Traffic Control Systems—Past, Present, and Future

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

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.

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.

The article presents the issue of digital standard interfaces of railway traffic control systems. The digitization and standardization of interfaces are digital rail elements, an essential feature of integrating various devices, facilities, and processes in one coherent system. The authors described the problems of connecting computer systems, starting from the interface's definition, including technical and functional aspects. Examples of interface types selected problems resulting from connecting incompatible devices and attempts to solve them are presented. It then provides background information on the project to develop a digital interface standard for selected rail traffic control systems. The project described in this article, under the name of "Standardization of selected interfaces of railway traffic control equipment and systems" POIR.04.01.01-00-0005/17, has been created as part of the BRIK (Research and Development in Railway Infrastructure) program and has been cofinanced both by the NCBiR (The National Centre for Research and Development), as well as the PKP Polskie Linie Kolejowe S.A. (PKP PLK - Polish State Railways). The project's goal is to develop, through research, specifications, and requirements for interfaces used in railway traffic control systems, which will become a standard enabling linking of systems from different manufacturers. The project results will be dedicated to using on the railway network managed by PKP PLK (and potentially also by other infrastructure managers) and documentation containing descriptions of standards, guidelines for the application and design of these interfaces.

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.

Suburban railway lines are emerging in China in the last few years. Not as definite as in high-speed railway or urban rail transit, the applications of signaling and traffic control system in suburban railway have not been strictly defined. Based on the investigation of the traffic scale and its characteristics in suburban railway, and traffic control systems implemented respectively in national railway and urban rail transit, an agent-based system, named D-Agent, is presented to support suburban railway traffic control and management. It consists of six basic modules: local data, knowledge base, skills, reasoning mechanism, data processing, and communication. Infrastructure characteristics, train performances, safety and operational restrictions are stored in local data and knowledge base. Functions of traffic control and adaptive support become the skills of D-Agent, and all possible interfaces of traffic control system are learned as the languages of D-Agent for communication with other systems. Through continuous learning, D-Agent can be trained to support the traffic control system or dispatcher in handling traffic problems, as well in adapting to different signaling systems that may be applied in suburban railway.

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.

The forthcoming of automated driving has led to vehicular heterogeneity, where vehicles with different automation levels, including connected and automated vehicles (CAVs), connected vehicles (CVs) and human-driven vehicles (HVs), coexist in the road traffic. However, the design of current traffic control systems fail to account for the vehicular traffic heterogeneity. In addition, the emerging artificial intelligence (AI) based traffic control strategies require large quantities of computation resources and generate high delay, which cannot meet the faultintolerance requirement of the traffic control system. Therefore, there is an urgent need for constructing a new traffic control framework to jointly satisfy the fault-intolerance requirement and realize ubiquitous control over the heterogeneous vehicles. Inspired by this, a digital twin (DT) empowered edge AI framework is proposed in this article. The DT's virtualization capability is utilized to enable virtual risk assessment and performance analysis under the fault-intolerant traffic control system. In addition, the DT's offline learning capability helps the edge AI understand the road traffic data more intelligently to perform enforced optimizations and decisions. Then, a three-layer vehicle control paradigm is discussed under the proposed framework. In the first layer, deep reinforcement learning (DRL) is utilized to intelligently control the CAV acceleration and lane-changing. In the second layer, indirect CV/HV control can be performed by adjusting the target speed and penetration rate of the DRLcontrolled CAVs which are mixed in the traffic flow. In the third layer, vehicle-to-everything (V2X) communications and variable message signs are utilized to realize the direct control of CVs and HVs. Experimental

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.

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.

HERMES is a DRIVE II project which focuses mainly on the enhancement and application of traffic control strategies that are based on on-line origin/destination information automatic incident detection. The biggest workpackage within HERMES aims at the development of a new on-line traffic signal network control system named MOTION (Method for the Optimization of Traffic Signals In Online controlled Networks) This work is to some extent based on results already achieved previously in a project study made for the SIEMENS AG and in the DRIVE I project ODIN, where algorithms for the on-line estimation of origins and destinations of the major streams at single intersections and in wider urban road networks have been developed together with initial concepts for the use of this information in traffic control. Simulation results have already shown that improvements of 8% to 20% in the traffic performance are possible, if signal control schemes are based on O/D-information.

In today's modern world, communication, transportation and the movement of people and merchandises are important, and doing so in the shortest possible time is also essential and vital. In the past decade, due to the significant increase in the number of passengers and vehicles along with the capacity limitations of communication arrays, it is absolutely necessary to apply new technologies to intelligent traffic control and management. The intelligent transportation system (ITS) utilizes advanced technologies in the fields of information processing, telecommunications and electronic control to meet transportation needs. The purpose of these systems is to streamline traffic in important and sensitive routes, and in addition to providing traffic safety, information, timely traffic control and the use of optimal capacity of transport arteries. This paper presents new method for extracting traffic parameters associated with a signalized highway using image processing and data mining KNN classification algorithm. These parameters include the length of red light LED, the volume of passing vehicles and the volume of pedestrians passing the highways in the green phase. In what follows, a Data Mining Traffic Light Control System is introduced, which by receiving the three traffic parameters mentioned above, proceeds to optimize the traffic signal timing. At the end, a two-phase common highway is simulated in the MATLAB software environment, and the results of the image processing algorithms and the Data Mining Traffic Light Control System designed for it are evaluated.

During the last 60 years, incessant efforts have been made to improve the efficiency of traffic control systems to meet ever-increasing traffic demands. Some recent works attempt to enhance traffic efficiency via vehicle-to-vehicle communications. In this paper, we aim to give a survey of some research frontiers in this trend, identifying early-stage key technologies and discussing potential benefits that will be gained. Our survey focuses on the control side and aims to highlight that the design philosophy for traffic control systems is undergoing a transition from feedback character to feedforward character. Moreover, we discuss some contrasting preferences in the design of traffic control systems and their relations to vehicular communications. The first pair of contrasting preferences are model-based predictive control versus simulation-based predictive control. The second pair are global planning-based control versus local self-organization-based control. The third pair are control using rich information that may be highly redundant versus control using concise information that is necessary. Both the potentials and drawbacks of these control strategies are explained. We hope these comparisons can shed some interesting light on future traffic control studies.

Traffic jam has become a common scene nowadays, due to the rapid increase of road users in the past few years. To solve this problem, the design of traffic control is required. Traffic control parameter can be determined from the traffic flow behavior of the traffic intersection. Currently, most of the traffic control system in Malaysia is using Webster Model, which its green time duration is pre-determined by collecting the data from the traffic intersection. Most of the waiting time at the intersection is wasted due to the constant green time duration. The aim of this paper is to optimize the average waiting time of a traffic intersection via a developed Fuzzy Logic traffic control system. Traffic light simulator hardware is built to deal with the difficulties of working in a real environment and to investigate the performance of the traffic control system. Conventional traffic control system, Webster Model and the Fuzzy Logic controller will be applied into the traffic signal simulator in order to investigate their performance.

This research is focused on laboratory based analysis on hardware module of traffic light with Programmable Logic Controller (PLC) software. There are four ways in hardware model for detection of vehicle; each way has one sensor button, three Light Emitted Diodes (LEDs) with individual colors say red, yellow and green represents the traffic light in each lane. Sensor and LEDs are connected to Mitsubishi Programmable Logic Controller (PLC) and each part on hardware being controlled by PLC. Ladder diagrams are programmed by software to monitor the system and helps to improve public transportation services, thereby improving traffic guidance and traffic light control. The system was developed by setting the appropriate time for the traffic lights to respond accordingly. The controller checks the priority and provides an exit signal to the traffic light post to turn the red, yellow or green lights on or off. Finally, the signal lights were successfully controlled by the PLC. Hence the system used in traffic control systems contain low power consumption, low engineering cost and increased safety.