Farnaz Khaghani
ABSTRACT
Assessment of the transportation infrastructure resilience as the ability of a system to recover after an incident is one of the priorities of engineers and decision-makers. Due to challenges of real-world data collection, performance and resilience assessment are commonly carried out through event simulations, which still call for validating field data. In this study, we have presented a novel approach for ground transportation resilience assessment that uses people-centric information retrieval through social media (i.e., Waze Twitter feeds), coupled with resilience models to move towards a more realistic performance assessment. Waze data and people-centric methods facilitate the analysis and help improve our understanding of realistic transportation system behavior in response to a disruptive event (i.e., accidents and traffic jams). Using the proposed approach, the resilience has been quantified for selected highway sections in Washington, DC area by using reports of delays, caused by disruptive events. The changes in travel time were used as the base for resilience quantification. The accident and traffic jam reports were collected and used as the performance indicators of the model. The findings demonstrate the potential of Waze as the real -world data source to evaluate and improve the performance of transportation infrastructure.
- Tamvakis, P. and Xenidis, Y. Resilience in transportation systems. Procedia-Social and Behavioral Sciences, 48 (2012), 3441--3450.Google Scholar
Cross Ref
- Leu, G., Abbass, H. and Curtis, N. Resilience of ground transportation networks: a case study on Melbourne (2010).Google Scholar
- Nieves-Meléndez, M. E. and de la Garza, J. M. Resilience frameworks instantiated to vehicular traffic applications. Sustainable and Resilient Infrastructure, 2, 2 (2017), 75--85.Google ScholarCross Ref
- Scott, D. M., Novak, D. C., Aultman-Hall, L. and Guo, F. Network robustness index: A new method for identifying critical links and evaluating the performance of transportation networks. Journal of Transport Geography, 14, 3 (2006), 215--227.Google ScholarCross Ref
- Tierney, K. and Bruneau, M. Conceptualizing and measuring resilience: A key to disaster loss reduction. TR news, 250 (2007).Google Scholar
- Henry, D. and Ramirez-Marquez, J. E. Generic metrics and quantitative approaches for system resilience as a function of time. Reliability Engineering & System Safety, 99 (2012), 114--122.Google ScholarCross Ref
- Chang, S. E. and Shinozuka, M. Measuring improvements in the disaster resilience of communities. Earthquake Spectra, 20, 3 (2004), 739--755.Google ScholarCross Ref
- Ouyang, M., Dueñas-Osorio, L. and Min, X. A three-stage resilience analysis framework for urban infrastructure systems. Structural safety, 36 (2012), 23--31.Google Scholar
- Fire, M., Kagan, D., Puzis, R., Rokach, L. and Elovici, Y. Data mining opportunities in geosocial networks for improving road safety. IEEE, City, 2012.Google ScholarCross Ref
- Silva, T. H., de Melo, P. O. V., Viana, A. C., Almeida, J. M., Salles, J. and Loureiro, A. A. Traffic condition is more than colored lines on a map: characterization of waze alerts. Springer, City, 2013.Google Scholar
Index Terms
- Resilience in urban transportation: towards a participatory sensing-based framework
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