Exploring the robustness of public transportation system on augmented network: A case from Nanjing China

A highly robust urban public transportation network is the foundation for the stable and sustainable development of a city. Managers may benefit from a reasonable investiga-tion into the robustness of the public transportation system to recognize and deliver improved public transportation services. This paper developed a robustness analysis framework for public transportation systems on augmented network (AN). Specifically, an AN was first built to treat the public transit network from a multi-layer perspective. The AN carefully considers the boarding, alighting, riding and transfer behaviors, and develops cost functions under the congestion effect, thus sufficiently simulating the real passengers' behaviors. Then, the user equilibrium (UE) assignment model, a model widely used for passenger flow forecasting, is applied to guide the robustness analysis under cascading failures. Finally, a real large-scale bus-rail hybrid network in Nanjing is used as a case study. The results show that the initial station capacities are significant to the impact of the cascading failure, from which we conclude that a larger initial capacity of stations can prevent the cascading failures. By investigating the relationship between passenger flow and cascade size, the comparative results further explain the mechanisms of cascading failures. Thanks to the AN and UE, three types of travel times can be compared to reflect the quality of public passenger trips, which should provide traffic managers with pertinent advice for improving service.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

A highly robust urban public transportation network is crucial for the stable and sustainable development of a city. This paper presents a robustness analysis framework for public transportation systems, using a large-scale bus-rail hybrid network in Nanjing as a case study. The results demonstrate the significant impact of initial station capacities on cascading failures.