Traffic Light Control System Research Articles

Street Light Controller Including Automatic Traffic Light Controller System Implementation on FPGA

— The number of vehicles has greatly expanded over time and is continually growing. Due to traffic congestion, a sizable amount of working hours must be spent on the roads, which has an indirect negative influence on the economy and leads to preventable road accidents that end in fatalities. While metropolitan infrastructures are developing much more slowly, the number of vehicles is rising. We require a system that can successfully manage such a circumstance. A system that can efficiently manage traffic is what we call an automatic traffic control system. Moreover, a system for automatic street lights is built within. This suggested concept tries to automate traffic regulation based on density and a controller for automatic street lights that only come on at night. It detects vehicles by using IR (Infra Red) sensors in every direction. IR sensor modules for traffic lights and an LDR sensor module for streetlights provide the inputs, respectively. Verilog HDL is used to implement the solution, and Xilinx Vivado is used to simulate it. An FPGA has this suggested model pre-programmed (Field Programmable Gate Array).

EVATL: A novel framework for emergency vehicle communication with adaptive traffic lights for smart cities

AbstractFixed cycle traffic lights primarily regulate road traffic, in which traffic light control systems are for specific lanes or crossings in urban areas. Also, not being appropriately installed can prolong the congestion delay and unnecessarily long wait times for crossing intersections, which can cause emergency vehicles to become stuck at intersections. Adaptive signal timing management technique that is more computationally viable than current fixed cycle signal control systems and can improve network‐wide traffic operations by reducing traffic delay and energy consumption. Even though specific adaptive control systems exist, there is no mechanism to communicate with emergency vehicles, which is crucial for smart cities. Motivated by this problem, a novel framework, Emergency Vehicle Adaptive Traffic Light (EVATL), is proposed for smart cities where an adaptive mode of operation for traffic lights is employed with emergency vehicle communication, improving their functioning and reducing overall congestion delay. EVATL detects emergency vehicle location using GPS with the Internet of Things(IoT), which integrates with traffic signals and works adaptively according to vehicle density at the traffic signal using YOLOv8. So, the primary goal of the proposed EVATL is to prioritise an emergency vehicle while simultaneously integrating adaptive traffic signals for smart cities. A GUI is developed for evaluating the proposed model by creating different scenarios for an adaptive traffic light and emergency vehicle communication. While analysing the simulation results of the proposed model EVATL, a clear improvement can be seen in the wait time of vehicles at a traffic light with the timely detection of an emergency vehicle at a set distance.

AI-based YOLO V4 Intelligent Traffic Light Control System

With the growing number of city vehicles, traffic management is becoming one of the most persistent challenges. Traffic bottlenecks cause more significant disturbances in our everyday lives and raise stress levels, negatively impacting the environment by increasing carbon emissions. Due to the population increase, megacities are experiencing severe challenges and significant delays in their day-to-day activities related to transportation. An intelligent traffic management system is required to assess traffic density regularly and take appropriate action. Even though separate lanes are available for various vehicle types, the effective result of wait times for the Commuters at the traffic signal point is not reduced. The proposed methodology employs Artificial Intelligence to collect live images from signals to address this issue in the current system. This approach calculates traffic density, utilizing the image processing technique YOLOv4 for effective traffic congestion management. The YOLOv4 algorithm produces better accuracy in the detection of multiple vehicles. Intelligent monitoring technology uses a signal-switching algorithm at signal intersections to coordinate time distribution and alleviate traffic congestion, resulting in lower vehicle waiting times.

ADAPTIVE TRAFFIC CONTROL AT THE INTERSECTION

The solution to the problem is a typical technology of a smart cross-logic system. Due to the rapid growth in the number of cars in the city, various problems such as congestion and environmental pollution are increasingly emerging.
 Adaptive intersection management system, often implemented to solve the problem of congestion in the city. Constantly used control systems are usually considered illogical, that is, the traffic light signal changes statically. The purpose of this work is to present an adaptive system of logical control of the intersection, i.e. changing traffic light signals in real time depending on the type of vehicles.
 This system is an example of an adaptive automotive communication model that implements data representation between vehicles and traffic lights. When crossing with cars at an intersection, it sends an instant message to the traffic light, after which the traffic light corrects the information and adjusts the signal time in real time. Each traffic light in both directions has its own management strategy, without requiring orthogonality. This traffic light control system allows you to maximize the number of cars crossing the intersection and, as a result, reduce congestion and pollution. The algorithm of traffic light control based on the type of car and its modeling is proposed. The safety and reliability of this control system are also discussed.

Simulation model of an intelligent transportation system for the “smart city” with adaptive control of traffic lights based on fuzzy clustering

This article presents a new simulation model of an intelligent transportation system (ITS) for the “smart city” with adaptive traffic light control. The proposed transportation model, implemented in the AnyLogic, allows us to study the behavior of interacting agents: vehicles (V) and pedestrians (P) within the framework of a multi-agent ITS of the “Manhattan Lattice” type. The spatial dynamics of agents in such an ITS is described using the systems of finite-difference equations with the variable structure, considering the controlling impact of the “smart traffic lights.” Various methods of traffic light control aimed at maximizing the total traffic of the ITS output flow have been studied, in particular, by forming the required duration phases with the use of a genetic optimization algorithm, with a local (“weakly adaptive”) switching control and based on the proposed fuzzy clustering algorithm. The possibilities of optimizing the characteristics of systems for individual control of the behavior of traffic lights under various scenarios, in particular, for the ITS with spatially homogeneous and periodic characteristics, are investigated. To determine the best values of individual parameters of traffic light control systems, such as the phases’ durations, the radius of observation of traffic and pedestrian flows, threshold coefficients, the number of clusters, etc., the previously proposed parallel real-coded genetic optimization algorithm (RCGA type) is used. The proposed method of adaptive control of traffic lights based on fuzzy clustering demonstrates greater efficiency in comparison with the known methods of collective impact and local (“weakly adaptive”) control. The results of the work can be considered a component of the decision-making system in the management of urban services.

A Novel High-Priority Traffic Light System Based on a Smart Technique

In the world today, traffic congestion in most cities is a severe problem. Recently, the system used is based on a fixed-time system in traffic, which cannot provide incompetence if one or more lane has higher traffic than others. The higher traffic on one side lane needs a longer operation time for a green signal as compared to the fixed time-based systems. This paper proposed a High Priority Traffic Light system HPTL as a technique to compute the period time of green and red lights based on the density of traffic present at that time. To fix this problem an automatic traffic light control system should proposed. The proposed system will be achieved by using PIR proximity Infrared Sensors. When the density is computed, the luminous period of the green and red signal will be selected to help the microcontroller. On each roadside, the sensors can detect the traffic density and calculate the vehicles in a specific lane then forward the density value to the microcontroller. The microcontroller will select the free lane and the high-traffic lane and then assign the priority system. The proposed system is successful in investing time, reducing vehicle density, and increasing reliability.

Review on Traffic Light Control Systems Based on Single-Chip Microcomputers and Smart Traffic Light Control Systems

With the development of the economy and the improvement of living standards, the number of private cars is increasing, which brings about traffic congestion and increased traffic safety hazards. Although some cities have widened some urban roads, traffic congestion and traffic safety hazards have not been effectively alleviated, and the speed of road construction and urban construction is far from meeting the needs of citizens. Therefore, traffic congestion is the primary problem that needs to be solved at present. This paper introduces single-chip microcomputers with their functions and applications as well as designs for traffic light control systems based on microcomputers. By comparing previous research designs and models, this paper points out the comparisons between different models and provides research suggestions and future prospects.

Heterogeneous traffic intersections control design based on reinforcement learning

AbstractTraffic light control is a cost‐effective method to alleviate traffic congestion and deep reinforcement learning (DRL) that is increasingly favored as a method for real‐time traffic light control. However, the complexities of modern urban intersections, including crossroads and T‐junctions, pose challenges for DRL‐based traffic light control systems that do not work well for such heterogeneous intersections. To address this problem, a Heterogeneous Advantage Actor‐Critic (HA2C) model is proposed to control traffic lights for heterogeneous intersections. First, HA2C employs an intersection structure transformation scheme to mask intersection heterogeneity. Second, it develops a two‐stage approach on top of an Advantage Actor‐Critic (A2C) reinforcement learning model to learn both general and structure‐specific policies, leading to more accurate decisions. The extensive simulations on both synthetic and real‐world maps demonstrate that HA2C outperforms the state‐of‐the‐art models in terms of higher throughput and faster travel time, while using a smaller model size in most scenarios.

Smart Traffic Light Control System with Automatic Vehicle Speed Breaker

The aim of the project is to design a smart traffic light control system interfaced with a barrier gate and an LDR based traffic light control system. When the signal timing changes a delay is provided with the help of a microcontroller, pop up barrier before the zebra crossing will open or close to allow vehicles to pass. When the signal is red the interfaced barrier gate closes and a buzzer notifies the closing of gate, thereby blocking the traffic but when the signal is green the same barrier opens and allows a proper flow of vehicles to avoid traffic congestion. We provide a time delay for all signals to change and buzzer action by using ESP32 microcontroller. ESP32 microcontroller is used for signal timing change as well as street light control according to LDR. Smart Street light which operates with the help of sensors Microcontrollers are programmed in such a way to adjust their timing and phasing to meet changing traffic conditions. The circuit, besides being reliable and compact, is also cost effective. This project presents the way of conservation of energy by reducing electricity wastage as well as to reduce the man power. The project uses light emitting diodes (LED) that do not consume an enormous amount of electricity. LDR is used as a sensor for the functioning of street lights effectively.

Cyber-Physical System for Smart Traffic Light Control.

In recent years, researchers have proposed smart traffic light control systems to improve traffic flow at intersections, but there is less focus on reducing vehicle and pedestrian delays simultaneously. This research proposes a cyber-physical system for smart traffic light control utilizing traffic detection cameras, machine learning algorithms, and a ladder logic program. The proposed method employs a dynamic traffic interval technique that categorizes traffic into low, medium, high, and very high volumes. It adjusts traffic light intervals based on real-time traffic data, including pedestrian and vehicle information. Machine learning algorithms, including convolutional neural network (CNN), artificial neural network (ANN), and support vector machine (SVM), are demonstrated to predict traffic conditions and traffic light timings. To validate the proposed method, the Simulation of Urban Mobility (SUMO) platform was used to simulate the real-world intersection working. The simulation result indicates the dynamic traffic interval technique is more efficient and showcases a 12% to 27% reduction in the waiting time of vehicles and a 9% to 23% reduction in the waiting time of pedestrians at an intersection when compared to the fixed time and semi-dynamic traffic light control methods.

MODELİNG OF TRAFFİC LİGHT CONTROL SYSTEMS

Traffic light control systems are commonly utilized to monitor and manage the flow of autos across multiple road intersections. Since traffic jams are ubiquitous in daily life, A crucial aim is to optimize the functioning of traffic signals for optimal traffic flow. Traffic light control systems aim to make sure automobiles flow smoothly along transit routes. However, considering the numerous parameters involved, synchronizing several traffic signal systems at neighboring junctions is a difficult challenge. Conventional systems are incapable of dealing with varied flows nearing intersections. Furthermore, the present traffic system does not account for reciprocal interference between nearby traffic signal systems, the asymmetry of automobile flow with time, accidents, the passage of emergency vehicles, and pedestrian crossings. This causes traffic congestion and jams. The goal is to provide an artificial intelligence-based method that enables adaptive management of traffic lights at an intersection to reduce traffic congestion in transportation. A simulation is also created to help visualize the problem and its solution. In this venture, an algorithm that enables adaptive control of congested crossings is presented to reduce traffic congestion in transportation networks. The algorithm modifies the optimal light timings to employ in the following cycle based on the number of cars in the junction and the rise in this number. To improve reliability, employing a doppler radar sensor to collect data from traffic is suggested. The transmitter pulses waves with the assistance of a doppler radar sensor, and the receiver listens for waves flowing back to the antenna as they strike objects in the atmosphere between each pulse. The distance between the antenna and the item (in this application, vehicles) may be measured simply by calculating the time it took for the wave to return to the antenna. The Doppler shift is used to illustrate the speed differential in traffic. The change in frequency or wavelength of a wave perceived by an observer moving relative to the wave's source is known as the Doppler shift. This is a common occurrence with sound and light waves. When the source of a wave approaches an observer, the frequency of the wave increases, resulting in a "blue shift." The frequency of the wave drops as the source moves away from the observer, resulting in a "redshift." As a result, the flexibility of each junction to select its signal lengths saves waiting times and guarantees that the intersection functions at peak performance. Keywords: Traffic control systems, intelligent traffic lights, modeling of traffic lights, traffic jam problems, reinforcement learning, doppler sensors.

Traffic Light Control Prototype Using PIR Motion and Microcontroller-Based Ultrasonic Sensors

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.

Design of Fuzzy and Conventional Controllers for Modeling and Simulation of Urban Traffic Light System with Feedback Control

Traffic patterns in urban areas present a complex and dynamic system that is characterized by inherent uncertainties. The presented study is a traffic light control system with feedback. The controller of the system is designed in a fuzzy and conventional way and is applied to a network of two junctions. The verification is performed using the MATLAB fuzzy toolbox platform (for designing the fuzzy controller) and Aimsun platform for microsimulation of the two junctions using the two types of controllers. To accomplish the control of the system a fuzzy controller on heuristic rules proposed to allow adaptive traffic control on signalized junctions in urban environments. The Fuzzy Toolbox in MATLAB is used to simulate the fuzzy controller. The Aimsun traffic simulator is used to model and simulate a traffic network of two intersections. The green light duration in the Aimsun model is based on the results for the two controllers from two separated experiments. Simulations of Aimsun models with the two types of controllers, the fuzzy and the conventional one, are compared. The experiment is performed under the premise that the traffic flow is oversaturated. Findings show that in a network of two junctions both controllers perform in a similar manner for the first junction. However, for the second junction, the fuzzy controller tends to have some advantages over the conventional controller with regard to higher traffic flow. In conclusion, the overall performance of the fuzzy controller is better than the one of the conventional controller, but further research is needed for more complex traffic networks.

IMPROVED TRAFFIC CONTROL SYSTEM THROUGH DETECTION AND CLASSIFICATION OF VEHICLES USING CAMERA BASED CROWD SENSING WITH DEEP LEARNING APPROACH.

Abstract - Intelligent traffic light control system can be considered as one of the main aspects of a smart city. There is significant increase in the number of People that own vehicles and this leads to traffic congestion in urban cities of developing countries. Traffic congestion is one of the highly challenging problems people face in their daily lives which affect their working productivity, causes accident, unnecessary delays and possibly loss of lives. The control of traffics based on the densities and classes of the vehicles using deep learning approach proved to be a better approach in handling traffic jams according to the current state of the studies. However, the current state of the studies did not have special consideration for the class of special vehicles (VIPs) which need special consideration due to their importance and tight schedules. Also, it did not consider allocating time based on the classification. Thus, a model to improve the detection and classification of vehicles on side of intersection as well as special consideration for VIPs was proposed. The proposed model was built using Faster R-CNN based on Tensorflow machine learning framework on a Google Colab environment. The model was able to detect and classify vehicles as “special vehicles (i.e. VIP), Emergency (EV) and Others” classes of vehicles, a time allocation simulator was proposed based on the specified classes with special timing for VIPs. Results show that using the proposed method, 94.43% accuracy was achieved.

Perancangan dan Pembuatan Alat Pemantau Lampu Lalu Lintas Simpang Lima Menggunakan Mikrokontroler Berbasis Web

Congestion has become a significant problem for road users, in particular, the occurrence of queues at intersections. Traffic lights have an important role related to the control system to regulate road users so that they are orderly in every queue at the crossroads. The current traffic light control system still uses a static timing system, so to solve this problem an innovation is needed in the form of making a traffic light monitoring device at a five-lane intersection using a web-based microcontroller as the input used to control road user traffic lights. The design of this tool uses a module with the ESP8266 series as a module capable of managing and sending data via internet media, by installing three LEDs each on five lanes which will be processed automatically, equipped with a buzzer component as a marker where traffic light signs are placed. . The circuit will be controlled using an Arduino type microcontroller with the ATmega2560 series. The results of the tests carried out show that the website that has been made can control LEDs, with a buzzer as a marker of information regarding the state of the traffic light, it can work with related provisions at intersections.

RATE: A model‐based testing approach that combines model refinement and test execution

AbstractIn this paper, we present an approach to conformance testing based on abstract state machines (ASMs) that combines model refinement and test execution (RATE) and its application to three case studies. The RATE approach consists in generating test sequences from ASMs and checking the conformance between code and models in multiple iterations. The process follows these steps: (1) model the system as an abstract state machine; (2) validate and verify the model; (3) generate test sequences automatically from the ASM model; (4) execute the tests over the implementation and compute the code coverage; (5) if the coverage is below the desired threshold, then refine the abstract state machine model to add the uncovered functionalities and return to step 2. We have applied the proposed approach in three case studies: a traffic light control system (TLCS), the IEEE 11073‐20601 personal health device (PHD) protocol, and the mechanical ventilator Milano (MVM). By applying RATE, at each refinement level, we have increased code coverage and identified some faults or conformance errors for all the case studies. The fault detection capability of RATE has also been confirmed by mutation analysis, in which we have highlighted that, many mutants can be killed even by the most abstract models.

EVALUATION OF THE EFFECTIVENESS OF THE TRAFFIC LIGHT COORDINATION PLAN ON NAUKI AVENUE IN KHARKIV

The paper presents the results of evaluating the performance measures of the newly developed and implemented in practice plan for coordinating the work of six local traffic lights on a 1.8 km long fragment of Nauki Avenue in Kharkiv. The analysis of existing methods of coordinated traffic light management has shown that at present they do not provide high performance and are roughly equal in effect to adaptive traffic light management methods. The main instrument of evaluation was the comparison of the results of the application of new parameters of traffic lights operation on the fragment of the street and road network, surrounding the coordinated section, with the traffic situation existing before its implementation. For this purpose, a study of travel time and the number of vehicle stops at intersections was conducted using the "floating car" method, and the speed of vehicles was calculated using TomTom service data. Each of these methods has its own advantages and disadvantages, and therefore they complemented each other in assessing the direct impact of the coordination plan on the state of traffic management on the studied fragment of the road network. The dual evaluation of the performance measures proved to be very appropriate in light of the operating conditions of the coordinated fragment of Nauki Avenue and the road network around it, which recorded a significant increase in traffic intensity during 2021 and after the implementation of the coordination plan in early 2022. The evaluation of its effectiveness in both ways convincingly showed the high efficiency of this measure, in terms of the number of stops and travel time, and the TomTom service data showed that even with an increase in traffic volume by almost one and a half times, the speed of cars in general increased by more than 14%. Taking into account the minimal costs for the implementation of the coordination plan, this testifies in favor of the means of arterial coordination in the Ukraine cities, as a priority direction for the development of local traffic lights systems in comparison with the creation of adaptive traffic light control systems. Keywords: coordination of traffic lights, urban arterial, travel time, vehicle speed.

The influence of the traffic light`s permissive signal share on the duration of traffic delays

Today, the motorization level and the value of traffic flow on the street-road network have increased. Often this leads to an excessive load on the existing traffic management system, highways, and roads. As a result, the number and duration of traffic delays are increasing, especially within populated areas. The consequence of this is the formation of traffic jams, increase of fuel and lubricants consumption, noise pollution, and emissions of exhaust gases. Another, no less critical consequence is an increase in traffic accidents, particularly at unsignalized intersections. Therefore, a traffic light control system is often implemented at street intersections to increase traffic safety and reduce the number of conflicting flows. In addition, this system should be optimized for signalized objects. The object of the study is a signalized intersection of multi-lane streets located in the city. The main problem that needs to be solved is the reduction of the vehicles` delay duration at the approaches to such intersections, in particular with the use of appropriate organizational measures and planning of the traffic light control system. According to the study's results, regularities were revealed regarding the change in the traffic's delay duration depending on the share of the permissive signal and the duration of the traffic light's cycle at the street intersection. At the same time, the received modulation results in the PTV Vissim software environment indicate an increase in delay values with an increase in the duration of the traffic light cycle. However, an equally important influence factor is the share of the permissive signal, as it reduces to a certain extent the duration of vehicles in standby mode at the approaches to the signalized intersection. The obtained results are recommended to be used in both developing new traffic organization schemes at signalized street intersections and improving existing ones.

Design and Implementation of a Traffic Control System Based on Congestion

Abstract: The traffic issues have garnered more and more attention on a global scale as the number of cars has grown. One of the biggest problems is the traffic congestion, also the fixed-time settings are still used by the majority of traffic systems today. These technologies are unable to dynamically alter the timing of traffic lights in response to heavy traffic. Thanks to technological advancements, sensors or cameras can now collect data on traffic volume and wait times. This study provided an illustration of a traffic light control system that can manage traffic according to the number of vehicles in each road. Additionally, it showed how the system was designed using the Proteus design suite software and how a prototype of the system was implemented using an Arduino Mega 2560 and an infrared sensor. Through the results obtained, the efficiency of the proposed system is clear by comparing it with the system that depends on the fixed time of traffic signals.

Evolutionary reinforcement learning multi-agents system for intelligent traffic light control: new approach and case of study

<span>Due to the rapid growth of urban vehicles, traffic congestion has become more serious. The signalized intersections are used all over the world and still established in the new construction. This paper proposes a self-adapted approach, called evolutionary reinforcement learning multi-agents system (ERL-MA), which combines computational intelligence and machine learning. The concept of this work is to build an intelligent agent capable of developing senior skills to manage the traffic light control system at any type of junction, using two powerful tools: learning from the confronted experience and the assumption using the randomization concept. The ERL-MA is an independent multi-agents system composed of two layers: the modeling and the decision layers. The modeling layer uses the intersection modeling using generalized fuzzy graph technique. The decision layer uses two methods: the novel greedy genetic algorithm (NGGA), and the Q-learning. In the Q-learning method, a multi Q-tables strategy and a new reward formula are proposed. The experiments used in this work relied on a real case of study with a simulation of one-hour scenario at Pasubio area in Italy. The obtained results show that the ERL-MA system succeeds to achieve competitive results comparing to urban traffic optimization by integrated automation (UTOPIA) system using different metrics.</span>