The Traffic Congestion Analysis Using Traffic Congestion Index and Artificial Neural Network in Main Streets of Electron
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he Traffic Congestion Analysis Using Traffic Congestion Index and Artificial Neural Network in Main Streets of Electronic City (Case Study: Hamedan City) Mohammad Mehdi ShirMohammadia,* and Mansour Esmaeilpourb,** aComputer
Engineering Department, Arak Branch, Islamic Azad University, Markazi Province, Arak Imam Khomeini Arak, Iran b Computer Engineering Department, Hamedan Branch, Islamic Azad Universit Hamadan Province, Hamedan, Hamedan, Iran *e-mail: [email protected] **e-mail: [email protected] Received October 27, 2019; revised January 28, 2020; accepted February 29, 2020
Abstract—Traffic is a major challenge for electronic cities and coping with it requires analyzing traffic congestion in the city road network. In this paper, the performance index of vehicle speed was estimated to evaluate the conditions of road networks. This study analyzes the traffic density for the main network of Hamedan communication routes based on the collected data of Speed performance of Hamedan traffic control system. According to this analysis, the congestion index and traffic peak hours were determined. Also the relationship between vehicle speed and traffic congestion was predictably predictable by neural network and the genetic algorithm. In this study areas of traffic were identified using Hamedan traffic control center due to the speed of vehicles. DOI: 10.1134/S0361768820060079
1. INTRODUCTION Smart cities are as Umbrellas of different technologies for responding to increasing urban population challenge. The priority of intelligent electronic cities is a strategy to collecting information about the city and its smart use to improve the provided services to citizens or to create new services [1]. These smart cities have weather, urban monitoring, pollution monitoring and various applications. the electronic city is the subset of the smart city [2]. The traffic in big cities has become a dilemma and reaching to smart cities is one of the important solutions in this area. Having accurate information about the city’s situation can help to make important decisions in urban management. The use of various sensors as a wireless sensor network can be used on a large scale in city and collect valuable information [3]. The data transmission with wireless signals in smart cities is one of the challenges because construction of high buildings and barriers reduces the power and quality of the signal [4]. Widespread use of wireless signals and equipment may lead to interference and reduce service quality [5]. Therefore, in order to solve the traffic problem, it is necessary to achieve traffic Congestion levels by collecting information, especially
with wireless signals so that it can be programmed to control and manage traffic. At the moment there are no fixed and stable evaluation tools for evaluating traffic conditions. In fact, there are various measures and evaluations in different areas based on specific applications and needs. However, timeAverage of peak travel can be calculated by defining the congestion assessment
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