摘要
随着城市轨道交通运营网络扩展和客流迅速增长,车站滞留风险和应急响应问题逐渐得到重视.本文在分析城市轨道交通车站乘客滞留影响因素的基础上,构建了风险评价指标体系和反向传播(Back-propagation,BP)神经网络模型,结合改进的P-中心选址模型求解得到应急公交驻车点的服务匹配方案.根据案例求解和方案对比研究表明,是否考虑风险权重影响了车站在选址过程中的相对重要度,基于风险分析的驻车点选址方案使部分权重较高的车站与驻车点之间的平均距离减少了0.8%~8.4%,但是对整个研究区域的公交应急服务覆盖效果影响很小,有利于应急资源的合理高效利用.
Along with the urban rail transit network expansion and rapid growth in passenger flow,the risk of passengers stranded at the station and emergency response problem are getting more attention.This paper sets up arisk evaluation index system and back-propagation neural network(BPNN)based on an analysis of passenger stranded risk,and combining the improved P-center location model,a service matching scheme for the URT stations and emergency bus reserve bases is proposed.The case solution and program comparison show that the risk weight has an influence on the relative importance among stations during location process,which decreases the average distance between high-risk stations and the EBRB by 0.8%~8.4%,but has little impact on the service coverage effect of the whole study area,so it's beneficial to the reasonable and efficient utilization of emergency resources.
作者
刘爽
支晓宇
陈绍宽
兴妍
LIU Shuang;ZHI Xiao-yu;CHEN Shao-kuan;XING Yan(MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology,Beijing Jiaotong University,Beijing 100044,China)
出处
《交通运输系统工程与信息》
EI
CSCD
北大核心
2018年第4期110-115,129,共7页
Journal of Transportation Systems Engineering and Information Technology
基金
国家自然科学基金(71621001
71571015)~~
关键词
城市交通
应急公交响应
BP神经网络
滞留风险
选址模型
urban traffic
emergency bus response
BP neural network
passenger stranded risk
location model
作者简介
通信作者:刘爽(1980-),女,北京人,副教授.liushuang@bjtu.edu.cn
