Abstract
Existing studies estimate the economic value (EcV) of network reliability and robustness separately. In this study, a model is proposed for estimating the EcV of reserve capacity in a rail transit network (RTN), which consists of: (1) the reduction of passengers’ total generalized travel cost (GTC) in normal operations, (2) the EcV of reliability enhancement in normal operations, and (3) the EcV of robustness enhancement when disturbances occur. The standard deviation of travel time (SDT) is used to measure travel time reliability (TTR). The reserve capacity helps to reduce passengers’ total GTC and the SDT during normal operations. The EcV of reliability enhancement equals the EcV converted from the reduction in SDT when the RTN operates normally. The EcV of robustness enhancement is the difference of passengers’ total GTC with and without reserve capacity when disturbances occur. To optimize reserve capacity, a model is developed to maximize its net benefit, equaling its EcV minus its cost, which is then solved with a quantum genetic algorithm. A case study of Chengdu’s RTN shows the effectiveness of the model with promising numerical results. The research findings show that the value of reserve capacity is severely underestimated without considering the EcV of reliability and robustness. The study can guide policymakers and operators in quantifying the EcV of reliability and robustness enhancement when expanding an RTN’s capacity.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
We are grateful for the support received from National Key R & D Program of China (No. 2017YFB1200700), the National Natural Science Foundation of China (Nos. 71701174 and 71861017), and Kunming University of Science and Technology (No. KUST-xk2022002).
References
Bell, M., and C. Cassir. 2000. Reliability of transport networks. Baldock, UK: Research Studies Press.
Black, I. G., and J. G. Towriss. 1993. Demand effects of travel time reliability, centre for logistics and transportation. Bedford, UK: Cranfield Institute of Technology.
Bruzelius, N. A. 1981. “Microeconomic theory and generalised cost.” Transportation 10 (3): 233–245. https://doi.org/10.1007/BF00148460.
Carrion, C., and D. Levinson. 2012. “Value of travel time reliability: A review of current evidence.” Transp. Res. Part A Policy Pract. 46 (4): 720–741.
Cats, O. 2016. “The robustness value of public transport development plans.” J. Transp. Geogr. 51 (Jul): 236–246. https://doi.org/10.1016/j.jtrangeo.2016.01.011.
Cats, O., and E. Jenelius. 2015. “Planning for the unexpected: The value of reserve capacity for public transport network robustness.” Transp. Res. Part A Policy Pract. 81 (Nov): 47–61.
Cats, O., G. J. Koppenol, and M. Warnier. 2017. “Robustness assessment of link capacity reduction for complex networks: Application for public transport systems.” Reliab. Eng. Syst. Saf. 167 (Nov): 544–553. https://doi.org/10.1016/j.ress.2017.07.009.
De Jong, G., M. Kouwenhoven, E. Kroes, P. Rietveld, and P. Warffemius. 2009. “Preliminary monetary values for the reliability of travel times in freight transport.” Eur. J. Transp. Infrastruct. Res. 9 (2): 83–99.
Derrible, S., and C. Kennedy. 2010. “The complexity and robustness of metro networks.” Phys. A 389 (17): 3678–3691. https://doi.org/10.1016/j.physa.2010.04.008.
Dixit, M., T. Brands, N. van Oort, O. Cats, and S. Hoogendoorn. 2019. “Passenger travel time reliability for multimodal public transport journeys.” Transp. Res. Rec. 2673 (2): 149–160. https://doi.org/10.1177/0361198118825459.
Fosgerau, M., and A. Karlström. 2010. “The value of reliability.” Transp. Res. Part A Policy Pract. 44 (1): 38–49. https://doi.org/10.1016/j.trb.2009.05.002.
Huang, P., J. Lessan, C. Wen, Q. Peng, L. Fu, L. Li, and X. Xu. 2020. “A Bayesian network model to predict the effects of interruptions on train operations.” Transp. Res. Part C Emerging Technol. 114: 338–358. https://doi.org/10.1016/j.trc.2020.02.021.
Huang, P., T. Spanninger, and F. Corman. 2022. “Enhancing the understanding of train delays with delay evolution pattern discovery: A clustering and Bayesian network approach.” IEEE Trans. Intell. Transp. Syst. 23 (9): 15367–15381. https://doi.org/10.1109/TITS.2022.3140386.
Ingvardson, J. B., O. A. Nielsen, S. Raveau, and B. F. Nielsen. 2018. “Passenger arrival and waiting time distributions dependent on train service frequency and station characteristics: A smart card data analysis.” Transp. Res. Part C Emerging Technol. 90 (May): 292–306. https://doi.org/10.1016/j.trc.2018.03.006.
Jackson, W. B., and J. V. Jucker. 1982. “An empirical study of travel time variability and travel choice behavior.” Transp. Sci. 16 (4): 460–475. https://doi.org/10.1287/trsc.16.4.460.
Jenelius, E., and O. Cats. 2015. “The value of new public transport links for network robustness and redundancy.” Transportmetrica A: Transp. Sci. 11 (9): 819–835. https://doi.org/10.1080/23249935.2015.1087232.
Jiao, J., F. Zhang, and J. Liu. 2020. “A spatiotemporal analysis of the robustness of high-speed rail network in China.” Transp. Res. Part D Transp. Environ. 89 (Dec): 102584. https://doi.org/10.1016/j.trd.2020.102584.
Kato, H., Y. Kaneko, and M. Inoue. 2010. “Comparative analysis of transit assignment: Evidence from urban railway system in the Tokyo Metropolitan Area.” Transportation 37 (5): 775–799. https://doi.org/10.1007/s11116-010-9295-8.
Kato, H., Y. Kaneko, and Y. Soyama. 2014. “Economic benefits of urban rail projects that improve travel-time reliability: Evidence from Tokyo, Japan.” Transport Policy 35 (Sep): 202–210. https://doi.org/10.1016/j.tranpol.2014.06.004.
Kato, T., and K. Uchida. 2018. “A study on benefit estimation that considers the values of travel time and travel time reliability in road networks.” Transportmetrica A: Transp. Sci. 14 (1–2): 89–109. https://doi.org/10.1080/23249935.2017.1321695.
Lee, D. H., Q. Meng, and W. Deng. 2010. “Origin-based partial linearization method for the stochastic user equilibrium traffic assignment problem.” J. Transp. Eng. 136 (1): 52–60. https://doi.org/10.1061/(ASCE)0733-947X(2010)136:1(52).
Lee, J. C., W. M. Lin, G. C. Liao, and T. P. Tsao. 2011. “Quantum genetic algorithm for dynamic economic dispatch with valve-point effects and including wind power system.” Int. J. Electr. Power Energy Syst. 33 (2): 189–197. https://doi.org/10.1016/j.ijepes.2010.08.014.
Leng, N., and F. Corman. 2020. “The role of information availability to passengers in public transport disruptions: An agent-based simulation approach.” Transp. Res. Part A Policy Pract. 133 (Mar): 214–236.
Li, B., E. Yap, T. Yamamoto, and N. Huan. 2020. “Passenger travel behavior analysis under unplanned metro service disruption: Using stated preference data in Guangzhou, China.” J. Transp. Eng. Part A Syst. 146 (2): 04019069.
Li, M., G. Wu, S. Johnston, and W. B. Zhang. 2009. Analysis toward mitigation of congestion and conflicts at light rail grade crossings and intersections. California PATH Program. Berkeley, CA: Univ. of California at Berkeley.
Li, Z., D. A. Hensher, and J. M. Rose. 2010. “Willingness to pay for travel time reliability in passenger transport: A review and some new empirical evidence.” Transp. Res. Part E Logist. Transp. Rev. 46 (3): 384–403. https://doi.org/10.1016/j.tre.2009.12.005.
Li, Z., P. Huang, C. Wen, X. Jiang, and F. Rodrigues. 2022. “Prediction of train arrival delays considering route conflicts at multi-line stations.” Transp. Res. Part C Emerging Technol. 138 (May): 103606. https://doi.org/10.1016/j.trc.2022.103606.
Litman, T. 2009. Transportation cost and benefit analysis–techniques, estimates and implications. 2nd ed. Victoria, BC: Victoria Transport Policy Institute.
Liu, H. X., X. He, and B. He. 2009. “Method of successive weighted averages (MSWA) and self-regulated averaging schemes for solving stochastic user equilibrium problem.” Networks Spatial Econ. 9 (4): 485–503. https://doi.org/10.1007/s11067-007-9023-x.
Liu, J., M. He, P. M. Schonfeld, H. Kato, and A. Li. 2022. “Measures of accessibility incorporating time reliability for an urban rail transit network: A case study in Wuhan, China.” Transp. Res. Part A Policy Pract. 165 (Nov): 471–489.
Liu, J., P. Schonfeld, Y. Yin, and Q. Peng. 2020a. “Effects of link capacity reductions on the reliability of an urban rail transit network.” J. Adv. Transp. 8: 1–15.
Liu, J., P. M. Schonfeld, Q. Peng, and Y. Yin. 2020b. “Measures of travel reliability on an urban rail transit network.” J. Transp. Eng. Part A Syst. 146 (6): 1–14.
Liu, J., P. M. Schonfeld, S. Zhan, Q. Peng, and Y. Liu. 2021. “Measuring and enhancing the connectivity reliability of a rail transit network.” Transportmetrica A: Transp. Sci. 18 (3): 1699–1733. https://doi.org/10.1080/23249935.2021.1965241.
Ma, F., Y. Liang, K. F. Yuen, Q. Sun, Y. Zhu, Y. Wang, and W. Shi. 2020. “Assessing the vulnerability of urban rail transit network under heavy air pollution: A dynamic vehicle restriction perspective.” Sustainable Cities Soc. 52 (Jan): 101851. https://doi.org/10.1016/j.scs.2019.101851.
Mattsson, L. G., and E. Jenelius. 2015. “Vulnerability and resilience of transport systems-A discussion of recent research.” Transp. Res. Part A Policy Pract. 81 (Nov): 16–34.
Mirjalili, S. 2019. “Genetic algorithm.” In Evolutionary algorithms and neural networks, 43–55. Cham, Switzerland: Springer.
Mooney, C. Z. 1997. Monte Carlo simulation. Thousand Oaks, CA: SAGE.
Muriel-Villegas, J. E., K. C. Alvarez-Uribe, C. E. Patiño-Rodríguez, and J. G. Villegas. 2016. “Analysis of transportation networks subject to natural hazards–Insights from a Colombian case.” Reliab. Eng. Syst. Saf. 152 (Aug): 151–165. https://doi.org/10.1016/j.ress.2016.03.006.
Nagurney, A., and Q. Qiang. 2007. “Robustness of transportation networks subject to degradable links.” EPL 80 (6): 68001. https://doi.org/10.1209/0295-5075/80/68001.
Nogal, M., and D. Honfi. 2019. “Assessment of road traffic resilience assuming stochastic user behaviour.” Reliab. Eng. Syst. Saf. 185 (May): 72–83. https://doi.org/10.1016/j.ress.2018.12.013.
Rakha, H., I. El-Shawarby, and M. Arafeh. 2010. “Trip travel-time reliability: Issues and proposed solutions.” J. Intell. Transp. Syst. 14 (4): 232–250.
Reilly, J., and H. Levinson. 2012. Public transport capacity analysis procedures for developing cities. Washington, DC: World Bank.
San, H. P., and M. I. M. Masirin. 2016. “Train dwell time models for rail passenger service.” In Proc., MATEC Web of Conf. London: EDP Sciences.
Sullivan, J. L., D. C. Novak, L. Aultman-Hall, and D. M. Scott. 2010. “Identifying critical road segments and measuring system-wide robustness in transportation networks with isolating links: A link-based capacity-reduction approach.” Transp. Res. Part A Policy Pract. 44 (5): 323–336.
Sun, Y., and R. Xu. 2012. “Rail transit travel time reliability and estimation of passenger route choice behavior: Analysis using automatic fare collection data.” Transp. Res. Rec. 2275 (1): 58–67. https://doi.org/10.3141/2275-07.
TranSafety, Inc. 1997. “Study compares older and younger pedestrian walking speeds.” Road Manage. Eng. J.
Uniman, D. L., J. Attanucci, R. G. Mishalani, and N. H. M. Wilson. 2010. “Service reliability measurement using automated fare card data: Application to the London underground.” Transp. Res. Rec. 2143 (1): 92–99. https://doi.org/10.3141/2143-12.
Wardman, M., and G. Whelan. 2011. “Twenty years of rail crowding valuation studies: Evidence from lessons from British experience.” Transp. Rev. 31 (3): 379–398. https://doi.org/10.1080/01441647.2010.519127.
Woodard, D., G. Nogin, P. Koch, D. Racz, M. Goldszmidt, and E. Horvitz. 2017. “Predicting travel time reliability using mobile phone GPS data.” Transp. Res. Part C Emerging Technol. 75 (Feb): 30–44. https://doi.org/10.1016/j.trc.2016.10.011.
Yang, Y., J. Cheng, Y. Zheng, and L. Chen. 2017. “Research on urban rail transit operation cost calculation.” J. Shijiazhuang Tiedao Univ. 30 (3): 93–97.
Yang, Y., Y. Liu, M. Zhou, F. Li, and C. Sun. 2015. “Robustness assessment of urban rail transit based on complex network theory: A case study of the Beijing Subway.” Saf. Sci. 79 (Nov): 149–162. https://doi.org/10.1016/j.ssci.2015.06.006.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 6 • June 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Sep 8, 2022
Accepted: Jan 31, 2023
Published online: Apr 7, 2023
Published in print: Jun 1, 2023
Discussion open until: Sep 7, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Zishuai Pang, Liwen Wang, Paul M. Schonfeld, Jie Liu, Qiyuan Peng, Li Li, A Disturbance-Driven Textual Model for Train Running Time Prediction on High-Speed Railways, Journal of Transportation Engineering, Part A: Systems, 10.1061/JTEPBS.TEENG-8389, 150, 10, (2024).
- Zhe Du, Weifeng Li, Guoyou Shi, Multi-USV Collaborative Obstacle Avoidance Based on Improved Velocity Obstacle Method, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 10.1061/AJRUA6.RUENG-1174, 10, 1, (2024).
- Zhiao Ma, Xin Yang, Wenlong Shang, Jianjun Wu, Huijun Sun, Resilience analysis of an urban rail transit for the passenger travel service, Transportation Research Part D: Transport and Environment, 10.1016/j.trd.2024.104085, 128, (104085), (2024).
- Di Liu, Yongxiang Zhang, Qiyuan Peng, Wei Wei, Haitao Li, Profit-oriented High-speed Railway Network Line Planning with Capacity Limitations, Journal of Modern Mechanical Engineering and Technology, 10.31875/2409-9848.2023.10.8, 10, (98-112), (2023).
- Jinqu Chen, Xiaowei Liu, Bo Du, Wenxin Li, Yong Yin, Xinyue Xu, Passenger-Oriented Resilience Assessment of an Urban Rail Transit Network under Partial Disturbances, Journal of Transportation Engineering, Part A: Systems, 10.1061/JTEPBS.TEENG-8017, 149, 11, (2023).
- Jinqu Chen, Cheng Liang, Jie Liu, Bo Du, Yong Yin, Qiyuan Peng, Resilience assessment of a highway–railway complementary network under rainstorms, Transportation Research Part D: Transport and Environment, 10.1016/j.trd.2023.103841, 121, (103841), (2023).
- Zhongcan Li, Ping Huang, Chao Wen, Jie Li, Filipe Rodrigues, Prediction of departure delays at original stations using deep learning approaches: A combination of route conflicts and rolling stock connections, Expert Systems with Applications, 10.1016/j.eswa.2023.120500, 229, (120500), (2023).