基于大数据的道路拥堵对实时交通安全的影响研究  

Research of road congestion effects on real-time traffic safety based on big data

作　　者：吕明新[1] 刘兆惠[1] 孙婷婷[1] 王超 

机构地区：[1]山东科技大学交通学院,山东青岛266590

出　　处：《山东交通科技》2016年第2期12-15,共4页

基　　金：山东科技大学科学研究基金资助;项目编号:2010AZZ194

摘　　要：通过对主动实时交通监控策略的可行性评估操作和安全进行探讨,提高城市道路的系统性能,减少交通拥堵和事故风险。一定规模的网络覆盖、密集部署的探测系统是大数据系统连续收集丰富信息的前提。实时交通安全分析,揭示了拥堵对交通碰撞所产生的影响。研究碰撞分级标准,在适当的时刻根据拥堵程度做出安全警告。通过卡尔曼滤波和贝叶斯推理技术实现实时预测模型,直接和间接拥堵指标证实拥堵对追尾碰撞的可能性有重大影响。In this study, the viability of a proactive real - time traffic monitoring strategy evaluating operation and safety was simultaneously explored. The objective is to improve the system performance of urban roads and reduce congestion and crash risk. A certain scale of the network coverage and dense deployment of the detection system are the premise of a big data system to collect abundant information. In this study, the l-sults of congestion evaluation encouraged real -time safety analysis to unveil the effects of traffic dynamics on crash occurrence. This paper introduces the reliability analysis and to determine the classification standard of the collision, and make safety warning according to the degree of e（mgestion at the appropriate moment . Finally, Kalman filter and Bayesian inference techniques were implemented in real - time （：rash prediction models. The identified effects, both direct and indirect congestion indicators confirmed the significant impact of congestion on rear - end crash likelihood.

关 键 词：大数据 实时 城市道路 交通拥堵 交通安全 

分 类 号：U491.31[交通运输工程—交通运输规划与管理]