Traffic Light Cycle Research Articles

Developing Hybrid Simulation Model to Improve Road Traffic Management

Yogyakarta has increasing trends in the number of vehicles and consequently intensifying the traffic volume and will effect to higher emission and air pollution. Traffic lights duration plays a vital role in congestion mitigation in the critical intersections of urban areas. This study has objective to minimize the number of vehicles waiting in line by developing the hybrid simulation method. First of all, the MKJI and Webster method were calculated to determine the green traffic light. Then, the simulation model was developed to evaluate the number of vehicles waiting in line according to different duration of green traffic lights from MKJI and Webster method. A case study will then be provided in Pelemgurih intersection located in Yogyakarta, Indonesia for demonstrating the applicability of the developed method. The result shows that the duration of green traffic lights calculated by Webster method provides lower number of vehicles waiting in line. It is due to the short duration of green traffic light resulted by Webster method so that the traffic light cycle becomes shorter and it effects the number of vehicles waiting in line which is lower than MKJI method. The results obtained can help the generating desired decision alternatives that will important for Department of Transportation, Indonesia to enhance the road traffic management with low number of vehicles waiting in line.

Development of an Efficient Driving Strategy for Connected and Automated Vehicles at Signalized Intersections: A Reinforcement Learning Approach

The concept of Connected and Automated Vehicles (CAVs) enables instant traffic information to be shared among vehicle networks. With this newly proposed concept, a vehicle’s driving behaviour will no longer be solely based on the driver’s limited and incomplete observation. By taking advantages of the shared information, driving behaviours of CAVs can be improved greatly to a more responsible, accurate and efficient level. This study proposed a reinforcement-learning-based car following model for CAVs in order to obtain an appropriate driving behaviour to improve travel efficiency, fuel consumption and safety at signalized intersections in real-time. The result shows that by specifying an effective reward function, a controller can be learned and works well under different traffic demands as well as traffic light cycles with different durations. This study reveals a great potential of emerging reinforcement learning technologies in transport research and applications.

Model calibration to simulate driving recommendations for traffic flow optimization in oversaturated city traffic

Long queues at signals cause fuel-consuming stop-and-go traffic. In this work we use a complete microscopic spatiotemporal measurement of congested city traffic at a signal to i) calibrate a both longitudinal and latitudinal driving model and then to ii) examine how changes in single vehicle’s driving behaviour could improve the situation. The model calibration is realized using a genetic algorithm. In this way, a realistic heterogeneous traffic scenario that has similar properties as empirical traffic could be simulated. We then show that already changing the behaviour of a single vehicle per traffic light cycle can significantly reduce the number of vehicles waiting in queues.

Optimal Supervisory Control of Two Phases Intersection Using Hybrid-based Model Approach

In the present paper, a hybrid approach that combines modular modelling and Timed Petri Nets model is proposed, the main interest of this combination is to ensure optimal supervisory control of urban traffic flows in two phases intersections of medium-sized cities. Each intersection is considered as a multi-agent system, composed of all agents who represent all physical spaces. The behaviours of interconnected agents are supervised by the Timed Petri Nets model that controls the traffic light cycles according to an optimal timing plan defined by the Continuous Genetic Algorithm. A real two phases intersection in Sidi Bel-Abbes City is presented as an example. The proposed timing plan is compared with the current one using SUMO software for the most important objective functions. Also, the proposed model's behaviour is compared with TSIS-CORSIM software's model, and all obtained experiments results show the effectiveness of the approach and its possibility to be extended and applied to a many-phases intersection.

Matlab algorithms for traffic light assignment using fuzzy graph, fuzzy chromatic number, and fuzzy inference system

We propose algorithms in Matlab that combine fuzzy graph, fuzzy chromatic number (FCN), and fuzzy inference system (FIS) to create traffic light assignment based on traffic flow, conflict, and queue length in an intersection. We evaluate the algorithms through two case studies each on a signalized intersection at Semarang City (Indonesia) and compare the result to the existing systems. The case studies show that the algorithm based on fuzzy graph-FCN-FIS could reduce traffic light cycle time on the intersections.We provide three results as follows:•A pseudocode to construct fuzzy graph of traffic data in an intersection.•Algorithm 1 is to Determine fuzzy graph model of a traffic light data and phase scheduling using FCN function which is presented using Matlab programming language.•Algorithm 2 is to Determine duration of green lights of each phase using Mamdani-FIS codes in Matlab.

Traffic Light Cycle Configuration of Single Intersection Based on Modified Q-Learning

In recent years, within large cities with a high population density, traffic congestion has become more and more serious, resulting in increased emissions of vehicles and reducing the efficiency of urban operations. Many factors have caused traffic congestion, such as insufficient road capacity, high vehicle density, poor urban traffic planning and inconsistent traffic light cycle configuration. Among these factors, the problems of traffic light cycle configuration are the focal points of this paper. If traffic lights can adjust the cycle dynamically with traffic data, it will reduce degrees of traffic congestion significantly. Therefore, a modified mechanism based on Q-Learning to optimize traffic light cycle configuration is proposed to obtain lower average vehicle delay time, while keeping significantly fewer processing steps. The experimental results will show that the number of processing steps of this proposed mechanism is 11.76 times fewer than that of the exhaustive search scheme, and also that the average vehicle delay is only slightly lower than that of the exhaustive search scheme by 5.4%. Therefore the proposed modified Q-learning mechanism will be capable of reducing the degrees of traffic congestions effectively by minimizing processing steps.

Study on reduction of the fuel consumption and the pollution by using traffic management systems in urban areas

The traffic management systems are implemented on a large scale during the last decades in urban areas due to their feature to optimize the traffic capacity, in a simultaneous and real time, not only at the individual junctions but also the entire city level. This is achieved through the short term predictions, using the traffic data bases collected by the specific sensors from the traffic controlled crossroads or from other places into the city street network, data that’s used on-line in order to optimize the traffic lights cycles onto all the controlled crossroads. In this way it is accomplished a optimal capacity for a certain traffic volume over a period of time. Using this method good results are obtained considering the reduction of the fuel consumption and of the pollution level. Another result is the increasing of the average speed of vehicles and the decreasing waiting times on the crossroads, which is beneficial for both cars and public transport. The case study for the Pitesti city highlights these aspects and shows how the urban transport model could be used for quantifying the effects of implementing the traffic management systems. The paperwork summary regarding the public transportation is attached.

Optimization of Traffic Flow On KS Tubun Street using Greenberg Model

KS Tubun Street is a street in Bogor, which has a fairly high vehicle volume and become one of a high-traffic jam area. This is caused by KS Tubun Street is the main road for road users from Jakarta and Bogor. Traffic jam problem that occurs due to the confluence interchange of traffic flow and traffic lights settings that are not proportional to the volume of vehicles across the road. Optimization of traffic flow at KS Tubun Street performed by the stages of forming a model of traffic flow, determining the density and velocity of the vehicle is based on the Greenberg model, and determining the length of the traffic lights to avoid a buildup of vehicles. The result is a traffic flow model with distance and time parameters. The density of vehicles that occurs on the streets of KS. Tubun street based on the Greenberg model between 180 to 240 unit car of passanger (ucp) with the average velocity of vehicles 15 to 19.5 km per hour. The density of vehicles on KS. Tubun street can be break down by increasing time. Traffic light cycle time can be reduced for 8 seconds with the red light glowing time is 80 seconds and the green light glowing time is 62 seconds.

Fuzzy Vehicle Control System for Single Intersection

Traffic light at the intersection is an important part of the daily life of the urban cities. Traffic light needs to be managed properly so that a large number of vehicles able to cross the intersection. Current traffic light system is not able to allot efficient traffic light cycle length to vehicles. It is because of the traffic system is based on the fixed cycle length. Today traffic is highly fluctuating. Number of vehicles in the traffic varies with respect to time and day. To handle this situation, the paper proposes a vehicle control system using fuzzy logic: Fuzzy Vehicle Control System (FVCS). FVCS allots dynamic cycle length signal duration to vehicles based on the present traffic available at the intersection. Experiments have been performed on the proposed FVCS architecture using MATLAB. Experiments show that a reduction of 28.92% can be achieved in the number of vehicles that are waiting at the intersection to cross it

Driving solutions for autonomous vehicles, and adaptive proposals to set the traffic lights times and to choose routes

Two objectives are pursued in this article: 1) with adaptive solutions, improve the traffic flow by setting the time cycle of traffic lights at intersections and reduce the travel time by selecting the vehicles route (treated as separated problems). 2) Avoid driving conflicts among autonomous vehicles (which have defined trajectories) and these with a non-autonomous vehicle (which follows a free path). The traffic lights times are set with formulas that continuously recalculate the times values according the number of vehicles on the intersecting streets. For selecting the vehicles route an algorithm was developed, this calculates different routes (connected streets that conform a solution from the origin to the destination) and selects a route with low density. The results of the article indicate that the adaptive solutions to set the traffic lights times and to select the vehicles path, present a greater traffic flow and a shorter travel time, respectively, than conventional solutions. To avoid collisions among autonomous vehicles which follow a linear path, an algorithm was developed, this was successfully tested in different scenarios through simulations, besides the algorithm allows the interaction of a vehicle manually controlled (circulating without restrictions) with the autonomous vehicles. The algorithm regulates the autonomous vehicles acceleration (deceleration) and assigns the right of way among these and with the human controlled vehicle.

Modeling and simulation of queue waiting time at traffic light intersection

Long queues of vehicles are often found at various traffic light intersection in cities. Such situation is caused by many factors, including the duration of traffic lights that do not match with the arrival of the vehicles. In this article, we propose a model of queue at a road intersection with traffic lights where it can be determine the appropriate traffic light duration based on the arrival of the vehicle, so that the queue waiting time can be obtained that is as far as possible under the driver’s time stress threshold. The waiting time in the queue is very dependent on the accuracy of the traffic light time duration setting, both red and green lights on all intersection lines. The research method that will be carried out is firstly to determine the vehicle waiting time model in the queue at the red light phase, followed by the waiting time model in the green light phase with the arrivals by Poisson process with M/M/1 queuing model, then, the waiting time of all vehicles will be determined in one traffic light cycle. Through the implementation of the queue waiting time model, it is expected to obtain an average waiting time for each of vehicle, based on the arrival process and the duration time of traffic lights. The application of SimEvents MATLAB-Simulink is used to demonstrate the calculation from the model that being built for the queue waiting time.

Development of Model for Traffic Flows on Urban Street and Road Network

The paper considers issues pertaining to creation of a model for controlling road traffic with the purpose to minimize delays on street and road network, which is proposed as an innovative one while developing an intelligent transport system of the large city that is Minsk. The developed model has a complex structure of algorithmic support. The first-level model has been implemented on the basis of fuzzy logic, for which a program has been developed and conditions have been determined, and operation of traffic light at a real local intersection of Minsk, which is included in the automated traffic management system, has been simulated. Innovation in the first-level model is an approach in determining conditions while detecting a fuzzy set without using a standard algorithm that is an algorithm of local flexible regulation. The paper proposes and investigates a model that works on the basis of operationally obtained parameters of traffic flow intensity at characteristic points (sections) of street and road network. Efficiency of the first-level model has been equal to 8 % due to optimization of a traffic light cycle (reduction of transport delays during passage of stop lines). Results of the simulation using the proposed computer program have made it possible to improve efficiency of traffic management on the studied highway (Logoysky trakt) in Minsk city of Minsk by 15 % due to decrease of delay level in case of unilateral coordination. The algorithm has been already implemented as part of the current automated traffic management system in the city of Minsk and it has shown its efficiency. However this efficiency can be increased if it is used together with an algorithm for searching maximum volume of motion in a cycle with a distributed intensity pulse. It has been planned to take into account this specific feature when increasing possibilities for algorithmization of traffic management.

The study of the influence of intelligent driver assistance systems on the capacity of urban roads

The article gives theoretical and experimental studies of the time delay of cars in the stream, due to the time of the driver’s reaction to changing traffic conditions. In the present study, it was switching of allowing signal of the traffic light which was assigned as the initial moment of the measurements. The observation showed the dependence of the delay time of the car on its position in the queue at the junction with traffic light regulation. Due to the presence of the human factor, the accumulated delay along the flow has had a great influence on the number of vehicles which had the opportunity to cross the junction within one traffic light cycle. A situation was simulated in which all vehicles in a stream were equipped with an assistant driver device when moving in a traffic jam. The value of the delay of the vehicle in this case depends not on the actions of a single person, but on the electronics. It was found that the delay of the car when using electronics is 5.44 times less than the delay caused by the human factor. The study was conducted in a part of Nadezhda Suslova street in the city of Nizhny Novgorod and showed the possibility to increase the speed and intensity of the considered actual traffic flow.

A Deep Reinforcement Learning Network for Traffic Light Cycle Control

Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy. To improve efficiency, taking real-time traffic information as an input and dynamically adjusting the traffic light duration accordingly is a must. In terms of how to dynamically adjust traffic signals' duration, existing works either split the traffic signal into equal duration or extract limited traffic information from the real data. In this paper, we study how to decide the traffic signals' duration based on the collected data from different sensors and vehicular networks. We propose a deep reinforcement learning model to control the traffic light. In the model, we quantify the complex traffic scenario as states by collecting data and dividing the whole intersection into small grids. The timing changes of a traffic light are the actions, which are modeled as a high-dimension Markov decision process. The reward is the cumulative waiting time difference between two cycles. To solve the model, a convolutional neural network is employed to map the states to rewards. The proposed model is composed of several components to improve the performance, such as dueling network, target network, double Q-learning network, and prioritized experience replay. We evaluate our model via simulation in the Simulation of Urban MObility (SUMO) in a vehicular network, and the simulation results show the efficiency of our model in controlling traffic lights.

INTELLIGENT SYSTEM OF TRAFFIC LIGHT CONTROL WITH DYNAMIC CHANGE PHASES OF TRAFFIC FLOWS ON CONTROLLED INTERSECTIONS


 
 
 
 
 There was method of making an effective system of traffic-light control of the traffic through the intersections in one direction according to which the phase coefficients for each cycle of traffic-light control are computed in real- time using the data of traffic intensity detected by transport detectors. Thus, the built-in traffic control system will be dynamically adapted to the change in the intensity of traffic flows, and the structure of the cycle and its duration will be changed taking into account the parameters of the traffic flow at the intersection. Accordingly, the traffic light cycle, where each cycle has the minimum required duration, will be most effective and will ensure uninterrupted traffic, the lack of traffic jams and the convenience for the pedestrian crossings.
 
 
 
 

Management of urban traffic flow based on traffic lights scheduling optimization

The automobile has become an essential element in the daily life of people. In the urban traffic network, an intersection is the ``bottleneck point'' of road network capacity. Most cities in Latin America have a traditional single control system or none. In this context, adequate programming of the traffic light cycles helps to reduce vehicles' waiting time and improve traffic flow. In this paper, we compare five metaheuristics to minimize vehicles' waiting time and maximize the velocity of vehicles in two real scenarios of Argentina and Uruguay. Analysis of experimental results shows that the proposed model optimizes the vehicular flow in a multiple-junction urban traffic network with different topologies and traffic flow.

Measures to ensure road traffic safety on controlled intersections

The paper discusses a new approach to ensuring the safety of controlled intersections. In the course of research, the authors found a change in the main characteristics of the traffic flow, which requires constant monitoring in the implementation of the intersection control using a traffic light. The heterogeneity of the composition of the traffic flow is determined and the influence of this parameter on the duration of the resolving signal is established. In addition, the authors determined the influence of weather conditions on the duration of the traffic light cycle and developed an approach to take into account changes in this factor in the calculation of the mode of operation of the traffic light object. As a result of the model experiment, the economic and environmental efficiency of the developed approach is determined. The results obtained in the course of the study are an integral part in the implementation of intelligent transport systems, which confirms the relevance and feasibility of the study. The use of this approach, in addition to integration into the its system, allows to reduce the accident rate of controlled intersections due to the rational distribution of the time of the traffic light object and reduce vehicle delays on the approaches, which together ensures the safety of regulated areas.

Congestion Adaptive Traffic Light Control and Notification Architecture Using Google Maps APIs

Traffic jams can be avoided by controlling traffic signals according to quickly building congestion with steep gradients on short temporal and small spatial scales. With the rising standards of computational technology, single-board computers, software packages, platforms, and APIs (Application Program Interfaces), it has become relatively easy for developers to create systems for controlling signals and informative systems. Hence, for enhancing the power of Intelligent Transport Systems in automotive telematics, in this study, we used crowdsourced traffic congestion data from Google to adjust traffic light cycle times with a system that is adaptable to congestion. One aim of the system proposed here is to inform drivers about the status of the upcoming traffic light on their route. Since crowdsourced data are used, the system does not entail the high infrastructure cost associated with sensing networks. A full system module-level analysis is presented for implementation. The system proposed is fail-safe against temporal communication failure. Along with a case study for examining congestion levels, generic information processing for the cycle time decision and status delivery system was tested and confirmed to be viable and quick for a restricted prototype model. The information required was delivered correctly over sustained trials, with an average time delay of 1.5 s and a maximum of 3 s.

ДО ПИТАННЯ ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ СВІТЛОФОРНОГО РЕГУЛЮВАННЯ

Предметом вивчення в статті є новий підхід визначення параметрів руху транспортних засобів і особливості сучасної організації моніторингу транспортних потоків (ТП) на таких регульованих перехрестях. Метою є розробка нового способу, що дозволяє підвищити рівень якості та комплексної оцінки визначення елементів циклу світлофорного регулювання (СР) в умовах зміни параметрів руху ТП в зоні перехрестя. Завдання дослідження: аналіз існуючих підходів моніторингу ТП на регульованих перехрестях; розробка нового способу, що дозволяє отримати більш повну інформацію для контролю та ефективного визначення оптимальних значень часу дії елементів циклу СР; розробка способу, що відповідає вимогам універсальних адаптивних систем, що дозволяють ефективно реагувати на зміни умов руху ТП на перехресті. Результати. Розкрито деякі особливості сучасної організації моніторингу ТП на локальних об'єктах, а також проаналізовано їх основні переваги та недоліки. Представлений новий спосіб визначення елементів циклу СР в умовах динамічного зміни параметрів руху ТП і адаптивного реагування системи на дорожню ситуацію. Наведено варіант спрощеної структури пристрою для реалізації запропонованого способу і ефективної обробки вихідної інформації, показана достовірність, і працездатність алгоритмів, закладених в спосіб. Висновки. Ефективне визначення параметрів руху ТП в зоні перехрестя по кожній смузі руху протягом світлофорного циклу дає можливість отримати більш повний комплекс інформації для контролю і подальшого оперативного управління рухом на перехресті. Показано шляхи розширення функціональних можливостей запропонованого способу за рахунок одночасного визначення додаткових параметрів стану ТП на перехресті і критеріїв оцінки якості НГ. Гнучка технологія і комплексна оцінка режимів СР дозволяє в реальному масштабі часу здійснювати як параметричну адаптацію, так і структурну адаптацію за рахунок реорганізації схем управління руху.

INTELLIGENT SYSTEM OF TRAFIC LIGHT CONTROL WITH DYNAMIC CHANGE PHASES OF TRAFFIC FLOWS ON UNCONTROLLED INTERSECTIONS


 
 
 There was method of making an effective system of traffic-light control of the traffic through the intersections in one direction according to which the phase coefficients for each cycle of traffic-light control are computed in real-time using the data of traffic intensity detected by transport detectors. Thus, the built-in traffic control system will be dynamically adapted to the change in the intensity of traffic flows, and the structure of the cycle and its duration will be changed taking into account the parameters of the traffic flow at the intersection. Accordingly, the traffic light cycle, where each cycle has the minimum required duration, will be most effective and will ensure uninterrupted traffic, the lack of traffic jams and the convenience for the pedestrian crossings.