Probe data-driven travel time forecasting for urban expressways by matching similar spatiotemporal traffic patterns |
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| Institution: | 1. School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Infrastructure System and Safety Control, Beihang University, Beijing 100191, China;2. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;1. Civil, Construction, and Environmental Engineering Department, Iowa State University, Ames, IA 50011, USA;2. Electrical and Computer Engineering Department, Iowa State University, Ames, IA 50011, USA;1. Department of Civil & Environmental Engineering, University of Utah, 110 Central Campus Drive, Suite 2000, Salt Lake City, UT 84112, United States;2. Department of Civil and Environmental Engineering, University of Hawaii at Manoa, 2540 Dole Street, Holmes 383, Honolulu, HI 96822, United States;1. Intelligent Transportation System Research Center, Southeast University, Nanjing 210096, Jiangsu Province, P.R. China;2. Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, USA;1. Guangdong Key Laboratory of Intelligent Transportation Systems, School of Engineering, Sun Yat-Sen University, Guangzhou, China;2. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region;3. Department of Civil Engineering, King Mongkuts Institute of Technology, Ladkrabang, Bangkok, Thailand;4. Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong;5. China Mobile Limited Group Guangdong, Guangzhou, China;1. Department of Electrical and Computer Engineering, 3500 Transportation Research Plaza, Blacksburg, VA 24061, United States;2. Charles E. Via, Jr. Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, 3500 Transportation Research Plaza, Blacksburg, VA 24061, United States |
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| Abstract: | Travel time is an effective measure of roadway traffic conditions. The provision of accurate travel time information enables travelers to make smart decisions about departure time, route choice and congestion avoidance. Based on a vast amount of probe vehicle data, this study proposes a simple but efficient pattern-matching method for travel time forecasting. Unlike previous approaches that directly employ travel time as the input variable, the proposed approach resorts to matching large-scale spatiotemporal traffic patterns for multi-step travel time forecasting. Specifically, the Gray-Level Co-occurrence Matrix (GLCM) is first employed to extract spatiotemporal traffic features. The Normalized Squared Differences (NSD) between the GLCMs of current and historical datasets serve as a basis for distance measurements of similar traffic patterns. Then, a screening process with a time constraint window is implemented for the selection of the best-matched candidates. Finally, future travel times are forecasted as a negative exponential weighted combination of each candidate’s experienced travel time for a given departure. The proposed approach is tested on Ring 2, which is a 32km urban expressway in Beijing, China. The intermediate procedures of the methodology are visualized by providing an in-depth quantitative analysis on the speed pattern matching and examples of matched speed contour plots. The prediction results confirm the desirable performance of the proposed approach and its robustness and effectiveness in various traffic conditions. |
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| Keywords: | Travel time forecast Probe data Pattern-matching Spatiotemporal traffic patterns Urban expressway |
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