Adaptive Traffic Light Control Research Articles

A Fuzzy Based Intelligent Traffic Light Control (ITLC) Method: An Implementation in Ankara City

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.

Adaptive Traffic Light Control With Deep Reinforcement Learning: An Evaluation of Traffic Flow and Energy Consumption

Adaptive Traffic Light Control With Deep Reinforcement Learning: An Evaluation of Traffic Flow and Energy Consumption

МОДЕЛИРОВАНИЕ АДАПТИВНОГО РЕЖИМА РАБОТЫ РЕГУЛИРУЕМОГО ПЕРЕСЕЧЕНИЯ В СОСТАВЕ МАГИСТРАЛЬНОЙ АСУДД

It is recommended to use modelling with the help of specialized software products to make managerial decisions about traffic control on urban streets. The object of the study is the regulated intersection of Tobolskiy Trakt St. and Sudoremontnaya St. in Tyumen. During the simulation micromodelling using the SmartAdaptive+ software product for potential implementation of adaptive traffic light control, we show that the current scheme of coordinated control can be supplemented with adaptive control at one of the nodes to minimize delays in both the main and crossing directions. Vehicle delays and maximum queue lengths under adaptive and tight regulation were calculated and compared using the statistical test method. With a maximum permissive phase of 75 s on the major direction and of 20 s on the minor direction, the shortest delays will be observed on both directions of intersection during the day.

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.

Distributed model-free adaptive predictive control of traffic lights for multiple interconnected intersections

Distributed model-free adaptive predictive control of traffic lights for multiple interconnected intersections

Adaptive traffic light control using deep reinforcement learning technique

Adaptive traffic light control using deep reinforcement learning technique

S-Edge: heterogeneity-aware, light-weighted, and edge computing integrated adaptive traffic light control framework

S-Edge: heterogeneity-aware, light-weighted, and edge computing integrated adaptive traffic light control framework

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.

Context-Aware Multiagent Broad Reinforcement Learning for Mixed Pedestrian-Vehicle Adaptive Traffic Light Control

Efficient traffic light control is a critical part of realizing smart transportation. In particular, deep reinforcement learning (DRL) algorithms that use deep neural networks (DNNs) have superior autonomous decision-making ability. Most existing work has applied DRL to control traffic lights intelligently. In this article, we propose a novel context-aware multiagent broad reinforcement learning (CAMABRL) approach based on broad reinforcement learning (BRL) for mixed pedestrian-vehicle adaptive traffic light control (ATLC). CAMABRL exploits the broad learning system (BLS) established in a flat network structure to make decisions instead of a deep network structure. Unlike previous works that consider the attributes of vehicles, CAMABRL also takes the states of pedestrians waiting at the intersection into consideration. Combining with the context-aware mechanism that utilizes the states of adjacent agents and potential state information captured by the long short-term memory (LSTM) network, agents can make farsighted decisions to alleviate traffic congestion. The experimental results show that CAMABRL is superior to several state-of-the-art multiagent reinforcement learning (MARL) methods.

DESAIN SISTEM KONTROL PENANGGULANGAN KEMACETAN LALU LINTAS ADAPTIF BERBASIS PLC

Traffic lights play an important role at every intersection in minimizing congestion, especially at intersections three and four. Today, the most common setting for traffic lights involves setting a fixed time. This pattern applies throughout the day, although the density of vehicles at each crossing is different. As a result, traffic jams on one side of the intersection are often unavoidable. Based on these problems, in this study an adaptive traffic light control system based on Programmable Logic Controller was built. In this test, the amber and green lights are set to about 3 and 5 seconds, respectively. During crowded situations, the duration of the green light is reduced to 10 seconds. It has been determined through testing that when the traffic light sensor detects a traffic jam, the green light will be on for 10 seconds, while under normal conditions it will be on for 5 seconds.

Traffic Behavior Recognition from Traffic Videos under Occlusion Condition: A Kalman Filter Approach

Real-time traffic data at intersections is significant for development of adaptive traffic light control systems. Sensors such as infrared radiation and GPS are not capable of providing detailed traffic information. Compared with these sensors, surveillance cameras have the potential to provide real scenes for traffic analysis. In this research, a You Only Look Once (YOLO)-based algorithm is employed to detect and track vehicles from traffic videos, and a predefined road mask is used to determine traffic flow and turning events in different roads. A Kalman filter is used to estimate and predict vehicle speed and location under the condition of background occlusion. The result shows that the proposed algorithm can identify traffic flow and turning events at a root mean square error (RMSE) of 10. The result shows that a Kalman filter with an intersection of union (IOU)-based tracker performs well at the condition of background occlusion. Also, the proposed algorithm can detect and track vehicles at different optical conditions. Bad weather and night-time will influence the detecting and tracking process in areas far from traffic cameras. The traffic flow extracted from traffic videos contains road information, so it can not only help with single intersection control, but also provides information for a road network. The temporal characteristic of observed traffic flow gives the potential to predict traffic flow based on detected traffic flow, which will make the traffic light control more efficient.

Анализ эффективности способов приоритета автобусам при проезде перекрестков с применением адаптивного управления светофорами

In recent years in the cities of the Russian Federation, within the framework of the national project “Safe and high-quality roads” the concept of “priority development of public transport” has been actively implemented and Intelligent transport systems have been created. The article discusses ways to prioritize public transport when passing through intersections using adaptive traffic light control as a possible alternative to a lane for route vehicles. The aim of the study is to reduce the loss of time for road users by determining the best ways to prioritize buses when passing regulated intersections, taking into account the number of passengers in public transport and the loss of time for all traffic participants. During the study, field observation methods, mathematical and simulation modeling were used. The account of the moments of the approach of buses to the regulated intersection and the analysis of the loss of time for buses when passing through the section on the central street of Tyumen was carried out. The scientific novelty lies in establishing the dependencies of the average speed of the bus on the approach to the intersection from the moment until the end of the traffic signal, the delay time of the buses on the number and moment of stopping the buses when passing the intersection. With the help of simulation micromodeling, the dependence of the delay time of buses and cars on traffic intensity was established for five options for priority to public transport. Two variants of priority for buses when passing through intersections are based on the methods of adaptive control of traffic lights and the algorithms “stage recall” and “green extension”. The use of a dedicated lane and the “stage recall” method at high traffic intensity reduces the delay time for buses by 94%, for personal vehicles – increases by 85%. At low traffic intensity, the delay time changes by 11 and 6%, respectively, for buses and cars. The use of the “green extension” method allows to reduce the total delay time by 3.2% with a high traffic volume (1900 veh/h) and a small number of buses (40 veh/h) and has a greater effect than the lane for fixed-route vehicles (deterioration by 148%). To determine the most efficient priority method, it is proposed to take into account the number of passengers in public transport. The dependence of the total delay time of all traffic participants on the traffic intensity of cars and buses with different priority methods has been established. This approach allows us to further determine the area of rational application of priority methods for public transport with the target function of reducing the loss of time for all traffic participants.

Traffic Signal Control With Reinforcement Learning Based on Region-Aware Cooperative Strategy

With the increase of private cars, traditional traffic signal control methods cannot alleviate the traffic congestion problem. Reinforcement learning (RL) is increasingly used in adaptive traffic light control. As urban traffic becomes more complex, reinforcement learning algorithms solely based on value or policy are not suitable for such scenarios. Moreover, the centralized method does not show a good effect on the multi-intersection, in particular, when the traffic flow is in the high-dimensional continuous state space. In this article, we propose a decentralized framework based on the advantage actor-critic (A2C) algorithm by assigning global control to each local RL agent or intersection. A2C algorithm involved in this article is a product of combining policy function and value function, which has good convergence ability and can be applied to continuous state space. The decentralized methods may put a new challenge: from the perspective of each local agent, the environment becomes partially observable. We overcome this problem by putting forward a region-aware cooperative strategy (RACS) based on graph attention network (GAT), which can incorporate the spatial information of the surrounding agents. We carry out experiments on synthetic traffic grid and real-world traffic network of Monaco city to compare with the existing A2C and Q-learning algorithms. Experimental results confirm that our RACS method has a shorter queue length and less waiting time than the two existing algorithms, and can reduce the total vehicle travel time.

Adaptive Traffic Light Controller Based on Congestion Detection Using Computer Vision

The transportation sector plays an important role in realizing a smart city. The increase in the number of vehicles is currently not supported by an increase in road capacity. Traffic jams or congestion will occur in many places. Congestion will increase the accident rate, bad effect on economic growth, and increase gas emissions. Effective traffic management is necessary to reduce congestion levels and its side effects. A traffic light is one of traffic management methods. Traffic lights control the flow of traffic at road intersections, zebra crossings, and other traffic flow points. Conventional traffic lights work on a pre-programmed time sequence. This system is effective if the vehicle density is relatively constant. The density of vehicles from various directions fluctuates with time. To increase the effectiveness of using traffic light, an adaptive system is needed. In this study, a simple adaptive traffic light mechanism was developed based on congestion on the road using computer vision. Vehicle congestion is detected using the YOLOv3 object detection which detects the type of vehicle. The detection system used by YOLOv3 with pretrained weight COCO has a true positive value for motorbikes of 60%, cars (light vehicles) 93%, and trucks/buses (heavy vehicles) 100%. The processing speed of the Jetson Nano mini-PC with the OpenCV library on the GPU is 2 times faster than the process with the CPU.

Smart traffic light control system using image processing

Traffic congestion has become a serious issue due to the growing number of vehicles in Malaysia. Traffic light control system is widely used to control the flow of road junction. Currently, most of the traffic light system used pre-time and count down timers to control traffic flow. Due to the fixed-time setting, often the system unable to handle unexpected heavy traffic flows and cause traffic jam. Thus, there is a need of adaptive traffic signals which are able to do real time monitoring to control traffic light signal based on traffic density. This study proposed an adaptive traffic light control system that uses image processing and image matching technique in controlling the traffic in an effective manner by taking images of each lane at a junction. The density of traffic in the images at each junction are compared. Results show that more time are allocated for the vehicles on the densest road to pass compared to other less dense road. Edge operation detector is used to detect the density of traffic at each lane. In this study, a comparison study was carried out by applying five different edge detectors namely Roberts, Sobel, Log, Canny and active contour. Among these detectors, Canny edge operation detector has found to be the best as it could extract actual edges with average time of 0.453 second.

A Vision-Based Real-Time Adaptive Traffic Light Control System Using Vehicular Density Value and Statistical Block Matching Approach

Abstract In India, traffic control management is a difficult task due to an increment in the number of vehicles for the same infrastructure and systems. In the smart-city project, the Adaptive Traffic Light Control System (ATLCS) is one of the major research concerns for an Intelligent Transportation System (ITS) development to reduce traffic congestion and accidents, create a healthy environment, etc. Here, we have proposed a Vehicular Density Value (VDV) based adaptive traffic light control system method for 4-way intersection points using a selection of rotation, area of interest, and Statistical Block Matching Approach (SBMA). Graphical User Interface (GUI) and Hardware-based results are shown in the result section. We have compared, the normal traffic light control system with the proposed adaptive traffic light control system in the results section. The same results are verified using a hardware (raspberry-pi) device with different sizes, colors, and shapes of vehicles using the same method.

Adaptive Traffic Light Control With Quality-of-Service Provisioning for Connected and Automated Vehicles at Isolated Intersections

Today’s traffic intersections primarily use fixed-cycle signaling, which is highly suboptimal and aggravates congestion and waste of fuel in urban areas. In addition, it offers minimal quality-of-service guarantee and makes travel time prediction hard. In this paper, we study how traffic signal scheduling can be done in order to not only reduce average wait time but also provide certain worst-case wait time guarantee to Connected and Automated Vehicles (CAVs). First, we derive the time a CAV spends at an intersection in the best-case and the worst-case, respectively. Using the worst-case wait time result, we propose two novel controllers which assign deadlines to CAVs based on the system state and then perform traffic signal control to meet these deadlines. We use the Simulation of Urban MObility (SUMO) simulator to implement and verify the performance of the proposed controllers. The simulation results show that they not only can guarantee the deadlines, but also outperform other methods such as the default and the delay-based controllers in terms of average delay in all scenarios.

Optimization of adaptive traffic light control modes based on machine vision

Urbanization leads to a significant increase in traffic density in large cities. The growing transport concentration is accompanied by an increase in traffic congestion and emissions of harmful substances. The policies and decisions developed by the authorities, such as the expansion of existing roads and construction of new roads, as well as increase in transport taxes, no longer make it possible to maintain the adequate mobility of the population. One of the solutions is to increase the efficiency of road infrastructure utilization by forecasting the traffic situation and using the adaptive adjustment of traffic lights operation. With dynamic collection and interpretation of traffic flow data from traffic monitoring cameras, it will be possible to use the dynamic parameters of vehicles as indicators for the adaptive adjustment of traffic light regulation. The first part of the paper describes the use of machine vision and a neural network (YOLOv4) in tracking the parameters of traffic flows on road sections in front of intersections. The second part of the paper presents a methodology based on the dynamic regulation of traffic lights cycles and their duration, taking into account the current and forecast parameters of the traffic flow. The algorithm for the adaptive adjustment of traffic lights regulation considers the following parameters: the number and dynamic dimensions of vehicles moving towards the intersection; the number of vehicles in the queue in front of the stop line and their acceleration at the start of the movement. We determined the relationship of the intersection capacity when driving straight ahead with the dynamic dimensions of vehicles and the formation of a queue in front of the stop line, waiting for the green light. The study resulted in the development of an algorithm for setting the duration of both a particular phase and the entire traffic lights cycle in the tasks of eliminating or minimizing the possibility of congestion.

Coordinated Signal Control System in Urban Road Network

Road congestion has become one of the major problems of urban cities due to the growth in the number of vehicles and the limitation of road infrastructure. The consequences of this congestion can be seen through a difficult movement of vehicles, an increase in CO2 emissions, increasing energy consumption, and stress for drivers. Intelligent Transport Systems (ITS) based on wireless sensor networks (WSN) have emerged with new solutions and application potentials in the context of the Intelligent city. In this article, we study an approach of using a wireless sensor network to control the traffic lights of several intersections and optimize their performance by reducing the average waiting time of users. the proposed adaptive traffic light controller determines the traffic lights sequence in real time based on the current traffic situation at the local intersection, the expected arrivals and the degree of congestion at destination intersections. To evaluate our approach, we construct an intersection network of twenty-four intersections with high traffic intensity using the SUMO simulator, the results of the simulation demonstrate the efficiency and practicality of the proposed algorithm.

Dynamic Traffic Light Control

Intelligent Transport System (ITS) is blooming worldwide. The Traditional Traffic management system is a tedious process and it requires huge man power, to overcome this we have proposed an automatic Traffic monitoring system that has effective fleet management. The current transportation system at intersections and junctions has Traffic Lights with Fixed durations which increase the unnecessary staying time which intern harms the environment. An Adaptive traffic light control is implemented using SUMO simulator, that changes the duration of Green and Red light according to the traffic flow. This is an effective and efficient way to reduce the Traffic congestion. The traffic congestion is determined by taking the object count using deep learning approach (Convolutional Neural Network).