NDLI: Car following regime taxonomy based on Markov switching

Content Provider	IEEE Xplore Digital Library
Author	Zaky, A.B. Gomaa, W.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Egypt-Japan Univ. of Sci. & Technol., Alexandria, Egypt (Zaky, A.B.; Gomaa, W.)
Abstract	Numerous regime classification models have been introduced to capture car following driving behavior and to simulate constrained longitudinal driving characteristics. However, these models disregard the switching process dynamics between different driving regimes. In this paper, we propose a model that incorporates stochastic Markov regime switching model to estimate individual drivers characteristics and extract different driving regimes features. The proposed model takes into consideration the time factor and analyzes sequences of observations on driving time series. Trajectory data such as: velocity, acceleration, and space gap between leader and follower drivers were used as well as switching features to learn the model. Evaluation of the proposed model using real car following data sets shows that the model is able to classify normal car following driving behavior, rare events, and short time events. More importantly, the model is able to determine the switching dynamics among different regimes by applying maximum likelihood estimates and Hamilton filter. Additionally, the proposed model can infer regime specific characteristics, such as: expected duration, the probability of moving from one regime to another, switching parameters and driving patterns. Application of the proposed model includes- but not limited to: crash predication, and driver assistance and assessment systems.
Starting Page	1329
Ending Page	1334
File Size	560013
Page Count	6
File Format	PDF
ISBN	9781479960781
DOI	10.1109/ITSC.2014.6957871
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Switches Mathematical model Hidden Markov models Biological system modeling Vehicles Acceleration Markov processes Markov switching Models Car following model regime classification driver behavior
Content Type	Text
Resource Type	Article

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