摘要
为准确预测大型铁路客运站旅客聚集能力和设施利用程度,分析大型铁路客运站旅客集散特点,提出旅客集散仿真的基本流程,构建基于个体活动的旅客集散微观仿真模型。将旅客在站内的活动过程划分为路径选择、节点选择和决策感知3个阶段,运用最小支撑子图求解旅客路径,建立基于多项LOGIT模型的节点选择模型,给出旅客复杂行为决策感知的统一表达式,开发大型铁路客运站旅客集散仿真系统。以北京南站为例进行综合仿真分析可知:全站绝大多数时间内服务水平较高,高峰时间段服务水平有所下降;在高峰时期最高聚集旅客人数达3 907人;在非高峰时期可以适当减少售票窗口,以提高售票设备利用率。
To accurately estimate the passenger mustering capacity and facility utilization, the characteristics of passenger mustering and evacuation in large scale railway station were analyzed. Based on the proposed simulation flow, an effective microscopic simulation model of railway station is developed by path planning, node selection and decision perception. Passenger path is calculated by minimum connected subgraph method. A MULTI-LOGIT node selection model is proposed. A universal expression of individual passenger behavior decision and perception is also set up. A simulation system has been developed for practical use. As an example, Beijing South Railway Station is simulated using the proposed method. It is found that high level of service prevails within the station for most of the time, while the level of service declines during peek time. The maximum passenger mustering approaches 3 907. It is also suggested that some vender machines should be closed during non-peek times to improve facility utilization.
出处
《中国铁道科学》
EI
CAS
CSCD
北大核心
2009年第3期119-124,共6页
China Railway Science
基金
国家自然科学基金资助项目(60674012)
国家"八六三"计划项目(2007AA11Z136)
北京交通大学校基金资助项目(2007RC039)
关键词
铁路客运站
旅客集散
个体行为
微观仿真
Railway passenger station
Passenger mustering and evacuation
Individual behavior
Microscopic simulation
