Road Traffic Management System with Load Balancing on Cloud Using VM Migration Technique

2018 ◽  
pp. 163-173
Author(s):  
Md. Rafeeq ◽  
C. Sunil Kumar ◽  
N. Subhash Chandra
Keyword(s):  
Load Balancing ◽  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Vm Migration ◽  
Traffic Management System ◽  
Migration Technique

scholarly journals Mobile Road Traffic Management System Using Weighted Sensor

2017 ◽  
Vol 11 (5) ◽  
pp. 147 ◽  
Author(s):  
Solomon Adegbenro Akinboro ◽  
Johnson A Adeyiga ◽  
Adebayo Omotosho ◽  
Akinwale O Akinwumi
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Urban Areas ◽  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Traffic Information ◽  
Traffic Light ◽  
Real Time Traffic ◽  
Traffic Management System

<p><strong>Vehicular traffic is continuously increasing around the world, especially in urban areas, and the resulting congestion ha</strong><strong>s</strong><strong> be</strong><strong>come</strong><strong> a major concern to automobile users. The popular static electric traffic light controlling system can no longer sufficiently manage the traffic volume in large cities where real time traffic control is paramount to deciding best route. The proposed mobile traffic management system provides users with traffic information on congested roads using weighted sensors. A prototype of the system was implemented using Java SE Development Kit 8 and Google map. The model </strong><strong>was</strong><strong> simulated and the performance was </strong><strong>assessed</strong><strong> using response time, delay and throughput. Results showed that</strong><strong>,</strong><strong> mobile devices are capable of assisting road users’ in faster decision making by providing real-time traffic information and recommending alternative routes.</strong></p>

scholarly journals A SUMO Based Simulation Framework for Intelligent Traffic Management System

2020 ◽  
pp. 1-5
Author(s):  
Shamim Akhter ◽  
◽  
Md. Nurul Ahsan ◽  
Shah Jafor Sadeek Quaderi ◽  
Md. Abdullah Al Forhad ◽  
...  
Keyword(s):  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Fuzzy Model ◽  
Road Segment ◽  
World Population ◽  
Dijkstra Algorithm ◽  
Neuro Fuzzy ◽  
Traffic Management System ◽  
Fuzzy Framework

Continuous increments in world population demands transportation with essential vehicle facilities and directly effect on road traffic volume or congestion, mostly in metropolitan cities, and thus it needs significant investigation, analysis, and maintenance. In these regards, an Intelligent Traffic Management System (ITMS) with a Deep-Neuro-Fuzzy model was proposed and implemented. Dijkstra algorithm is used to select optimum path from source to destination on the basis of calculated road segment weights from Deep-Neuro-Fuzzy framework. However, Deep-Neuro-Fuzzy framework needs some comprehensive analysis, other means some simulation or emulation, and etc, to proof the efficiency and workability of the model. In this paper, we are going to explore the Deep-Neuro-Fuzzy model in pragmatic style with an open-source traffic simulation model (SUMO) and helps to explore traffic-related issues including route choice, simulate traffic light or vehicular communication, etc in our ITMS. In addition, a new GUI is developed to control the simulation input attributes and presents the feedbacks into the traffic flow in SUMO environment. Results highlight that the proposed SUMO model can realistically simulate ITMS based on the road segment weights from Deep-Neuro-Fuzzy model. Different built-in routing algorithms are also used to proof the workability of this model.

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