Long short-term memory model for traffic congestion prediction with online open data

CASIA OpenIR > 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

	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
会议名称	2016 IEEE 19th International Conference on Intelligent Transportation Systems
会议日期	1-4 Nov. 2016
会议地点	Rio de Janeiro, Brazil
摘要	Traffic 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.
收录类别	EI
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/20168
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
作者单位	1.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
第一作者单位	中国科学院自动化研究所
推荐引用方式 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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