Intelligent Traffic Signal Research Articles

Peer-to-Peer Priority Signal Control Strategy in a Connected Vehicle Environment

This paper presents a methodology that enhances the priority signal control model in the multi-modal intelligent traffic signal system (MMITSS). To overcome the range limit of vehicle to infrastructure (V2I) and the intersection geometry message (MAP) distance limits, peer-to-peer intersection communications are utilized to send priority requests from adjacent intersections. Through integrated communication, the peer priority control strategy can create a signal plan for prioritized vehicles that considers longer term (headway) arrival times. Transit vehicles are considered in this study. The longer-term signal plan provides a flexible signal schedule that allows local phase actuation. The peer priority strategy is effective in reducing the number of stops and delay for priority eligible vehicles, while minimizing the negative impact on regular vehicles. To validate the strategy, a simulation experiment was designed to compare fully actuated control, coordination, and MMITSS priority control using two different VISSIM simulation networks (Arizona and Utah). The result shows that the peer-to-peer long term planning strategy can improve transit service reliability while limiting the adverse impact on other traffic.

Trajectory Length Prediction for Intelligent Traffic Signaling: A Data-Driven Approach

Ship trajectory length prediction is vital for intelligent traffic signaling in the controlled waterways of the Yangtze River. In current intelligent traffic signaling systems (ITSSs), ships are supposed to travel exactly along the central line of the Yangtze River, which is often not a valid assumption and has caused a number of problems. Over the past few years, traffic data have been accumulated exponentially, leading to the big data era. This trend allows more accurate prediction of ships’ travel trajectory length based on historical data. In this paper, ships’ historical trajectories are first grouped by using the fuzzy c-means clustering algorithm. The relationship between some known factors (i.e., ship speed, loading capacity, self-weight, maximum power, ship length, ship width, ship type, and water level) and the resultant memberships are then modeled using artificial neural networks. The trajectory length is then estimated by the sum of the predicted probabilities multiplied by the trajectory cluster centers’ length. To the best of our knowledge, this is the first time to predict the overall trajectory length of manually controlled ships. The experimental results show that the proposed method can reduce the probability of generating incorrect traffic control signals by 74.68% over existing ITSSs. This will significantly improve the efficiency of the Yangtze River traffic management system and increase the traffic capacity by reducing the traveling time.

An Approach for Intelligent Traffic Signal Control System for Ambulance Using IoT

Today, many human lives are being expired due to traffic jam and get delayed in reaching the hospital within time. In an emergency condition when a patient is in the ambulance, he/she has to reach the hospital with utmost fast to the nearest hospital should be done in a speedy way to save their lives, hence there is a need to tackle the traffic jam and control the traffic lights accordingly so that ambulance’s lane must be made green and thus providing a stoppage free way for the Ambulance to reach the hospital without delay in time.Normally traffic light systems operate on a timing mechanism that changes the light after a given interval of time. Thus, automated traffic signal releasing system for ambulance will not spend unnecessary time waiting for the traffic lights to change and manage of traffic jam. This proposed project aims to provide facilities and make easier for vehicle in emergency case especially for an ambulance. In an emergency situation, we are using the transmitter and receiver circuit which is using the Radio Frequency (RF) Sensor respectively. The transmitter will send the signal to the receiver that placed at every single traffic light to turn the traffic light from red to green light as the normal traffic is passed through, the traffic light will operate as usual until ambulance vehicle press the button by passing through it. This project aims to design an intelligent traffic light signal which makes the arrival of ambulance free way by halting traffic in case of emergencies. Hence this project will act as a life saver.

Iterative Tuning With Reactive Compensation for Urban Traffic Signal Control

To utilize the utmost capacities of junctions with traffic signals, having preemptive and predictive capabilities with integrated traffic sensors are important elements for an intelligent traffic signal control system. Recently, an efficient and preemptive traffic signal control strategy known as iterative tuning (IT) has been developed by exploring repetitive characteristic of traffic demand on working days, whereas on weekends or days with special events, severe nonrepetitive disturbances limit the performance of IT strategy practically. This paper proposes a preemptive and predictive methodology, i.e., IT strategy with reactive compensation (ITRC), to reduce network delay time and eliminate unnecessary stops for vehicles. With anticipation of repetitive traffic flows based on historical data, nominal traffic signals are tuned by IT controller over repetitions. The reactive compensation, which is junction-based model predictive control strategy (JMPC), makes adjustments on nominal traffic signals and compensates the nonrepetitive elements. Rigorous analysis provides sufficient condition for guaranteeing the convergence of ITRC. Case study of Commonwealth Avenue in Singapore with heavy traffic from morning peak to evening peak hours is presented, and the simulation results show the effectiveness, robustness, and convergence of ITRC over pure IT strategy and pure JMPC strategy for scenarios with various traffic variations, respectively.

INTELLIGENT TRAFFIC SIGNAL CONTROL SYSTEM

Adaptive traffic signal control system is presents an intelligent traffic signal control system using image processing technique. With the help of specialized algorithm, morphology and image needed to avoid traffic congestion. This paper processing technique, the vehicles are detected, recognized and density is calibrated for controlling traffic density. This is the most reliable and upcoming technology in the road transit system

Analysing improvements to on-street public transport systems: a mesoscopic model approach

Light rail transit and bus rapid transit have shown to be efficient and cost-effective in improving public transport systems in cities around the world. As these systems comprise various elements, which can be tailored to any given setting, e.g. pre-board fare-collection, holding strategies and other advanced public transport systems (APTS), the attractiveness of such systems depends heavily on their implementation. In the early planning stage it is advantageous to deploy simple and transparent models to evaluate possible ways of implementation. For this purpose, the present study develops a mesoscopic model which makes it possible to evaluate public transport operations in details, including dwell times, intelligent traffic signal timings and holding strategies while modelling impacts from other traffic using statistical distributional data thereby ensuring simplicity in use and fast computational times. This makes it appropriate for analysing the impacts of improvements to public transport operations, individually or in combination, in early planning stages. The paper presents a joint measure of reliability for such evaluations based on passengers’ perceived travel time by considering headway time regularity and running time variability, i.e. taking into account waiting time and in-vehicle time. The approach was applied on a case study by assessing the effects of implementing segregated infrastructure and APTS elements, individually and in combination. The results showed that the reliability of on-street public transport operations mainly depends on APTS elements, and especially holding strategies, whereas pure infrastructure improvements induced travel time reductions. The results further suggested that synergy effects can be obtained by planning on-street public transport coherently in terms of reduced travel times and increased reliability.

Long-Term Ship Speed Prediction for Intelligent Traffic Signaling

Yangtze River is probably the world's busiest inland waterway. Ships need to be guided when passing through a controlled waterway based on their long-term speed prediction. Inaccurate ship speed prediction leads to nonoptimal traffic signaling, which may cause a significant traffic jam. For the existing intelligent traffic signaling system, the ship speed is assumed to be constant, which has caused many problems and issues. This paper proposes a novel algorithm to construct an improved multilayer perceptron (MLP) network for accurate long-term ship speed prediction, in which the hidden neurons of the MLP are optimized by the particle swarm optimization method. The effectiveness and efficiency of the method are guaranteed by using the orthogonal least squares method, which is the fast approach for the construction of the MLP network in a stepwise forward procedure. The model is driven by easily acquired dynamic data of the ships, including the speed and the position. The effectiveness of the proposed method is further confirmed by comparing with several traditional modeling techniques. To the best of our knowledge, this is the first time that a ship speed model is built for long-term prediction. The experimental results show that the developed model is in good agreement with the real-life data, with more than 97% accuracy. It will help to generate the optimal traffic commands for Yangtze River in an intelligent traffic signaling system.

An intelligent control system for traffic lights with simulation-based evaluation

This paper introduces an intelligent control system for traffic signal applications, called Fuzzy Intelligent Traffic Signal (FITS) control. It provides a convenient and economic approach to improve existing traffic light infrastructure. The control system is programmed on an intermediate hardware device capable of receiving messages from signal controller hardware as well as overriding traffic light indications during real-time operations. Signal control and optimization toolboxes are integrated into the embedded software in the FITS hardware device. A fuzzy logic based control has been implemented in FITS. In order to evaluate the effects of FITS system, this study attempts to develop a computational framework to evaluate FITS system using microscopic traffic simulation. A case study is carried out, comparing different commonly used signal control strategies with the FITS control approach. The simulation results show that the control system has the potential to improve traffic mobility, compared to all of the tested signal control strategies, due to its ability in generating flexible phase structures and making intelligent timing decisions. In addition, the effects of detector malfunction are also investigated in this study. The experiment results show that FITS exhibits superior performance than several other controllers when a few detectors are out-of-order due to its self-diagnostics feature.

Traffic Image Classification using Horizontal Slice Algorithm

Traffic image classification to identify the traffic density will support the traffic problems such as intelligent traffic signal control, traffic planning, etc. This paper proposes a novel traffic image classification method based on horizontal slice algorithm and histogram. The system model is designed and evaluated with the image datasets of Ho Chi Minh city, Vietnam. The best accuracy result can obtain 91%. General Terms Traffic image classification, horizontal slice algorithm, histogram

Automatic Traffic Light Control for Emergency Vehicle

The paper describes the design of an intelligent auto traffic signal control system. Traffic congestion is the major issues that would be considered. At the junctions of the road, vehicular traffic intersects and it can be controlled by the traffic signals. One of the main need of the traffic signal is that good coordination and control among the vehicles. The traffic on the road is very high during the peak hours. This leads to the emergency vehicles to stick in the traffic jam. Dynamic control of traffic is needed during peak hours. Hence we propose an automatic traffic light control for emergency vehicles. The traffic light in the path of any emergency vehicles is controlled. It does not depend on the way of monitoring people to be manual which results in time delay. So, Human life is not affected due to the time delay in the arrival emergency vehicles. To control the traffic, the man power is not required in this method. The emergency vehicles find the shortest path to the location of the accident. If the train crosses the junction, the emergency vehicle gets alerted before five km. In front of the zebra line crossing, a divider can be created for safety crossing of people. The system uses AT mega 8micro controller, IR sensors. This system is used to control the traffic lights. And also save the time in emergency periods.

Multimodal Intelligent Traffic Signal System Simulation Model Development and Assessment

The study demonstrated the multimodal intelligent traffic signal system (MMITSS) simulation tool to assess the potential effects of a broader MMITSS deployment that would ultimately facilitate the site-independent analysis of MMITSS applications. The study also identified the most beneficial operational conditions for each MMITSS operational scenario by considering a combination of simulation variables and traffic demand levels. The MMITSS simulation platform consists of the Vissim microscopic traffic simulation software, the basic safety message distributor program, an Econolite ASC/3 traffic controller emulator that runs on a Windows platform, and a roadside equipment module that runs on a Linux platform. The study demonstrated that the intelligent traffic signal system reduced vehicle delay by up to 35% and increased average traffic stream speed by up to 27%. In addition, transit signal priority (TSP) reduced travel time for both transit and passenger vehicles on the corridor by up to 29% and 28%, respectively. However, TSP can increase systemwide delay because it reduces green times on the side streets. Freight signal priority (FSP) can be used effectively along major freight routes. Although FSP significantly reduced truck delay, networkwide delay was increased substantially, especially in the scenario with a large percentage of trucks in the traffic composition. The TSP and FSP bundle simulation study demonstrated that the bundle operation successfully executed a hierarchical level of priority and provided higher priority for trucks. MMITSS applications effectively improve vehicle travel time and delay for equipped and potentially nonequipped vehicles, depending on the scenarios considered. Typically, systemwide disbenefits are observed when TSP, FSP, or both are applied.

Intelligent traffic signal controller based on type-2 fuzzy logic and NSGAII

Intelligent traffic signal control (TSC) system is important for the alleviation of traffic congestion. Usually, most of the researches about TSC focused on single intersection based on type-1 fuzzy set. Compared with type-1 fuzzy logic controller (FLC), type-2 FLC can deal with more uncertainties in the road traffic control system. Therefore, a type-2 FLC optimized by NSGAII (T2-NSGAII) is designed for TSC in a complex road network, in which the intersection's traffic signal time is dynamically adjusted by its own and adjacent intersections' traffic volumes to reduce global delay time and traffic congestion. In T2-NSGAII, the expert rule set and the parameters of the fuzzy membership functions are simultaneously optimized by NSGAII to achieve less time delay and traffic congestion. In the simulations of a six-intersection traffic network with different vehicular arrival rates, it is demonstrated that T2-NSGAII has better performance compared with vehicle actuated controller based on fixed-time control (FTC), type-1 FLC, type-2 FLC and isolatedly optimized Type-2 FLC and the total delay time could be reduced by 76.3%, 65.1%, 58.3% and 35.4% respectively.

Dynamic Simulation and Optimization of Traffic Signal System

With the economic development and urbanization, the number of motor vehicles increased rapidly, because the contradictions between urban traffic supply and demand imbalances are becoming increasingly acute. We used the cell transmission model (CTM) to optimize on the basis of a single intersection by observing the delay time and the traffic capacity, research into the intelligent urban traffic signal control system, improve the operating efficiency of the existing transportation system. To a certain extent, it addresses the issue of urban traffic congestion.

Research on Strategy Intelligent Control of Traffic Signal

In order to make the traffic flow smoothly through the intersection, to minimize the delay time, must ensure that the control decision of the traffic signal is real-time and accuracy. Aiming at the existing problems of the traditional timing control method, analysis of traffic signal control parameters, performance indicators, put forward the fuzzy inference is applied to the traffic signal control system, puts forward an intelligent traffic signal control system based on fuzzy control; the vehicle queue length, inlet flow rate, green extension as the control parameters and simulation the results show that the proposed control method can effectively improve the traffic congestion, improve the vehicle capacity.

A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System

This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC) and Genetic Algorithms (GAs) and its application on a traffic signal system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and fuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space. They have a great influence over the performance of the system. On the other hand, the Genetic Algorithm is a metaheuristic that provides a robust search in complex spaces. In this work, it has been used to adapt the decision rules of FLCs that define an intelligent traffic signal system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid algorithm show an improvement of up to 34% in the performance with respect to a standard traffic signal controller, Conventional Traffic Signal Controller (CTC), and up to 31% in the comparison with a traditional logic controller, FLC.

An Intelligent Traffic Signal Control System Based on ARM Cortex-M3

In order to alleviate traffic jam, an intelligent traffic signal control system base on ARM Cortex-M3 is implemented. In the system, STM32F207 is processor. Embedded RTOS CoOS is transplanted to achieve multi-task control of traffic signal in software design. A new multi-population genetic algorithm is developed to optimize green ratio. The result analysis shows that the system has stable performance and it makes the optimization of green ratio convenient and swift.

An Intelligent Traffic Signal Controller Based on CAN Bus

According to the characteristics of mixed traffic flow of China, An intelligent traffic signal controller is introduced. STM32F207 and w5500 are used to design hardware structure. The software design is completed based on CoOS. Single point fuzzy control is used to improve dynamic traffic flow. Experimental results show that the intelligent traffic controller has high level of integration, powerful functions, and reliable performance.

A New Design of Intelligent Traffic Signal Control

Dynamic traffic signal control in Intelligent Transportation System (ITS) recently has received increasing attention. This paper proposed an adaptive and cooperative multi-agentfuzzy system for a decentralized traffic signal control. The proposed model has three levels of control, the current intersection traffic situation, its neighboring intersections recommendations and a knowledge base, which provides the current intersection traffic pattern. 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. Also every intersection flow is predicted in two different ways: 1- through a recursive algorithm. 2- based on a two stage fuzzy clustering algorithm. The proposed solution is tested with traffic control of a large connected junction and the result obtained is promising in comparison to the conventional fixed sequence traffic signal and to the vehicle actuated traffic signal control strategies which are the most applicable strategies in this area. Also to simulate the proposed traffic control solutions, a Netlogo-based traffic simulator has been developed as the agents’ world which simulates the roads, traffic flow and intersections.

Distributed and adaptive traffic signal control within a realistic traffic simulation

As traffic congestion rises within urban centers around the world, the intelligent control of traffic signals within cities is becoming increasingly important. Previous research within the area of intelligent traffic signal control has several shortcomings, including a reliance on historical data, the use of centralized systems which cannot handle city-sized problem instances and solutions which are not capable of addressing real-world traffic scenarios (e.g., constantly varying volumes and complex network structures). The research reported here proposes algorithms capable of controlling traffic signals that rely on traffic observations made by available sensor devices and local communication between traffic lights. This solution allows signals to be updated frequently to match current traffic demand, while also allowing for significantly large problem sizes to be addressed. To evaluate the developed system, a realistic traffic model was developed using information supplied by the City of Ottawa, Canada. It was found, through simulation within the SUMO traffic simulation environment, that the proposed adaptive system resulted in higher overall network performance when compared to the current fixed signal plan controllers, which were recreated using information from the City of Ottawa. This work also includes examples of why fixed signal controllers are inferior to an adaptive control system.

Intelligent Traffic Signal System for Isolated Intersections

One critical issue of traffic control is the optimization of signalized intersections for improved multimodal safety and operations. Accommodating pedestrian traffic at intersections is challenging because the demands of multimodal service compete fiercely on limited green time resources. The Highway Capacity Manual prescribes that the parallel vehicle green must exceed “Walk” plus pedestrian clearance interval (PCI) timed by a design walking speed. This static PCI timing is unsafe because seniors and children are likely to be slower than the design pedestrian. Furthermore, a vehicle-flow issue arises when the prolonged PCI exceeds the operationally efficient parallel green: additional vehicle right-of-way, unnecessary for operational efficiency, preempts green time from conflicting phase(s) and increases intersectionwide queuing delays. Queuing delays necessitate a trade-off between competing multifaceted traveler needs. Fuzzy logic control (FLC) proves effective, flexible, and robust in handling competing objectives. With the dynamic PCI concept, this research developed an intelligent traffic signal system that performed friendly pedestrian accommodation and also incorporated FLC into fulfilling multifaceted vehicle needs. The potential benefits from the new system optimized with a genetic algorithm were quantified through a comparison with a standard dual-ring, eight-phase, vehicle-actuated controller, conventionally cited as NEMA (National Electrical Manufacturers Association) control. Microsimulation experiments revealed that the current countermeasure, which lowered PCI timing design speed to strengthen crossing safety, was operationally deficient. The existing timing standard cannot offer adequate safety for all pedestrians, and the NEMA system omits multifaceted vehicle needs in control logic. In contrast, the FLC system fully protects all pedestrians through dynamic PCI and smartly serves manifold vehicle needs well. The FLC system outperforms the NEMA control by embodying a reasonable trade-off between competing objectives in the management of an isolated intersection.