Traffic light optimization using non-dominated sorting genetic algorithm (NSGA2)

Intelligent Traffic Lights: Green Time Period Negotiaton

A simulation-based multiclass, multimodal traffic assignment model with departure time for evaluating traffic control plans of planned special events

State departments of transportation (DOTs) develop traffic control plans (TCPs) as part of construction projects. TCPs communicate required traffic control measures to the construction team and help ensure safe travel through construction work zones and a safe environment for construction workers. TCP quality can vary from project to project as a result of many factors, including the process by which the TCP is designed, reviewed, implemented, and inspected; the features and layout of the TCP and roadway; and the communication of the TCP to the constructor. This paper presents a research study conducted to understand how to improve work zone safety performance by improving the TCP design and implementation processes, identifying the TCP and roadway attributes that made TCP implementation challenging, and identifying factors that affected TCP quality and consistency. A sample of 39 construction projects of varying types and sizes was studied with a mixed methods research approach that included a survey of 12 key personnel on each project, a quantitative evaluation of each project's TCP, analysis of project crash data, and an on-site evaluation of the work zone. TCP quality and consistency and worker and motorist safety were found to be higher on projects for which constructability reviews and design peer reviews had been conducted and on projects on which the contractor super intendent and TCP designer had more years of experience and had undergone specialized TCP training. The TCP features found to increase TCP complexity to the greatest extent were multiple traffic control stages, multilane closures, and dense temporary signage.

Traffic congestion in cities includes the complex and dangerous passing of emergency vehicles, which is a time-consuming task. This problem requires the optimisation of traffic lights in favour of emergency vehicles. To accomplish this, this paper discusses an optimized traffic light system using machine learning that prioritizes the passing of emergency vehicles into city areas. It integrates SVM and Random Forest models by dynamically adjusting traffic light signals based on traffic density to accelerate emergency vehicles. The results reveal that the proposed system would lead to improved emergency response times while enhancing overall transportation efficiency with reduced congestion of traffic. Additionally, the study further went on to establish the effectiveness of the proposed model as a solution in traffic flow optimization and management. Results show that the performance of the proposed model is effective for the purpose of traffic light optimization. The SVM+SAFS and RF+SAFS methods figured prominently as high-performance methods with accuracy rates of 94.89% and 95.02%, respectively. Furthermore, in the case of the RF+SAFS method used for traffic light optimization, it was possible to reduce the average waiting time by 20%, increase the capacity of transit by 15%, and decrease fuel consumption by 10%. Overall, combining the outputs in the model led to the following performance, an 18% decrease in total travel time.

As the growth of vehicles and the acceleration of urbanization, the urban traffic congestion problem becomes a burning issue in our society. Constructing a software defined Internet of things(SD-IoT) with a proper traffic control scheme is a promising solution for this issue. However, existing traffic control schemes do not make the best of the advances of the multi-agent deep reinforcement learning area. Furthermore, existing traffic congestion solutions based on deep reinforcement learning(DRL) only focus on controlling the signal of traffic lights, while ignore controlling vehicles to cooperate traffic lights. So the effect of urban traffic control is not comprehensive enough. In this article, we propose Modified Proximal Policy Optimization (Modified PPO) algorithm. This algorithm is ideally suited as the traffic control scheme of SD-IoT. We adaptively adjust the clip hyperparameter to limit the bound of the distance between the next policy and the current policy. What's more, based on the collected data of SD-IoT, the proposed algorithm controls traffic lights and vehicles in a global view to advance the performance of urban traffic control. Experimental results under different vehicle numbers show that the proposed method is more competitive and stable than the original algorithm. Our proposed method improves the performance of SD-IoT to relieve traffic congestion.

This chapter provides an in-depth overview of the state of the art on traffic light control and optimization. Several classical control systems, methods, concepts, tools, and strategies are described, and their related pros and cons are discussed. Four different types of control strategies are considered, pretimed, actuated, adaptive, and self-organized, and it is demonstrated that some strategies can be appropriate for local control, area control, or both (local and area). Further, the chapter develops a system of coupled nonlinear oscillators, that is used for traffic light control and optimization both at isolated junctions (i.e., local control) and in a network of coupled traffic junctions (i.e., area control). The case of an isolated traffic junction is modeled by coupled oscillators, each of which represents a specific phase group of the traffic light at the junction. The case of a network of coupled traffic junctions is also considered, and we show the possibility of modeling the traffic light at each junction by a single oscillator. The system developed is viewed as a modified version of the self-organized Kuramoto model for traffic light control due to some important features that are common to both systems (i.e., Kuramoto model and the system developed). The main advantage of the system developed is the possibility of dynamically monitoring the signals’ phases (delays), signal-splits, or signal timings according to the dynamic variation of the traffic demand in all conflicting approaches of the traffic junctions under investigation. The system of coupled nonlinear oscillators is considered a flexible platform, which is appropriate for modeling the four types of traffic light control strategies. Another advantage of the system developed is the possibility of an easy hardware implementation using electronic devices. This allows a straightforward possibility for designing appropriate electronic prototypes of traffic light controllers (appropriate for both local and area controls).

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.

Insufficient capacity of the road network is a problem in most cities. This problem can be partially solved by optimizing the operation of the traffic light. Since selecting traffic light settings in real time can lead to a deterioration in the traffic situation, it is advisable to pre-configure the model and work out various traffic light operating modes on it, and select the optimal ones. The purpose of the study was to simulate changes in the average number of cars in a queue before a traffic light and the average travel time of an intersection. To achieve this goal, the following tasks are formulated: creating a model of a road section with real flows at the entrances; determining the average number of cars in a queue under different traffic light operating modes; creating an experiment to find the average time of a left turn with and without the introduction of a specialized left-turn phase of the traffic light. Materials and methods. To solve the tasks set, the method of using the AnyLogic environment for analyzing a traffic section has been tested, and the possibility of using simulation modeling to identify the dependence of the intersection's capacity on the operating mode of the traffic light has been shown. Results. In the course of the research, a model of the existing intersection was created and the dependence of the number of cars in the queue in front of the forbidding traffic light signal on the duration of its full cycle was considered. To conduct the experiment, the existing 84-second traffic light cycle was changed to 30 seconds, one, two, three, and five minutes. To visualize the obtained dependencies, a graph is constructed that reflects the number of cars in a traffic jam under different traffic light operating modes. The created model is also used to determine the dependence of the travel time of an intersection at a left turn on the number of turning cars and the number of cars in the opposite direction. Traffic light options with and without an additional left signal are considered. The results are presented in a table and graphically. Conclusion. The study showed that with a small amount of oncoming traffic, the introduction of a specialized left-turn phase of the traffic light increases the travel time of the intersection, but with an increase in oncoming traffic, the situation changes to the opposite.

In order to identify the spatial distribution of congested roads in the road network, a traffic congestion state discrimination method suitable for road network is proposed in this paper. Firstly, the regional road network is discretized and the fuzzy comprehensive evaluation method is used to calculate traffic congestion index based on the collected traffic indicator data. Then, the spatial distribution of traffic congestion in the road network is described by calculating variation coefficient of the road network and the Moran's Index. Simulation results show that the method can effectively identify the spatial distribution of the congestion state in the road network, and provide guidance for urban traffic control and traffic planning.

The main concern of this research is to control traffic flow and monitor highways by installing wireless sensors. Therefore, a new multi-objective model is proposed to find the optimal location of wireless sensors along highways. The sensors are paired and each pair of sensors communicates and interacts with each other to receive information from passing cars. The speed estimation of the vehicles passing through this pair of sensors must have the minimum deviation from the actual speed that is obtained by an accurate measurement. This deviation is called measurement error, which is minimized in the first objective function. In this research when traffic jams happens, some sensors located in proper distance with enough energy, move into the traffic area to reduce the measurement error which is caused by traffic congestion. In fact, for each traffic area a new location problem should be solved to relocate sensors so that the maximum decrease in error rate happens. The second objective maximizes the error reduction resulted from sensors movement. In this paper, movement of sensors is considered based on the amount of solar energy stored in the sensor at that moment. Finally, the third objective function maximizes the benefits resulted from detecting the bottlenecks in highways. Since some parameters of the objective functions such as error rates, error reduction resulted from movement of sensors and benefits are uncertain, this research employs a multi-objective robust optimization approach which results in a traffic control plan which is less sensitive to the realization of uncertain parameters.

Traffic congestion has become a big problem for most people because it increases noise, air pollution, and wasting time. Current normal traffic light system is not enough to manage the traffic problematic congestions because they operate on a fixed-time length plan. In recent years, internet of things led to introducing new models of intelligent traffic light systems; by utilizing different techniques such as predictive-based model, radiofrequency identification, and ultrasonic-based model. The most essential one of these techniques is depends of image processing and microcontroller communications. In this paper, we propose an intelligent, low cost, and efficient microcontroller circuit-based system for controlling cars in traffic light. This system can manage car traffics smarter than traditional approaches, it is capable to dynamically adjust timings of traffic signal. It can rapidly respond to traffic conditions to reduce traffic congestion. For implementing this system, a server, microcontroller board, cameras, as hardware and wireless network between traffic lights as infrastructure for communication are used. The system uses machine learning technique (i.e.,Yolov3 model and OpenCV) for decision depending on existence of emergency cars and number of cars. The experiment results show higher accuracy in managing traffic lights and recognizing the emergency cars.

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%.

In present-day the most common issues that concerns everyone is traffic congestion on road at crossings where traffic light poles are placed. The objective is to flow and maintain traffic system automatically but unfortunately jamming queue exists while everything is fine from technology point of view because of predefined static time duration. This predefined time slots fail to capture real time scenarios in terms of traffic load and that leads to congestion. To avoid such type of problem, this work contributes the concept of dynamic traffic light slots as a best possible solution. Dynamic traffic light is a concept of controlling the time duration of RED and GREEN light according to traffic flow or by sensing flow of traffic on road in movement. This can be achieved by using Global Positioning System which will convey the details of traffic flow and by considering that, time slots are provided to traffic lights i.e. whole controlling and managing will work like Real Time System.

Recently evolutionary algorithms have created more interest among researchers and manufacturing engineers for solving multiple-objective problems. The objective of this chapter is to give readers a comprehensive understanding and also to give a better insight into the applications of solving multi-objective problems using evolutionary algorithms for manufacturing processes. The most important feature of evolutionary algorithms is that it can successfully find globally optimal solutions without getting restricted to local optima. This chapter introduces the reader with the basic concepts of single-objective optimization, multi-objective optimization, as well as evolutionary algorithms, and also gives an overview of its salient features. Some of the evolutionary algorithms widely used by researchers for solving multiple objectives have been presented and compared. Among the evolutionary algorithms, the Non-dominated Sorting Genetic Algorithm (NSGA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) have emerged as most efficient algorithms for solving multi-objective problems in manufacturing processes. The NSGA method applied to a complex manufacturing process, namely plateau honing process, considering multiple objectives, has been detailed with a case study. The chapter concludes by suggesting implementation of evolutionary algorithms in different research areas which hold promise for future applications.

In the context of large cities, the growth in traffic congestion has become a major problem. Static control systems may block emergency vehicles due to traffic congestion. The goal of detecting, forecasting, and reducing traffic congestion is to improve the level of service provided by the transportation system. One of the main issues that automatic way of traffic management plan will address is the current urban traffic congestion. Due to its capability to cope with dynamic behavior over time and with vast numbers of parameters in massive data, neural networks (NN) and machine-learning (ML) techniques are being employed more and more to tackle real-world issues, surpassing analytical and statistical methods. Therefore, deep learning is becoming more relevant for these jobs as access to bigger datasets at greater resolution increases. This study focuses on a critical assessment of the state of the art for the use of Artificial Intelligence in this specific field of Intelligent Transportation Systems. The literature that is now accessible uses a variety of methods to identify and dassify traffic congestion. The conclusions obtained from a review of recent articles using two taxonomic criteria are finally presented.