Clustering driver behavior using dynamic time warping and hidden Markov model-Reference-Cited by-同舟云学术

Clustering driver behavior using dynamic time warping and hidden Markov model

Published:2019-08-12 Issue:3 Volume:25 Page:249-262
ISSN:1547-2450
Container-title:Journal of Intelligent Transportation Systems
language:en
Short-container-title:Journal of Intelligent Transportation Systems

Author:

Yao Ying¹,Zhao Xiaohua¹,Wu Yiping¹,Zhang Yunlong²,Rong Jian¹

Affiliation:

1. Beijing Key Laboratory of Traffic Engineering and the College of Metropolitan Transportation, Beijing University of Technology, Beijing, China

2. Zachry Department of Civil Engineering, Texas A&M University, College Station, TX, USA

Funder

National Natural Science Foundation of China

Natural Science Foundation of Beijing Municipality

Beijing Engineering Research Center of Urban Transport Operation Guarantee, Beijing University of Technology

Publisher

Informa UK Limited

Subject

Applied Mathematics,Computer Science Applications,Aerospace Engineering,Automotive Engineering,Information Systems,Software,Control and Systems Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/15472450.2019.1646132

Reference25 articles.

1. Investigating the motivational factors influencing drivers intentions to unsafe driving behaviours: Speeding and overtaking violations

2. Bicego, M., Murino, V. & Figueiredo, M. A. (2003). Similarity-based clustering of sequences using hidden Markov models. Paper presented at International Workshop on Machine Learning and Data Mining in Pattern Recognition, Leipzig, Germany, (pp. 85–95). Heidelberg, Berlin: Springer.

3. Nonlinear path control in automated vehicle guidance

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