Urban transportation system long-term resilience assessment using multi-dimensional dynamic Bayesian network
Transportation Research Part D: Transport and Environment, ISSN: 1361-9209, Vol: 136, Page: 104427
2024
- 10Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures10
- Readers10
- 10
Article Description
Resilience has increasingly been recognized as crucial for coping with disruptions and sustaining urban transportation systems (UTSs). However, long-term and dynamic resilience research is lacking. Therefore, this study redefines resilience and develops a comprehensive dynamic long-term resilience assessment model for UTSs. To capture the dynamic characteristics of UTS, we constructed a dynamic Bayesian network model to explore the system’s latent learning ability. To reflect the multidimensional considerations in measuring system resilience, leading indicators from four dimensions (economic, environmental, social, and technological) are selected. Case studies reveal that 1) UTS resilience shows dynamic characteristics, 2) environmental and technical indicators enhance resilience, 3) learning capability is positively related to resilience, and 4) resilience does not always correlate with economic development or urban GDP. The proposed research framework offers a reference for integrating subjective and objective data, and the evaluation model serves as a guide for dynamic assessments of system resilience.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1361920924003845; http://dx.doi.org/10.1016/j.trd.2024.104427; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85205436148&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1361920924003845; https://dx.doi.org/10.1016/j.trd.2024.104427
Elsevier BV
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