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首页> 外文会议>2017 IEEE SmartWorld, Ubiquitous Intelligence amp; Computing, Advanced amp; Trusted Computed, Scalable Computing amp; Communications, Cloud amp; Big Data Computing, Internet of People and Smart City Innovation >Research on propagation of passenger flow in urban rail transit network for large-scale events
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Research on propagation of passenger flow in urban rail transit network for large-scale events

机译：大型事件在城市轨道交通网络中客流传播的研究

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摘要

Large-scale events in cities will attract a large number of audiences and bring great pressure on the nearby traffic operation and management. However, little attention has been given to the evaluation and propagation in urban rail transit network. The objective of this work is to propose an effective dynamic traffic assignment method for large-scale events and capture the temporal-spatial nature of passenger flow in the complex metro network. In the method framework, a stochastic utility function including in-vehicle time, extra waiting time and transfer penalties is developed to describe passenger perception and the corresponding estimation method of parameters is given. What's more, an improved multinomial logit model is proposed to take correlation between alternative routes into consideration. Finally, an empirical experiment based on Beijing Metro is conducted to illustrate the propagation of passenger flow after a large-scale event ends and validate the proposed method. The results indicate that an acceptable accuracy can be obtained for analyzing the dynamic influence of large-scale events in urban rail transit network.
机译：城市中的大型活动将吸引大量观众，并给附近的交通运营和管理带来巨大压力。但是，很少有人关注城市轨道交通网络的评估和传播。这项工作的目的是为大型事件提出一种有效的动态交通分配方法，并捕获复杂地铁网络中客流的时空特性。在方法框架中，开发了一种包含车辆时间，额外等待时间和中转罚金的随机效用函数来描述乘客的感知，并给出了相应的参数估计方法。此外，提出了一种改进的多项式对数模型，以考虑替代路径之间的相关性。最后，以北京地铁为例进行了实证试验，以说明大规模事件结束后客流的传播并验证了所提方法的有效性。结果表明，分析城市轨道交通大事件的动态影响可以得到可接受的精度。

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