Resilience-Based Optimization of Postdisaster Restoration Strategy for Road Networks
This work proposes a framework for the optimization of postdisaster road network restoration strategies from a perspective of resilience. The network performance is evaluated by the total system travel time (TSTT). After the implementation of a postdisaster restoration schedule, the network flows in...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/8871876 |
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| _version_ | 1832550829643530240 |
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| author | Xinhua Mao Jibiao Zhou Changwei Yuan Dan Liu |
| author_facet | Xinhua Mao Jibiao Zhou Changwei Yuan Dan Liu |
| author_sort | Xinhua Mao |
| collection | DOAJ |
| description | This work proposes a framework for the optimization of postdisaster road network restoration strategies from a perspective of resilience. The network performance is evaluated by the total system travel time (TSTT). After the implementation of a postdisaster restoration schedule, the network flows in a certain period of days are on a disequilibrium state; thus, a link-based day-to-day traffic assignment model is employed to compute TSTT and simulate the traffic evolution. Two indicators are developed to assess the road network resilience, i.e., the resilience of performance loss and the resilience of recovery rapidity. The former is calculated based on TSTT, and the latter is computed according to the restoration makespan. Then, we formulate the restoration optimization problem as a resilience-based bi-objective mixed integer programming model aiming to maximize the network resilience. Due to the NP-hardness of the model, a genetic algorithm is developed to solve the model. Finally, a case study is conducted to demonstrate the effectiveness of the proposed method. The effects of key parameters including the number of work crews, travelers’ sensitivity to travel time, availability of budget, and decision makers’ preference on the values of the two objectives are investigated as well. |
| format | Article |
| id | doaj-art-4c32666990f048ad9bacbc659cf6d917 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-4c32666990f048ad9bacbc659cf6d9172025-02-03T06:05:35ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/88718768871876Resilience-Based Optimization of Postdisaster Restoration Strategy for Road NetworksXinhua Mao0Jibiao Zhou1Changwei Yuan2Dan Liu3College of Transportation Engineering, Chang’an University, Xi’an 710064, ChinaCollege of Transportation Engineering, Tongji University, Shanghai 201804, ChinaEngineering Research Center of Highway Infrastructure Digitalization, Ministry of Education, Xi’an 710064, ChinaCollege of Transportation Engineering, Chang’an University, Xi’an 710064, ChinaThis work proposes a framework for the optimization of postdisaster road network restoration strategies from a perspective of resilience. The network performance is evaluated by the total system travel time (TSTT). After the implementation of a postdisaster restoration schedule, the network flows in a certain period of days are on a disequilibrium state; thus, a link-based day-to-day traffic assignment model is employed to compute TSTT and simulate the traffic evolution. Two indicators are developed to assess the road network resilience, i.e., the resilience of performance loss and the resilience of recovery rapidity. The former is calculated based on TSTT, and the latter is computed according to the restoration makespan. Then, we formulate the restoration optimization problem as a resilience-based bi-objective mixed integer programming model aiming to maximize the network resilience. Due to the NP-hardness of the model, a genetic algorithm is developed to solve the model. Finally, a case study is conducted to demonstrate the effectiveness of the proposed method. The effects of key parameters including the number of work crews, travelers’ sensitivity to travel time, availability of budget, and decision makers’ preference on the values of the two objectives are investigated as well.http://dx.doi.org/10.1155/2021/8871876 |
| spellingShingle | Xinhua Mao Jibiao Zhou Changwei Yuan Dan Liu Resilience-Based Optimization of Postdisaster Restoration Strategy for Road Networks Journal of Advanced Transportation |
| title | Resilience-Based Optimization of Postdisaster Restoration Strategy for Road Networks |
| title_full | Resilience-Based Optimization of Postdisaster Restoration Strategy for Road Networks |
| title_fullStr | Resilience-Based Optimization of Postdisaster Restoration Strategy for Road Networks |
| title_full_unstemmed | Resilience-Based Optimization of Postdisaster Restoration Strategy for Road Networks |
| title_short | Resilience-Based Optimization of Postdisaster Restoration Strategy for Road Networks |
| title_sort | resilience based optimization of postdisaster restoration strategy for road networks |
| url | http://dx.doi.org/10.1155/2021/8871876 |
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