Development and evaluation of a knowledge-based system for traffic congestion management and control

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.

In this paper, we have made a comprehensive study about the key technologies including applying wireless sensor networks to the traffic monitoring network, its traffic flow forecast based on gray forecasting model and traffic congestion control. According to the features that wireless sensor networks have no space constraints, flexible distribution, mobile convenience and quick reaction, we present a scheme that uses wireless sensor networks to monitor city transport vehicles and have designed a traffic monitoring system based on wireless sensor network that is applicable to all types of city environment. With the system, we can monitor the important roads that are easily blocked and find out the time changing law of traffic congestion, and then put the focus on monitoring them in order to greatly reduce the investment and achieve high efficiency. As far as the traffic flow forecasting methods concerned, we use Adaptive GM (1, 1) Model which have a real-time rolling forecast for city traffic and have a better forecast results. Because of the fewer study about the traffic congestion control in the current academia, we make a deep study about the traffic congestion control issues in this article. Learn from mature congestion control algorithm in computer network, we have designed an algorithm of traffic flow congestion control and scheduling for traffic network, which is called TRED. We have used it for real-time traffic scheduling and have opened up a new way to study and solve traffic congestion control problems.

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.

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.

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 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

Because of the population increasing so high, and traffic density remaining the same, traffic prediction has become a great challenge today. Creating a higher degree of communication in automobiles results in the time wastage, fuel wastage, environmental damage, and even death caused by citizens being trapped in the middle of traffic. Only a few researchers work in traffic congestion prediction and control systems, but it may provide less accuracy. So, this paper proposed an efficient IoT-based traffic prediction using OWENN algorithm and traffic signal control system using Intel 80,286 microprocessor for a smart city. The proposed system consists of ‘5’ phases, namely IoT data collection, feature extraction, classification, optimized traffic IoT values, and traffic signal control system. Initially, the IoT traffic data are collected from the dataset. After that, traffic, weather, and direction information are extracted, and these extracted features are given as input to the OWENN classifier, which classifies which place has more traffic. Suppose one direction of the place has more traffic, it optimizes the IoT values by using IBSO, and finally, the traffic is controlled by using Intel 80,286 microprocessor. An efficient OWENN algorithm for traffic prediction and traffic signal control using a Intel 80,286 microprocessor for a smart city. After extracting the features, the classification is performed in this step. Hereabout, the classification is done by using the optimized weight Elman neural network (OWENN) algorithm that classifies which places have more traffic. OWENN attains 98.23% accuracy than existing model also its achieved 96.69% F-score than existing model. The experimental results show that the proposed system outperforms state-of-the-art methods.

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.

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.

A Report on Urban Traffic Control Research Conducted by the Federal Highway Administration

Traffic crisis often happen because of traffic burden by the large number automobiles on the path. Increasing transportation move and decreasing the average waiting time of each vehicle are the aims of cooperative intelligent traffic control system. Each signal wishes to catch better travel move. During the course, signals form a strategy of cooperation in addition to restriction for neighboring signals to exploit their individual benefit. A superior traffic signal scheduling strategy is used to resolve the difficulty. The several parameters may influence the traffic control model. So it is hard to learn the best possible result. Traffic light controllers are not expert to study from previous results. Due to this, they are unable to include the uncertain transformation of traffic flow. Reinforcement learning algorithm based traffic control model used to get fine timing rules by properly defining real-time parameters of the real traffic scenario. The projected real-time traffic control optimization prototype is able to continue with the traffic signal scheduling rules successfully. The model expands traffic value of the vehicle, which consists of delay time, the number of vehicles stopped at a signal, and the newly arriving vehicles to learn and set up the optimal actions. The experimentation outcome illustrates a major enhancement in traffic control, demonstrating the projected model is competent of making possible real-time dynamic traffic control.

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.

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.

Traffic crisis frequently happen because of traffic burden by the large number automobiles are on the path. Increasing transportation move and decreasing the average waiting time of each vehicle are the objectives of cooperative intelligent traffic control system. Each signal wishes to catch better travel move. During the course, signals form a strategy of cooperation in addition to restriction for neighboring signals to exploit their individual benefit. A superior traffic signal scheduling strategy is useful to resolve the difficulty. The several parameters may influence the traffic control model. So it is hard to learn the best possible result. Traffic light controllers are not expert to study from previous results. Due to this they are unable to include uncertain transformation of traffic flow. Reinforcement learning algorithm based traffic control model can be used to obtain fine timing rules by properly defining real time parameters of the real traffic scenario. The projected real-time traffic control optimization prototype is able to continue with the traffic signal scheduling rules successfully. The model expands traffic value of the vehicle, which consists of delay time, the number of vehicles stopped at signal, and the newly arriving vehicles to learn and establish the optimal actions. The experimentation outcome illustrates a major enhancement in traffic control, demonstrating the projected model is competent of making possible real-time dynamic traffic control.

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.