Full Text:   <4418>

CLC number: U491

On-line Access: 2013-04-03

Received: 2012-08-30

Revision Accepted: 2013-01-18

Crosschecked: 2013-03-26

Cited: 14

Clicked: 11544

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

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


Share this article to: More |Next Article >>>

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: 
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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

References

[1] Allen, B.L., Shin, B.T., Cooper, D.J., 1978. Analysis of traffic conflicts and collision. Transportation Research Record, 667:67-74. 

[2] Almquist, S., Hyden, C., Risser, R., 1991. Use of speed limiters in cars for increased safety and a better environment. Transportation Research Record, 1318:34-39. 

[3] Archer, J., 2004.  Methods for the Assessment and Prediction of Traffic Safety at Urban Intersections and Their Application in Micro-Simulation Modeling. PhD Thesis, Royal Institute of Technology,Stockholm, Sweden :

[4] Cunto, F., Saccomanno, F.F., 2007. Micro-Level Traffic Simulation Method for Assessing Crash Potential at Intersections. , Proceedings of 86th Annual Meeting, Transportation Research Board, Washington DC, :

[5] Dempster, A.P., Laird, N.M., Rubin, D.B., 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39(1):1-38. 

[6] Digalakis, V., Rohlicek, J.R., Ostendorf, M., 1993. ML estimation of a stochastic linear system with the EM algorithm and its application to speech recognition. IEEE Transactions on Speech and Audio Proceeding, 1(4):431-442. 


[7] FHWA, 2003.  Surrogate Safety Measures from Traffic Simulation Models. Final Report, Publication No. FHWA-RD-03-050. Federal Highway Administration,USA :

[8] Guido, G., Saccomanno, F., Vitale, A., Astarita, V., Festa, D., 2011. Comparing safety performance measures obtained from video capture data. Journal of Transportation Engineering, 137(7):481-491. 


[9] Haykin, S., 2001.  Kalman Filtering and Neural Networks. John Wiley and Sons, Inc.,New York :

[10] Hayward, J., 1971.  Near Misses as a Measure of Safety at Urban Intersections. PhD Thesis, Department of Civil Engineering, The Pennsylvania State University,University Park, PA :

[11] Hsieh, C.T., Lai, E., Wang, Y.C., 2003. Robust speaker identification system based on wavelet transform and Gaussian mixture model. Journal of Information Science and Engineering, 19(2):267-282. 

[12] Hydn, C., 1996. Traffic Conflicts Technique: State-of-the-art.  Traffic Safety Work with Video-Processing. University Kaiserslautern, Transportation Department,Kaiserslautern, Germany :

[13] Jiang, H., He, W., 2012. Grey relational grade in local support vector regression for financial time series prediction. Expert Systems with Applications, 39(3):2256-2262. 


[14] Jin, S., Wang, D.H., Yang, X.R., 2011. Non-lane-based car following model using visual angle information. Transportation Research Record Journal of the Transportation Research Board, 2249:7-14. 


[15] Jin, S., Qu, X., Wang, D.H., 2011. Assessment of expressway traffic safety using Gaussian mixture model based on time to collision. International Journal of Computational Intelligence Systems, 4(6):1122-1130. 


[16] Jin, S., Wang, D.H., Xu, C., Huang, Z.Y., 2012. Staggered car-following induced by lateral separation effects in traffic flow. Physics Letters A, 376(3):153-157. 


[17] Jordan, M., Jacobs, R., 1994. Hierarchical mixtures of experts and the EM algorithm. Neural Computation, 6(2):181-214. 


[18] Khashei, M., Bijari, M., 2012. A new class of hybrid models for time series forecasting. Expert Systems with Applications, 39(4):4344-4357. 


[19] Kim, S.C., Kang, T.J., 2007. Texture classification and segmentation using wavelet packet frame and Gaussian mixture model. Pattern Recognition, 40(4):1207-1221. 


[20] Lord, D., Mannering, F., 2010. The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives. Transportation Research Part A, 44:291-305. 

[21] McAndrews, C., 2011. Traffic risks by travel mode in the metropolitan regions of Stockholm and San Francisco: a comparison of safety indicators. Injury Prevention, 17(3):204-207. 


[22] McLachlan, G., 1988.  Mixture Models. Marcel Dekker,New York, NY :

[23] Meng, Q., Qu, X., 2012. Estimation of vehicle crash frequencies in road tunnels. Accident Analysis and Prevention, 48(1):254-263. 


[24] Meng, Q., Qu, X., Wang, X., Yuanita, V., Wong, S.C., 2011. Quantitative risk assessment modeling for nonhomogeneous urban road tunnels. Risk Analysis, 31(3):382-403. 


[25] Meng, Q., Qu, X., Yong, K.T., Wong, Y.H., 2011. QRA model-based risk impact analysis of traffic flow in urban road tunnels. Risk Analysis, 31(12):1872-1882. 


[26] Minderhoud, M.M., Bovy, P.H.L., 2001. Extended time-to-collision measures for road traffic safety assessment. Accident Analysis and Prevention, 33(1):89-97. 


[27] Nasseri, M., Moeini, A., Tabesh, M., 2011. Forecasting monthly urban water demand using Extended Kalman Filter and Genetic Programming. Expert Systems with Applications, 38(6):7387-7395. 


[28] Park, H.S., Cho, S.B., 2012. Evolutionary attribute ordering in Bayesian networks for predicting the metabolic syndrome. Expert Systems with Applications, 39(4):4240-4249. 


[29] Qu, X., Meng, Q., Yuanita, V., Wong, Y.H., 2011. Design and implementation of a quantitative risk assessment software tool for Singapore’s road tunnels. Expert Systems with Applications, 38(11):13827-13834. 

[30] Redner, R., Walker, H., 1984. Mixture densities, maximum likelihood and the EM algorithm. SIAM Review, 26(2):195-237. 


[31] Stauffer, C., Grimson, W.E.L., 1999. Adaptive background mixture models for real-time tracking. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2:246-252. 

[32] Stephens, M.A., 1974. EDF statistics for goodness of fit and some comparisons. Journal of the American Statistical Association, 69(347):730-737. 


[33] Svensson, A., 1998.  A Method for Analyzing the Traffic Process in a Safety Perspective. PhD Thesis, University of Lund,Lund, Sweden :

[34] Titterington, D., Smith, A., Makov, U., 1985. Statistical Analysis of Finite Mixture Distributions, John Wiley & Sons,:

[35] Topp, H.H., 1998.  Traffic Safety Work with Video-Processing. University Kaiserslautern, Transportation Department,Green Series No. 43, Kaiserslautern, Germany :

[36] van der Horst, R., Kraay, J., 1986. The Dutch Conflict Observation Technique-DOCTOR. , Proceedings of Workshop-Traffic Conflicts and Other Intermediate Measures in Safety Evaluation, Budapest, Hungary, :

[37] Vrhelyi, A., 1996. Dynamic Speed Adaptation based on Information Technologya Theoretical Background, PhD Thesis, Bulletin 142, Lund University,:

[38] Vogel, K., 2003. A comparison of headway and time to collision as safety indicators. Accident Analysis and Prevention, 35(3):427-433. 


[39] Xie, Y.C., Zhang, Y.L., Ye, Z.R., 2007. Short-Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition. Computer-Aided Civil and Infrastructure Engineering, 22(5):326-334. 


[40] Yim, J., Joo, J., Park, C., 2011. A Kalman filter updating method for the indoor moving object database. Expert Systems with Applications, 38(12):15075-15083. 



Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE