Traffic Light Control System

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

Traffic congestion is one of the main problems in transportation sector and it causes a lot of drawbacks to public. The traffic light system is used to reduce the level of occurring traffic congestion. Generally, the available traffic light systems use a fixed time setting. This old traffic control system is no longer able to manage the ever-changing traffic conditions effectively and efficiently, causing a long queue of vehicles. To overcome this problem, a traffic light control system that can adapt to actual conditions of road density and can run automatically is offered. This system utilizes Google Map API as a road density data source. The result of this study is a traffic control system that can adjust the green light time duration based on the obtained density values and density trends, simulation of this adaptive system as well as simulation results analysis. A prototype of this adaptive control system was also produced in this study.

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 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 present paper is designed to give a sharper focus on the development of a traffic signal control system for a particular intersection. The study sets forth to examine the movements of vehicles, pedestrians and other types of traffic participants on their approaching, stopping and passing through a traffic-light regulated intersection. Accomplished is a critical examination and made is a road traffic model of a given intersection zone in order to help reduce traffic obstructions and congestion. Singled out, accordingly, are the respective traffic indicators along with some traffic flow dependencies and determined, subsequently, are the quantitative values of the key intersection performance indicators. Established is the criterion of optimality and refined are the algorithms to optimize the relevant length of the phases and the traffic signal cycles at the intersection area. Proposed, further, is a concrete solution for a definite traffic light control system.

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.

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.

Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)‐based models. For example, developing AI‐driven dynamic traffic system models that accurately capture high‐resolution traffic attributes and formulate robust control algorithms for traffic signal optimization is difficult. Additionally, uncertainties in traffic system modeling and control processes can further complicate traffic signal system controllability. To partially address these challenges, this study presents a novel, hybrid neural network model enhanced with a probability density function kernel shaping technique to formulate traffic system dynamics better and improve comprehensive traffic network modeling and control. The numerical experimental tests were conducted, and the results demonstrate that the proposed control approach outperforms the baseline control strategies and reduces overall average delays by 11.64% on average. By leveraging the capabilities of this innovative model, this study aims to address major challenges related to traffic congestion and energy inefficiency toward more effective and adaptable AI‐based traffic control systems.

Urban traffic signal control robust optimization against Risk-averse and Worst-case cyberattacks

With the development of autonomous vehicles, a mixed traffic flow scenario of Connected Autonomous Vehicles (CAV) and Human-Driven Vehicles (HVs) would be popular in the near future. The traditional traffic light control systems (TLCSs) for HVs do not make full use of traffic information collected via VANET; meanwhile emerging traffic light systems for CAV assume complicated and quick reactions, which human drivers may not be able to handle. Therefore, they are not suitable for the mixed scenario. This paper proposed a novel TLCS, named PALM, for tackling the challenge for handling mixed traffic scenarios. PALM considers the traffic flow at each intersection and adjacent ones and adjusts the traffic lights schedule for the next few phases accordingly. It also optimizes the signal timing and phases to better serve the platoons formed by CAV. The simulation results show that our approach achieves up to 75.34% and 33.02% drop in the average waiting time compared to the static and actuated TLCS, respectively.

In this paper, a smart and centralized traffic light control and monitoring system is proposed to control the modern transportation systems and make the city safer, using programmable logic controllers (PLCs) and programmable electronic microcontrollers. A camera is used to monitor the mishaps during the traffic flow of vehicles. The system has four modes, i.e., auto-control mode (ACM), manual control mode (MCM), central control mode (CCM), and remote control mode (RCM). In the auto-control mode (ACM), the traffic light signals are controlled automatically through programmable electronic microcontrollers at specific times, while the manual control mode (MCM) controls the traffic light signals manually (on–off switches) according to the traffic congestion. The central control mode (CCM) is considered to be a centralized mode, where the programmable logic controller (PLC) is used by a computer workstation. In this mode, the traffic light signals are controlled by a ladder logic program of the PLC. The third mode, RCM, is linked with the second mode, CCM; in this mode, the traffic light signals are remotely controlled through the software by transferring programmable logic controller (PLC) functions to the software interface. As a result, this transportation system can also be controlled remotely. The designed system delivers suitable, flexible, and reliable control for traffic signaling and transportation.

Traffic congestion poses serious challenges to urban infrastructures through the unpredictable dynamical loading of their vehicular arteries. Despite the advances in traffic light control systems, the problem of optimal traffic signal timing is still resistant to straightforward solutions. Fundamentally nonlinear, traffic flows exhibit both locally periodic dynamics and globally coupled correlations under deep uncertainty. This paper introduces Oscillator-Based modelling and control using Efficient neural Learning for Intelligent road traffic Signal Calculation (OBELISC), an end-to-end system capable of modelling the cyclic dynamics of traffic flow and robustly compensate for uncertainty while still keeping the system feasible for real-world deployments. To achieve this goal, the system employs an efficient representation of the traffic flows and their dynamics in populations of spiking neural networks. Such a computation and learning framework enables OBELISC to model and control the complex dynamics of traffic flows in order to dynamically adapt the green light phase. In order to emphasize the advantages of the proposed system, an extensive experimental evaluation on real-world data completes the study.

Traffic light control systems are widely used to monitor and control vehicles’ flow to provide a smooth and orderly flow of traffic by controlling which lane should be granted to pass through the intersection point without compromising on safety standards. However, a conventional traffic light system handles neither dynamic traffic flow nor the prioritization of emergency vehicles (EVs). Consequently leads to congestion as the conventional traffic light grants underutilized road lanes to have the equal cycle length as a congested lane and possibly causes an accident and delay when multiple EVs are approaching the intersection. This paper proposes an intelligent system that dynamically adjusts the cycle length for each of the lanes at an intersection based on the vehicular density and grants the different types of EVs to pass through the intersection point by assigning different priorities. The system consists of three modules, the traffic light control module, the emergency Radio-frequency identification (RFID) moduleandtheinternetmodule. Thetrafficlightcontrolmoduledetectsvehiculardensityusing ultrasonicsensorsandassignsadynamicsetofcyclelengthbasedontheindividuallanedensity condition. TheemergencyRFIDsareinstalledon differenttypesofEVsbasedonthepreset priority weights. The internet module allows the dynamic traffic light system to be controlled by authorized personnel in a real-time application.


 
 
 Traffic and road safety is a major concern of many governments around the world as it ensures the safety of its road users. Due to an increase in the number of road users, traffic-related accidents and fatalities have increased annually. Therefore, governments have stepped up their efforts to ensure the safety of road users and to reduce the number of traffic-related accidents and fatalities. However, these efforts have proven ineffective as the data gathered to measure the effectiveness of these efforts either lack credibility or are insufficient. This has led to inconclusive findings concerning the culture of road users and the extent of their awareness of traffic safety. Therefore, this paper performs a systematic data- and text-driven analytics and review of published literature on public awareness of traffic and road safety. The selected papers were first subjected to content analysis before being grouped according to the frequency of use of keywords discussing the issue at hand. As this method enabled us to successfully identify trends as well as the efficacy of traffic and road safety research, therefore, data and text analytics is an effective method of conducting a systematic literature review. The findings of this study will help traffic authorities gain credible insights into public perception, behavior, and attitude towards traffic safety awareness. Our assessment indicates that there are two important perspectives to be considered with regard to road safety; namely human and technology; the latter of which will be the main topic of study on public awareness of road and traffic safety. Moreover, an emerging trend in the literature suggests that more efforts should be allocated to promoting public awareness of road and traffic safety.