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施工环境下高速公路应急车辆资源配置 被引量:1

Resource Allocation of Expressway Emergency Vehicle in the Environment of Construction
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摘要 提出了一种施工环境下的高速公路应急车辆资源配置方法。通过分析高速公路施工区域对高速公路通行能力的影响,构建了施工环境下高速公路路段危险程度计算模型,求解各路段的危险程度;根据现有的高速公路应急资源的配置模型,充分考虑施工区域影响下的路段危险程度、最佳救援时间、资源需求等参数的影响,采用数学规划的方法构建了基于施工区通行能力与历史事故数据的应急资源配置模型,采用粒子群算法求解模型。并以山东高速济青高速段施工区域路段为例进行了应急车辆资源配置。结果表明:在保证各路段资源需求的前提下,应急车辆的资源利用效率最高,实现了施工环境下高速公路应急车辆的合理配置。 A resource allocation method of expressway emergency vehicle under construction environment was proposed.According to the analysis on the influence of construction area on highway capacity,the expressway risk degree calculation model under construction environment was established to get the risk degree of each section.According to the current expressway emergency resource allocation models,the influence of factors such as risk degree,the best rescue time and resource demands in the construction area was fully taken into account;the emergency resource allocation model based on the traffic capacity and historical accident data of the construction area was established and the particle swarm optimization algorithm was used to solve the proposed model.Taking the construction area of Ji nan-Qinghai section of Shandong expressway as an example,the emergency vehicle resource allocation was carried out.The results show that under the premise of guaranteeing the resource demand of each section,the utilization efficiency of emergency vehicle is the highest,which realizes the reasonable allocation of expressway emergency vehicle under the construction environment.
作者 于德新 赵阳 武毅 瞿卫东 YU Dexin;ZHAO Yang;WU Yi;QU Weidong(School of Transportation,Jilin University,Changchun 130022,Jilin,P.R.China;Jilin Province Key Laboratory of Road Traffic,Changchun 130022,Jilin,P.R.China)
出处 《重庆交通大学学报(自然科学版)》 CAS 北大核心 2018年第10期110-116,共7页 Journal of Chongqing Jiaotong University(Natural Science)
基金 国家自然科学基金项目(51308249) 山东省省管企业科技创新项目(20122150251-1)
关键词 交通运输工程 高速公路 施工环境 应急资源配置 粒子群算法 路段危险程度 traffic and transportation engineering expressway construction conditions emergency resource allocation particle swarm optimization risk degree of road
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