灾害条件下高速公路行车安全评估理论与方法研究
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摘要
目前因灾害天气、交通突发事件等灾害条件引起的高速公路交通事故屡见不鲜。灾害条件下的行车安全评估研究是灾害条件下行车安全管理系统的重要组成部分，是对灾害条件下行车危险程度进行准确判断，进而采取措施主动预防和减少交通事故的重要手段。但国内在灾害条件下的行车安全评估方面还存在一定的问题。本文对灾害条件下高速公路行车安全评估方法进行了较为系统的研究，行而有效的行车安全评估方法可以判断该灾害的严重程度，即行车安全等级，交通管理者根据行车安全等级，确定相应的交通疏导与控制对策，进而最大限度地提高灾害条件下高速公路行车安全水平。
灾害条件致灾机理及其安全评估基础数据采集研究。明确了灾害类型：冰雪、降雨、雾、大风以及交通突发事件，分析了各种灾害条件在道路、驾驶员、行车环境等方面对行车安全造成的影响及致灾机理，进而为行车安全评估指标的确定提供理论基础，并给出了安全评估指标计算的基础数据采集及检测设备布设方法。
冰雪、降雨条件下高速公路行车安全评估方法研究。应用ADAMS/Car仿真软件模拟车辆在冰雪、降雨条件下以不同的行驶速度在曲线路段的运行状况，在冰雪、短时强降雨情况下，将车辆在灾害条件下的侧向位移与正常条件下的侧向位移之差作为行车安全评估指标，根据仿真结果进行行车安全等级划分；在强降雨情况下，除将车辆的侧向位移差作为行车安全评估指标外，同时考虑了能见度对行车安全的影响，通过对车辆在强降雨条件下的制动距离与此时的能见度值作比较，将车辆的制动距离差作为另一行车安全评估指标，综合以上两个评估指标的仿真结果对行车安全进行等级划分。
雾、大风条件下高速公路行车安全评估方法研究。应用ANFIS（自适应模糊神经网络）追尾概率模型，结合能见度、前后车速度差、跟随车车速，利用MATLAB编程计算车辆在雾天条件下各种行车间距的追尾概率，根据追尾概率对行车安全等级进行划分；应用ANSYS仿真软件模拟车辆在大风条件下高速公路的运行状况，通过对车辆施加不同的侧向面载荷使车辆产生侧向位移，根据车辆侧向位移的大小对行车安全等级进行划分。
交通突发事件下高速公路行车安全评估方法研究。结合元胞自动机模型，应用MATLAB数学软件模拟交通突发事件下不同大车比例与饱和度组合情况下的交通流状态，记录车辆运行中的交通冲突数以及相邻路段和车道间的速度标准差，并作为行车安全评估指标，根据仿真结果对行车安全等级进行划分。
Now the freeway accidents caused by atrocious weather, traffic incident etc are common.Traffic safety assessment under disaster conditions is part of traffic safety managementsystem and an accurate judgment of traffic safety level.And then proactive measures are takento prevent or reduce traffic accidents. But there are still some problems in domestic trafficsafety assessment. Based on this background，this paper makes a research about freewaytraffic safety assessment under disaster conditions in order to improve traffic safety levelmaximize.
Study on formation mechanism of disaster conditions and the underlying data collectionfor its safety assessment. In this part, the type of disaster is defined which contain snow andice, rain, fog, strong wind and traffic incident, and what impacts and formation mechanismfrom disaster conditions to driving safety is analyzed on road, driver, driving environment etc,through which provide a theoretical basis for the indicators of traffic safety assessment andgiven the layout method of calculation of the basic data acquisition and equipment testing inthe safety assessment indicators.
Assess the freeway traffic safety under snow and ice and rain conditions. Hereapplicates ADAMS/Car to simulate the vehicle moving in the snow and ice, rainfallconditions, with different speed in the curved sections. Under the snow and ice, short-termheavy rainfall condition, the difference of the lateral displacement of the vehicle underdisaster conditions and under normal conditions is the security evaluation index, the drivingsafety is divided in to different grades according to the simulation results. In case of heavyrainfall, the paper not only takes the difference of lateral displacement of the vehicle asdriving safety assessment indicators but takes the impact from visibility into account. Inaddition, here use difference of braking distance of the vehicles as another traffic safetyevaluation index through the comparison between the braking distance and and visibilityvalue under heavy rainfall conditions. Above two evaluation indicators’ simulation results,thispaper divide driving safety into different grades.
Assess the freeway traffic safety under fog and strong wind conditions. ANFIS (adaptivefuzzy neural network) rear-end probability model is used, combined with the visibility,vehicle speed difference between the former and the after, the following vehicle’s speed, thispaper uses MATLAB programming to calculate the rear-end probability of different drivingspace in fog and divide traffic safety into grades according to the rear-end probability; What’smore,the paper uses ANSYS to simulate the vehicles’ running conditions in freeway under windy conditions, and make driving safety classification according to the vehicle lateraldisplacement through imposing different lateral surface loads on the vehicle.
Assess the freeway traffic safety under traffic incidents. Combined with cellularautomaton simulated traffic flow state in the proportion of different carts and saturation usingMATLAB mathematical software under traffic incidents, recorded the number of trafficconflict and speed standard deviation between adjacent sections and lane as the safetyevaluation index, divided traffic safety rating based on the simulation results.
灾害条件致灾机理及其安全评估基础数据采集研究。明确了灾害类型：冰雪、降雨、雾、大风以及交通突发事件，分析了各种灾害条件在道路、驾驶员、行车环境等方面对行车安全造成的影响及致灾机理，进而为行车安全评估指标的确定提供理论基础，并给出了安全评估指标计算的基础数据采集及检测设备布设方法。
冰雪、降雨条件下高速公路行车安全评估方法研究。应用ADAMS/Car仿真软件模拟车辆在冰雪、降雨条件下以不同的行驶速度在曲线路段的运行状况，在冰雪、短时强降雨情况下，将车辆在灾害条件下的侧向位移与正常条件下的侧向位移之差作为行车安全评估指标，根据仿真结果进行行车安全等级划分；在强降雨情况下，除将车辆的侧向位移差作为行车安全评估指标外，同时考虑了能见度对行车安全的影响，通过对车辆在强降雨条件下的制动距离与此时的能见度值作比较，将车辆的制动距离差作为另一行车安全评估指标，综合以上两个评估指标的仿真结果对行车安全进行等级划分。
雾、大风条件下高速公路行车安全评估方法研究。应用ANFIS（自适应模糊神经网络）追尾概率模型，结合能见度、前后车速度差、跟随车车速，利用MATLAB编程计算车辆在雾天条件下各种行车间距的追尾概率，根据追尾概率对行车安全等级进行划分；应用ANSYS仿真软件模拟车辆在大风条件下高速公路的运行状况，通过对车辆施加不同的侧向面载荷使车辆产生侧向位移，根据车辆侧向位移的大小对行车安全等级进行划分。
交通突发事件下高速公路行车安全评估方法研究。结合元胞自动机模型，应用MATLAB数学软件模拟交通突发事件下不同大车比例与饱和度组合情况下的交通流状态，记录车辆运行中的交通冲突数以及相邻路段和车道间的速度标准差，并作为行车安全评估指标，根据仿真结果对行车安全等级进行划分。
Now the freeway accidents caused by atrocious weather, traffic incident etc are common.Traffic safety assessment under disaster conditions is part of traffic safety managementsystem and an accurate judgment of traffic safety level.And then proactive measures are takento prevent or reduce traffic accidents. But there are still some problems in domestic trafficsafety assessment. Based on this background，this paper makes a research about freewaytraffic safety assessment under disaster conditions in order to improve traffic safety levelmaximize.
Study on formation mechanism of disaster conditions and the underlying data collectionfor its safety assessment. In this part, the type of disaster is defined which contain snow andice, rain, fog, strong wind and traffic incident, and what impacts and formation mechanismfrom disaster conditions to driving safety is analyzed on road, driver, driving environment etc,through which provide a theoretical basis for the indicators of traffic safety assessment andgiven the layout method of calculation of the basic data acquisition and equipment testing inthe safety assessment indicators.
Assess the freeway traffic safety under snow and ice and rain conditions. Hereapplicates ADAMS/Car to simulate the vehicle moving in the snow and ice, rainfallconditions, with different speed in the curved sections. Under the snow and ice, short-termheavy rainfall condition, the difference of the lateral displacement of the vehicle underdisaster conditions and under normal conditions is the security evaluation index, the drivingsafety is divided in to different grades according to the simulation results. In case of heavyrainfall, the paper not only takes the difference of lateral displacement of the vehicle asdriving safety assessment indicators but takes the impact from visibility into account. Inaddition, here use difference of braking distance of the vehicles as another traffic safetyevaluation index through the comparison between the braking distance and and visibilityvalue under heavy rainfall conditions. Above two evaluation indicators’ simulation results,thispaper divide driving safety into different grades.
Assess the freeway traffic safety under fog and strong wind conditions. ANFIS (adaptivefuzzy neural network) rear-end probability model is used, combined with the visibility,vehicle speed difference between the former and the after, the following vehicle’s speed, thispaper uses MATLAB programming to calculate the rear-end probability of different drivingspace in fog and divide traffic safety into grades according to the rear-end probability; What’smore,the paper uses ANSYS to simulate the vehicles’ running conditions in freeway under windy conditions, and make driving safety classification according to the vehicle lateraldisplacement through imposing different lateral surface loads on the vehicle.
Assess the freeway traffic safety under traffic incidents. Combined with cellularautomaton simulated traffic flow state in the proportion of different carts and saturation usingMATLAB mathematical software under traffic incidents, recorded the number of trafficconflict and speed standard deviation between adjacent sections and lane as the safetyevaluation index, divided traffic safety rating based on the simulation results.
引文
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