混合交通流下的高速公路作业区最佳预警距离  

Optimal Warning Distance for Expressway Construction Area under Mixed Traffic Flow

作　　者：袁锐 陈宁 仝瑶 贾建林 陈艳艳 YUAN Rui;CHEN Ning;TONG Yao;JIA Jian-lin;CHEN Yan-yan(School of Metropolitan Transportation,Beijing University of Technology,Beijing 100124,China;Transport Planning and Research Institute,Ministry of Transport,Beijing 100028,China;Toyota Urban Transport Research Institute,Aichi-ken 471-0024,Japan)

机构地区：[1]北京工业大学城市交通学院,北京100124 [2]交通运输部规划研究院,北京200028 [3]丰田都市交通研究所,爱知471-0024,日本

出　　处：《公路交通科技》2023年第7期185-190,206,共7页Journal of Highway and Transportation Research and Development

基　　金：交通运输部交通运输行业重点科技项目(2021-ZD2-047,2022-ZD6-116);山东省交通运输科技计划项目(2021B49)。

摘　　要：为缓解高速公路作业区因车道数量变少或路障设置而引起的交通运行效率急剧下降问题,降低车辆在高速公路作业区因频繁换道或加减速而带来的交通事故发生风险,以江苏省五峰山北向南四车道过江通道为研究环境,针对高速公路典型作业区开展了相关研究,构建了高速公路作业区效率、安全、油耗综合指标体系。基于Vissim仿真软件,考虑不同的流量、装配率和预警距离条件,构建了道路场景和预警模型。利用贝叶斯神经网络进行预测,研究了高速公路作业区不同条件下车辆的最佳预警距离。结果表明:该方法对高速公路作业区综合、安全、能耗指标的预测平均相对误差为0.4%,0.4%,0.2%;通过对综合效率、安全和能耗指标的最佳预警距离分析得出,更高的装配率下3项指标结果更优,最佳预警距离更短;在自由流下,最佳预警距离为300~800 m,且最佳预警距离随着流量的增大而增加;在饱和交通流下,最佳预警距离为800~1200 m,但此时流量的增加对预警距离的影响并不大;在流量过饱和后,最佳预警距离为1200~1800 m,且随着流量增大而继续增大。研究结果可以为高速公路作业区车辆的安全驾驶和交通管理部门发布道路预警提供一定参考,同时也提供了一种高速公路作业区车辆预警最佳距离的预测方法。To alleviate the sharp decline in traffic efficiency caused by reduced number of lanes or the presence of obstacles and reduce the risk of accidents due to frequent lane changes or acceleration and deceleration in expressway operation area,taking the Wufengshan north-south 4-lane river crossing channel in Jiangsu Province as the research environment,the related research on typical operation area of expressway is conducted,a comprehensive indicator system for efficiency,safety and fuel consumption in expressway operation area is constructed.Based on Vissim simulation software,considering different traffic volumes,assembly rates and warning distance conditions,the road scenario and warning model are created.The optimal warning distance for vehicles under different conditions in expressway operation area is studied by using Bayesian neural network for prediction.The result shows that the average relative errors of this method for predicting the comprehensive,safety and energy consumption indicators of expressway operation area are 0.4%,0.4%and 0.2%respectively.Through the analysis of the optimal warning distance for comprehensive efficiency,safety and energy consumption indicators,it is concluded that(1)higher assembly rates lead to better result in all the 3 indicators,and the optimal warning distance becomes shorter;(2)under free flow condition,the optimal warning distance is 300-800 m and it increases with the increase of traffic volume;(3)under saturated traffic flow condition,the optimal warning distance is 800-1200 m,but the increase in traffic volume at this time has little influence on the warning distance;(4)after traffic volume is oversaturated,the optimal warning distance is 1200-1800 m,and it continues to increase as the traffic volume increases.The research result can provide some reference for the safe driving of vehicles in expressway operation area and the issuance of road warning by traffic management departments,and also provide a method for predicting the optimal distance of vehicle warning in exp

关 键 词：智能交通 最佳预警距离 仿真模拟 贝叶斯神经网络 高速公路作业区 

分 类 号：U491.4[交通运输工程—交通运输规划与管理]