Road Traffic Signal Survey

Scope, 2022

Transport and road traffic demand has caused signs of congestion, delays, and accidents in the last two decades, particularly in developing cities, mainly during peak hours. Hence, a multidisciplinary effort (for example, the improvement of driving habits, the provision of better infrastructure and traffic management measures, and the rationalisation of the use of public roads) is needed. This project proposed the design and implementation of a smart traffic light control system to improve the vehicular circulation of Queens Drive-Layard Street intersection, especially during peak hours. Finally, it evaluated the factors generating problems and social impact (for example, lost man-hours, environmental pollution, and accidents) to propose strategies to improve the traffic flow, reduce operational conflict, and provide a safer environment for the community. From the analysis and simulation carried out with the VISSIM software during rush hour at the intersection with traffic lights, it was found that the number of vehicles in the queue was reduced from 351 to 270 in the six possible movements within the intersection. By reducing the vehicles in the queue, pollution emissions are also reduced (297.1 g/h CO emissions and 57.9 g/h NO emissions). Finally, 270 vehicles queuing generate an extra cost in fuel, although in less quantity, reducing consumption from 17.8 to 16.7 L/h. Therefore, the proposed situation has reduced the delay and number of vehicles, yet the service level remains the same as the current situation. In addition, the proposed case includes improvements in horizontal and vertical signage, which will help mitigate risks and reduce unnecessary delays.

2014

Adaptive traffic signal control system is presents an intelligent traffic signal control system using image processing technique. With the help of specialized algorithm, morphology and image needed to avoid traffic congestion. This paper processing technique, the vehicles are detected, recognized and density is calibrated for controlling traffic density. This is the most reliable and upcoming technology in the road transit system

preprint.org, 2025

Rapid urbanization and increased vehicular traffic demanded the creation of intelligent traffic management systems. Smart traffic signal optimization employing real-time data has emerged as a critical approach for improving traffic flow, reducing congestion, and increasing overall urban mobility. This research paper digs into improvements in traffic signal control systems, focusing on the integration of real-time data and artificial intelligence (AI) approaches. We investigate a variety of methodologies, including reinforcement learning, fuzzy logic, and connected car technologies, highlighting their strengths and limits. The report also analyses the problems and potential future approaches for deploying these intelligent systems.

International Journal of Computer Applications, 2018

Traffic congestion problem is a phenomenon on road networks that occurs as use increases, and is characterized by slower speeds, longer trip times, and increased vehicular queuing and contributes huge impact to the transportation system in the country.These TLC have limitations because it uses the pre-defined hardcode that does not have the flexibility of modification on real time basis. Due to the fixed time intervals of green, orange and red signals, the waiting time is more and the delay of respective light is not dependent on traffic. Thus, a car uses more fuel. Through this paper we intend to present an improvement in existing traffic control system at the intersection using different techniques i.e. Intelligent Traffic Light Controller using Embedded System, Traffic Control System Based on Image Processing Technique, Intelligent Traffic Light Using RFID Technique. Existing automatic traffic control system at the intersection with pre-set timing signals is proved to be ineffici...

IEEE International Conference on Information and Automation for Sustainability, 4 (2008) 301-306

An adaptive neuro-fuzzy inference system is developed and tested for traffic signal controlling. From a given input data set, the developed adaptive neuro-fuzzy inference system can draw the membership functions and corresponding rules by its own, thus making the designing process easier and reliable compared to standard fuzzy logic controllers. Among useful inputs of fuzzy signal control systems, gap between two vehicles, delay at intersections, vehicle density, flow rate and queue length are often used. By considering the practical applicability, the average vehicle inflow rate of each lane is considered in this work as inputs to model the adaptive neuro-fuzzy signal control system. In order to define the desired objectives of reducing the waiting time of vehicles at the signal control, the combined delay of vehicles within one signal cycle is minimized using a simple mathematical optimization method The performance of the control system was tested further by developing an event driven traffic simulation program in Matlab under Windows environment. As expected, the neuro-fuzzy logic controller performed better than the fixed time controller due to its real time adaptability. The neuro-fuzzy controlling system allows more vehicles to pass the junction in congestion and less number of vehicles when the flow rate is low. In particular, the performance of the developed system was superior when there were abrupt changes in traffic flow rates.

IJIRST, 2014

In the recent days, the traffic density has risen to a very high level. Increase in traffic density has resulted in spending long hours in traffic for short distances. The solution can be implemented by automating the traffic signals using the concept of "Autonomic Computing"-computing systems that can manage themselves. Using the characteristics of Autonomic computing and Wireless Sensor Networks [8], the traffic signals can me made smarter and intelligent by making it react to the traffic density and thus increasing or decreasing the waiting time accordingly.

Journal of Advanced Transportation, 2022

e development of Information and Communication Technologies (ICT) has now reached some entirely unexpected domains. Many applications in modern cities lead to novelties, resulting in new habits of the citizens of tomorrow's smart cities. ese innovations include Intelligent Transportation Systems (ITS). An important application domain of ITS is undoubtedly represented by the dynamic and optimized management of traffic-lighted road intersections. Although several works have already been presented in the literature over the years, many have not considered the new perspectives regarding the source of data to manage traffic lights, i.e., not only Wireless Sensor Networks (WSNs), but, mainly, the vehicular communications. is paper introduces an innovative approach to dynamically regulating traffic light cycles and phases in an isolated intersection. e suggested method can fit data management from WSNs and vehicular communications through IEEE 802.11p and LTE-V2V, employing various Fuzzy Logic Controllers (FLCs) that manage vehicles turning movements for dynamic controls of both the phase and the green time of traffic lights. e results obtained will allow us to observe that the proposed application is better than the others. Each of the implemented configurations will bring advantages and disadvantages that allow choosing one configuration or another based on specific project requirements.

Citation/Export MLA Shubhada P. Mone, Sachin Wankhede, Rohini Kadam, Aditya Mahakulkar, Poonam Kauthale, “An Intelligent Traffic Light Controlling System”, March 15 Volume 3 Issue 3 , International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 940 - 943, DOI: 10.17762/ijritcc2321-8169.150309 APA Shubhada P. Mone, Sachin Wankhede, Rohini Kadam, Aditya Mahakulkar, Poonam Kauthale, March 15 Volume 3 Issue 3, “An Intelligent Traffic Light Controlling System”, International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 940 - 943, DOI: 10.17762/ijritcc2321-8169.150309

2015

With the increase in population, traffic density on roads has increased to a level that makes navigating through cities extremely difficult. The apex of the problem is seen at traffic junctions, where the traffic light systems are hard coded and not adaptive to the real-time traffic conditions. The proposed system involves a closed loop control of traffic lights using a camera that acquires images of all four roads, followed by image processing to ascertain the real-time traffic congestion on the roads. The Canny Edge Detection technique is used in order to detect the edges of the vehicles in the images. The density of traffic on the road is determined by obtaining the Standard Variation value of the edge detected image. The traffic flow control algorithm has two aspects: the first being prioritization of roads, and the second, the duration of the traffic signal. The system has been developed using Laboratory Virtual Instrument Engineering Workbench (LabVIEW) which allows for effect...

International Journal of Computer Applications, 2016

In the present time the number of private vehicles is increasing rapidly. As result the traffic congestion is growing and becoming a huge problem in big cities. Due to traffic congestion many huge problems are occurring like pollution, wastage of time, money, accidents etc. Traffic congestion occurred very frequently in the present time which affect the daily life and introduce so many problems and challenges. To solve all these problems traffic signal control is a best solution. Fuzzy set theory have been widely applied to many applications and it can easily deal with uncertainty, complexity and imprecision in many systems. The problems in traffic engineering are mainly characterized by the imprecise, ambiguous and uncertain parameters. Due to such type of parameters fuzzy logic method is best suitable approach for traffic signal control. The performance of fuzzy logic based controller is better for two one way street based on extension of green light time. Later fuzzy logic controller was developed for two way intersection without turns, single intersection with all movements, multiple intersection etc. Better performance of fuzzy logic based traffic signal control in comparison of traditional traffic signal control increase the research in this field. Fuzzy logic approach will definitely help in