Green Light Duration Research Articles

Smart Traffic Light Control System

Abstract—Traffic congestion is a significant issue in urban areas, leading to increased travel time, fuel consumption, and environmental pollution. This project introduces an adaptive Traffic Light Control System that dynamically prioritizes traf- fic direction based on real-time vehicle counts obtained from cameras. The system ensures efficient traffic flow by allocating green light duration proportionally to the vehicle density in each direction while maintaining fairness through a minimum green time of 3 seconds for all directions. The implementation includes a clockwise rotation of the green signal, preventing any direction from being perpetually delayed. The maximum green light duration is capped at 75 seconds to maintain system balance and efficiency. The results demonstrate improved traffic clearance, reduced waiting times, and significant optimization in signal utilization, making this system an effective solution for modern traffic management challenges. Index Terms—Smart Traffic Light, Traffic Light, OpenCV, Yolov.

Design of a Crossroad Traffic Light System

Nowadays, the situation of traffic jams is gradually increasing, and the demand for intelligent traffic lights is increasing as well. This paper designs a crossroad traffic light based on sequential logic using NI Multisim 14.0 virtual simulation software. The design includes: using 555 timer to build a second pulse oscillator circuit which outputs clock signal. Using 74LS169 counting chip to build timing circuit. Set up state control with 74LS74D trigger. Using nixie tubes and 74LS47 decoder chips to build the timing display circuit. Finally, logic gate and LED lights are used to build the display circuit of traffic lights. Next, how to design intelligent traffic lights is discussed based on this design combined with image monitoring technology. The feedback bit of image processing technology is added to the designed circuit, so that the circuit can change the green light duration with the traffic flow, and the designed circuits are also becoming smarter.

A Fuzzy Based Intelligent Traffic Light Control (ITLC) Method: An Implementation in Ankara City

The escalating global population and increased vehicle usage have worsened traffic congestion in metropolitan areas, a significant urban challenge. Addressing this, adaptive traffic light control methods, especially at intersections, are being developed to improve traffic flow and reduce waiting times. This study significantly contributes to this field by implementing Fuzzy Logic in intelligent traffic light systems, focusing on Ankara's Polatlı Refik Cesur intersection. Using the SUMO simulation platform and Python programming, it analyzed waiting times and queue lengths. The initial phase used queue length for each intersection arm as an input. Fuzzy logic rules then determined the output, prioritizing street or phase order for optimal flow. The study further proposed an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based control plan. ANFIS merges neural network capabilities with fuzzy logic, using waiting time and queue length as inputs to regulate the green light duration. Compared to existing traffic systems, this model showed a substantial improvement. It achieved a 36.5% reduction in waiting times, underlining the efficiency of the Fuzzy Logic-based method. This approach not only enhances traffic management but also contributes significantly to the literature on intelligent traffic light control systems. By addressing key urban traffic issues, the study paves the way for future advancements in traffic management technologies. The findings highlight the potential of combining advanced computational methods, like ANFIS, with traditional traffic control techniques to optimize urban traffic flow, offering a blueprint for similar challenges in other metropolitan areas.

Ant Colony Optimization With Look-Ahead Mechanism for Dynamic Traffic Signal Control of IoV Systems

In the era of Internet of Vehicle (IoV), traffic data are obtained by wireless sensor networks (WSNs), which provides convenience for real-time traffic signal control and is expected to further improve the efficiency of the whole system. Traffic signal controllers designed for IoV systems usually fully depend on data transmitted from the IoV system and are limited to the penetration rate of connected vehicles. This work proposes a dynamic ant colony optimization algorithm with the look-ahead mechanism (DALM) with eight no-conflict phases. The needed traffic information in the DALM algorithm is small and can be obtained independently by the system, which makes this method does not fully rely on the vehicle trajectories data and can better protect customer privacy and deal with cyber attack. With the look-ahead mechanism, the forthcoming traffic flow together with the current waiting queue are involved to predict the length of waiting vehicles and the optimal phase sequence is dynamically selected according to shortest green-light duration of candidate phase sequences obtained form the collected real-time traffic data. Experiments conducted under five scenarios with different kinds of traffic flow on SUMO platform show that compared with other four methods, both the total waiting time and the total carbon emissions of vehicles at the intersection are greatly reduced by using the DALM algorithm, especially in high traffic flow situations. The proposed algorithm can greatly improve the efficiency of the intersection in high traffic flow situation and is predictable to benefit the real-time traffic signal control in IoV system.

Optimization Models for Urban Traffic Management

The main control tool for traffic management in urban areas is traffic light settings. The goal is to decrease the queue lengths at intersections. Usually, the duration of the green light of the traffic light is used for control. The control approach is based on the so-called “store-and-forward” model. However, this model does not reflect the stochastic nature of traffic dynamics. This study presents a model with some probabilistic conditions approximating real traffic behavior. An additional contribution concerns the definition of a bi-level optimization model that simultaneously optimizes the green light and traffic light cycle duration of an urban network of four intersections. Three traffic management optimization problems are defined and solved. Their solutions are graphically illustrated and commented on. Bi-level optimization outperforms by giving lower values of queue lengths compared to classical and stochastic nonlinear optimization problems in the considered network.

Optimasi Waktu Tunggu Lampu Lalu Lintas dengan Mengaplikasikan Teori Graf dan Metode Webster

Mobility in the city of Manado is a trigger for the occurrence of traffic volume density, leading to congestion at several points, particularly at intersections. The Toar-Lumimuut intersection is one such vulnerable location where congestion often occurs, despite the presence of traffic lights controlling the flow of vehicles. In fact, many drivers still have to wait for the red light more than once at the Toar-Lumimuut intersection. Based on these findings, it indicates that the waiting time for traffic lights at the Toar-Lumimuut intersection is still inefficient. This study aims to determine the synchronized waiting time for traffic lights according to the traffic situation, in order to achieve a more optimal waiting time for traffic lights. This problem can be addressed using graph theory and the Webster method. The research yielded results, with the optimum cycle time increasing to 285 seconds, accompanied by an increase in green light duration and a decrease in red light duration as a percentage for each road segment. Therefore, the research concludes that this provides a more effective waiting duration.

Controlling Traffic Congestion in Urbanised City: A Framework Using Agent-Based Modelling and Simulation Approach

Urbanised city transportation simulation needs a wide range of factors to reflect the influence of certain real-life events accurately. The vehicle composition and the timing of the traffic light signal scheduling play an important role in controlling the traffic flow and facilitate road users, particularly in densely populated urban cities. Since road capacity in urban cities changes throughout the day, an optimal traffic light signal duration might be different. Hence, in this paper, the effect of vehicle composition and traffic light phases on traffic flow during peak and off-peak hours in Georgetown, Penang, one of the highly populated cities in Malaysia, is investigated. Through Agent-Based Modelling (ABM), this complex system is simulated by integrating the driver’s behaviour into the model using the GIS and Agent-Based Modelling Architecture (GAMA) simulation platform. The result of predicted traffic flow varies significantly depending on the vehicle composition while the duration of the traffic signal timing has little impact on traffic flow during peak hours. However, during off-peak hour, it is suggested that 20 s duration of green light provides the highest flow compared to 30 s and 40 s duration of green light. This concludes that the planning for traffic light phasing should consider multiple factors since the vehicle composition and traffic light timing for an effective traffic flow varies according to the volume of road users.

Design of Fuzzy and Conventional Controllers for Modeling and Simulation of Urban Traffic Light System with Feedback Control

Traffic patterns in urban areas present a complex and dynamic system that is characterized by inherent uncertainties. The presented study is a traffic light control system with feedback. The controller of the system is designed in a fuzzy and conventional way and is applied to a network of two junctions. The verification is performed using the MATLAB fuzzy toolbox platform (for designing the fuzzy controller) and Aimsun platform for microsimulation of the two junctions using the two types of controllers. To accomplish the control of the system a fuzzy controller on heuristic rules proposed to allow adaptive traffic control on signalized junctions in urban environments. The Fuzzy Toolbox in MATLAB is used to simulate the fuzzy controller. The Aimsun traffic simulator is used to model and simulate a traffic network of two intersections. The green light duration in the Aimsun model is based on the results for the two controllers from two separated experiments. Simulations of Aimsun models with the two types of controllers, the fuzzy and the conventional one, are compared. The experiment is performed under the premise that the traffic flow is oversaturated. Findings show that in a network of two junctions both controllers perform in a similar manner for the first junction. However, for the second junction, the fuzzy controller tends to have some advantages over the conventional controller with regard to higher traffic flow. In conclusion, the overall performance of the fuzzy controller is better than the one of the conventional controller, but further research is needed for more complex traffic networks.

Network-Level Traffic Signal Cooperation: A Higher-Order Conflict Graph Approach

Traffic signal control and cooperation are extremely important to alleviate traffic congestion in a large traffic network. This study develops a higher-order conflict graph approach for network-wide traffic signal control and cooperation. A conflict graph is applied to model the traffic signal configurations, which identifies the conflict and unconflicted movements for each intersection. In conflict graph, the node represents each movement. The weight of each node can be defined as traffic volume, queue length, fuel consumption, or any weighted combinations of these measurements. The calculation of the optimal green light duration and green light sequence (for different movements) is equivalent to sequentially finding the maximum weight independent set (MWIS) in the conflict graph. The conflict graph also provides a uniform and efficient way to connect traffic signal operations among nearby intersections spatially. Then, we introduced the concept of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -th order neighborhood to model the degree of connectivity between each movement to the movements at upstream or downstream intersections. The weight of each node in the higher-order conflict graph not only represents its own congestion level, but also relates to the traffic conditions of nearby intersections. Through this approach, the cooperation of multiple intersections can be realized by incorporating their spatial connectivity into conflict graph and solving the MWIS problem. A simulation network is built in SUMO to test the effectiveness of the proposed method. Results suggested that the proposed model outperformed other state-of-the-art signal control methods. Also, the scheme maintains good performance under varying traffic demands.

Intelligent Traffic Control System for Congestion Control, Ambulance Clearance and Stolen Vehicle Detection

Abstract: Over the years, the number of vehicles has increased dramatically, which has led to serious problems such as traffic jams, accidents, and many other problems, as cities turn into smart cities. In recent years, traffic jams have become one of the main challenges for engineers and designers to create an intelligent traffic management system capable of effectively detecting and reducing the overall density of traffic in most urban areas. This paper presents an intelligent traffic control system to pass emergency vehicles smoothly. Each individual vehicle is equipped with special RFID tag (placed at a strategic location), which makes it impossible to remove or destroy. We use RFID reader, NSK EDK-125–TTL and PIC16F877A system-on-chip to read the RFID tags attached to the vehicle. It counts number of vehicles that passes on a particular path during a specified duration. It also determines the network congestion, and hence the green light duration for that path. If the RFID-tag-read belongs to the stolen vehicle, then a message is sent using GSM SIM300 to the police control room. Also, when an ambulance is approaching the junction, it will communicate to the traffic controller in the junction to turn on the green light. This module uses ZigBee modules on CC2500 and PIC16F877A system-on-chip for wireless communications between the ambulance and traffic controller. The prototype was tested under different combinations of inputs in our wireless communication laboratory and experimental results were found as expected. Keywords: ZigBee, CC2500, GSM, SIM300, PIC16F877A, Ambulance Vehicle, Stolen Vehicle, Congestion Control, Traffic Junction.

Traffic Light Optimization Based on Modified Webster Function

Intersection traffic lights are a basic means of ensuring the normal operation of road traffic. A good signal timing scheme is essential for improving traffic congestion. To obtain the signal timing scheme of the designated intersection, the method proposed in this article is based on a modified Webster function. The method uses the signal cycle and proportion of green light duration as independent variables to establish the corresponding intersection vehicle delay function. This function is converted from a multiobjective optimization to a single-objective optimization formulation; a modified genetic algorithm is then used to find the optimal solution to this function. The experimental results show that the timing scheme optimized by the improved genetic algorithm can reduce the intersection delay by nearly 15.64%. The proposed traffic signal timing based on the modified Webster function will be of value as an important reference for the optimization of traffic lights at urban intersections.

Influence of the Speed of Motion of Public Motor Transport and the Time of the Green Signal of the Light Traffic on the Formation of Their Transport Flow

The optimal traffic flow of public transport through a controlled intersection can be achieved by taking into account its speed and the time of the green traffic light. Regularities of the influence of the green traffic light period on the number of public transport passing through the intersection, the acceleration time of public transport on its speed, the green light duration on a distance that public transport can cover, and the distance covered by public transport on the passing time are revealed. The results of the study suggest that there are reserves for increasing traffic flow by controlling the high-speed mode of public transport and taking into account the operating time of a traffic light object.

Traffic Lights Analysis and Simulation Using Fuzzy Inference System of Mamdani on Three-Signaled Intersections.

Abstract As one of the main characters at road intersections, traffic lights have lost its limelight and functions as of late. The unbalanced timing on its green light/green time duration settings with less to no regard to actual traffic in each lane becomes one of its main problems. Therefore in this paper, the author will formulate a more dynamic green time setting system by using Fuzzy Inference System type Mamdani. To help with the analyzing process, a desktop-based application is designed to simulate the green light duration setting for the traffic light based on the previously conducted analysis. The green light/green time output obtained during the implementation of this application will be further compared to the data obtained from transportation office of DKI Jakarta (Dinas Perhubungan Provinsi DKI Jakarta). From this comparison, it can be deduced that the output results from the implementation of this methods show a more dynamic value.

Knapsack problem-based control approach for traffic signal management at urban intersections: Increasing smooth traffic flows and reducing environmental impact

Urbanism development makes cities more congested and then more polluted. Hence, the primary factor that influences the urban environment directly is traffic flow. Therefore, this complex system requires efficient control methods to reduce its impact at urban zones, in particular for traffic light within intersections. In this paper, a dynamic control strategy of traffic signal at urban intersections is proposed. This strategy is based on Knapsack-problem and enables to manage the green light duration autonomously following the queue length for each road lane. The dynamic behavior of this system is considered as a discrete event system. Consequently, a modular Timed Synchronized Petri Net (TSPN) model is developed. Depending on real-time communication, the TSPN modules represent the ”slaves”, and the responsive controller represents the ”master” that manage optimally the vehicles evacuation at the intersection. Moreover, for the system dependability, some interesting properties of the system are checked through the developed Petri net model. SUMO based simulations are performed and analyzed to validate the proposed approach. Through the performed simulations and the analysis of the proposed dynamic control approach and findings show the efficiency of our control policy about smooth traffic increasing and environmental impact reducing.

An Intelligent Fuzzy Logic Based Traffic Controller

Increasing road congestion, travel time, number of accidents, carbon dioxide emissions, and fuel consumption are some of the consequences of growth in the vehicle population. Therefore, intelligent traffic controllers are required to solve road traffic congestion problems. The results of prevalent methods, including preset cycle time controller and vehicle-actuated controller, indicated that they do not effectively perform at traffic peak moments. Therefore, due to the deficiency of common methods, fuzzy logic based traffic signal controllers have attracted a lot of attention among researchers. In this article, a fuzzy logic based algorithm for 4-way intersections is proposed and it consists of two main stages for sorting the phase and determining the green light duration. The proposed system is simulated in the MATLAB programming environment and the performance of the designed controller and a conventional controller is compared for some of the presumed conditions. The results of applying the proposed system indicate that this algorithm has a better performance in different traffic conditions in contrast to a preset cycle time controller and it can reduce the number of vehicles behind traffic lights at intersections and the waiting time of passengers.

Fog-based dynamic traffic light control system for improving public transport

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.

Dynamic Traffic Light Control

Intelligent Transport System (ITS) is blooming worldwide. The Traditional Traffic management system is a tedious process and it requires huge man power, to overcome this we have proposed an automatic Traffic monitoring system that has effective fleet management. The current transportation system at intersections and junctions has Traffic Lights with Fixed durations which increase the unnecessary staying time which intern harms the environment. An Adaptive traffic light control is implemented using SUMO simulator, that changes the duration of Green and Red light according to the traffic flow. This is an effective and efficient way to reduce the Traffic congestion. The traffic congestion is determined by taking the object count using deep learning approach (Convolutional Neural Network).

Optimization of Traffic Light Control Using Fuzzy Logic Sugeno Method

Congestion is one of the big problems around the world, especially for big cities. Intersections are the scene of congestion because the lane is the meeting point of two or more roads which has a major influence on the smooth flow of vehicles on the road network. This congestion occurs due to various factors, one of which is the statistical traffic light duration, which does not match traffic conditions. Based on this, there needs to be a development in the timing of a more adaptive green light. This study describes the design of a traffic light controller using the Sugeno method fuzzy logic. This study aims to design a green light duration calculation by applying fuzzy logic that results in adaptive traffic light duration at intersections, by entering the density of each intersection path, which is divided into 4 inputs, namely regulated lane density, opposing lane density I, and opposite lane density. II, the density of the opposite lane III, with the aim of the system being able to produce a duration that is in accordance with the current traffic situation with an output in the form of a green light duration on the regulated lane.

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.

Modular design for an urban signalized intersections network using synchronized timed Petri nets and responsive control

Abstract Traffic flow at urban intersections fluctuates randomly throughout the day. It depends on several dynamic factors and requires adequate regulation and adaptable control strategies in particular for traffic signals regulation. This paper deals with this problem, and our proposal focuses on the adaptive management of traffic lights within urban intersections. To do so, Timed Synchronized Petri Nets (TSPNs) models are first developed to model and study the system behaviour. Then a real-time adaptive control strategy is proposed for traffic regulation within urban signalized intersections. The control logic is shared between two communicating components of the system. The slaves (TSPNs sub-models) control the traffic signals displays, phases transitions, and traffic flow fluctuations. The master (controller) decides and selects the next phase that should be serviced and determines its green light duration. Due to the used modularity approach, the developed models decrease the modelling complexity, and more importantly, they can be adapted easily to a cluster of intersections. Furthermore, various traffic signal control strategies could be implemented using these models. Moreover, some simulations are performed, and the obtained results are analysed and discussed. Our control strategy is validated through these simulation results.