Work

Quantifying the resilience of rapid transit systems: A composite index using a demand-weighted complex network model

Year: 2022

Type: article

Abstract: Quantifying the impact of disruptions on rapid transit resilience is crucial in transport planning. We propose a composite resilience score for rapid transit systems comprising four indicators that measure different physical aspects of resilience. These are computed using a weighted network model incorporating the network structure of stations, differences in line capacities, and travel demand. Our method provides a holistic assessment of network resilience and allows for straightforward comparisons of different scenarios including rail expansions and changes in demand. Applying our methodology to multiple configurations of Singapore's rapid transit system, we demonstrate its effectiveness in capturing the impact of planned future lines. We also showcase through simulated studies how tipping points in resilience arise when demand varies. Furthermore, we demonstrate that system resilience could be unintentionally reduced by redistributing commuting demand to peripheral areas. Our methodology is easily applied to other rapid transit systems around the world.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0267222

Authors: Hong En Tan, Jeremy Hong Wen Oon, Nasri bin Othman, Erika Fille Legara, Christopher Monterola, Muhamad Azfar Ramli

Institutions: Agency for Science, Technology and Research, Institute of High Performance Computing, Asian Institute of Management

Language: English

Topic: Infrastructure Resilience and Vulnerability Analysis

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $1,805