scholarly journals A review of computational intelligence methods for traffic management systems

2021 ◽  
Vol 67 (4) ◽  
pp. 25-30
Author(s):  
Vladimir Ilin ◽  
Dragan Simić

One of the most important challenges in modern city life is to enable effective and efficient traffic management system. Recently, computational intelligence methods have become increasingly popular for traffic management system design, application, and monitoring. Computational intelligence methods are often deployed for managing traffic, that is for reducing mileage, congestion, the use of fuels, and environmental impact. The aim of this paper is twofold. First, to present the three main areas in a computational intelligence approach, namely neural networks, fuzzy logic systems, and evolutionary computation. Second, to emphasize their impact on various traffic management domains, including traffic flow forecasting, traffic light control, traffic fatalities prediction, traffic sign detection, and optimization of transportation networks.

2021 ◽  
Vol 67 (4) ◽  
pp. 25-30
Author(s):  
Vladimir Ilin ◽  
Dragan Simić

One of the most important challenges in modern city life is to enable effective and efficient traffic management system. Recently, computational intelligence methods have become increasingly popular for traffic management system design, application, and monitoring. Computational intelligence methods are often deployed for managing traffic, that is for reducing mileage, congestion, the use of fuels, and environmental impact. The aim of this paper is twofold. First, to present the three main areas in a computational intelligence approach, namely neural networks, fuzzy logic systems, and evolutionary computation. Second, to emphasize their impact on various traffic management domains, including traffic flow forecasting, traffic light control, traffic fatalities prediction, traffic sign detection, and optimization of transportation networks.


Author(s):  
Solomon Adegbenro Akinboro ◽  
Johnson A Adeyiga ◽  
Adebayo Omotosho ◽  
Akinwale O Akinwumi

<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>


2020 ◽  
Vol 1 (2) ◽  
pp. 65-70
Author(s):  
Daniel Shunu

In this study, a proposed intelligent traffic management system is presented making use of the wireless sensor network for improving traffic flow.  By making use of the clustering algorithm, VANET environment is utilized for the proposed system. The components of the proposed system include sensor node hardware, vehicle detection system through magnetometer, and UDP protocol for communication between the nodes. The intersection control agent receives the information about the vehicles and by making use of its algorithm, it dynamically changes the traffic light timings. By making use of the greedy algorithm, the system can be enhanced to a wider area by connecting multiple intersections.


2019 ◽  
Vol 18 (1) ◽  
pp. 47-54
Author(s):  
D. V. Kapskiy ◽  
D. V. Navoy ◽  
P. A. Pegin

The paper considers issues pertaining to creation of a model for controlling road traffic with the purpose to minimize delays on street and road network, which is proposed as an innovative one while developing an intelligent transport system of the large city that is Minsk. The developed model has a complex structure of algorithmic support. The first-level model has been implemented on the basis of fuzzy logic, for which a program has been developed and conditions have been determined, and operation of traffic light at a real local intersection of Minsk, which is included in the automated traffic management system, has been simulated. Innovation in the first-level model is an approach in determining conditions while detecting a fuzzy set without using a standard algorithm that is an algorithm of local flexible regulation. The paper proposes and investigates a model that works on the basis of operationally obtained parameters of traffic flow intensity at characteristic points (sections) of street and road network. Efficiency of the first-level model has been equal to 8 % due to optimization of a traffic light cycle (reduction of transport delays during passage of stop lines). Results of the simulation using the proposed computer program have made it possible to improve efficiency of traffic management on the studied highway (Logoysky trakt) in Minsk city of Minsk by 15 % due to decrease of delay level in case of unilateral coordination. The algorithm has been already implemented as part of the current automated traffic management system in the city of Minsk and it has shown its efficiency. However this efficiency can be increased if it is used together with an algorithm for searching maximum volume of motion in a cycle with a distributed intensity pulse. It has been planned to take into account this specific feature when increasing possibilities for algorithmization of traffic management.


This paper proposes an internet of things (IoT) based intelligent traffic management system that can aid problematic traffic situations in smart cities by classifying congestions via sensory data, and then controlling traffic lights and creating alternate routes for incoming vehicles to the congested zones in order to relieve or avoid congestions completely. The proposed intelligent traffic management system consists of different subsystems such as Test Operation, Supervisory, Traffic Light, and Pathfinder subsystems. The system is represented by flowcharts with their explanations and its operation with some defined scenarios is validated with the CupCarbon simulation environment.


Nowadays traffic in metropolitan cities is becoming a challenging task and the violation of traffic rules leads to fatal accidents. The commuters are also facing lot of delay in their destination when they caught up in traffic which makes them unstable in their working environments. The proposed bollards based intelligent traffic management system determines the traffic scenario using sensors deployed in various locations and thereby regulating the traffic thereby providing chaos free travel. The installation of the bollard system, including traffic lights and communication pillars, is one among the intelligent traffic management system. This system is working in combination with the traffic light control, bollards to regulate the traffic. This system can be used in restricted areas, crowded malls, public access locations, and tourist locations also to regulate the traffic and movement of the people.


The traffic congestion is one of the major problems in crowded cities, which causes people to spend hours on the road. In traffic congestion situations, finding alternate routes for emergency vehicles, which provides shortest travel time to nearby hospital is critically life-saving issue. In this paper, we propose a traffic management system and an algorithm for routing of an emergency vehicle. The algorithm uses distance between source and destination, maximum vehicle count, maximum speed, average speed, traffic light conditions on the roads, which are assumed to support vehicle-to-infrastructure (V2I) communication in 5G IoT network. Simulations are performed on CupCarbon IoT simulator platform for various test scenarios. The performance of the proposed emergency vehicle routing algorithm is compared against well known Link State algorithm. And, the results demonstrate the effectiveness of the proposed method.


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