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

作　　者：Sheng JIN Dian-hai WANG Cheng XU Dong-fang MA 

机构地区：[1]College of Civil Engineering and Architecture,Zhejiang University [2]Zhejiang Police College

出　　处：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》2013年第4期231-243,共13页浙江大学学报（英文版）A辑（应用物理与工程）

基　　金：Project (No. 2011AA110304) supported by the National High-Tech R&D Program of China (863 program)

摘　　要：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.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 the TTC samples, and the maximum likelihood （ML） estimation Then, the GMM is used to model the bimodal distribution of 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 ex- pressway 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.

关 键 词：Forecasting Traffic safety Gaussian mixture model Kalman filter 

分 类 号：U491[交通运输工程—交通运输规划与管理]