多方式诱导下基于出行链的随机用户平衡模型  

Stochastic user equilibrium model based on trip chain analysis under multi-modal guidance

作　　者：赵丹[1,2] 邵春福[1,2] 岳昊[1,2] 李娟[1,2] 孟梦[1,2] 

机构地区：[1]北京交通大学交通运输学院,北京100044 [2]北京交通大学城市交通复杂系统理论与技术教育部重点实验室,北京100044

出　　处：《吉林大学学报（工学版）》2015年第1期82-88,共7页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(51178032;11172035);'973'国家重点基础研究发展计划项目(2012CB725403);中央高校基本科研业务费专项基金项目(2013JBM051);国家'十二五'科技支撑计划项目(2013BAG18B01);中国人民公安大学基本科研业务费项目(2014JKF1130)

摘　　要：为了评价多方式诱导信息对综合交通网络中交通流分配的影响,通过构建基于出行链的概率型随机用户平衡模型,并设计内嵌Monte-Carlo模拟的MSA求解算法,研究多方式诱导下组合出行方式选择、出行链费用和出行链结构的变化。求解过程中,引入超级网络理论,并基于超级网络进行流量分配。研究结果表明:多方式诱导信息使公共交通模式分担率增加5.63%,得到网络总收益为196.254,说明多方式诱导信息对引导出行者减少使用小汽车有显著效果,也有助于降低出行链费用,简化出行链结构。In order to evaluate the impact of multi-modal guidance information on the traffic assignment in the integrated transportation network, a trip-chain-based probit stochastic user equilibrium model was proposed to study the variations of travelers＇ choice of combined trip mode, trip-chain cost and trip-chain structure under the multi-modal guidance. A MSA algorithm with Monte-Carlo method embedded was presented to solve the model. In the model, travelers were divided into two groups by accepting multi-modal guidance information or not, and both groups make choices according to their own expected cost of trip chains. In the course of solution, hyper-network theory was introduced, and traffic flow assignment was carried out on the hyper-network. The results show that multi-modal guidance information helps to increase the mode split rate of public transport by 5. 63%, and the network benefit reach 196. 254. It can be confirmed that multi-modal guidance information contributes to encourage travelers to decrease the use of car, and reduce trip-chain cost and simplify trip-chain structure.

关 键 词：交通运输系统工程 出行链 随机用户平衡 交通诱导信息 组合出行方式 

分 类 号：U491.1[交通运输工程—交通运输规划与管理]