Learning error distribution kernel‐enhanced neural network methodology for multi‐intersection signal control optimization

The traffic congestion problem in urban areas is worsening since traditional traffic signal control systems cannot provide efficient traffic control. Therefore, dynamic traffic signal control in intelligent transportation system (ITS) recently has received increasing attention. This study devised an adaptive and cooperative multi-agent fuzzy system for a decentralized traffic signal control. To achieve this goal we have worked on a model, which has three levels of control. Every intersection is controlled by its own traffic situation, its neighboring intersections recommendations and a knowledge base, which provides the traffic pattern of each intersection in any particular day of the week and hour of the day. The proposed architecture comprises a knowledge base, prediction module and a traffic observer that provide data to real traffic data preparation module, then a decision-making layer takes decision to how long should the intersection green light be extended. The proposed architecture can achieve dynamic traffic signal control. We have also developed a NetLogo-based traffic simulator to serve as the agents' world. Our approach is tested with traffic control of a large connected junction and the result obtained is promising; The average delay time can be reduced by 21.76% compared to the conventional fixed sequence traffic signal and 14.77% compared to the vehicle actuated traffic signal control strategy.

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

This work aims to minimize average delay for signalized traffic network along Jalan UMS under oversaturated condition using decentralized genetic algorithm (DGA). Relieving traffic network is a key challenge for a nation for improving its socio-economic systems and welfare to society. Control the traffic signal timing becomes a cost effective solution to reduce congestion since the space constraint limits the authority to build more infrastructures in city. Regrettably, most of the current traffic signal control systems are not yet optimized. They are preset based on historical traffic data; thus they are inadequate to cope with the dynamic changes in real traffic scenario. Therefore, this work proposes DGA to optimize the traffic network signal for reducing average delay during the morning peak hour. A comprehensive traffic model based on Public Works Department, Malaysia has been developed as the platform. The average delay experienced by vehicles to traverse the crossed intersection during morning peak hour is used as the performance metric to evaluate performances of the proposed algorithm. Simulation results show developed DGA is able to reduce the average network delay by optimizing the traffic signal.

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.

Rapid urbanization and increasing ownership of private vehicles have led to severe traffic congestion, which has become a critical challenge in modern urban areas operated with signalized intersections. This study proposes and evaluates a novel AI-based composite framework for traffic signal control optimization, which integrates a Long Short-Term Memory (LSTM) model for traffic flow prediction, a Deep Neural Network (DNN) for vehicle speed estimation, and a Genetic Algorithm (GA) for signal phase optimization under open-loop and closed-loop objectives. It has been validated through high-fidelity microscopic simulations (i.e. VISSIM), calibrated with field data at the atypical six-leg intersection in Busan, Korea. This study found that while the overall performance of the framework for 24 hours exhibits clear trade-offs (i.e. slight total traffic throughput decrease of 2.2%), its primary value lies in its demonstrated ability to effectively mitigate severe congestion during peak hours (i.e. speed increase of up to 38.5% during the evening peak), delivering significant gains in traffic quality in need. This study also revealed that the system actively performs traffic flow redistribution as an intelligent traffic control strategy. The contributions of this study are threefold. First, it provides a methodological blueprint for an interpretable, modular control framework that decouples prediction processes from optimization, offering a transparent alternative to end-to-end Reinforcement Learning (RL) models. Second, this study offers crucial empirical insight for the evaluation of adaptive traffic control systems. Their true effectiveness may be best measured by peak hour performance rather than 24-hour averages. Finally, this study makes a conceptual contribution by identifying and quantifying the ‘reality gap’ between the predictions of an offline-trained optimizer and the outcomes of a high-fidelity simulation, highlighting a fundamental challenge for the field. HIGHLIGHTS A hierarchical AI framework is an alternative to reinforcement learning model Closed-loop outperforms open-loop in optimizing real time traffic signal control A speed stabilization objective is adopted over a speed maximization approach Leading to tangible gains in traffic quality during peak hour over 24-hour average

A traffic signal system established due to the congestion of vehicles at road intersections. Traffic choking at road convergence becomes a controversy for daily riders and proportionally allowing the violation of traffic rules or other hazards. The conventional traffic signal system has lots of disadvantages. Even the system is automatic but due to fixed time range it gives rise to other problem like traffic violation, air pollution indirectly this would lead to deceitful affair. The signal system has been executed using different types of controller some have fast execution time, a greater number of inputs and outputs, reliability also plays an important role in traffic signal system design. To overcome such issues, we proposed a design in this paper which works on the compactness of vehicles on traffic signals and also detects the traffic signal breach done by the vehicles. This paper shows the simulation of dynamic traffic signal and traffic breach control system with IR sensor or piezo sensor to detect the density of vehicles and traffic breach or jumping of red light. This paper related with an FPGA controller as a VLSI design using Verilog in Xilinx software.

The integration of autonomous systems in traffic management has become increasingly important as urban populations and vehicle numbers continue to rise, leading to significant congestion. Traditional traffic signal control systems, which rely on fixed timing, are no longer sufficient to handle the dynamic and complex nature of urban traffic. To address these challenges, the proposed explainable Deep Reinforcement Learning (DRL) framework aims to optimize traffic signal control by dynamically adjusting traffic signals based on real-time data. This approach enhances traffic flow efficiency, reduces congestion, and improves overall system performance. The framework leverages Vehicle-to-Everything (V2X) communication, which enables real-time data exchange between vehicles, infrastructure, and other road users, extending the perception range of autonomous vehicles and providing valuable insights for traffic signal optimization. Additionally, the integration of smart infrastructure, such as smart intersections, plays a crucial role in enabling adaptive traffic management and facilitating better coordination across multiple intersections. One of the key advantages of the proposed system is its transparency, achieved through the implementation of explainable AI (XAI) techniques. These mechanisms provide clear insights into the decision-making processes, ensuring that traffic management authorities and system users can understand the rationale behind the system’s decisions. Although challenges such as data accuracy, scalability, and cybersecurity risks remain, the proposed DRL framework shows great promise in revolutionizing traffic management systems. Future research directions include enhancing data collection methods, improving the scalability of the system for larger cities, and further developing explainability features to improve trust and adoption in real-world applications.

The traffic light control system is a key component of urban traffic management, and its performance directly impacts the smooth flow of traffic and traffic safety. Based on some issues existing in the current traffic signal control systems, this paper proposes a novel traffic light control system based on intelligent algorithms. This system combines artificial intelligence technology with traditional traffic signal control methods to achieve intelligent control of traffic signals, thereby enhancing traffic efficiency and road safety. The paper provides a detailed explanation of the system's design principles, algorithm implementation, and experimental results. Comparative experiments demonstrate that the system has significant advantages in reducing traffic congestion and improving vehicle passage efficiency. Finally, the paper discusses possible future improvement directions and application scenarios, envisioning the development prospects of traffic light control systems in the future.

A smart city has being studied to improve the quality of life. A traffic control system is a part of what smart cities need to deal with. The traffic control system identifies the traffic flow and controls the vehicle at an intersection. This study proposes a traffic signal control system that uses Q-learning to maximize throughput and minimize waiting time. The proposed system has optimized traffic signals by adjusting the time of the green signal and allocating the green signal. We conducted an experiment by applying two kinds of traffic load. The performance is also analyzed by applying data with urban traffic density distribution.

Traffic congestion and delays are two major challenges in modern vehicle traffic control systems. These issues can be mitigated through an efficient and autonomous traffic scheduling system. The objective of the proposed methodology is to automate the traffic control system based on the density of vehicles approaching to the traffic signal without any human intervention. Unlike the conventional traffic signal systems that rely on preset timers which is often unsuitable for unpredictable traffic conditions. Therefore, the proposed approach dynamically adjusts signal timings based on real-time data. The methodology utilizes proximity sensors strategically placed at a predetermined distance from the traffic signal to detect approaching vehicles. The speed and density of vehicles are monitored based on the readings from these sensors. A Edge-Impulse-based machine learning model is proposed to predict the density and arrival time of the vehicles to the traffic signal. Using machine learning algorithms, the system can forecast future traffic conditions and optimize real-time traffic control by significantly reducing congestion and delays. Moreover, by automating the traffic scheduling process, the proposed methodology can help to reduce human error and improve the safety of road users. The proposed methodology has the potential to transform existing traffic control systems, making them more intelligent, efficient, and autonomous. The model is rigorously tested and validated to ensure its reliability and accuracy in real-world traffic scenarios.

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.

In recent days number of vehicles is increasing day by day, especially in metropolitan cities. As vehicles increases, traffic density also increases on the road, that results us in slowing the speed, it takes more time to reach the destination, increases in vehicle queuing. If there is no control over traffic signals there may chance of getting accidents also. Improvement in the traffic control is required to overcome all these problems. Traditional traffic control system is not enough to control the current traffic density; traffic control system requires some enhancement to the existing system to avoid the traffic congestion. In the proposed work Intelligent Traffic Control System using Global System for Mobile communication (GSM) is used to overcome the congestion on the roads. The density of the vehicles is obtained by connecting the IR sensors. And depending on the density of the vehicles the traffic light signals are controlled by the microcontroller. The vehicles can also change the route if the traffic is heavy in the current route and information regarding heavy traffic is provided to vehicles in advance through the GSM.

To utilize the utmost capacities of junctions with traffic signals, having preemptive and predictive capabilities with integrated traffic sensors are important elements for an intelligent traffic signal control system. Recently, an efficient and preemptive traffic signal control strategy known as iterative tuning (IT) has been developed by exploring repetitive characteristic of traffic demand on working days, whereas on weekends or days with special events, severe nonrepetitive disturbances limit the performance of IT strategy practically. This paper proposes a preemptive and predictive methodology, i.e., IT strategy with reactive compensation (ITRC), to reduce network delay time and eliminate unnecessary stops for vehicles. With anticipation of repetitive traffic flows based on historical data, nominal traffic signals are tuned by IT controller over repetitions. The reactive compensation, which is junction-based model predictive control strategy (JMPC), makes adjustments on nominal traffic signals and compensates the nonrepetitive elements. Rigorous analysis provides sufficient condition for guaranteeing the convergence of ITRC. Case study of Commonwealth Avenue in Singapore with heavy traffic from morning peak to evening peak hours is presented, and the simulation results show the effectiveness, robustness, and convergence of ITRC over pure IT strategy and pure JMPC strategy for scenarios with various traffic variations, respectively.

This paper deals with modeling method and application of Fuzzy Discrete Event System(FDES). FDES have characteristics which Crisp Discrete Event System(CDES) can't deals with and is constituted with the events that is determined by vague and uncertain judgement like biomedical or traffic control. We proposed Real-time Fuzzy Temporal Logic Framework(RFTLF) to model Fuzzy Discrete Event System. It combines Temporal Logic Framework with Fuzzy Theory. We represented the model of traffic signal systems for intersection to have the property of Fuzzy Discrete Event System with Real-time Fuzzy Temporal Logic Framework and designed a traffic signal controller for smooth traffic flow. Moreover, we proposed the method to find the minimum-time route to reach the desired destination with information obtained in each intersection. In order to evaluate the performance of Real-time Fuzzy Temporal Logic Framework model proposed in this paper, we simulated unit-time extension traffic signal controller model of the latest signal control method on the same condition.

Most computerized traffic control systems that are now operational use a central computer to perform virtually all data processing tasks. These systems operate satisfactorily, but it is believed that this system design approach is now stretched to its limit of practical capability. The recent availability of economical microprocessors and their successful application to traffic signal controllers suggests that investigations should be conducted into the formulation of traffic control system concepts that make full use of microprocessor capabilities and thereby identify improved systems for future applications, particularly those that relate to the concept of traffic management systems that is currently in favor. This paper describes a Multilevel Distributed System (MLDS) concept designed to meet future traffic control and traffic management system requirements as currently envisioned.