Technical Papers
Oct 31, 2020

Evacuation Traffic Management under Low Visibility and Toxic-Gas Leakage Disasters

Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 1

Abstract

The majority of existing evacuation models neglect the fact that leaking toxic gas usually degrades atmospheric visibility under chemical disasters. This paper takes the impact of leaking toxic gas on atmospheric visibility into account in evacuation problems. The reduction of traffic speed and road capacity under conditions of low atmospheric visibility is introduced and has stochastic features. Further, a robust evacuation traffic management model for evacuation scenarios of low visibility and toxic-gas-leakage disasters is developed to obtain optimal evacuation plans. The proposed robust optimization model can guarantee the feasibility of optimal evacuation plans to the evacuation scenarios of low atmospheric visibility under corresponding robustness requirements. Numerical experiments show that neglecting the impact of low atmospheric visibility overestimates the performance of optimal evacuation plans and even assigns evacuation traffic to roads with bad driving conditions under chemical disasters.

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Data Availability Statement

All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

This work was supported by the National Key R&D Program of China ( Grant No. 2017YFC0803300), the National Natural Science Foundation of China (Grant Nos. 71621001, 71631002, and 71771021), and the Fundamental Research Funds for the Central Universities (Grant No. 2019YJS089).

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Go to Journal of Transportation Engineering, Part A: Systems
Journal of Transportation Engineering, Part A: Systems
Volume 147Issue 1January 2021

History

Received: May 8, 2020
Accepted: Aug 17, 2020
Published online: Oct 31, 2020
Published in print: Jan 1, 2021
Discussion open until: Mar 31, 2021

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Ph.D. Student, Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong Univ., Beijing 100044, People’s Republic of China. Email: [email protected]
Associate Professor, Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong Univ., Beijing 100044, People’s Republic of China (corresponding author). ORCID: https://orcid.org/0000-0002-5956-0999. Email: [email protected]
Professor, Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong Univ., Beijing 100044, People’s Republic of China. Email: [email protected]
Professor, Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong Univ., Beijing 100044, People’s Republic of China. Email: [email protected]

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