An Optimal Schedule for Urban Road Network Repair Based on the Greedy Algorithm

Abstract

The schedule of urban road network recovery caused by rainstorms, snow, and other bad weather conditions, traffic incidents, and other daily events is essential. However, limited studies have been conducted to investigate this problem. We fill this research gap by proposing an optimal schedule for urban road network repair with limited repair resources based on the greedy algorithm. Critical links will be given priority in repair according to the basic concept of the greedy algorithm. In this study, the link whose restoration produces the ratio of the system-wide travel time of the current network to the worst network is the minimum. We define such a link as the critical link for the current network. We will re-evaluate the importance of damaged links after each repair process is completed. That is, the critical link ranking will be changed along with the repair process because of the interaction among links. We repair the most critical link for the specific network state based on the greedy algorithm to obtain the optimal schedule. The algorithm can still quickly obtain an optimal schedule even if the scale of the road network is large because the greedy algorithm can reduce computational complexity. We prove that the problem can obtain the optimal solution using the greedy algorithm in theory. The algorithm is also demonstrated in the Sioux Falls network. The problem discussed in this paper is highly significant in dealing with urban road network restoration.

A novel review on optimization techniques used in wind farm modelling

“…Recently new domains of evolutionary algorithms have emerged to handle Wind Farm applications. Among all the algorithms presented in the previous section, the greedy algorithm [5] , [121] stands tall in terms of convergence value and It is noteworthy to mention that theoretical error in this method is 0.0001%. In the evolution algorithm [65] the convergence characteristics are achieved in very less generations and very minimum computational effort.…”

“…According to procedural and implementation complexity the following soft computing wind farm optimization layouts techniques are ranked. (1) Multilevel extended pattern search algorithm [11] (2) Greedy Algorithm [5] , [121] (3) Ant Colony algorithm [58] (4) Quadratic Interpolation Optimization [90] (5) Particle Swam optimization [53] (6) Binary Particle Swarm Optimization [54] (7) Genetic Algorithm [2] , [112] (8) Definite Point Selection [91] (9) Evolutive algorithm [92] (10) Random Search [93] (11) Simulated Annealing [94] .…”

“…Recently, a number of numerical tools are under development, some based on stochastic mathematical models, each presenting specific features in terms of accuracy, convergence, stability, robustness, and calculation speed [10] . Among the most promising methods which have applied wind farm layout optimization, are Greedy Algorithm [5] , [121] , Multilevel Extended pattern search algorithm [11] and Sequential convex programming (SCP) [12] . As of now, there is no single research article which summarizes the research of wind farm layout optimization.…”

Optimal Route Determination to provide Relief Following an Earthquake using the Traffic Density Ratio (Case Study: Isfahan's Fire Stations)

“…They used greedy algorithm to retrieve the network after the crisis. The results have shown that this method is the most effective solution for identifying the critical links on the network [16].…”

“…Khademi et al studied a postearthquake response and determined the optimum route for cars in the network [15]. Lu et al (2016) planned to rebuild the urban road network caused by rain, snow and other adverse weather conditions, traffic accidents and the daily urban network conditions. They used greedy algorithm to retrieve the network after the crisis.…”