A multilevel distributed system concept for computerized traffic control applications

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

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 paper presented the PRT (Personal Rapid Transit) control and traffic management system. The genesis of the use of information systems for control and PRT traffic management was made. The concept of the PRT system was defined, a review of these systems and their structure was carried out. The mechanical and ergonomic systems of PRT vehicles have been described. The chassis structure and the ergonomics of the PRT vehicle cabs have been characterized. The process of estimating traffic conditions and simulations of exemplary PRT vehicles have been described. An analysis of the PRT system was carried out, the purpose of which is to determine the optimal number of PRT stations guaranteeing optimal service provision, in terms of the demand for PRT services. The stages of designing the PRT circulation system for districts on the outskirts of the city are illustrated. The OPTIMAL program tool (C-CAD) has been used.

Urban and metropolitan areas lack the well-developed and properly managed traffic management system. Recent studies estimate that a large segment of population will migrate to urban areas by the next decade making these areas highly congested and populated. The complication of traffic control in urban areas has elevated road congestion. The mismanagement of traffic due to the fixed traffic light system (FTLS) has widely affected the road transportation. This paper proposes an Intelligent Traffic Light System (ITLS) for Ludhiana Road transportation and makes a comparison between the FTLS and ITLS. In the normal traffic system, they have an average of 180seconds time duration to bypassing the signal whereas in ITLS mode of operation, it looks for the heavy traffic lane and increases the 90 more seconds to allow the vehicles to bypass the signals from the heavy load lane. With the help of image sensor, CANNY Edge detection algorithm detecting the edges of the vehicles and store the information at edge server. Edge server then computes the green signal activation for heavy load lane information and makes the decision to give the way of concerned lane. values of the used here to carry the heavy load information to the traffic signal system to the implementing ITLS. The comparison regards the ITLS as an efficient system for traffic management and congestion control, plunging the delay and the average waiting time of the vehicles. This research paper discusses the overall architecture of ITLS, implementation of CANNY Edge detection algorithm and green signal activation algorithm.

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.

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 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.

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.

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.

The Columbus metropolitan area is one of the few growing areas of the Midwest and has been the fastest growing metropolitan area in Ohio for some time. Delaware County, north of Columbus, is one of the highest growth counties in the nation. While generally the employment and population growth in the Columbus area has been positive, unprecedented traffic problems have been developing in the Columbus area due to the growth, and due to the continuous increase in single occupancy auto travel. Traffic congestion that has now become common in the Columbus area, is only expected to worsen as growth continues, and as needed expansion of transportation infrastructure falls further behind. The traffic congestion that has resulted, affects not only the freeway mainline, but also many of the interchanges and the arterial streets serving the interchanges. Columbus is designing a state-of-the-art Freeway Traffic Management System, is expanding an advanced Urban Traffic Control System, and is maintaining a number of closed-loop systems. Each one of these systems has been designed to operate very efficiently and optimize the traffic operations in their own domain; nevertheless, the combined operations are bound to be sub-optimal, because the systems operate uncooperatively on the same large urban traffic domain. The ultimate goal of this project is to develop an interface that by utilizing the existing traffic signal control systems infrastructure (control devices, software and hardware) and by building relatively inexpensive software and hardware, will produce a prototype for an integrated freeway-surface street control system.

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 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 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.