Trip reservation and train operation plan optimization method of urban rail transit under demand responsive mechanism

ZHANG Song-liang; LI De-wei; YIN Yong-hao

doi:10.19818/j.cnki.1671-1637.2022.04.022

Volume 22 Issue 4

Aug. 2022

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ZHANG Song-liang, LI De-wei, YIN Yong-hao. Trip reservation and train operation plan optimization method of urban rail transit under demand responsive mechanism[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 285-294. doi: 10.19818/j.cnki.1671-1637.2022.04.022

Citation:

ZHANG Song-liang, LI De-wei, YIN Yong-hao. Trip reservation and train operation plan optimization method of urban rail transit under demand responsive mechanism[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 285-294. doi: 10.19818/j.cnki.1671-1637.2022.04.022

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Trip reservation and train operation plan optimization method of urban rail transit under demand responsive mechanism

doi: 10.19818/j.cnki.1671-1637.2022.04.022

1.
School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
2.
Frontiers Science Center for Smart High-Speed Railway System, Beijing Jiaotong University, Beijing 100044, China
3.
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, Hunan, China

Funds:

National Natural Science Foundation of China 71971019

Fundamental Research Funds for the Central Universities 2020JBZD007

Fundamental Research Funds for the Central Universities 2022JBQY006

Natural Science Foundation of Hunan Province 2022JJ40651

More Information

Author Bio:
ZHANG Song-liang(1997-), male, doctoral student, 21114041@bjtu.edu.cn

LI De-wei(1982-), male, professor, PhD, lidw@bjtu.edu.cn
Received Date: 2022-03-13
Available Online: 2022-10-08
Publish Date: 2022-08-25

Abstract

Abstract

In rail transit systems, the planning of the supply side conflicts with the time-varying characteristics of the demand side. Therefore, an optimization method of trian operation plan for urban rail transit under a demand responsive mechanism was proposed to coordinate the supply and demand relationship. The optimization method includes two steps: trip reservation and demand response. A collaborative optimization model of demand response and train operation plan was established to minimize passenger trip cost and train operation cost, and the delay cost of passengers due to trip reservation was emphasized. The factors such as train operation, transport capacity, train marshalling and passenger distribution were considered, and an adaptive large-scale neighborhood search algorithm based on passenger priority was designed. It was featured with an outer layer optimizing the train operation plan and an inner layer optimizing the passenger allocation, which realizes the matching between the supply and demand of passenger flows. With Beijing Subway Batong Line as an example, a numerical experiment was carried out on its all-day demand management and transportation organization based on the demand responsive mechanism. The results were analyzed from three aspects including locomotive application, passenger waiting time and load rate distribution. Analysis results show that the optimization method can reduce the number of operation trains by 13.8%, and 29.8% of units can be saved by the multi-group mode, which can effectively reduce the operating miles of trains and cut down corporate expenses. Furthermore, the method can shorten the average passenger waiting time at stations by up to 35.3% while ensuring the basic trip of passengers, and the increase in the proportion of reservations has an obvious effect on the reduction of passenger waiting time. The optimized operation plan can make the train load rate maintain at a set level and effectively reduce personnel density to avoid large-scale gathering of passengers, which is a useful exploration and can effectively prevent and control the pandemic appearing in urban rail transit. 2 tabs, 8 figs, 31 refs.
- urban rail transit,
- demand response,
- train operation plan,
- adaptive large neighborhood search,
- trip reservation,
- passenger flow control

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References(31)

References

[1]	LI Zi-hao, TIAN Xiang-liang, LI Zhong-wen, et al. Risk analysis of metro station passenger flow based on passenger flow patterns[J]. Journal of Tsinghua University (Science and Technology), 2019, 59(10): 854-860. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-QHXB201910009.htm
[2]	LI Chen, WANG Bo, BAI Yun-yun, et al. Impact analysis of passenger flow under urban rail transit emergency conditions based on AFC data[J]. Journal of Railway Science and Engineering, 2019, 16(10): 2620-2627. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD201910031.htm
[3]	MIAO Qin. How to solve unexpected large passenger flow with metro[J]. Urban Rapid Rail Transit, 2015, 28(4): 62-64. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DSKG201504020.htm
[4]	ZHANG Qi, XIAO Wen-jin, PAN Gang. A CA-based simulation model of urban railway large passenger flow congestion transmission[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(4): 83-89. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201704013.htm
[5]	JIANG Zhi-bin, FAN Wei, LIU Wei, et al. Reinforcement learning approach for coordinated passenger inflow control of urban rail transit in peak hours[J]. Transportation Research Part C: Emerging Technologies, 2018, 88(3): 1-16.
[6]	YUAN Fu-ya, SUN Hui-jun, KANG Liu-jiang, et al. Passenger flow control strategies for urban rail transit networks[J]. Applied Mathematical Modelling, 2020, 82: 168-188. doi: 10.1016/j.apm.2020.01.041
[7]	YANG Rui-xia, ZHOU Wei-teng, HAN Bao-ming, et al. Research on coordinated passenger inflow control for the urban rail transit network based on the station-to-line spatial- temporal relationship[J]. Journal of Advanced Transportation, 2022, 2022: 8895935.
[8]	LIU Sha-sha, YAO En-jian, LI Bin-bin, et al. Forecasting passenger flow distribution between urban rail transit stations based on behavior analysis under emergent events[J]. Journal of the China Railway Society, 2018, 40(9): 22-29. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201809005.htm
[9]	YE Hong-xia. On the prediction method of passenger flow redistribution under urban rail transit network emergency[J]. Urban Mass Transit, 2018, 21(8): 63-66. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GDJT201808015.htm
[10]	ANTONIOU C, BARCELÓ J, BREEN M, et al. Towards a generic benchmarking platform for origin-destination flows estimation/updating algorithms: design, demonstration and validation[J]. Transportation Research Part C: Emerging Technologies, 2016, 66: 79-98. doi: 10.1016/j.trc.2015.08.009
[11]	CANCA D, BARRENA E, DE-LOS-SANTOS A, et al. Setting lines frequency and capacity in dense railway rapid transit networks with simultaneous passenger assignment[J]. Transportation Research Part B: Methodological, 2016, 93: 251-267. doi: 10.1016/j.trb.2016.07.020
[12]	YIN Yong-hao, LI De-wei, BEŠINOVI AC'G N, et al. Hybrid demand-driven and cyclic timetabling considering rolling stock circulation for a bidirectional railway line[J]. Computer-Aided Civil and Infrastructure Engineering, 2019, 34(2): 164-187. doi: 10.1111/mice.12414
[13]	ROBENEK T, MAKNOON Y, AZADEH S S, et al. Passenger centric train timetabling problem[J]. Transportation Research Part B: Methodological, 2016, 89: 107-126. doi: 10.1016/j.trb.2016.04.003
[14]	CANCA D, BARRENA E, ALGABA E, et al. Design and analysis of demand-adapted railway timetables[J]. Journal of Advanced Transportation, 2014, 48(2): 119-137. doi: 10.1002/atr.1261
[15]	BARRENA E, CANCA D, COELHO L C, et al. Single-line rail rapid transit timetabling under dynamic passenger demand[J]. Transportation Research Part B: Methodological, 2014, 70: 134-150. doi: 10.1016/j.trb.2014.08.013
[16]	BUCAK S, DEMIREL T. Train timetabling for a double-track urban rail transit line under dynamic passenger demand[J]. Computers and Industrial Engineering, 2022, 163: 107858.
[17]	BARRENA E, CANCA D, COELHO L C, et al. Exact formulations and algorithm for the train timetabling problem with dynamic demand[J]. Computers and Operations Research, 2014, 44: 66-74.
[18]	NIU Hui-min, CHEN Ming-ming, ZHANG Ming-hui. Optimization theory and method of train operation scheme for urban rail transit[J]. China Railway Science, 2011, 32(4): 128-133. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTK201104024.htm
[19]	WU Xing-tang, DONG Hai-rong, TSE C K. Multi-objective timetabling optimization for a two-way metro line under dynamic passenger demand[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(8): 4853-4863.
[20]	WANG Jing. Regulatory quandary and its solutions of internet chauffeured car in China[J]. Administrative Law Review, 2016(2): 49-59. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XZFX201602006.htm
[21]	RITZINGER U, PUCHINGER J, HARTL R F. Dynamic programming based metaheuristics for the dial-a-ride problem[J]. Annals of Operations Research, 2016, 236(2): 341-358.
[22]	HUANG Di, GU Yu, WANG Shuai-an, et al. A two-phase optimization model for the demand-responsive customized bus network design[J]. Transportation Research Part C: Emerging Technologies, 2020, 111: 1-21.
[23]	WANG Jing-peng, LI Xin-wei, HUANG Hai-jun, et al. Operations mechanism of ride-sourcing platform with diversified products and services[J]. Systems Engineering—Theory and Practice, 2022, 42(7): 1873-1883. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XTLL202207011.htm
[24]	SHU Wan-neng, LI Yan. A novel demand-responsive customized bus based on improved ant colony optimization and clustering algorithms[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, DOI: 10.1109/TITS.2022.3145655.
[25]	GARCÍA-RÓDENAS R, LÓPEZ-GARCÍA M L, LÓPEZ-GÓMEZ J A, et al. Passenger centric train timetabling problem with elastic demand[J]. Transportation Research Procedia, 2020, 47: 465-472.
[26]	CACCHIANI V, QI Jian-guo, YANG Li-xing. Robust optimization models for integrated train stop planning and timetabling with passenger demand uncertainty[J]. Transportation Research Part B: Methodological, 2020, 136: 1-29.
[27]	ZHOU Wen-liang, FAN Wen-zhuang, YOU Xiao-rong, et al. Demand-oriented train timetabling integrated with passenger train-booking decisions[J]. Sustainability, 2019, 11(18): 4932.
[28]	ROBENEK T, AZADEH S S, MAKNOON Y, et al. Train timetable design under elastic passenger demand[J]. Transportation Research Part B: Methodological, 2018, 111: 19-38.
[29]	SHI Jun-gang, YANG Jing, ZHOU Feng, et al. Integrate optimization model of operation schedule for metro express/local train[J]. Journal of Traffic and Transportation Engineering, 2018, 18(1): 130-138. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC201801016.htm
[30]	WANG Yi-zhen, LI De-wei, CAO Zhi-chao, Integrated timetable synchronization optimization with capacity constraint under time-dependent demand for a rail transit network[J]. Computers and Industrial Engineering, 2020, 142: 106374.
[31]	NIU Hui-min, ZHOU Xue-song, GAO Ru-hu. Train scheduling for minimizing passenger waiting time with time-dependent demand and skip-stop patterns: Nonlinear integer programming models with linear constraints[J]. Transportation Research Part B: Methodological, 2015, 76: 117-135.

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Trip reservation and train operation plan optimization method of urban rail transit under demand responsive mechanism

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