Citation

Wang HW, Peng ZR, Wang D, Meng Y, Wu T, Sun W, Lu QC. Transp. Res. C Emerg. Technol. 2020; 115: e102619.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.trc.2020.102619

PMID

unavailable

Abstract

Resilience offers a broad social-technical framework to deal with breakdown, response and recovery of transportation networks adapting to various disruptions. Although current research works model and simulate transportation resilience from different perspectives, the real-world resilience of urban road network is still unclear. In this paper, a novel end to end deep learning framework is proposed to estimate and predict the spatiotemporal patterns of transportation resilience under extreme weather events. Diffusion Graph Convolutional Recurrent Neural Network and a dynamic-capturing algorithm of transportation resilience jointly form the backbone of this framework. The presented framework can capture the spatiotemporal dependencies of urban road network and evaluate transportation resilience based on real-world big data, including on-demand ride services data provided by DiDi Chuxing and grid meteorological data.

RESULTS show that aggregate data of related precipitation events could be used for transportation resilience modeling under extreme weather events when facing sample imbalance problem due to limited historical disaster data. In terms of observed transportation resilience, transportation network demonstrates different characteristics between sparse network and dense network, as well as general precipitation events and extreme weather events. The response time is double or triple of the recovery time, and an elastic limit exists in the recovery process of network resilience. In terms of resilience prediction, the proposed model outperforms competitors by incorporating topological information and has better predictions of the system performance degradation than other resilience indices. The above results could assist researchers and policy makers clearly understand the real-world resilience of urban road networks in both theory and practice, and take effective responses under emergent disruptive events.

Language: en

Keywords

Extreme weather events; Deep Learning; Graph Convolutional Neural Network; Spatiotemporal traffic prediction; Transportation resilience; Urban road networks