Research on Prediction Method of Freeway Operation Situation Based on Short-Term Traffic Flow Multi-Parameters Regression
Research Institute of Highway, Ministry of Transport, 8 Xitucheng Road, Haidian District, Beijing 100088, China
In order to realize the short-term prediction of traffic flow operation security situation, so as to enhance the operation safety of freeway, based on the traffic flow detection data and traffic accident data of Beijing section in Jing-ha freeway, the security situation prediction model based on short-term multi-parameters was established in the paper. Firstly, we extracted all the traffic flow data of microwave coils and videos, as well as the traffic accidents after updating the electromechanical system of Beijing section in Jing-ha freeway, so as to establish the risk prediction database, and developed the pre-analysis software of basic data; secondly, the traffic flow data of 30 minutes prior to the time of the accident were divided into six slices at 5-minute level, and the volume, speed, occupancy as well as their statistical parameters were selected as the chosen parameters of the model; next, single-parameter Logistic regression analysis was carried out in each time slice respectively; finally, based on the results of the correlation analysis of parameters and the single-parameter modeling significance, the multi-parameters Logistic regression model was established in each time slice respectively, thus obtaining the short-term prediction model of the traffic flow operation security situation. The results indicate that, the modeling fitting effect of slice 1 is the best, that is the change of the traffic flow parameters and their statistics in 5 minutes prior to the time of the accident can effectively predict the possibility of the accident, in which the average speed has a significant impact on the risk of the accident.
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.