АВТОМАТИЗИРОВАННАЯ ТРАНСПОРТНО-ТЕХНОЛОГИЧЕСКАЯ СИСТЕМА ОРГАНИЗАЦИИ ДОРОЖНОГО ДВИЖЕНИЯ И СОЗДАНИЯ ТРАНСПОРТНЫХ КОРИДОРОВ

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.

Efficient traffic control and management is a fundamental and urgent challenge in our ever-growing cities. Maximizing roadway capacity usage, balancing traffic flows, decreasing emission, improving traffic safety and providing motorists with the best end-to-end transportation experience are the most important goals of modern intelligent traffic control infrastructures. Evolving traffic light or traffic signal controller systems are playing an important role in these architectures by adaptively and dynamically coordinating individual traffic lights to achieve city-wide objectives of traffic operations. The integration of such systems with vehicle-to-vehicle (V2V) and vehicle-to- infrastructure (V2I) communication techniques (together referred as V2X) could easily be the next step of this evolution allowing for dynamic real-time information exchange between all the players of the traffic controlling ecosystem and fostering cooperative urban mobility. In this paper we propose a possible architecture for a V2X-integrated traffic light controller systems and present the testbed with our proof-of-concept implementation of the proposed scheme.

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.

Introduction Drag on city traffic has escalated to be a pivotal problem in almost all cities in the world, and commuter travel time, fuel consumption and emission have also soared. Fixed-time signal control systems have been traditionally used in traffic signal control, but their optimality highly depends on the time of day they are applied and they are not capable of real-time traffic control. In order to overcome these drawbacks, intelligent and adaptive traffic control approaches are being explored by both scientists and traffic developers. Objective The purpose of this research is to create and assess a dynamic traffic light control system by utilizing Snake Optimization (SO), a novel meta-heuristic algorithm. In order to minimize vehicle queues, cut down on delays, and enhance overall traffic flow, the main goal is to examine how well SO optimizes traffic signal timings based on real-time traffic data. by contrasting conventional static control techniques and meta-heuristics with the SO-based dynamic approach. Proposed Method To find the best traffic light phase durations at intersections, the suggested approach uses the Snake Optimization (SO) algorithm, a bio-inspired meta-heuristic technique. Real traffic data from the Algerian city of Algiers, which depicts realistic traffic volumes and patterns, is used to test the method. The SUMO (Simulation of Urban Mobility) simulator, an open-source program popular for simulating microscopic traffic, is used to construct the experimental framework. Traffic scenario modeling, control algorithm implementation, and performance evaluation in a simulated urban setting are all made possible by SUMO. The following steps are part of the optimization process: gathering of data, using the SUMO simulator to model traffic flow and intersections, incorporating the SO algorithm to modify traffic light timings in real time in response to inputs, evaluation of performance using metrics like vehicle throughput, average waiting time, reduction of CO2 emission and total travel time, and evaluation of the benefits obtained by the SO-based approach through comparison with a baseline static control system and ACO-DSOS meta-heuristic. Conclusion According to the simulation results, the suggested approach, which is based on the Snake Optimization algorithm, performs better than other meta-heuristics and conventional static control systems. The algorithm's effectiveness in controlling traffic flows is validated by quantitative comparisons. According to the study's findings, SO is a practical and successful strategy for maximizing traffic light control in practical settings.

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.

Traffic control at road intersections is based on traffic lights (TLs). The control mechanism typically used for traffic lights operates based on a periodic schedule to change the light (red/yellow/green). In many cases, a different schedule is used in late night/early morning hours. This fixed control mechanism does not adequately account for changing traffic conditions, and is unaware of/unresponsive to congestion. In this work, we propose a framework for dynamic traffic light control at intersections. The framework relies on a simple sensor network to collect traffic data and includes novel protocols for traffic flow control to handle congestion and facilitate flow. We show that our proposed algorithms have low overhead and are practical to employ in live traffic flow scenarios. Through extensive simulations, we demonstrate the benefits of our framework in optimizing traffic flow metrics, such as traffic throughput, average vehicle waiting time, and vehicle waiting line length.

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.

Traffic congestion is a major concern for many cities throughout the world. Developing a sophisticated traffic monitoring and control system would result in an effective solution to this problem. In a conventional traffic light controller, the traffic lights change at constant cycle time. Hence it does not provide an optimal solution. Many traffic light controllers implemented in current practice, are based on the 'time-of-the-day' scheme, which use a limited number of predetermined traffic light patterns and implement these patterns depending upon the time of the day. These automated systems do not provide an optimal control for fluctuating traffic volumes. A traffic light controller based on fuzzy logic can be used for optimum control of fluctuating traffic volumes such as over saturated or unusual load conditions. The objective is to improve the vehicular throughput and minimize delays. The rules of fuzzy logic controller are formulated by following the same protocols that a human operator would use to control the time intervals of the traffic light. The length of the current green phase is extended or terminated depending upon the 'arrival' i.e. the number of vehicles approaching the green phase and the 'queue' that corresponds to the number of queuing vehicles in red phases. A prototype system for controlling traffic at an intersection is designed using VB6 and Matlab tool. The traffic intersection is simulated in VB6 and the data regarding the traffic parameters is collected in VB6 environment. The decision on the duration of the extension is taken using the Matlab tool. This decision is based on the Arrival and Queue of vehicles, which is imported in Matlab from VB6 environment. The time delay experienced by the vehicles using the fixed as well as fuzzy traffic controller is then compared to observe the effectiveness of the fuzzy traffic controller.

Fuzzy logic and deep Q learning based control for traffic lights

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.

Traffic congestion especially at road intersections is becoming an issue for which road traffic users contend with daily. The conventional traffic light applies a fixed logic of allocating equal “GO” time to lanes of traffic at road intersections irrespective of the density of traffic on each lane. Using the PIC18F4550 microcontroller interfaced with infrared sensors, a new traffic light control system was developed to ease the flow of traffic at a particular troubled spot in Uyo Metropolis (Abak Road, Udo Eduok, Udo Obio streets intersection) in Akwa Ibom State, Nigeria. Simulation of the proposed design was performed using the Proteus Software while implementation was carried out on a prototype. Performance evaluation of the prototype implemented showed that the 4 arrays (IR Transmitter and Receiver) of infrared sensors interfaced with the microcontroller using AND gates and strategically positioned to read the density of traffic on each lane at the intersection triggered when a vehicle comes between the transmitter and the receiver. Obtaining inputs from these sensors together with the logic from the microcontroller, a new traffic light control system was developed capable of controlling traffic based on the density of each lane of traffic. Results obtained from simulation and implementation of the design indicates that the traffic control system with the PIC18F4550 microcontroller and the infrared sensors gives a better performance compared to the conventional traffic light control system.Keywords: Road traffic density, Traffic light, Uyo metropolis, ‘+’ road intersection, Proteus

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 jams and congestion in our cities are a major problem because of the huge increase in the number of cars on the road. To remedy this problem, several control methods are proposed to prevent or reduce traffic congestion based on traffic lights. There are few works using reinforcement learning technique for traffic light control and recent studies have shown promising results. However, existing works have not yet tested the methods on the real-world traffic data and they only focus on studying the rewards without interpreting the policies. In this paper, we proposed a reinforcement learning algorithm to address the traffic signal control problem in real multi-phases isolated intersection. A case study based on Algiers city is conducted the simulation results from the different scenarios show that our proposed scheme reduces the total travel time of the vehicles compared to those obtained with traffic-adaptive control.

The conventional traffic light control frameworks are governed by fixed time interims of the traffic lights. These traditional fixed traffic light controllers have certain constraints and are less effective on the grounds that they utilize an equipment, which have capacities as indicated by the program that comes up with limitations of the inconsistent adaptability of adjustments. Along these lines, due to the fixed time interims of green and red signs, there is overabundance average waiting time and superfluous holding up time on lanes, due to which vehicles tend to devour more fuel. This in the long run indicates the ecological contamination and makes a few medical related problems among the individuals on street and living close by, as they tend to inhale the impure air. Likewise, these customary traffic light control frameworks do not have any advancements or arrangements to give any data on traffic densities on different streets, which prompt traffic clogs. Consequently, to make the traffic light scheme control and traffic guideline progressively productive, the proposed work makes use of the development of a novel strategy called as Adaptive Traffic Light Control System, modelled with highly proficient framework. The proposed framework utilizes a complete system of cluster of sensors for detecting the traffic with priority based traffic management. On positioning this effectively detected traffic, the planning interims of red and green light signals across the streets are cleverly chosen and changed in order to keep the holding up time least. In this way, enhancement of the traffic light with periodically exchanges, builds the street limit, spares time for voyaging and forestalls traffic blockages. The traffic related details are intermittently updated to the multi-cloud environment. The model is assessed by means of rebuilding in the Simulation of Urban Mobility framework (SUMO) in a vehicular system using Reinforced Decision Process (RDP), and the reenactment results show the significantly improved efficiency of the model in controlling and regulating the traffic with dynamic and self-adaptive management.

Traffic congestion and delays at signalized intersections remain a challenge in urban mobility. To minimize congestion, adaptive traffic signal control systems have been adopted across the United States. However, for these systems to function at their best, systems require an accurate estimation of traffic states, such as traffic density and queue length. This paper implements a relatively novel method, Physics-Informed Neural Networks (PINNs), for real-time traffic density estimation to enhance adaptive intersection signal control. The methodology leverages PINNs, which integrate both data-driven learning and physical constraints imposed by the Lighthill-Whitham-Richards (LWR) macroscopic traffic flow model. A simulation of a four-way intersection was used to generate realistic spatio-temporal traffic data to evaluate the performance of the model. Separate PINNs models, one trained for each incoming approach using simulation-based spatio-temporal density data, provide segment-level density predictions. These real-time density estimates are then employed within an adaptive traffic signal control algorithm designed to adjust green light phasing based on detected demand. The proposed PINNs control strategy was evaluated through simulation, comparing its performance against the Webster traffic timing method using key metrics such as average vehicle delay at the intersection and average vehicle travel time. The findings show an approximate 18% decrease in average vehicle delay and more than a 6% reduction in average travel time when utilizing adaptive traffic control based on the PINNs method for determining density values. These findings highlight the potential of leveraging PINNs for accurate, real-time traffic state estimation, which can effectively inform and improve the performance of adaptive traffic signal control systems, leading to reduced congestion and enhanced mobility at intersections.