Y. Lv, Z. Li and F. Y. Wang, "Long short-term memory model for traffic congestion prediction with online open data," 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, 2016, pp. 132-137.
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