Congestion Propagation Quantization Model about Rail Transit System

Abstract

Abstract:

The rail transit has become the backbone of the integrated passenger transport system. It affects the normal operation of the urban passenger transport and the social economy as well. The study of the law of the congestion propagation rate helps to improve the safety and the efficiency of the rail transit system. Previous attempts to describe the oversaturated condition propagation are lack of detailed parameter or not in accordance with the actual situation. The parameters for the oversaturated condition propagation model are estimated in this paper and the congestion propagation model considered the passenger flow can be obtained. Firstly, it analyzes the parameters and their influence factors in the oversaturated conditions propagation model and the comparison about the congestion propagation quantization is proposed. Secondly, the character of the passenger flow is conducted as its research objective. Thirdly, the threshold of the large passenger flow can be established through the data of the passenger flow and remain capacity of the train. Fourthly, the quantitative model of the congestion propagation rate is constructed. The influence of the passenger flow can be obtained by the susceptible-infected-recovered (SIR) model. Finally, the Beijing rail transit line 13 is illustrated. The congestion propagation rate in different time and different transfer situations can be conducted. It is beneficial for help optimizing the service of the rail transit system, and for the emergency disposal of the urging surging passenger flow.

Key words: urban traffic, congestion propagation, SIR model, passenger flow, threshold

摘要：

轨道交通作为综合客运交通系统的骨干，影响着城市客运交通乃至社会经济的正常运转，对其大客流拥挤传播规律的研究有助于提高轨道交通系统的运输安全和效率.以往学者研究的大客流传播模型中往往参数量化缺失或者缺乏实际意义，因此，本文着眼于量化大客流传播模型中的参数，旨在构建客流影响下的拥挤传播模型.首先，分析大客流传播模型中参数及其影响因素，阐述现有传播速率量化模型；其次，以客流的不确定性作为研究对象，分析客流传播特征；再次，通过轨道交通客流数据量化模型参数相关的客流和列车剩余载客能力，界定大客流影响阈值；然后，构建考虑客流因素的传染病模型，量化拥挤传播速率，据此分析客流对轨道交通拥挤传播的影响；最后，以北京地铁13号线为例，比较不同时段、不同换乘情况下其大客流拥挤传播影响范围的差异，分析其原因，从而为优化轨道交通系统的服务水平及大客流事件应急处置提供参考和借鉴.

关键词: 城市交通, 拥挤传播, 传染病模型, 客流, 影响阈值

CLC Number:

U291.69

XIONG Zhi-hua, YAO Zhi-sheng. Congestion Propagation Quantization Model about Rail Transit System[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(3): 146-151.

熊志华，姚智胜. 轨道交通拥挤传播速率量化模型研究[J]. 交通运输系统工程与信息, 2018, 18(3): 146-151.

Add to citation manager EndNote|Ris|BibTeX

URL: http://www.tseit.org.cn/EN/abstract/abstract19639.shtml

http://www.tseit.org.cn/EN/Y2018/V18/I3/146

References

[1] 李凌燕. 城市轨道交通网络突发大客流传播机理及组织优化 [D]. 成都: 西南交通大学, 2015. [LI L Y. Sudden passenger flow propagation mechanism analysis and organizational optimization in urban rail transit network[D]. Chengdu: Southwest Jiaotong University, 2015.]

[2] 骆晨, 刘澜, 牛龙飞. 城市轨道交通超大客流网络拥挤传播研究[J]. 石家庄铁道大学学报(自然科学版), 2014, 27(2): 83- 86. [LUO C, LIU L, NIU L F. The research on the network congestion for large passenger flow of urban rail transit[J]. Journal of Shijiazhuang Tiedao University (Natural Science), 2014, 27(2): 83-86.]

[3] 陈菁菁. 城市轨道交通网络运营可靠性研究[D]. 上海: 同济大学, 2007. [CHEN J J. The study on the operational reliability of the urban transit transportation network[D]. Shanghai: Tongji University, 2007.]

[4] 牛龙飞. 城市轨道交通大客流的网络传播特性及运输组织协调研究 [D]. 成都: 西南交通大学, 2013. [NIU L F. Crowding propagation and organization coordination of urban rail network under large passenger flow[D]. Chengdu: Southwest Jiaotong University, 2013.]

[5] 曹志超. 网络条件下城市轨道交通突发大客流演化机理和应急策略研究[D]. 北京: 北京交通大学, 2013. [CAO Z C. Unexpected passenger flow evolution mechanism analysis in urban rail transit network and emergency strategy study[D]. Beijing: Beijing Jiaotong University, 2013.]

[6] 刘小霞. 城市轨道交通网络突发客流传播影响分析 [D]. 北京: 北京交通大学, 2011. [LIU X X. Unexpected passenger flow propagation analysis in urban rail transit network[D]. Beijing: Beijing Jiaotong University, 2011.]

[7] 吴璐. 城市轨道交通网络突发客流特性及拥挤控制研究[D]. 成都: 西南交通大学, 2013. [WU L. Analysis of unexpected passenger demand and resulting congestion in urban rail transit[D]. Chengdu: Southeast Jiaotong University, 2013.]

[8] 徐娟. 传染病动力学模型中的阈值[J]. 新乡学院学报 (自然科学版), 2012, 29(5): 392-395. [XU J. Threshold value of infectious disease dynamic model[J]. Journal of Xinxiang University (Natural Science Edition), 2012, 29 (5): 392-395.]

[1]	LIU Xiao-bing , LI Feng-xiao , TIAN Xin-mei, YAN Xue-dong. Identifying Metropolitan Center Structure Based on Commuting Patterns [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 17-28.
[2]	XU Qi, CHEN Yue , HUANG Jing-ru , GAO Shun-xiang , ZHANG Zhi-jian. Job Accessibility Analysis Considering Travel Cost [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 37-44.
[3]	XU Meng , LIU Tao , ZHONG Shao-peng , JIANG Yu. Urban Smart Public Transport Studies: A Review and Prospect [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 91-108.
[4]	LIU Chun-yu , LIU Yong-hong , LUO Xia , ZHU Ying. Trajectory Optimization of Connected Vehicles at Isolated Intersection in Mixed Traffic Environment [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 154-162.
[5]	ZHANG Xiao-yu , SHAO Chun-fu , WANG Bo-bin , HUANG Shi-chen. Travel Mode Choice Analysis with Shared Mobility in Context of COVID-19 [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 186-196.
[6]	LI Xin , DAI Zhang , LI Huai-yue, HU Jia. Joint Optimization of Urban Rail Transit and Local Bus Transit: Continuous Approximation Approach [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 206-213.
[7]	HAO Yan-xi, LIU Rong-yang, HU Hua , FANG Yong, LIU Zhi-gang. One-way Pedestrian-bicycle Mixed Flow Model and Self-organization Phenomenon [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 223-229.
[8]	ZHAO Chuan-lin , HE Shao-song, SUN Shu-min, WANG Yu-han. Bi-level Optimization Model in Transportation Evacuation Network Based on Rational Inattention [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 239-246.
[9]	CHEN Xiao-hong , LAN Qiu-yu. Interval Optimization Model of Intersection Signal Timing Under Uncertain Traffic Demand [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 247-256.
[10]	LI Bing, WANG Zheng-hui, MA Ming-wei, YANG Hong-yu, FENG Yue. Capacity and Delay of Right-turn Vehicles at Signalized Intersections Under Influence of Pedestrian Two-stage Crossing [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 257-267.
[11]	ZHU Tong , QIN Dan, DONG Ao-ran, DU Yu-meng, WEI Wen. Relational Analysis Between Bus Drivers' Violation Type and Traffic Accident [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 322-329.
[12]	MAO Bao-hua , WANG Min , HO Tin-kin , CHEN Hai-bo. A Review and Prospect of Urban Public Transit Level-of-Service Research [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1): 2-13.
[13]	WANG Zi-jia , JIA Hui-hui, ZHU Ya-di , CHEN Feng. Commuting Mode Choice Behavior in Rail and Bus Composite Network Based on Smart Card Data [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1): 67-73.
[14]	JIA Bin , ZHU Ling, LI Shu-kai, LIU Jia-lin. Collaborative Optimization Strategy of Short-turning Plan and Passenger Flow Control in Urban Rail Transit [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1): 124-132.
[15]	MOU Zhen-hua , WANG Han-bing , LIN Ben-jiang , CHEN Yi-qun, JIN Cheng-cheng , CHEN Yan-yan. Utility Simulation Evaluation of Dynamic Fines Strategy for Illegal Parking Based on Evolutionary Game [J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(1): 152-162.