JZUS - Journal of Zhejiang University SCIENCE

Journal of Zhejiang University SCIENCE A 2013 Vol.14 No.4 P.231-243

http://doi.org/10.1631/jzus.A1200218

Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter*

Author(s): Sheng Jin¹, Dian-hai Wang¹, Cheng Xu², Dong-fang Ma¹
Affiliation(s): 1. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China; more
Corresponding email(s): jinsheng@zju.edu.cn
Key Words: Forecasting, Traffic safety, Gaussian mixture model, Kalman filter

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Sheng Jin, Dian-hai Wang, Cheng Xu, Dong-fang Ma. Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter[J]. Journal of Zhejiang University Science A, 2013, 14(4): 231-243.

@article{title="Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter",
author="Sheng Jin, Dian-hai Wang, Cheng Xu, Dong-fang Ma",
journal="Journal of Zhejiang University Science A",
volume="14",
number="4",
pages="231-243",
year="2013",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1200218"
}

%0 Journal Article
%T Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter
%A Sheng Jin
%A Dian-hai Wang
%A Cheng Xu
%A Dong-fang Ma
%J Journal of Zhejiang University SCIENCE A
%V 14
%N 4
%P 231-243
%@ 1673-565X
%D 2013
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1200218

TY - JOUR
T1 - Short-term traffic safety forecasting using Gaussian mixture model and Kalman filter
A1 - Sheng Jin
A1 - Dian-hai Wang
A1 - Cheng Xu
A1 - Dong-fang Ma
J0 - Journal of Zhejiang University Science A
VL - 14
IS - 4
SP - 231
EP - 243
%@ 1673-565X
Y1 - 2013
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1200218

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: In this paper, a prediction model is developed that combines a gaussian mixture model (GMM) and a kalman filter for online forecasting of traffic safety on expressways. Raw time-to-collision (TTC) samples are divided into two categories: those representing vehicles in risky situations and those in safe situations. Then, the GMM is used to model the bimodal distribution of the TTC samples, and the maximum likelihood (ML) estimation parameters of the TTC distribution are obtained using the expectation-maximization (EM) algorithm. We propose a new traffic safety indicator, named the proportion of exposure to traffic conflicts (PETTC), for assessing the risk and predicting the safety of expressway traffic. A kalman filter is applied to forecast the short-term safety indicator, PETTC, and solves the online safety prediction problem. A dataset collected from four different expressway locations is used for performance estimation. The test results demonstrate the precision and robustness of the prediction model under different traffic conditions and using different datasets. These results could help decision-makers to improve their online traffic safety forecasting and enable the optimal operation of expressway traffic management systems.

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