Intelligent Traffic Light Control System with Priority Lane Intervention

The congestion of the urban traffic is becoming one of critical issues with increasing population and automobiles in cities. Traffic jams not only cause extra delay and stress for the drivers, but also increase fuel consumption, add transportation cost, and increase carbon dioxide air pollution. The traffic controller is one of critical factors affecting the traffic flow. The conventional traffic patterns are nonlinear and complex. As a result, the fixed traffic light controller is not optimized to reduce the traffic jam. Moreover, it does not improve the response time for ambulances, fire trucks, police cars and other emergency vehicles. This paper is written with the endeavor to provide the readers an idea of the research that has been carried out in the intelligent traffic light field and the microcontroller based traffic light control system. The historical review of the technology and the recent development of the intelligent traffic light control system are stated in this paper. In addition, the future studies of the related work are also presented.

The escalating global population and increased vehicle usage have worsened traffic congestion in metropolitan areas, a significant urban challenge. Addressing this, adaptive traffic light control methods, especially at intersections, are being developed to improve traffic flow and reduce waiting times. This study significantly contributes to this field by implementing Fuzzy Logic in intelligent traffic light systems, focusing on Ankara's Polatlı Refik Cesur intersection. Using the SUMO simulation platform and Python programming, it analyzed waiting times and queue lengths. The initial phase used queue length for each intersection arm as an input. Fuzzy logic rules then determined the output, prioritizing street or phase order for optimal flow. The study further proposed an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based control plan. ANFIS merges neural network capabilities with fuzzy logic, using waiting time and queue length as inputs to regulate the green light duration. Compared to existing traffic systems, this model showed a substantial improvement. It achieved a 36.5% reduction in waiting times, underlining the efficiency of the Fuzzy Logic-based method. This approach not only enhances traffic management but also contributes significantly to the literature on intelligent traffic light control systems. By addressing key urban traffic issues, the study paves the way for future advancements in traffic management technologies. The findings highlight the potential of combining advanced computational methods, like ANFIS, with traditional traffic control techniques to optimize urban traffic flow, offering a blueprint for similar challenges in other metropolitan areas.

Traffic congestion has been causing many setbacks and challenges in the major developing and developed smart cities in India and all over the world. The typical traffic light sequence works on the specific switching of Red, Green and Yellow lights in a particular way with stipulated time irrespective of peak traffic hour and situation, this makes typical traffic light sequence method inefficient for present situation where density of traffic is exponentially increasing over the time. In this work, we propose and develop a design of Intelligent Traffic Light Control System to manage the road traffic according to the real time traffic situation. This work is intended to propose methods of dynamic time allocation for traffic lights according to traffic density and situation. In this paper, we acquired real time traffic density using image sensors and obstacle detector, where according to the traffic density, traffic controller dynamically generates “ON” and “OFF” time of traffic light sequence. The EDGE Spartan 6 FPGA (Field Programmable Gate Array) development board is programmed as controller to implement the proposed work and MATLAB programming in used to capture real time traffic image and generate dynamic time for the traffic light sequence to control the traffic situation in real time.

VANET-Based Intelligent Traffic Light Control System by Detecting Congestion Using Fuzzy C-Means Clustering Technique in a Smart City

The acceleration of urbanization has left cities around the world facing significant challenges, including traffic congestion and inefficient management. Intelligent transportation systems provide new technological means to address these issues, especially in traffic signal control, where advanced communication technologies play an important role in facilitating the process. The potential of long range radio (LoRa) technology in intelligent traffic signal control systems is attracting growing interest, largely due to its capacity for long-range transmission and low power consumption. This paper explores the potential benefits of LoRa technology in traffic flow monitoring and signal control by reviewing existing studies, especially in the field of long-distance transmission and low-power communication. The study indicates that LoRa technology can effectively boost the efficacy of traffic management, reduce traffic congestion, and provide technical support for the development of future intelligent transportation systems. Nevertheless, the stability of LoRa technology in high-density urban environments and its integration with other technologies still present challenges. Further research can investigate the anti-jamming ability, communication rate optimization, and its potential for wider application of LoRa technology in Intelligent Transport Systems (ITS) to promote more efficient urban traffic management

This research is devoted to the issue of regulating traffic congestion in major cities using light-dependent resistors coupled with the PIC16F877A microcontroller. This study proposes an intelligent traffic control system for T-Junctions, utilizing sensing and control to optimize traffic flow through dynamic phase adjustments and congestion reduction, enabled by a microcontroller-based decision-making system. The proposed system reduces traffic congestion, automates control, and enhances safety, minimizing accidents and lowering infrastructure costs. Under simulated environment, it demonstrates an average response time of 50 ms and achieves 99% accuracy in displaying the correct countdown. Finally, the number of state transitions handled per minute is 60 transitions per minute under normal traffic conditions.

A method is proposed for neural network prediction of congestion on road sections outside the control zones of transport detectors in the systems of “flexible” transport flow control at intersections with the traffic light regulation. The paper describes the operating principle of the original control system based on fuzzy logic. To develop a universal neural network solution that can be used to predict traffic on most road sections with common characteristics without the need for training for each case separately, it is proposed to identify 9 main types of sections and, accordingly, 9 neural networks. Each typical neural network differs in the amount of the input data and parameters. All neural networks are trained on samples obtained from analyzing traffic flow on road sections of each type. Typical characteristics of road sections, architecture, and parameters of neural networks are determined. Neural networks are trained and tested on the model data. Using computer simulation methods, the author assesses the control quality of the original intelligent traffic light control system, which has complete information about the transport flow (however, it is difficult to implement in practice), as well as a control system that uses a set of data obtained using transport detectors and supplemented with a neural network forecast. According to the experiment results, a system with a neural network forecast of congestion on road sections is inferior in quality of control by no more than 1.92% compared to an idealized system, which gives grounds to consider this method applicable in intelligent transport flow control systems at intersections with traffic light regulation.

Parking and running red lights are the major problems in traffic violations. This study presents two application systems for finding parking spaces and controlling traffic lights. An intelligent traffic light system adaptively controls the time of traffic lights so that the period of green lights is adjusted according to the real-time traffic flow. The traffic jam is thus effectively reduced. An intelligent parking system is also implemented to help drivers find parking spaces efficiently. Initially, a web camera is utilized to capture images. Hence, vehicles are identified to compute the remaining number of parking spaces. A website shows the remaining parking spaces. This system can help a user to find suitable parking spaces quickly. Therefore, parking violations are reduced. The YOLO (You Only Look Once) neural network is employed to identify vehicles, where the networks are trained through transfer learning. Only a small quantity of training images is required. The experimental results show that the vehicle identification accuracy rate can reach 93%, the recall rate reaches 83%, and the overall F measure reaches 87.7%. Therefore, the vehicle identification system can be applied to intelligent traffic light control and roadside parking spaces.

Traffic congestion has been a major problem for almost every city around the globe. Most of those cities get their traffic even worse in rush hour. In order to address the limitation of existing traffic light which is operated using fixed-time intervals, many researchers have been developing an intelligent traffic light that works in real time based on a machine learning algorithm. Unfortunately, it takes a lot of time to process the algorithm as the traffic is getting worse. In this paper, we propose a Q-Learning Accelerator Architecture for Intelligent Traffic Light Controller. It allows a faster learning time computation. Furthermore, the controller will allow traffic lights to work adaptively in real time. This architecture is compatible with a 4-arm crossroad using 4 possible actions which are traffic light signals operation. This architecture has been successfully implemented on PYNQ-ZI and achieve maximum clock frequency of 100 MHz. The achievable clock offers acceleration ratio approximately 1400 times against fully software Q-Learning on 2.3 GHz Intel Core i5 With 8 GB Memory. In addition, we also perform HW/SW co-verification by integrating HW design to the traffic simulator in order to validate system in realistic condition. Evaluation result shows that the proposed system offers better performance compare to conventional method, specifically can maintain the number of waiting vehicles. This result corresponds to reduction of congestion for around 75%.

The paper describes the design of an intelligent auto traffic signal control system. Traffic congestion is the major issues that would be considered. At the junctions of the road, vehicular traffic intersects and it can be controlled by the traffic signals. One of the main need of the traffic signal is that good coordination and control among the vehicles. The traffic on the road is very high during the peak hours. This leads to the emergency vehicles to stick in the traffic jam. Dynamic control of traffic is needed during peak hours. Hence we propose an automatic traffic light control for emergency vehicles. The traffic light in the path of any emergency vehicles is controlled. It does not depend on the way of monitoring people to be manual which results in time delay. So, Human life is not affected due to the time delay in the arrival emergency vehicles. To control the traffic, the man power is not required in this method. The emergency vehicles find the shortest path to the location of the accident. If the train crosses the junction, the emergency vehicle gets alerted before five km. In front of the zebra line crossing, a divider can be created for safety crossing of people. The system uses AT mega 8micro controller, IR sensors. This system is used to control the traffic lights. And also save the time in emergency periods.

Traffic lights at crossroads help regulate the flow of vehicles so they can run smoothly and avoid congestion. One of the causes of traffic congestion is a traffic control light system that is not optimal because it still uses a fixed timer system, so the lights' duration is less efficient in its application. When the green light is on, the streets are deserted. But there is a traffic jam on another road. Therefore the concept of traffic light timing needs to be developed to get efficient timing. This study aims to create a traffic light control system that uses a PIR (Passive Infrared) motion sensor and an ultrasonic sensor based on the Arduino Mega2560 microcontroller based on the congestion level. The method used in this traffic control prototype is an experimental method of reading vehicle objects mounted with PIR sensors and ultrasonic sensors. This system can detect the movement of vehicles approaching a traffic light, measure the distance between the car and the traffic light, and set the timing of the light signal according to the level of congestion detected. This system has been tested using vehicle movement simulations brought closer to the PIR and ultrasonic sensors at different distances. The test results show the system can work properly and produce the light signal according to the detected traffic conditions. For its application to traffic lights in the future, it is feasible to help the traffic unit.

Deep reinforcement learning based intelligent traffic light is an increasing tendency for improving the traffic condition due to its promising control performance. The current works about intelligent traffic lights tend to control the signal phase or phase time under fixed-lane condition. However, the fixed-lane condition cannot fit the different vehicle flow very well, thus the traffic condition cannot be enhanced further. In this paper, for improving the traffic condition further, we extend the existing intelligent traffic lights to the two-dimension control problem where the agent needs to control not only the traffic light signal, but also the direction of changeable lanes. As a result, the traffic light signal and lane allocation satisfy the need of current vehicle flow simultaneously. Furthermore, for the joint problem of lane allocation and the control of traffic light control, we develop a Q-network structure to build its deep reinforcement learning model. The simulation results demonstrate that the performance of the proposed method outperforms that of the existing intelligent traffic light with fixed lane.

Present Traffic Light Controllers (TLC) are based on microcontroller and microprocessor. These TLC have limitations because it uses the pre-defined hardware, which is functioning according to the program 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 car uses more fuel. To make traffic light controlling more efficient, we exploit the emergence of new technique called as "Intelligent traffic light controller". This makes the use of Sensor Networks along with Embedded Technology. The timings of Red, Green lights at each crossing of road will be intelligently decided based on the total traffic on all adjacent roads. Thus, optimization of traffic light switching increases road capacity and traffic flow, and can prevent traffic congestions. GSM cell phone interface is also provided for users those who wish to obtain the latest position of traffic on congested roads. This is a unique feature of this project which is very useful to car drivers to take an alternate route in case of congestion. The various performance evaluation criteria are average waiting time, average distance traveled by vehicles, switching frequency of green light at a junction, efficient emergency mode operation and satisfactory operation of SMS using GSM Mobile. The performance of the Intelligent Traffic Light Controller is compared with the Fixed Mode Traffic Light Controller. It is observed that the proposed Intelligent Traffic Light Controller is more efficient than the conventional controller in respect of less waiting time, more distance traveled by average vehicles and efficient operation during emergency mode and GSM interface. Moreover, the designed system has simple architecture, fast response time, user friendliness and scope for further expansion.

Traffic congestion has become a major problem in the cities where the population and the number of vehicles are continuously increasing. To ensure the safety of people, traffic light systems are incorporated at all necessary intersections of roads. Most of the traffic control systems used are now working based on fixed time for vehicular movement. So, a smart traffic control system is built which changes the waiting time of the vehicles based on situations, conditions, and priorities. Smart traffic system considers the vehicular density and special conditions such as the presence of an emergency vehicle at any of the roads and increases the waiting or clearance time accordingly. The system works such that, more the density of traffic at any side of the road, the longer would be the clearance time of that road with an increase in the duration of green light so as to allow more vehicles to pass through and hence reduce the traffic density. This avoids traffic congestion, wastage of time, and makes the system more efficient. A priority is given to the passage of emergency vehicles to prevent delays due to traffic congestion. So, the system is built such that during the presence of an emergency vehicle, the traffic light remains green till the emergency vehicle passes the signal and then returns to normal working. The system also considers the prevention of accidents and safety of the pedestrians during the night, when the traffic signals have only Amber lights, by use of sensors to detect any presence. The traffic lights along with alarms are used to indicate such presence. The system is built using Proteus software as a simulation tool for single-way and four ways traffic signals. Thus, the proposed work aims for a better and efficient traffic control system.

Using the improved NaSch traffic model, we simulate the traffic flow on the crossroad under open boundary condition. The simulations are controled by ordinary traffic light and intelligent traffic light respectively. The results show that the average speed and the traffic flux under control of the later are greater than that controled by the former. Moreover, the vehicles controled by intelligent traffic light can cut down the unnecessary waiting time.