scholarly journals IoT based Road Traffic Management System for Metropolitan Cities

2020 ◽  
Vol 8 (12s3) ◽  
pp. 75-79
Keyword(s):  
Traffic Congestion ◽  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Traffic Density ◽  
The Road ◽  
Metropolitan Cities ◽  
Traffic Management System ◽  
Road Traffic Congestion ◽  
Jamming Problems

Many cities in the world face jamming problems in road traffic, particularly in metropolitan cities. At present the traffic controlling systems aresemiautomatic in nature. With the introduction of IoT in road traffic management systems, it revolutionizes the field of road traffic management system and improves the road traffic congestion problem.This paper proposes an IoT-based road traffic management system for metropolitan cities. The proposed system provides the hassle free movement of the vehicles to avoid inconvenience and reroute the higher priority vehicles. Experimental results show that the proposed system gives higher success rate for the low traffic density in the lane.

Neural Network (NN) Based Route Weight Computation for Bi-Directional Traffic Management System

2020 ◽  
pp. 750-765
Author(s):  
Shamim Akhter ◽  
Rahatur Rahman ◽  
Ashfaqul Islam
Keyword(s):  
Neural Network ◽  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Integrated Approach ◽  
Management Support ◽  
The Road ◽  
Multiple Data ◽  
Management Support Systems ◽  
Traffic Management System

Low-cost, flexible, easily maintainable and secure traffic management support systems are in demand. Internet-based real time bi-directional communication provides significant benefits to monitor road traffic conditions. Dynamic route computation is a vital requirement to make the traffic management system more realistic and reliable. Therefore, an integrated approach with multiple data feeds and Backpropagation (BP) Neural Network (NN) with Levenberg-Marquardt (LM) optimization is applied to predict the road weights. The results indicate that the proposed traffic system/tool with NN based dynamic weights computation is much more effective to find the optimal routes. The BP NN with LM optimization achieves 96.67% accuracy.

Design and Implementation of an Intelligent Traffic Management System

2019 ◽  
pp. 58-79 ◽  
Author(s):  
Shamim Akhter ◽  
Sakhawat Hosain Sumit ◽  
Md. Rahatur Rahman
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Route Planning ◽  
Efficient Solutions ◽  
The Road ◽  
Decision Attributes ◽  
Software Modules ◽  
Traffic Management System

Intelligence traffic management system (ITMS) provides effective and efficient solutions toward the road traffic management and decision-making problems, and thus helps to reduce fuel consumption and emission of greenhouse gases. Software-based real-time bi-directional TMS with a neural network was proposed and implemented. The proposed TMS solves a decision problem, dynamic road weights calculation, using different environmental, road and vehicle related decision attributes. In addition, the development of the real-time operational models as well as their solving challenges has increased in a rapid manner. Therefore, the authors integrate the design and development of a neural-based complete real-time operational ITMS, with the combination of software modules including traffic monitoring, road weight updating, forecasting, and optimum route planning decision. Collecting, extracting the insights and inherit meaning, and modeling the tremendous amount of continuous data is a challenging task. A discussion is also included with the future improvements on ITMS.

scholarly journals Efficient Traffic Management System

2018 ◽  
Vol 7 (3.12) ◽  
pp. 926
Author(s):  
Suraj Kumar G Shukla ◽  
Aadithya Kandeth ◽  
D Sai Santhiya ◽  
Kayalvizhi Jayavel
Keyword(s):  
Image Processing ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Management System ◽  
Large Scale ◽  
Traffic Signals ◽  
Input Image ◽  
Traffic Density ◽  
Use Of Technology ◽  
Traffic Management System

Traffic Management is a big issue which impacts us almost daily. Use of technology such as IoT and image processing can lead to a smooth traffic management system. The common reason for traffic congestion is due to the lack of an efficient traffic prioritization system. The internet of things is a network of devices. The embedded systems includes sensors, actuators, and electronics.With software and connectivity locally or over internet  helps in  transfer of data. Each of these devices is uniquely identifiable in the network and are highly interoperable. Image processing using OpenCV is a technique that is used to process an input image and obtain the traffic densities along various lanes in a junction. Existing traffic management solutions include using RFID tags on vehicles to obtain a vehicle count. This can also be done using ultrasonic sensors. The problem with these methods is that when implemented in a large scale, the cost of the entire system can be exponentially higher than an image processing approach as each vehicle will have to be fitted with an RFID tag. Hence, implementing this model at a largescale level, for example in a metropolitan city will be time consuming and expensive. This has led to the development of our algorithm, which uses image processing and IoT along with CCTV cameras. This system is efficient, as it uses CCTV cameras that are already present in traffic signals of most major metropolitan cities. Hence implementing the system at a largescale level is feasible. This algorithm also takes care of corner cases like heavy utility vehicles and motorbikes. It can also be used at night and during unfavorable weather conditions.The algorithm used detects the density of traffic as opposed to the count of vehicles by taking input images from a CCTV camera, comparing it with a sample image of an empty road and obtaining a match percentage. The traffic density can be found easily using this since it has a disproportionate relation with the match percentage. The traffic signals can be altered accordingly using the traffic density. The output is then sent to the ThingSpeak cloud where it can be analyzed.

scholarly journals A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System

2020 ◽  
Vol 12 (11) ◽  
pp. 4660 ◽  
Author(s):  
Tanzina Afrin ◽  
Nita Yodo
Keyword(s):  
Traffic Congestion ◽  
Traffic Management ◽  
Road Traffic ◽  
Mitigation Strategies ◽  
Decision Makers ◽  
Economic Losses ◽  
Advantages And Disadvantages ◽  
Traffic Management System ◽  
Transportation Development ◽  
Road Traffic Congestion

Traffic congestion is a perpetual problem for the sustainability of transportation development. Traffic congestion causes delays, inconvenience, and economic losses to drivers, as well as air pollution. Identification and quantification of traffic congestion are crucial for decision-makers to initiate mitigation strategies to improve the overall transportation system’s sustainability. In this paper, the currently available measures are detailed and compared by implementing them on a daily and weekly traffic historical dataset. The results showed each measure showed significant variations in congestion states while indicating a similar congestion trend. The advantages and disadvantages of each measure are identified from the data analysis. This study summarizes the current road traffic congestion measures and provides a constructive insight into the development of a sustainable and resilient traffic management system.

Neural Network (NN) Based Route Weight Computation for Bi-Directional Traffic Management System

2016 ◽  
Vol 7 (4) ◽  
pp. 45-59 ◽  
Author(s):  
Shamim Akhter ◽  
Rahatur Rahman ◽  
Ashfaqul Islam
Keyword(s):  
Neural Network ◽  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Low Cost ◽  
Integrated Approach ◽  
Management Support ◽  
The Road ◽  
Multiple Data ◽  
Traffic Management System

Low-cost, flexible, easily maintainable and secure traffic management support systems are in demand. Internet-based real time bi-directional communication provides significant benefits to monitor road traffic conditions. Dynamic route computation is a vital requirement to make the traffic management system more realistic and reliable. Therefore, an integrated approach with multiple data feeds and Backpropagation (BP) Neural Network (NN) with Levenberg-Marquardt (LM) optimization is applied to predict the road weights. The results indicate that the proposed traffic system/tool with NN based dynamic weights computation is much more effective to find the optimal routes. The BP NN with LM optimization achieves 96.67% accuracy.

scholarly journals Road Traffic Congestion Management Based on a Search-Allocation Approach

2017 ◽  
Vol 18 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Jamal Raiyn
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Road Traffic ◽  
Smart Cities ◽  
Self Regulation ◽  
Congestion Management ◽  
Shortest Route ◽  
Road Traffic Congestion

Abstract This paper introduces a new scheme for road traffic management in smart cities, aimed at reducing road traffic congestion. The scheme is based on a combination of searching, updating, and allocation techniques (SUA). An SUA approach is proposed to reduce the processing time for forecasting the conditions of all road sections in real-time, which is typically considerable and complex. It searches for the shortest route based on historical observations, then computes travel time forecasts based on vehicular location in real-time. Using updated information, which includes travel time forecasts and accident forecasts, the vehicle is allocated the appropriate section. The novelty of the SUA scheme lies in its updating of vehicles in every time to reduce traffic congestion. Furthermore, the SUA approach supports autonomy and management by self-regulation, which recommends its use in smart cities that support internet of things (IoT) technologies.

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>

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