Establishment and Application of Passenger Flow Safety Management Evaluation Model with Entropy Weight and TOPSIS for Metro Stations

Heng YU

doi:10.7307/ptt.v36i6.765

Authors

Heng YU Chengdu University, School of Architecture and Civil Engineering

DOI:

https://doi.org/10.7307/ptt.v36i6.765

Keywords:

metro station, passenger flow, safety, entropy weight, TOPSIS, evaluation model

Abstract

As a critical component of urban transportation, metro systems demand rigorous passenger flow safety management. This study proposes a comprehensive decision-making analysis method for metro station passenger flow safety management by integrating the entropy weight and TOPSIS methods. It aims to develop an evaluation model that accurately assesses and ranks the safety management practices of metro stations. To achieve this, 17 indicators related to station scale, safety management equipment, safety or security measures, investment in safety management and the effects of passenger flow management are selected to form an evaluation indicator system. The entropy weight method is employed to allocate weights to these indicators, reflecting their interrelatedness and importance. Subsequently, the TOPSIS method is used to establish a decision model that calculates the closeness of each station’s management practice to an optimal plan, allowing for the ranking of different stations’ safety management practices. The algorithms are developed and optimised using MATLAB, enabling efficient calculation and analysis. A case study involving real metro stations is conducted to validate the feasibility and effectiveness of the proposed evaluation method. The results demonstrate that this model provides an accurate assessment of metro station passenger safety management and offers decision-makers clear directions for improvement.

References

Rao Y, et al. Urban growth pattern and commuting efficiency: Empirical evidence from 100 Chinese cities. Journal of Cleaner Production. 2021;302:126994. DOI: 10.1016/j.jclepro.2021.126994.

Aboul-Atta TA, Elmaraghy SBE. Factors affecting performance improvement of the metro system in cities. Journal of Engineering and Applied Science. 2022;69:27. DOI: 10.1186/s44147-022-00078-4.

Al-Nami WT. Ranking and analysis the strategies of crowd management to reduce the risks of crushes and stampedes in crowded environments and ensure the safety of passengers. Neutrosophic Systems with Applications. 2023;8:61–78. DOI: 10.61356/j.nswa.2023.50.

Çelebi D; İmre Ş. Measuring crowding-related comfort in public transport. Transportation Planning and Technology. 2020;43(7):735–750. DOI: 10.1080/03081060.2020.1805546.

Christensen CB. Congested metro materialities: How platform escalators afford “friction” in the Copenhagen metro. Applied Mobilities. 2023;8(4):287–304. DOI: 10.1080/23800127.2022.2034098.

Moodie, K. The King’s Cross fire: Damage assessment and overview of the technical investigation. Fire Safety Journal. 1992;18(1):13–33. DOI: 10.1016/0379-7112(92)90045-E.

Zhang L, et al. Discovering worst fire scenarios in subway stations: A simulation approach. Automation in Construction. 2019;99:183–196. DOI: 10.1016/j.autcon.2018.12.007.

Huan N, Yao E, Li B. Early warning mechanism for the surge of passengers in metro systems based on automated fare collection data: Case study of Guangzhou, China. Transportation Research Record. 2019;2673:917–929. DOI: 10.1177/0361198119838847.

Wang Y, Li M, Zhou J, Zheng H. Sudden passenger flow characteristics and congestion control based on intelligent urban rail transit network. Neural Computing and Applications. 2022;34(9):6615–6624. DOI: 10.1007/s00521-021-06062-y.

Xue G, et al. Forecasting the subway passenger flow under event occurrences with multivariate disturbances. Expert Systems with Applications. 2022;188:116057. DOI: 10.1016/j.eswa.2021.116057.

Xiong J, Li Y, Liu X. Optimization of passenger flow guidance in rail transit station based on AnyLogic. 2021 6th International Conference on Intelligent Transportation Engineering (ICITE 2021). Lecture Notes in Electrical Engineering, vol 901. 29-31 October. 2021, Beijing, China. 2021. p. 160-169. DOI: 10.1007/978-981-19-2259-6_14.

Owais M, Ahmed AS, Moussa GS, Khalil AA. Design scheme of multiple-subway lines for minimizing passengers transfers in mega-cities transit networks. International Journal of Rail Transportation. 2021;9(6):540–563. DOI: 10.1080/23248378.2020.1846632.

Ni W, et al. Study on optimization of passenger flow at a metro station based on AnyLogic—Case study of Youfangqiao station of Nanjing metro line 2. Complex System Modeling and Simulation 2021;1(3):242-252. DOI: 10.23919/CSMS.2021.0009.

Jiang Z, et al. Reinforcement learning approach for coordinated passenger inflow control of urban rail transit in peak hours. Transportation Research Part C: Emerging Technologies. 2018;88:1–16. DOI: 10.1016/j.trc.2018.01.008.

Wang X, et al. Multistation coordinated and dynamic passenger inflow control for a metro line. IET Intelligent Transport Systems. 2020;14(9):1068–1078. DOI: 10.1049/iet-its.2019.0337.

Kazanskaya L, Proskuryakova E. Improvement of work of urban public transport based on passenger traffic simulation. Urbanism. Arhitectură. Construcţii. 2021;12:5–12.

Mulerikkal J, Thandassery S, Rejathalal V, Kunnamkody DMD. Performance improvement for metro passenger flow forecast using spatio-temporal deep neural network. Neural Computing and Applications. 2022;34:983–994. DOI: 10.1007/s00521-021-06522-5.

Motvallian Naeini H, Shafahi Y, Safari Taherkhani M. Optimizing and synchronizing timetable in an urban subway network with stop-skip strategy. Journal of Rail Transport Planning & Management. 2022;22:100301. DOI: 10.1016/j.jrtpm.2022.100301.

Blanco V, Conde E, Hinojosa Y, Puerto J. An optimization model for line planning and timetabling in automated urban metro subway networks. A Case Study. Omega. 2020;92:102165. DOI: 10.1016/j.omega.2019.102165.

Canavan S, et al. Best practices in operating high frequency metro services. Transportation Research Record. 2019;2673(9):491–501. DOI: 10.1177/0361198119845356.

Demirel T, Demirel NÇ, Kahraman C. Fuzzy analytic hierarchy process and its application. In: Kahraman C (ed.) Fuzzy Multi-Criteria Decision Making: Theory and Applications with Recent Developments. Springer US: Boston; 2008. p. 53–83.

Kepaptsoglou K, Karlaftis MG, Gkountis J. A fuzzy AHP model for assessing the condition of metro stations. KSCE Journal of Civil Engineering. 2013;17:1109–1116. DOI: 10.1007/s12205-013-0411-0.

Zhang Z, et al. Resilience assessment of metro stations based on AHP-PSO fuzzy combined empowerment method with baggage impact perspective. Journal of Infrastructure, Policy and Development. 2024;8(2):3003. DOI: 10.24294/jipd.v8i2.3003.

Baradaran V. Assessment and prioritizing the risks of urban rail transportation by using grey analytical hierarchy process (GAHP). International Journal of Transportation Engineering. 2017;4,255–273. DOI: 10.22119/IJTE.2017.44430

Sarkar D, Singh M. Development of risk index for mass rapid transit system project in western India through application of fuzzy analytical hierarchy process (FAHP). International Journal of Construction Management. 2021;21(5):439–451. DOI: 10.1080/15623599.2018.1557997.

Javanmardi E, Liu S, Xie N. Exploring the philosophical foundations of Grey systems theory: Subjective processes, information extraction and knowledge formation. Foundations of Science. 2021;26:371–404. DOI: 10.1007/s10699-020-09690-0.

Wang Y, Li M, Yang B, Yang C. An urban rail transit hazard evaluation methodology based on Grey system theory. Procedia - Social and Behavioral Sciences. 2012;43:764–772. DOI: 10.1016/j.sbspro.2012.04.150.

Matara C, Cheng X. Research on railway passenger transportation risks evaluation using LEC and Grey system theory. Special Issue in IET Intelligent Transport Systems - “Big Data Analytics and Artificial Intelligence (AI) Applications for Smart Transportation”, 18-21 June, Bejing, China. 2018. p. 1-11.

Wang Y, Ma J, Zhang J. Metro passenger flow forecast with a novel Markov-Grey model. Periodica Polytechnica Transportation Engineering. 2020;48(1):70–75. DOI: 10.3311/PPtr.11131.

Panik MJ. Data envelopment analysis (DEA). In: Linear Programming and Resource Allocation Modeling. John Wiley & Sons; 2018. pp. 373–404.

Azizi H. DEA Efficiency Analysis: A DEA approach with double frontiers. International Journal of Systems Science. 2013;45(11):2289–2300. DOI: 10.1080/00207721.2013.768715.

Khadem Sameni M, Preston J, Khadem Sameni M. Evaluating efficiency of passenger railway stations: A DEA approach. Research in Transportation Business & Management. 2016;20:33–38. DOI: 10.1016/j.rtbm.2016.06.001.

Swami M, Parida M. Comparative appraisal of metro stations in Delhi Using data envelopment analysis in a multimodal context. Journal of Public Transportation. 2015;18(3):29–51. DOI: 10.5038/2375-0901.18.3.3.

Azadeh A, Salehi V, Kianpour M. Performance evaluation of rail transportation systems by considering resilience engineering factors: Tehran railway electrification system. Transportation Letters. 2018;10(1):12–25. DOI: 10.1080/19427867.2016.1207928.

Roh JS, Ryou HS, Park WH, Jang YJ. CFD simulation and assessment of life safety in a subway train fire. Tunnelling & Underground Space Technology. 2009;24(4):447-453. DOI: 10.1016/j.tust.2008.12.003.

Teodosiu CI, Ilie V, Dumitru RG, Teodosiu RS. Assessment of ventilation efficiency for emergency situations in subway systems by CFD modeling. Building Simulation. 2016;9:319–334. DOI: 10.1007/s12273-015-0269-9.

Hosseini M, Madani H, Shahriar K. CFD-based modeling of sarin gas dispersion in a subway station–A hypothetical scenario. Journal of Mining and Environment. 2022;13(1):235–251. DOI: 10.22044/jme.2022.11604.2150.

Song Y, Liu B, Li L, Liu J. Modelling and simulation of crowd evacuation in terrorist attacks. Kybernetes. 2022;53(4):1229–1249. DOI: 10.1108/K-02-2022-0260.

Peng J, et al. Passenger flow bottleneck decongestion in subway stations: A simulation study. Simulation. 2024;00375497241240003. DOI: 10.1177/00375497241240003.

Zhou J, Koutsopoulos HN, Saidi S. Evaluation of subway bottleneck mitigation strategies using microscopic, agent-based simulation. Transportation Research Record. 2020;2674(5):649–661. DOI: 10.1177/0361198120917384.

Lei Y, et al. Optimizing total passenger waiting time in an urban rail network: A passenger flow guidance strategy based on a multi-agent simulation approach. Simulation Modelling Practice and Theory. 2022;117:102510. DOI: 10.1016/j.simpat.2022.102510.

Liu J, Hu L, Xu X, Wu J. A queuing network simulation optimization method for coordination control of passenger flow in urban rail transit stations. Neural Comput & Applic. 2021;33(17):10935–10959. DOI: 10.1007/s00521-020-05580-5.

Wang H, Yu L, Qin S. Simulation and optimization of passenger flow line in Lanzhou west railway station. In: Sierpiński, G. (eds) Advanced Solutions of Transport Systems for Growing Mobility. TSTP 2017. Advances in Intelligent Systems and Computing, vol 631. Springer, Cham. DOI: 10.1007/978-3-319-62316-0_5.