基于大数据分析的实时交通安全监测与预警研究

Research on Real-time Traffic Safety Monitoring and Early Warning Based on Big Data Analysis

研究了主动实时交通监控策略评估的可行性,同时探讨了其可操作性和安全性.目标是通过减少拥塞和碰撞风险来改进和提高城市交通系统的性能.通过数据挖掘和贝叶斯推理技术在实时碰撞预测模型中的使用,确定影响交通运行的因素,包括间接(高峰时段,数量和上游低速)拥塞位置和直接拥塞位置(下游的拥塞指数较高),其指标证实了拥塞对追尾事故造成的重大影响.如何解决此问题,是实际交通问题解决中的一个难题.作为回应,引入可行性分析以确定根据拥塞强度触发安全警告的时间.实验仿真结果表明共同监测、改善交通运营和安全的重要性.

In this paper,the feasibility of active real-time traffic monitoring strategy evaluation is studied,and its operability and security are discussed.The goal is to increase and improve the performance of urban traffic system by reducing congestion and collision risk.Through the use of data mining and Bayesian inference technology in the real-time collision prediction model,the factors affecting traffic operation are determined,including indirect(peak hours,quantity and upstream low-speed)congestion location and direct congestion location(downstream congestion index is high).Its indicators verify the significant impact of congestion on rear end collision accidents.How to solve this problem is a difficult problem in the actual traffic problem solving.In response,a feasibility analysis is introduced to determine the time to trigger a security warning according to the congestion intensity.The experimental simulation results show the importance of joint monitoring and improving traffic operation and safety.