An Improved Simulated Annealing Technique for Enhanced Mobility in Smart Cities

Abstract

Vehicular traffic congestion is a significant problem that arises in many cities. This is due to the increasing number of vehicles that are driving on city roads of limited capacity. The vehicular congestion significantly impacts travel distance, travel time, fuel consumption and air pollution. Avoidance of traffic congestion and providing drivers with optimal paths are not trivial tasks. The key contribution of this work consists of the developed approach for dynamic calculation of optimal traffic routes. Two attributes (the average travel speed of the traffic and the roads' length) are utilized by the proposed method to find the optimal paths. The average travel speed values can be obtained from the sensors deployed in smart cities and communicated to vehicles via the Internet of Vehicles and roadside communication units. The performance of the proposed algorithm is compared to three other algorithms: the simulated annealing weighted sum, the simulated annealing technique for order preference by similarity to the ideal solution and the Dijkstra algorithm. The weighted sum and technique for order preference by similarity to the ideal solution methods are used to formulate different attributes in the simulated annealing cost function. According to the Sheffield scenario, simulation results show that the improved simulated annealing technique for order preference by similarity to the ideal solution method improves the traffic performance in the presence of congestion by an overall average of 19.22% in terms of travel time, fuel consumption and CO 2 emissions as compared to other algorithms; also, similar performance patterns were achieved for the Birmingham test scenario.

Centralized simulated annealing for alleviating vehicular congestion in smart cities

“…The advantage of the centralized algorithm structure in this case is that information about the wider road network can be embedded into the system. This differs to the work in [14], where only the local traffic information is used to optimize the route of each vehicle, reducing the potential improvement in travel time etc.…”

“…In a previous work [14], an Improved Simulated Annealing TOPSIS (ISA-TOPSIS) algorithm is proposed to enhance the mobility in smart cities. ISATOP-SIS is a decentralized method that can only reduce the local traffic congestion, by considering only two attributes (vehicles average speed and road length) in its optimization to provide optimal paths.…”

“…How to construct a new path Un based on an initial path Uc[14] a d } is substitute by (a i ,á i+1 , . .…”

A geometrical path planning method for unmanned aerial vehicle in 2D/3D complex environment

“…After several years of studies, many researchers have proposed a number of representative methods about path planning. These methods can be roughly divided into the following categories: the methods based on graph, e.g., Voronoi diagram [1] and probabilistic roadmap (PRM) [2], the methods based on heuristic search, e.g., A-star (A*) [3] and D-Star [4], the methods based on random search, e.g., ant colony (AC) [5], particle swarm optimization (PSO) [6] and genetic algorithm (GA) [7], the methods based on potential field, e.g., simulated annealing (SA) [8], stream function [9] and boundary value problem (BVP) [10].…”

Computing new optimized routes for GPS navigators using evolutionary algorithms

“…In [1] the authors propose the ISATOPSIS (Improved Simulated Annealing Technique for Order Preference by Similarity to the Ideal Solution) algorithm to address the tra c congestion problem in smart cities. It comprises a multi-objective optimization algorithm, which combines simulated annealing (SA) with cost function based on both, multi-a ribute decision making (MADM) and TOPSIS [7] to provide the driver with optimal paths.…”