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Research Article | Open Access

Resilience assessment framework toward interdependent bus–rail transit network: Structure, critical components, and coupling mechanism

Bing LiuaXiaoyue LiubYang YangaXi ChencXiaolei Maa,d( )
School of Transportation Science and Engineering, Beihang University, Beijing, 100191, China
Department of Civil & Environmental Engineering, University of Utah, Salt Lake City, UT, 84112, USA
School of Computer Science and Engineering, Beihang University, Beijing, 100191, China
Key Laboratory of Intelligent Transportation Technology and System, Ministry of Education, Beijing, 100191, China
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Abstract

Understanding the interdependent nature of multimodal public transit networks (PTNs) is vital for ensuring the resilience and robustness of transportation systems. However, previous studies have predominantly focused on assessing the vulnerability and characteristics of single-mode PTNs, neglecting the impacts of heterogeneous disturbances and shifts in travel behavior within multimodal PTNs. Therefore, this study introduces a novel resilience assessment framework that comprehensively analyzes the coupling mechanism, structural and functional characteristics of bus–rail transit networks (BRTNs). In this framework, a network performance metric is proposed by considering the passengers’ travel behaviors under various disturbances. Additionally, stations and subnetworks are classified using the k-means algorithm and resilience metric by simulating various disturbances occurring at each station or subnetwork. The proposed framework is validated via a case study of a BRTN in Beijing, China. Results indicate that the rail transit network (RTN) plays a crucial role in maintaining network function and resisting external disturbances in the interdependent BRTN. Furthermore, the coupling interactions between the RTN and bus transit network (BTN) exhibit distinct characteristics under infrastructure component disruption and functional disruption. These findings provide valuable insights into emergency management for PTNs and understanding the coupling relationship between BTN and RTN.

References

 

Arriagada, J., Munizaga, M.A., Guevara, C.A., Prato, C., 2022. Unveiling route choice strategy heterogeneity from smart card data in a large-scale public transport network. Transport. Res. C Emerg. Technol. 134, 103467.

 

Bholowalia, P., Kumar, A., 2014. EBK-Means: a clustering technique based on Elbow method and k-Means in WSN. Int. J. Comput. Appl. 105, 975–8887.

 

Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E., 2008. Fast unfolding of communities in large networks. J. Stat. Mech. Theor. Exp. 2008, 10008.

 

Borjigin, S.G., He, Q., Niemeier, D.A., 2023. COVID-19 transmission in U.S. transit buses: a scenario-based approach with agent-based simulation modeling (ABSM). Commun. Transp. Res. 3, 100090.

 

Chen, J., Liu, J., Peng, Q., Yin, Y., 2022. Resilience assessment of an urban rail transit network: a case study of Chengdu subway. Phys. A Stat. Mech. its Appl. 586, 126517.

 

Cimellaro, G.P., Reinhorn, A.M., Bruneau, M., 2010. Framework for analytical quantification of disaster resilience. Eng. Struct. 32, 3639–3649.

 

Dai, Z., Liu, X.C., Chen, X., Ma, X., 2020. Joint optimization of scheduling and capacity for mixed traffic with autonomous and human-driven buses: a dynamic programming approach. Transport. Res. C Emerg. Technol. 114, 598–619.

 

De-Los-Santos, A., Laporte, G., Mesa, J.A., Perea, F., 2012. Evaluating passenger robustness in a rail transit network. Transport. Res. C Emerg. Technol. 20, 34–46.

 

Feng, X., He, S., Li, G., Chi, J., 2021. Transfer network of high-speed rail and aviation: structure and critical components. Phys. A Stat. Mech. its Appl. 581, 126197.

 

Gehrke, S.R., Wang, L., 2020. Operationalizing the neighborhood effects of the built environment on travel behavior. J. Transport Geogr. 82, 102561.

 

Goldbeck, N., Angeloudis, P., Ochieng, W.Y., 2019. Resilience assessment for interdependent urban infrastructure systems using dynamic network flow models. Reliab. Eng. Syst. Saf. 188, 62–79.

 

Gu, Y., Fu, X., Liu, Z., Xu, X., Chen, A., 2020. Performance of transportation network under perturbations: reliability, vulnerability, and resilience. Transport. Res. Part E Logist. Transp. Rev. 133, 1–16.

 

Hao, Y., Si, B., Zhao, C., 2022. Topology transformation-based multi-path algorithm for urban rail transit network. Transport. Res. C Emerg. Technol. 136, 103540.

 

Huang, W., Zhou, B., Yu, Y., Yin, D., 2021. Vulnerability analysis of road network for dangerous goods transportation considering intentional attack: based on Cellular Automata. Reliab. Eng. Syst. Saf. 214, 107779.

 

Ip, W.H., Wang, D., 2011. Resilience and friability of transportation networks: evaluation, analysis and optimization. IEEE Syst. J. 5, 189–198.

 

Li, W., Chen, S., Dong, J., Wu, J., 2021. Exploring the spatial variations of transfer distances between dockless bike-sharing systems and metros. J. Transport Geogr. 92, 103032.

 

Lin, P., Weng, J., Fu, Y., Alivanistos, D., Yin, B., 2020. Study on the topology and dynamics of the rail transit network based on automatic fare collection data. Phys. A Stat. Mech. its Appl. 545, 123538.

 

Liu, S., Yin, C., Chen, D., Lv, H., Zhang, Q., 2021. Cascading failure in multiple critical infrastructure interdependent networks of syncretic railway system. IEEE Trans. Intell. Transport. Syst. 23 (6), 5740–5753.

 

Luo, Z., Yang, B., 2021. Towards resilient and smart urban road networks: connectivity restoration via community structure. Sustain. Cities Soc. 75, 103344.

 

Ma, F., Liu, F., Yuen, K.F., Lai, P., Sun, Q., Li, X., 2019. Cascading failures and vulnerability evolution in bus–metro complex bilayer networks under rainstorm weather conditions. Int. J. Environ. Res. Publ. Health 16 (3), 329.

 

Ma, F., Shi, W., Yuen, K.F., Sun, Q., Xu, X., Wang, Y., Wang, Z., 2020. Exploring the robustness of public transportation for sustainable cities: a double-layered network perspective. J. Clean. Prod. 265, 121747.

 

Martello, M.V., Whittle, A.J., Keenan, J.M., Salvucci, F.P., 2021. Evaluation of climate change resilience for Boston's rail rapid transit network. Transport. Res. Transport Environ. 97, 102908.

 

Miao, Q., Feeney, M.K., Zhang, F., Welch, E.W., Sriraj, P.S., 2018. Through the storm: transit agency management in response to climate change. Transport. Res. Transport Environ. 63, 421–432.

 

Ouyang, M., 2016. Critical location identification and vulnerability analysis of interdependent infrastructure systems under spatially localized attacks. Reliab. Eng. Syst. Saf. 154, 106–116.

 

Pan, X., Dang, Y., Wang, H., Hong, D., Li, Y., Deng, H., 2022. Resilience model and recovery strategy of transportation network based on travel OD-grid analysis. Reliab. Eng. Syst. Saf. 223, 108483.

 

Pu, Z., Li, Z., Ash, J., Zhu, W., Wang, Y., 2017. Evaluation of spatial heterogeneity in the sensitivity of on-street parking occupancy to price change. Transport. Res. C Emerg. Technol. 77, 67–79.

 

Qu, X., Wang, S., Niemeier, D., 2022. On the urban-rural bus transit system with passenger-freight mixed flow. Commun. Transp. Res. 2, 100054.

 

Rodríguez-Núñez, E., García-Palomares, J.C., 2014. Measuring the vulnerability of public transport networks. J. Transport Geogr. 35, 50–63.

 
Russo, B., Velasco, M., Locatelli, L., Sunyer, D., Yubero, D., Monjo, R., Martínez-Gomariz, E., Forero-Ortiz, E., Sánchez-Muñoz, D., Evans, B., Gómez, A.G., 2020. Correction: Russo, B., et al. Assessment of urban flood resilience in barcelona for current and future scenarios. the resccue project. (Sustainability 2020, 12, 5638). Sustain, vol. 12, pp. 1–2.
 

Serdar, M.Z., Koc, M., Al-Ghamdi, S.G., 2022. Urban transportation networks resilience: indicators, disturbances, and assessment methods. Sustain. Cities Soc. 76, 103452.

 

Shenoi, S., 2013. International journal of critical infrastructure protection. Int. J. Crit. Infrastruct. Prot. 6, 61–62.

 

Smith Jr., R.L., Brennan, T.S., 1980. Traffic-assignment techniques for smaller cities. Transp. Eng. J. ASCE 106 (1), 85–98.

 

Sun, D.J., Guan, S., 2016. Measuring vulnerability of urban metro network from line operation perspective. Transport. Res. Part A Policy Pract. 94, 348–359.

 

Sun, X., Wandelt, S., Zanin, M., 2017. Worldwide air transportation networks: a matter of scale and fractality? Transp. A Transp. Sci. 13, 607–630.

 

Sun, L., Huang, Y., Chen, Y., Yao, L., 2018. Vulnerability assessment of urban rail transit based on multi-static weighted method in Beijing, China. Transport. Res. Part A Policy Pract 108, 12–24.

 

Tamakloe, R., Hong, J., Tak, J., 2021. Determinants of transit-oriented development efficiency focusing on an integrated subway, bus and shared-bicycle system: application of Simar-Wilson’s two-stage approach. Cities 108, 102988.

 

Tang, Y., Jiang, Y., Yang, H., Nielsen, O.A., 2020. Modeling and optimizing a fare incentive strategy to manage queuing and crowding in mass transit systems: modeling and optimizing a fare incentive strategy to manage queuing and crowding in mass transit systems. Transp. Res. Part B Methodol. 138, 247–267.

 

Wang, H.W., Peng, Z.R., Wang, D., Meng, Y., Wu, T., Sun, W., Lu, Q.C., 2020a. Evaluation and prediction of transportation resilience under extreme weather events: a diffusion graph convolutional approach. Transport. Res. C Emerg. Technol. 115, 102619.

 

Wang, H.W., Peng, Z.R., Wang, D., Meng, Y., Wu, T., Sun, W., Lu, Q.C., 2020b. Evaluation and prediction of transportation resilience under extreme weather events: a diffusion graph convolutional approach. Transport. Res. C Emerg. Technol. 115, 102619.

 

Wang, B., Su, Q., Chin, K.S., 2021. Vulnerability assessment of China–Europe Railway Express multimodal transport network under cascading failures. Phys. A Stat. Mech. its Appl. 584, 126359.

 

Xiong, G.Q., Lei, J.Y., 2020. Emergency evacuation model and simulation analysis of urban metro. Ind. Eng. J. 23, 99–106 (in Chinese).

 

Xu, X., Chen, A., Cheng, L., Yang, C., 2017. A link-based mean-excess traffic equilibrium model under uncertainty. Transp. Res. Part B Methodol. 95, 53–75.

 

Yin, D., Huang, W., Shuai, B., Liu, H., Zhang, Y., 2022a. Structural characteristics analysis and cascading failure impact analysis of urban rail transit network: from the perspective of multi-layer network. Reliab. Eng. Syst. Saf. 218, 108161.

 

Yin, J., Ren, X., Liu, R., Tang, T., Su, S., 2022b. Quantitative analysis for resilience-based urban rail systems: a hybrid knowledge-based and data-driven approach. Reliab. Eng. Syst. Saf. 219, 108183.

 

Zhang, L., Lu, J., Fu, B. bai, Li, S. bin, 2019a. A cascading failures model of weighted bus transit route network under route failure perspective considering link prediction effect. Phys. A Stat. Mech. its Appl. 523, 1315–1330.

 

Zhang, Z., Zhao, Y., Liu, J., Wang, S., Tao, R., Xin, R., Zhang, J., 2019b. A general deep learning framework for network reconstruction and dynamics learning. Appl. Netw. Sci. 4.

 

Zhou, Y., Wang, J., Yang, H., 2019. Resilience of transportation systems: concepts and comprehensive review. IEEE Trans. Intell. Transport. Syst. 20, 4262–4276.

 
Zhou, H., Zhang, Shanghang, Peng, J., Zhang, Shuai, Li, J., Xiong, H., Zhang, W., 2021. Informer: beyond efficient transformer for long sequence time-series forecasting. In: 35th AAAI Conf. Artif. Intell. AAAI 2021 12B, pp. 11106–11115.
 

Zhu, K., Cheng, Z., Wu, J., Yuan, F., Sun, L., 2022. Quantifying out-of-station waiting time in oversaturated urban metro systems. Commun. Transp. Res. 2, 100052.

Communications in Transportation Research
Article number: 100098
Cite this article:
Liu B, Liu X, Yang Y, et al. Resilience assessment framework toward interdependent bus–rail transit network: Structure, critical components, and coupling mechanism. Communications in Transportation Research, 2023, 3: 100098. https://doi.org/10.1016/j.commtr.2023.100098

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Received: 15 March 2023
Revised: 05 May 2023
Accepted: 26 May 2023
Published: 06 July 2023
© 2023 The Authors.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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