Categorizing Car-Following Behaviors: Wavelet-Based Time Series Clustering Approach
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 8
Abstract
The categorization analysis of car-following behaviors is beneficial to enrich the current car-following models and the applications of connected and automated vehicles (CAVs) in a mixed traffic environment. Previous studies categorized the car-following behaviors during the traffic oscillations using artificially designed behavior patterns, but they are not quietly flexible and are limited to distinguish the complicated car-following behaviors. To address such a problem, the study proposes a wavelet-based time series clustering approach to automatically categorize the car-following behaviors. First, the response time series of the car-following behaviors are extracted using general Newell’s car-following model. Second, the discrete wavelet transformation algorithm is employed to extract the following behavior features from the original time series. Finally, the hierarchical clustering algorithm is used to categorize the car-following behaviors according to the calculated similarity between the transformed time series. Numerical tests on Next Generation Simulation (NGSIM) show that the proposed algorithm can effectively and automatically categorize the typical car-following behavior patterns summarized in the previous studies. The proposed algorithm is also flexibly implemented to discover the potential car-following behavior patterns. Findings suggest that a wavelet-based time series clustering by combing the Haar wavelet transformation algorithm and hierarchical clustering algorithm is a superior approach to automatically categorize car-following behaviors with a time series trajectory.
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Data Availability Statement
Some or all data, models, or code used during the study were provided by a third party (NGSIM). Direct requests for these materials may be made to the provider, as indicated in the Acknowledgments.
Acknowledgments
This study is partially supported by the National Key R&D Program in China (Grant No. 2018YFB1600600), Science and Technology Major Project, Transportation of Jiangsu Province, and Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX18_0136). Authors thank Federal Highway Administration (FHWA) for making the NGSIM trajectory data available.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 8 • August 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Aug 3, 2019
Accepted: Mar 13, 2020
Published online: May 28, 2020
Published in print: Aug 1, 2020
Discussion open until: Oct 28, 2020
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