An Agent-Based Support System for Suburban Railway Traffic Control

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.

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.

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 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.

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.

Traffic control systems are technological laggards when compared with modern control systems used in industry today. The traffic control industry lacks—and often refuses to adopt—necessary standards that will allow it to use the benefits of emerging technologies in a cost‐effective, timely fashion. The same industry cannot keep looking at itself if the promise of future data communications and computing technologies are to be used advantageously. This paper discusses the state of traffic signal systems, how they came to be, the technologies they use, and their technological position with respect to state‐of‐the‐art hardware and software systems. Standards bodies that should play a relevant role in the design of tomorrow's traffic control systems are introduced. Surveys of current and future directions for traffic control hardware, software, and data communications facilities are presented. Traffic control's technological generation gap is identified, and a course of action for academia, manufacturers, and users is suggested.

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.

이 논문에서는 도심 지역의 교통 제어 시스템의 동적 응답 성능 향상을 위하여 적응형 Q-Learning 강화 학습 메커니즘을 설계 하였다. 도로, 자동차, 교통 제어 시스템을 지능 시스템으로 모델링 하고, 자동차와 도로 사이는 무선 통신을 이용한 네트워크가 구성된다. 도로와 대로변에 필요한 센터네트워크가 설치되고 Q-Learning 강화 학습은 제안한 메커니즘의 구현을 위해 핵심 알고리즘으로 채택하였다. 교통 신호 제어 규칙은 자동차와 도로에서 매 시간 업데이트된 정보에 따라서 결정되며, 이러한 방법은 기존의 교통 제어 시스템에 비하여 도로를 효율적으로 활용하며 결과적으로 교통 흐름을 개선 한다. 알고리즘을 활용한 최적의 신호 체계는 온라인상에서 자동으로 학습함으로서 구현된다. 시뮬레이션을 통하여 제안한 알고리즘이 기존 시스템에 비하여 효율성 개선과 차량의 대개 시간에 대한 성능 지수가 모두 30% 이상 향상되었다. 실험 결과를 통하여 제안한 시스템이 교통 흐름을 최적화함을 확인하였다. A flexible mechanism is proposed in this paper to improve the dynamic response performance of a traffic flow control system in an urban area. The roads, vehicles, and traffic control systems are all modeled as intelligent systems, wherein a wireless communication network is used as the medium of communication between the vehicles and the roads. The necessary sensor networks are installed in the roads and on the roadside upon which reinforcement learning is adopted as the core algorithm for this mechanism. A traffic policy can be planned online according to the updated situations on the roads, based on all the information from the vehicles and the roads. This improves the flexibility of traffic flow and offers a much more efficient use of the roads over a traditional traffic control system. The optimum intersection signals can be learned automatically online. An intersection control system is studied as an example of the mechanism using Q-learning based algorithm, and simulation results showed that the proposed mechanism can improve the traffic efficiency and the waiting time at the signal light by more than 30% in various conditions compare to the traditional signaling system.

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.

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.

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.

Nowadays, traffic congestion is increasingly aggravating in almost every urban area and existing traffic light controllers cannot satisfy the rising demands efficiently to handle the traffic pressure. An intelligent transportation system (ITS) aims to provide major innovations for improving the performance of a public transport system including the traffic light control systems. In this paper, we propose a fog-based distributed architecture for a dynamic traffic light control system for ITS. In the proposed architecture, the local gateway collects real-time local traffic data using a wireless sensor network at each intersection. It also gets the neighboring traffic data from the distributed fog nodes. Using this local and global traffic data, the proposed Efficient Dynamic Traffic Light Control algorithm for Multiple (EDTLCM) intersections calculates the optimal green light sequence and duration (that maximizes the benefits), considering three objectives i) reducing the average waiting time ii) minimizing the fuel consumption and iii) maximizing the throughput. The simulation result shows that the proposed EDTLCM minimizes the waiting time, reduces fuel consumption and improves system throughput compared to the other dynamic strategies.

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.

This paper proposes a new intelligent traffic control (ITC) system which is more efficient than the traffic control system currently used in the state of Kuwait.The proposed ITC system is designed as a dynamic system by using a fuzzy expert system; the fuzzy rules are applied in the visual basic and computer-based program (Excel) to run the validation process.The developed control system applied on five intersections in the grid network at four periods.The results show that the number of vehicles passing the intersection phases is increased in most phases by an average of 12.9% at the first period, 23.3% at the second period, 10.4% at the third period and by 21.2% at the fourth period.For the same periods, the phases green time is increased by an average of 9.1%, 5.8%, 9.9% and 6.3%.And the number of intersection cycles remains constant at the most time which means that the developed control system distributes the phases green time dynamically based on the traffic situation.