|Table of Contents|

Dynamic marshalling and scheduling of trains in major epidemics(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2020年03期
Page:
120-128
Research Field:
交通运输规划与管理
Publishing date:

Info

Title:
Dynamic marshalling and scheduling of trains in major epidemics
Author(s):
CAO Yuan1 WEN Jia-kun2 MA Lian-chuan1
(1. National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing 100044, China; 2. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China)
Keywords:
urban rail transit virtual coupling dynamic scheduling epidemic prevention and control infection risk analysis social force model
PACS:
U113
DOI:
10.19818/j.cnki.1671-1637.2020.03.011
Abstract:
In order to reduce the infection risk of passengers who travel by urban rail transit in the context of the global epidemics outbreak, the marshalling and scheduling of trains were taking as the research objects, and a dynamic marshalling and scheduling method of trains based on the virtual coupling under the major epidemic was proposed. In order to improve the flexibility of train marshalling and scheduling in urban rail transit, the virtual coupling was applied to the train marshalling in urban rail transit. The nonlinear programming model of train dynamic marshalling based on the passenger flow was established to optimize the scheduling of urban rail transit trains, so as to improve the transport efficiency of urban rail transit, reduce the station personnel density and consequently reduce the risk of disease infection. The improved Wells-Riley model was used for the infection analysis. The pedestrian movement model based on the social force was used to calculate the parameters in the improved Wells-Riley model, so as to analyze the infection risk of passengers who travel under the dynamic marshalling of virtual coupling. The infection probability under the virtual coupling system was simulated and compared with the result under the traditional method by using the MATLAB. Analysis result shows that the virtual coupling technology can significantly improve the train transport efficiency of urban rail transit and shorten the tracking time interval between trains to 34.6 s. The dynamic marshalling and scheduling method of trains based on the virtual coupling can effectively reduce the infection risk of passengers. In the same conditions, the infection risk of passengers from the proposed method is only 85.1% of that from the traditional way, and the infection risks in the carriages and channel are 50.0% and 8.7% of those from the traditional way, respectively. If the proposed method is combined with the measures such as the control of off-peak travel and passenger flow,the in-station epidemic prevention and testing, the infection risk of passengers can reduce further. 3 tabs, 11 figs, 30 refs.

References:

[1] CAO Yuan, WANG Zheng-chao, LIU Feng, et al. Bio-inspired speed curve optimization and sliding mode tracking control for subway trains[J]. IEEE Transactions on Vehicular Technology, 2019, 68(7): 6331-6342.
[2] CAO Yuan, SUN Yong-kui, XIE Guo, et al. Fault diagnosis of train plug door based on a hybrid criterion for IMFs selection and fractional wavelet package energy entropy[J]. IEEE Transactions on Vehicular Technology, 2019, 68(8): 7544-7551.
[3] SU Shuai, WANG Xue-kai, CAO Yuan, et al. An energy-efficient train operation approach by integrating the metro timetabling and eco-driving[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20: 1-17.
[4] ZHANG Yu-zhuo, CAO Yuan, WEN Ying-hong, et al. Optimization of information interaction protocols in cooperative vehicle-infrastructure systems[J]. Chinese Journal of Electronics, 2018, 27(2): 439-444.
[5] CAO Yuan, MA Lian-chuan, XIAO Shuo, et al. Standard
analysis for transfer delay in CTCS-3[J]. Chinese Journal of Electronics, 2017, 26(5): 1057-1063.
[6] CAO Yuan, LI Peng, ZHANG Yu-zhuo. Parallel processing algorithm for railway signal fault diagnosis data based on cloud computing[J]. Future Generation Computer Systems, 2018(88): 279-283.
[7] HALTUF M. Shift2Rail JU from member state's point of view[J]. Transportation Research Procedia, 2016, 14: 1819-1828.
[8] WANG Yu-jian, SONG Yong-duan, GAO Hui, et al.
Distributed fault-tolerant control of virtually and physically interconnected systems with application to high-speed trains under traction/braking failures[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(2): 535-545.
[9] FELEZ J, KIM Y J, BORRELLI F. A model predictive control approach for virtual coupling in railways[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(7): 2728-2739.
[10] SCHUMANN T. Increase of capacity on the Shinkansen
high-speed line using virtual coupling[J]. International Journal of Transport Development and Integration, 2017, 1(4): 666-676.
[11] 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 Jing, CHEN Ming-liang, NING Bin, et al. Train tracking performance measurement under virtual coupling in subway[J]. Journal of Beijing Jiaotong University, 2019, 43(1): 96-103.(in Chinese)
[12] 刘 鹏.客流自适应地铁运营调度策略研究[D].成都:西南交通大学,2015.
LIU Peng. Research on the traffic self-adaptive of subway operation dispatch strategy[D]. Chengdu: Southwest Jiaotong University, 2015.(in Chinese)
[13] VAZIFEH M M, SANTI P, RESTA G, et al. Addressing
the minimum fleet problem in on-demand urban mobility[J]. Nature, 2018, 557(7706): 534-538.
[14] SONE S. Comparison of the technologies of the Japanese
Shinkansen and Chinese High-speed Railways[J]. Journal of Zhejiang University: Science A, 2015, 16(10): 769-780.
[15] GHOSEIRI K, SZIDAROVSZKY F, ASGHARPOUR M J. A multi-objective train scheduling model and solution [J]. Transportation Research Part B: Methodological, 2004, 38(10): 927-952.
[16] ARIANO A, PACCIARELLI D, PRANZO M. A branch and bound algorithm for scheduling trains in a railway network[J]. European Journal of Operational Research, 2007, 183(2): 643-657.
[17] ESPINOSA-ARANDA J L, GARCÍA-RÓDENAS R. A demand-based weighted train delay approach for rescheduling railway networks in real time[J]. Journal of Rail Transport Planning and Management, 2013, 3(1/2): 1-13.
[18] QUAGLIETTA E, CCRMAN F, GOVERDE R M P. Stability analysis of railway dispatching plans in a stochastic and dynamic environment[J]. Journal of Rail Transport Planning and Management, 2013, 3(4): 137-149.
[19] SALIM V, CAI Xiao-qiang. A genetic algorithm for railway scheduling with environmental considerations[J]. Environmental Modelling and Software, 1997, 12(4): 301-309.
[20] CAPRARA A, MONACI M, TOTH P, et al. A Lagrangian heuristic algorithm for a real-world train timetabling problem[J]. Discrete Applied Mathematics, 2006, 154(5): 738-753.
[21] ZOU You, XIE Jia-rong, WANG Bing-hong. Evacuation of pedestrians with two motion modes for panic system[J]. PloS One, 2016, 11(4): 1-13.
[22] GOU Ren-yong. New insights into discretization effects in
cellular automata models for pedestrian evacuation[J]. Physica A: Statistical Mechanics and Its Applications, 2014, 400: 1-11.
[23] QIU Guo, SONG Rui, HE Shi-wei, et al. The pedestrian
flow characteristics of Y-shaped channel[J]. Physica A: Statistical Mechanics and Its Applications, 2018, 508: 199-212.
[24] SONG Xiao, XIE Hong-nan, SUN Jiang-han, et al. Simulation of pedestrian rotation dynamics near crowded exits[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(8): 3142-3155.
[25] MITCHELL I. ERTMS level 4, train convoys or virtual
coupling[J]. IRSE News, 2016, 219: 14-15.
[26] CAO Yuan, ZHANG Yu-zhuo, WEN Tao, et al. Research on dynamic nonlinear input prediction of fault diagnosis based on fractional differential operator equation in high-speed train control system[J]. Chaos, 2019, 29(1): 013130-1-7.
[27] QIAN Hua, LI Yu-guo, NIESEN P V, et al. Spatial
distribution of infection risk of SARS transmission in a hospital ward[J]. Building and Environment, 2009, 44(8): 1651-1658.
[28] BENTHAM R, WHILEY H. Quantitative microbial risk
assessment and opportunist waterborne infections-are there too many gaps to fill?[J]. International Journal of Environmental Research and Public Health, 2018, 15(6): 1-11.
[29] 钱 华,郑晓红,张学军.呼吸道传染病空气传播的感染概率的预测模型[J].东南大学学报(自然科学版),2012,42( 3):468-472.
QIAN Hua, ZHENG Xiao-hong, ZHANG Xue-jun. Prediction of risk of airborne transmitted diseases[J]. Journal of Southeast University(Natural Science Edition), 2012, 42(3): 468-472.(in Chinese)
[30] 孟 琦.基于社会力的车站交叉行人流特性分析及疏散研究[D].北京:北京交通大学,2019.
MENG Qi. Intersecting pedestrian flows characteristics analysis and evacuation in stations based on social force[D]. Beijing: Beijing Jiaotong University, 2019.(in Chinese)

Memo

Memo:
-
Last Update: 2020-07-10