Bayesian Network Inference on Departure Time Choice Behavior

机译：贝叶斯网络对出发时间选择行为的推断

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Departure time choice behavior plays an important role in travel decision for metro passengers during morning peak hours. Different from statistical models, this paper proposed Bayesian networks (BNs) to model the departure time choices of metro passengers. The structure of BNs is learned through K2 algorithm and its parameters are estimated by maximum likelihood estimation (MLE) method using the stated preference (SP) survey data. Main results are obtained as follows: (1) passengers can accept departure earlier than usual in the range of 0-20 min; (2) passengers will prefer to choose departure earlier if they enjoy a 20% or more discount on metro fare; and (3) passengers are willing to departure at usual time with slight crowding while they prefer to departure earlier under serious crowding. These findings contribute to making strategies for passenger flow control and safety operation for metro stations.

机译：出发时间选择行为在早上高峰时段对于地铁乘客的出行决策起着重要作用。与统计模型不同，本文提出了贝叶斯网络（BNs）来模拟地铁乘客的出发时间选择。通过K2算法学习BN的结构，并使用陈述的偏好（SP）调查数据通过最大似然估计（MLE）方法估计其参数。获得的主要结果如下：（1）旅客可以在0-20分钟的范围内比平常更早地接受出发；（2）如果乘客享受地铁票价20％或更多的折扣，他们会更愿意选择较早的出发时间；（3）乘客愿意在平时稍有拥挤的情况下出发，而宁愿在严重拥挤的情况下提早出发。这些发现有助于制定地铁站的客流控制和安全运营策略。

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《Proceedings of the 3rd international conference on electrical and information technologies for rail transportation: transportation》|2017年|599-607|共9页
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作者
Xian Li; Haiying Li; Linqiao Qin; Xinyue Xu;
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State Key Laboratory of Rail Traffic Control Safety, Beijing Jiaotong University, Beijing, China,School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China;

State Key Laboratory of Rail Traffic Control Safety, Beijing Jiaotong University, Beijing, China;

Traffic Operations and Safety (TOPS) Laboratory, Department of Civil and Environmental Engineering, University of Wisconsin-Madison, 53706 Madison, WI, USA;

State Key Laboratory of Rail Traffic Control Safety, Beijing Jiaotong University, Beijing, China;

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原文格式 PDF
正文语种 eng
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关键词
Departure time choice; Bayesian network; Peak-hour congestion; Metro passengers;

机译：出发时间选择；贝叶斯网络高峰时段交通挤塞；地铁乘客;

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专利

1. A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice [J] . Zhu Zheng, Chen Xiqun, Xiong Chenfeng, Transportation . 2018,第5期

机译：用于出发时间和模式选择的二维决策建模的混合贝叶斯网络
2. Time-dependent congestion pricing system for large networks: Integrating departure time choice, dynamic traffic assignment and regional travel surveys in the Greater Toronto Area [J] . Aboudina Aya, Abdelgawad Hossam, Abdulhai Baher, Transportation Research . 2016,第deca期

机译：大型网络的时间依赖性拥堵定价系统：整合出发时间选择，动态交通分配和大多伦多地区的区域旅行调查
3. The role of intention as mediator between latent effects and behavior: application of a hybrid choice model to study departure time choices [J] . Thorhauge Mikkel, Cherchi Elisabetta, Walker Joan L., Transportation . 2019,第4期

机译：意图作为潜在效应和行为之间的中介者的作用：混合选择模型在研究出发时间选择中的应用
4. Bayesian Network Inference on Departure Time Choice Behavior [C] . Xian Li, Haiying Li, Linqiao Qin, International conference on electrical and information technologies for rail transportation: transportation . 2018

机译：贝叶斯网络推论出发时间选择行为
5. Optimized Time-Dependent Congestion Pricing System for Large Networks: Integrating Distributed Optimization, Departure Time Choice, and Dynamic Traffic Assignment in the Greater Toronto Area. [D] . Aboudina, Aya Tollah Moustafa S. M. 2016

机译：大型网络的优化的时变拥塞定价系统：在大多伦多地区集成分布式优化，出发时间选择和动态交通分配。
6. Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method [O] . Bin Yu, Jia-Meng Xu, Shan Li, 2017

机译：基于动态贝叶斯网络混合学习方法的时延基因调控网络推断
7. Time-dependent model for departure time and route choices in networks with queues [O] . Lam WHK, Zhang Y, Yafeng Y 1999

机译：与时间相关的模型，用于带有队列的网络中的出发时间和路线选择

Bayesian Network Inference on Departure Time Choice Behavior

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