Recovery Model and Maintenance Optimization for Urban Road Networks with Congestion

Hongyan Dui, Yulu Zhang, Songru Zhang and Yun-An Zhang ()
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Hongyan Dui: School of Management, Zhengzhou University, Zhengzhou 450001, China
Yulu Zhang: School of Management, Zhengzhou University, Zhengzhou 450001, China
Songru Zhang: School of Management, Zhengzhou University, Zhengzhou 450001, China
Yun-An Zhang: Laboratory of Science and Technology on Integrated Logistics Support, College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China

Mathematics, 2023, vol. 11, issue 9, 1-17

Abstract: Urban road networks have promoted high-quality travel for residents by increasing connectivity and intelligence. But road congestion has not been effectively alleviated, causing a loss of time and energy. At present, the recovery of urban road networks mainly considers removing the failed edges. Considering the recovery cost and time, it is important to take active maintenance behavior to restore these networks. One of the key problems is dispatching traffic workers reasonably to achieve timely maintenance. In this paper, a flow-distribution-based process and execution (FD-PE) model is established for solving congestion. The maintenance centers (MC) study the reasons for and spread of congestion by edge flow. Based on the genetic algorithm (GA), two models of maintenance for urban road networks are developed, which include a single MC-centered dispatching plan and the co-scheduling of MCs. Both models aim at minimizing recovery time and allocating maintenance resources. The road network in Zhengzhou is borrowed as a case to explain the feasibility of the proposed models. The results show that on the premise of dividing network regions, it is reasonable to take a single MC to recover congestion. Compared with a single MC, the co-scheduling of MCs may save more time.

Keywords: recovery model; maintenance optimization; urban road networks; congestion (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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