基于动态安全间隔的城轨大小交路列车运行仿真研究

doi:10.16097/j.cnki.1009-6744.2025.01.015

交通运输系统工程与信息 ›› 2025, Vol. 25 ›› Issue (1): 146-159.DOI: 10.16097/j.cnki.1009-6744.2025.01.015

基于动态安全间隔的城轨大小交路列车运行仿真研究

许得杰^*，钟苗苗，巩亮，惠昌武，曾俊伟

兰州交通大学，交通运输学院，兰州730070

收稿日期:2024-06-15 修回日期:2024-11-25 接受日期:2024-12-05 出版日期:2025-02-25 发布日期:2025-02-24
作者简介:许得杰(1986—)，男，甘肃武威人，副教授，博士。
基金资助:
国家自然科学基金(72261025)；中国国家铁路集团有限公司重点研发计划项目(N2023X007)；兰州交通大学—天津大学联合创新基金(2021056)。

Train Tracking Operation Simulation for Full-length and Short-turn Routings in Urban Rail Transit Based on Dynamic Safety Interval

XU Dejie^*, ZHONG Miaomiao, GONG Liang, HUI Changwu, ZENG Junwei

School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China

Received:2024-06-15 Revised:2024-11-25 Accepted:2024-12-05 Online:2025-02-25 Published:2025-02-24
Supported by:
National Natural Science Foundation of China (72261025)；Key Research and Development Projects of China National Railway Group Co Ltd (N2023X007)；The United Fund of Lanzhou Jiaotong University and Tianjin University (2021056)。

摘要/Abstract

摘要： 城市轨道交通大小交路运营组织方式是目前解决线路客流不均衡性的主要方法，针对大小交路的列车运行仿真研究，有助于提高列车运营效率，保障行车安全。本文根据单一交路和大小交路两种典型交路模式的特点，考虑列车制动性能、信号系统和线路条件等因素对列车运行加速度的影响，提出列车最小安全追踪距离计算方法，构建基于元胞自动机的城市轨道交通列车运行仿真模型。以某城市轨道交通1号线为例，通过模型标定，对列车追踪及折返运行情况进行仿真验证，分析列车运行速度和制动性能参数对线路运营能力的影响，并探讨大小交路列车不同开行比例下通过能力和车底运用数量的变化规律。研究结果表明：考虑列车运行过程中加速度的变化能显著提高系统的仿真精度；基于动态安全间隔的城轨列车追踪运行仿真模型能有效缩短列车最小安全追踪距离；大小交路列车开行比例为1∶1时，通过能力较单一交路提高约33.33%；车底运用数量的增量随着大小交路周转时间比值的增加呈周期性变化趋势；车底运用数量不变时，大小交路列车开行比例为1∶2时通过能力最大。研究结论可为城市轨道交通时刻表编制及列车运营管理提供理论依据。

关键词: 城市交通, 运行仿真, 元胞自动机, 城市轨道交通, 大小交路, 通过能力

Abstract: The operation mode of full-length and short-turn routings is the main method to solve the imbalance of passenger flow in urban rail transit system. The simulation study of train operation for full-length and short-turn routings is helpful to improve the efficiency and ensure the safety of train operation. Considering the characteristics of two typical routing modes of single-routing and full-length and short-turn routings, this paper proposes a method to calculate the minimum safe tracking distance of trains and develops a simulation model of urban rail transit train operation based on cellular automata. The influence of train braking performance, signal system and line conditions on train operation acceleration are also analyzed. The train tracking and turn-back operation are simulated and verified using an urban rail transit line as an example. The influence of train speed and braking performance parameters on line operation capacity is analyzed, and the variation law of the carrying capacity and the number of rolling stocks is discussed under different operation ratios of full-length and short-turn routings. The results show that considering the change of acceleration in the process of train operation can significantly improve the simulation accuracy of the system. The simulation model of urban rail transit train tracking operation based on dynamic safety interval can effectively shorten the minimum safe tracking distance of trains. When the ratio of full-length and short-turn routings is 1∶1, the carrying capacity is about 33.33% higher than that of single routing. The increment of the number of rolling stocks shows a periodic trend with the increase of the ratio of the turn-around times of the full-length and short-turn routings. When the number of rolling stocks is constant, the carrying capacity is the largest under the operation ratio of 1∶2. This study can provide a theoretical basis for the urban rail transit timetable optimization and train operation management.

Key words: urban traffic, operation simulation, cellular automata, urban rail transit, full-length and short-turn routings, carrying capacity

中图分类号:

U121

许得杰, 钟苗苗, 巩亮, 惠昌武, 曾俊伟. 基于动态安全间隔的城轨大小交路列车运行仿真研究[J]. 交通运输系统工程与信息, 2025, 25(1): 146-159.

XU Dejie, ZHONG Miaomiao, GONG Liang, HUI Changwu, ZENG Junwei. Train Tracking Operation Simulation for Full-length and Short-turn Routings in Urban Rail Transit Based on Dynamic Safety Interval[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(1): 146-159.

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参考文献

[1]刘海东,毛保华,何天健,等.不同闭塞方式下城轨列车追踪运行过程及其仿真系统的研究[J].铁道学报,2005, 27(2): 120-125. [LIU H D, MAO B H, HE T J, et al. Study on tracking operations between trains of different block modes and simulation system[J]. Journal of the China Railway Society, 2005, 27(2): 120-125.]

[2]张海,倪少权,吕苗苗.基于元胞自动机模型的地铁列车折返间隔分析[J].交通运输工程与信息学报,2021,19(2): 37-45. [ZHANG H, NI S Q, LV M M. Analysis of subway trains' turn-back headway based on the cellular automaton model[J]. Journal of Transportation Engineering and Information, 2021, 19(2): 37-45.]

[3] WANG X, XING K J, WANG J, et al. Predictive control algorithm for speed and displacement tracking of urban rail trains[J]. IAENG International Journal of Applied Mathematics, 2024, 54(7): 1362-1370.

[4]张英贵,赵明慧,张云丽.面向虚拟连挂的城轨列车群组追踪运行仿真研究[J].交通运输系统工程与信息,2024, 24(1): 199-209. [ZHANG Y G, ZHAO M H, ZHANG Y L. Simulation research on train group tracking operation with virtual coupling for urban mass rail transit [J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24 (1): 199-209.]

[5]帅斌,罗佳楠,冯心妍,等.虚拟编组下基于跟驰模型的列车群运行控制方法研究[J].交通运输系统工程与信息,2024, 24(3): 1-11. [SHUAI B, LUO J N, FENG X Y, et al. A train group control method based on car following model under virtual coupling[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(3): 1-11.]

[6]张淼,何仪娟,杨博宇,等.基于LSTM-KF模型的高速列车群组追踪运行轨迹预测方法[J].交通运输工程学报, 2024, 24(3): 296-310. [ZHANG M, HE Y J, YANG B Y, et al. Trajectory prediction method for high-speed train group tracking operation based on LSTM-KF mode [J]. Journal of Traffic and Transportation Engineering, 2024, 24(3): 296-310.]

[7]荀径,陈明亮,宁滨,等.虚拟重联条件下地铁列车追踪运行性能衡量[J]. 北京交通大学学报,2019,43(1): 96-103. [XUN J, CHEN M L, NING B, et al. Train tracking performance measurement under virtual coupling in subway[J]. Journal of Beijing Jiaotong University, 2019, 43(1): 96-103.]

[8]林颖,王长林.基于CBTC的车载ATP安全制动曲线计算模型研究[J]. 铁道学报,2011,33(8): 69-72. [LIN Y, WANG C L. Computational model of safe braking curve of onboard ATP based on CBTC[J]. Journal of the China Railway Society, 2011, 33(8): 69-72.]

[9]CARVAJAL-CARRENO W, CUCALA A P, FERNANDEZ-CARDADOR A. Fuzzy train tracking algorithm for the energy efficient operation of CBTC equipped metro lines[J]. Engineering Applications of Artificial Intelligence, 2016, 100(53): 19-31.

[10] XUN J, YIN Y, LIU R H, et al. Cooperative control of high-speed trains for headway regulation: A self triggered model predictive control based approach[J]. Transportation Research Part C: Emerging Technologies, 2019, 102: 106-120.

[11] 常鸣, 崔科, 王维旸,等.基于动势能转换的虚拟编组列车动态最小安全间隔[J].同济大学学报(自然科学版), 2024, 52(1): 41-48. [CHANG M, CUI K, WANG W Y, et al. Dynamic minimum safety distance of virtual coupled trains based on kinetic-potential energy conversion[J]. Journal of Tongji University(Natural Science), 2024, 52(1): 41-48.]

[12] QUAGLIETTA E, SPARTALIS P, WANG M, et al. Modelling and analysis of virtual coupling with dynamic safety margin considering risk factors in railway operations[J]. Journal of Rail Transport Planning &Management, Elsevier, 2022, 22: 100313.

[13] 许得杰, 毛保华,陈绍宽,等.考虑开行比例的大小交路列车开行方案优化[J].交通运输工程学报,2021,21(2): 173-186. [XU D J, MAO B H, CHEN S K, et al. Optimization of operation scheme for full-length and short-turn routings considering operation proportion[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 173-186.]

[14] 高国飞, 张星臣,罗强,等.市域快速轨道交通快慢车混合运行时线路通过能力计算方法[J].中国铁道科学, 2021, 42(1): 156-165. [GAO G F, ZHANG X C, LUO Q, et al. Calculation method of line passing capacity in mixed operation of fast and slow trains of urban rapid rail transit[J]. China Railway Science, 2021, 42(1): 156-165.]

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基于动态安全间隔的城轨大小交路列车运行仿真研究

Train Tracking Operation Simulation for Full-length and Short-turn Routings in Urban Rail Transit Based on Dynamic Safety Interval

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