运营突发事件下城市轨道交通诱导信息发布策略研究

Guidance Information Release Strategy During Operation Emergencies in Urban Rail Transit

运营突发事件下,向城市轨道交通(城轨)乘客有针对性地发布诱导信息,是实现高效疏导、确保城轨运营安全的重要举措。本文考虑乘客异质性,开展突发事件下城轨诱导信息发布策略研究。首先,基于潜在分类模型(Latent Class Model,LCM)构建城轨乘客在突发事件和诱导信息双重作用下的路径选择行为模型,分析乘客对诱导信息接受程度及路径选择偏好的差异。其次,以线网乘客总出行时间和线网客流分布均衡性基尼系数为优化目标,构建基于路径选择行为分析的信息诱导优化模型,并应用非支配排序遗传算法(Non-dominated Sorting Genetic AlgorithmⅡ,NSGA-Ⅱ)求解,以获得突发事件下受影响起讫点(Origin and Destination,OD)间各有效路径的最优推荐指数。最后,以北京市工作日早高峰城轨区域线网进行案例分析。结果表明:城轨乘客在突发事件下对诱导信息存在“诱导服从型”“诱导中立型”“诱导无视型”这3类不同接受程度的群体。提供诱导信息后,线网乘客总出行时间减少3.906%,线网客流分布均衡性基尼系数下降4.063%,同时减少了高满载率区间数量。事件结束后,诱导信息的继续发布可降低突发事件受影响区间7.08%的满载率,并避免乘客在刚恢复正常运营区间上的再次聚集。

During operation emergencies in urban rail transit,providing targeted guidance information to passengers is essential for efficient evacuation and ensuring safety.This study considers the heterogeneity of passengers and investigates the strategy of issuing guidance information during operation emergencies.A route choice model is constructed using the Latent Class Model(LCM),taking into account both operational emergencies and guidance information.Passenger acceptance of guidance information and route preferences are analyzed.An informationinduced optimization model based on route choice behavior is formulated.The optimization objective is to minimize total travel time and the Gini coefficient of passenger flow distribution.The Non-dominated Sorting Genetic Algorithm II(NSGA-II)is used to solve the mode for the optimized recommendations for effective routes between Origin and Destination(OD)pairs.A case study is conducted on the regional line network of Beijing's urban rail transit during weekday morning peak hours.The results reveal three groups of passengers with different levels of acceptance of guidance information:"induced obedience","induced neutrality",and"induced ignorance".Providing guidance information reduces the total travel time by 3.906%,the Gini coefficient by 4.063%,and the number of high fullness intervals.Continuing the release of guidance information after the emergency reduces the full load rate of affected sections by 7.08%and prevents the re-gathering of passengers as sections resume normal operation.