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Network Reconfiguration for Increasing Transportation System Resilience Under Extreme Events
Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, USA.
Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, USA..
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics. Systems Sciences Lab, Palo Alto Research Center, Palo Alto, CA, USA.
2019 (English)In: Risk Analysis, ISSN 0272-4332, E-ISSN 1539-6924Article in journal (Refereed) Epub ahead of print
Abstract [en]

Evacuating residents out of affected areas is an important strategy for mitigating the impact of natural disasters. However, the resulting abrupt increase in the travel demand during evacuation causes severe congestions across the transportation system, which thereby interrupts other commuters' regular activities. In this article, a bilevel mathematical optimization model is formulated to address this issue, and our research objective is to maximize the transportation system resilience and restore its performance through two network reconfiguration schemes: contraflow (also referred to as lane reversal) and crossing elimination at intersections. Mathematical models are developed to represent the two reconfiguration schemes and characterize the interactions between traffic operators and passengers. Specifically, traffic operators act as leaders to determine the optimal system reconfiguration to minimize the total travel time for all the users (both evacuees and regular commuters), while passengers act as followers by freely choosing the path with the minimum travel time, which eventually converges to a user equilibrium state. For each given network reconfiguration, the lower-level problem is formulated as a traffic assignment problem (TAP) where each user tries to minimize his/her own travel time. To tackle the lower-level optimization problem, a gradient projection method is leveraged to shift the flow from other nonshortest paths to the shortest path between each origin-destination pair, eventually converging to the user equilibrium traffic assignment. The upper-level problem is formulated as a constrained discrete optimization problem, and a probabilistic solution discovery algorithm is used to obtain the near-optimal solution. Two numerical examples are used to demonstrate the effectiveness of the proposed method in restoring the traffic system performance.

Place, publisher, year, edition, pages
John Wiley & Sons, 2019.
Keywords [en]
Network reconfiguration, optimization, resilience, traffic assignment
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-74600DOI: 10.1111/risa.13320PubMedID: 31039286Scopus ID: 2-s2.0-85065195673OAI: oai:DiVA.org:ltu-74600DiVA, id: diva2:1325690
Available from: 2019-06-17 Created: 2019-06-17 Last updated: 2019-06-25

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Goebel, Kai

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