A Causal Inference Approach to Measure the Vulnerability of Urban Metro Systems

Transit operators and passengers need vulnerability measures to understand the level of service degradation under disruptions. This paper contributes to the literature with a novel causal inference approach for estimating station-level vulnerability in metro systems. The empirical analysis is based on large-scale data on historical incidents and population-level passenger demand, thus obviates the need for assumptions made by previous studies on human behaviour and disruption scenarios. We develop three empirical vulnerability metrics based on the causal impact of disruptions on travel demand and the average travel speed. The unbiased estimates of disruption impact are obtained by adopting a propensity score matching method, which adjusts for the confounding biases caused by non-random occurrence of disruptions. An application of the proposed framework to London Underground indicates that the vulnerability of a metro station depends on the location, topology, and other characteristics. We find that in 2013 central London stations are more vulnerable in terms of travel demand loss. However, the loss of average travel speed reveals that passengers from outer London stations suffer from longer individual delays due to lack of alternative routes.

[1]  Jianhua Zhang,et al.  Comparison analysis on vulnerability of metro networks based on complex network , 2018 .

[2]  Katja Berdica,et al.  AN INTRODUCTION TO ROAD VULNERABILITY: WHAT HAS BEEN DONE, IS DONE AND SHOULD BE DONE , 2002 .

[3]  Shauhrat S Chopra,et al.  A network-based framework for assessing infrastructure resilience: a case study of the London metro system , 2016, Journal of The Royal Society Interface.

[4]  Daniel J. Graham,et al.  Determinants of Delay Incident Occurrence in Urban Metros , 2011 .

[5]  C. Brewin,et al.  Psychological and behavioural reactions to the bombings in London on 7 July 2005: cross sectional survey of a representative sample of Londoners , 2005, BMJ : British Medical Journal.

[6]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[7]  Sybil Derrible,et al.  The complexity and robustness of metro networks , 2010 .

[8]  Daniel Sun,et al.  Vulnerability Analysis of Urban Rail Transit Networks: A Case Study of Shanghai, China , 2015 .

[9]  Stanislav Sobolevsky,et al.  Vulnerability of Transportation Networks: The New York City Subway System under Simultaneous Disruptive Events , 2017 .

[10]  Fan Zhang,et al.  Resiliency assessment of urban rail transit networks: Shanghai metro as an example , 2018, Safety Science.

[11]  Moshe Ben-Akiva,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1985 .

[12]  M. Lechner.  Identification and Estimation of Causal Effects of Multiple Treatments Under the Conditional Independence Assumption , 1999, SSRN Electronic Journal.

[13]  C. Willmott,et al.  Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance , 2005 .

[14]  Erik Jenelius,et al.  Vulnerability and resilience of transport systems : A discussion of recent research , 2015 .

[15]  Ricardo Silva,et al.  Predicting traffic volumes and estimating the effects of shocks in massive transportation systems , 2015, Proceedings of the National Academy of Sciences.

[16]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[17]  Chenfeng Xiong,et al.  Travel Behavior Reactions to Transit Service Disruptions , 2017 .

[18]  Zhe Li,et al.  Evaluating the alignment of new metro line considering network vulnerability with passenger ridership , 2019, Transportmetrica A: Transport Science.

[19]  Qing-Chang Lu,et al.  A station-based rail transit network vulnerability measure considering land use dependency , 2018 .

[20]  J. C. G. Palomares,et al.  Measuring the vulnerability of public transport networks , 2014 .

[21]  Yongxue Liu,et al.  Robustness assessment of urban rail transit based on complex network theory: a case study of the Beijing Subway , 2015 .

[22]  D. Rubin,et al.  Causal Inference for Statistics, Social, and Biomedical Sciences: A General Method for Estimating Sampling Variances for Standard Estimators for Average Causal Effects , 2015 .

[23]  One Gateway Plaza,et al.  Metropolitan Transportation Authority , 1998 .

[24]  Jingfeng Yuan,et al.  Metro passenger behaviors and their relations to metro incident involvement. , 2015, Accident; analysis and prevention.

[25]  Martin Skitmore,et al.  An Incident Database for Improving Metro Safety: The Case of Shanghai , 2016 .

[26]  Hyun Kim,et al.  Assessing network vulnerability of heavy rail systems with the impact of partial node failures , 2019 .

[27]  Daniel Sun,et al.  Measuring Vulnerability of Urban Metro Network from Line Operation Perspective , 2016 .

[28]  D. Stephens,et al.  Quantifying Causal Effects of Road Network Capacity Expansions on Traffic Volume and Density via a Mixed Model Propensity Score Estimator , 2014 .

[29]  Shuliang Wang,et al.  Networked analysis of the Shanghai subway network, in China , 2011 .

[30]  Catherine Morency,et al.  Smart card data use in public transit: A literature review , 2011 .

[31]  Ziyou Gao,et al.  Estimating the influence of common disruptions on urban rail transit networks , 2016 .

[32]  Oded Cats,et al.  Predicting disruptions and their passenger delay impacts for public transport stops , 2020, Transportation.

[33]  Liya Yao,et al.  Vulnerability assessment of urban rail transit based on multi-static weighted method in Beijing, China , 2018 .

[34]  Yasuo Asakura,et al.  Behavioural data mining of transit smart card data: A data fusion approach , 2014 .

[35]  D. Rubin.  Estimating causal effects of treatments in randomized and nonrandomized studies. , 1974 .

[36]  Daniel J. Graham,et al.  Crowding cost estimation with large scale smart card and vehicle location data , 2017 .

[37]  Kpotissan Adjetey-Bahun,et al.  A model to quantify the resilience of mass railway transportation systems , 2016, Reliab. Eng. Syst. Saf..

[38]  Elise Miller-Hooks,et al.  Measuring the performance of transportation infrastructure systems in disasters: a comprehensive review , 2015 .

[39]  Brian Caulfield,et al.  Weather and rail delays: Analysis of metropolitan rail in Dublin , 2017 .

[40]  Tom Petersen,et al.  Importance and Exposure in Road Network Vulnerability Analysis , 2006 .