Evaluating transit network resilience through graph theory and demand-elastic measures: Case study of the Toronto transit system

Abstract

The reliability of public transit networks is of critical importance the world over. As many transit systems are increasingly exposed to various causes of service disruptions, there exists a need to quantitatively measure the operational resilience of a transit network. This paper presents an approach for transit resilience measurement that combines several metrics from the existing literature. As a case study, the paper examines and quantifies the resilience of the public transit network in Toronto, Canada to operational disruptions. The approach adopted in this work is a combination of quantitative methods founded in Graph Theory, where the public transit network is represented as a directional graph, and demand-elastic methods using transportation network simulation models to complement the network science approaches. The research findings revealed the critical stations in Toronto's subway network, which if disrupted, would create major negative impacts on passenger trip times. The reasons for their inherent critical nature are discussed and analyzed. This work was also able to spatially quantify transit resilience by identifying low-risk and at-risk areas within Toronto. Although the results are specific to Toronto, making it the first study to analyze transit resilience elaborately in this city, the techniques employed can be applied to any sufficiently detailed transit network.