Chapter
Aug 12, 2020
Method of Flow Curve Classification Based on Curve Similarity
Authors: Sheng Li [email protected], Rui Bi [email protected], Wenzhong Tang [email protected], Junfeng Zhang [email protected], Ying Zou [email protected], and Qian Li [email protected]Author Affiliations
Publication: CICTP 2020
ABSTRACT
Highway managers could use traffic characteristic analysis to understand capacity changes and forecast and manage traffic better. The flow curve is a common index to reflect highway operation in management and flow forecasting. Using flow curve classification, one can explore changes and deliver scientific traffic management. Clustering is widely applied in curve classification, but rarely in traffic flow curve analysis. This paper presents a traffic flow curve classification method based on curve similarity using density based spatial clustering of applications with noise (DBSCAN) algorithm. Due to sectional flow curve discreteness, discrete Frechet distance was selected to measure flow curve similarity. DBSCAN clustering algorithm was used to classify flow curves into different categories. Characteristics of each flow curve type were achieved and change characteristics were obtained. The method was applied in a real sectional flow data-based flow curve classification and results showed the proposed curve similarity-based flow curve classification method would be more accurate and efficient.
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© 2020 American Society of Civil Engineers.
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Published online: Aug 12, 2020
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1School of Computer Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beihang Univ., Beijing 100191, China; The Third Research Institute of the Ministry of Public Security, Shanghai 200031, China. Email: [email protected]
2Beijing Key Lab of Urban Intelligent Traffic Control Technology, North China Univ. of Technology, Beijing 100144, China. Email: [email protected]
3School of Computer Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beihang Univ., Beijing 100191, China. Email: [email protected]
4School of Computer Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beihang Univ., Beijing 100191, China. Email: [email protected]
5Beijing Municipal Commission of Transport, Beijing 100073, China. Email: [email protected]
6Beijing Municipal Commission of Transport, Beijing 100073, China. Email: [email protected]
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