Duration Of Traffic Lights Research Articles

IMPLEMENTASI METODE FUZZY TSUKAMOTO UNTUK PENENTUAN DURASI FASE LALU LINTAS (Studi Kasus di Perempatan Jalan Soekarno Hatta – Ibrahim Adjie, Bandung)

The Soekarno Hatta - Ibrahim Adjie Intersection is an intersection that has a red light duration that is quite long, reaching 5 minutes. Setting the duration of traffic lights that are not adjusted to the density of vehicles against the capacity of the road can cause congestion which affects traffic violations by drivers such as running red lights which can cause accidents. Therefore, this study aims to optimize traffic management using the Tsukamoto fuzzy method. The data on the number of vehicles, road capacity, and traffic light duration are data taken from direct observation. Based on the results of the study, it was found that the Soekarno Hatta - Ibrahim Adjie intersection required a shorter total traffic phase duration to reduce vehicle waiting time thereby reducing congestion.

Application of Compatible Graph and Webster Methods for Traffic Light Arrangement in Jepara

The problem of congestion has become a common problem that is often found on the highways, especially at the crossroads, without exception in Jepara Regency. The Mayong-Jepara Highway intersection is classified as a busy intersection that is full of vehicles during every departure and return hours, which are dominated by factory employees. Congestion often occurs at these intersections even though there are traffic lights that regulate the movement of vehicles. So, This study aims to find the solutions to congestion problems at Mayong-Jepara Highway intersection through compatible graph modeling and optimizing traffic light cycle time using the Webster method. Compatible graphs are used to determine traffic flows that are worked to move simultaneously without causing collisions, and that are used to carry out the graph weighting to determine the number of phases. The variables used in this applied research are the data on the number of vehicles, road width, and traffic light duration that taken directly in the field. Because the traffic density in the morning and afternoon is different for each intersection arm. Thus, the cycle time is determined in 2 sessions namely the morning and the afternoon. From the modeling of the compatible graph that has been weighted, it is obtained that the morning and afternoon sessions have 3 phases as the total which are then used to determine the traffic light cycle time. As a result, the cycle times for the morning session are 98 seconds, and the afternoon session is 155 seconds. The results were then simulated with PTV Vissim software and assessed to be quite effective in dealing with congestion and heavy traffic at the Mayong-Jepara Highway intersection especially in the afternoon session.

FairLight: Fairness-Aware Autonomous Traffic Signal Control With Hierarchical Action Space

Although Reinforcement Learning (RL) approaches are promising in autonomous Traffic Signal Control (TSC), they often suffer from the unfairness problem that causes extremely long waiting time at intersections for partial vehicles. This is mainly because traditional RL methods focus on optimizing the overall traffic performance, while the fairness of individual vehicles is neglected. To address this problem, we propose a novel RL-based method named FairLight for the fair and efficient control of traffic with variable phase duration. Inspired by the concept of User Satisfaction Index (USI) proposed in the transportation field, we introduce a fairness index in the design of key RL elements, which specially considers the travel quality (e.g., fairness). Based on our proposed hierarchical action space method, FairLight can accurately allocate the duration of traffic lights for selected phases. Experimental results obtained from various well-known traffic benchmarks show that, compared with state-of-the-art RL-based TSC methods, FairLight can not only achieve better fairness performance, but also improve the control quality from the perspectives of the average travel time of vehicles and RL convergence speed.

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.

PROTOTYPE SMART TIME SCHEDULER LAMPU LALU LINTAS MENGGUNAKAN ALGORITMA HAAR CASCADE

The traffic light functions as a traffic controller at crossroads and also as smoothness at intersections to avoid congestion. The current application of conventional traffic lights still has weaknesses in determining the duration of traffic lights which are not adjusted to the number of vehicle volumes which often change every time. Especially when there is a dense flow of vehicles at intersections, especially at certain times. Based on observations of traffic density at the intersection that connects Soekarno - Hatta road and Arifin Achmad road in Pekanbaru City. There is often a high density of vehicles from the direction of Jalan Soekarno-Hatta (Morning Market) to Jalan Jenderal Sudirman during working hours so that the duration of the traffic lights cannot reduce the amount of queue density in the light traffic. The implementation of the Smart Time Scheduler is a solution for adjusting the duration of traffic lights based on the level of traffic density, by building a tool to detect and calculate the number of vehicle queues at the Traffic Light and then enter the calculation results into 3 categories, namely Normal, Medium and Dense Density. The system uses Haar Cascade with the open cv library on the Raspberry pi, the results of the system testing that has been carried out can count the number of vehicles and instruct Arduino to set the duration of the traffic lights where in the category of normal vehicle queues, the green traffic light gets a waiting time of 10 seconds in countdown, Moderate category for 15 seconds, and Solid Category, for 20 seconds, while the calculation error value uses the Error Percentage equation formula, namely comparing the results of vehicle calculations by the system with Manual Calculation Results multiplied by 100% Then the System Calculation Error is 16% and the accuracy level of the tool is 100% - 16% = 84%, the size of the error value is influenced by the quality of the light intensity, and the distance of the camera's detection of the object when performing the detection.

Deep Reinforcement Learning for Traffic Light Timing Optimization

Existing inflexible and ineffective traffic light control at a key intersection can often lead to traffic congestion due to the complexity of traffic dynamics, how to find the optimal traffic light timing strategy is a significant challenge. This paper proposes a traffic light timing optimization method based on double dueling deep Q-network, MaxPressure, and Self-organizing traffic lights (SOTL), namely EP-D3QN, which controls traffic flows by dynamically adjusting the duration of traffic lights in a cycle, whether the phase is switched based on the rules we set in advance and the pressure of the lane. In EP-D3QN, each intersection corresponds to an agent, and the road entering the intersection is divided into grids, each grid stores the speed and position of a car, thus forming the vehicle information matrix, and as the state of the agent. The action of the agent is a set of traffic light phase in a signal cycle, which has four values. The effective duration of the traffic lights is 0–60 s, and the traffic light phases switching depends on its press and the rules we set. The reward of the agent is the difference between the sum of the accumulated waiting time of all vehicles in two consecutive signal cycles. The SUMO is used to simulate two traffic scenarios. We selected two types of evaluation indicators and compared four methods to verify the effectiveness of EP-D3QN. The experimental results show that EP-D3QN has superior performance in light and heavy traffic flow scenarios, which can reduce the waiting time and travel time of vehicles, and improve the traffic efficiency of an intersection.

Traffic-light control in urban environment exploiting drivers’ reaction to the expected red lights duration

Traffic congestion in urban environment is one of the most critical issue for drivers and city planners for both environment and efficiency reasons. Traffic lights are one of the main tools used to regulate traffic by diverting the drivers between different paths. Rational drivers, in turn, react to the traffic light duration by evaluating their options and, if necessary, by changing direction in order to reach their destination quicker. In this paper, we introduce a macroscopic traffic model for urban intersections that incorporates this rational behavior of the drivers. Then, we exploit it to show that, by providing additional information about the expected red-time duration to the drivers, one can decrease the amount of congestion in the network and the overall length of the queues at the intersections. Additionally, we develop a control policy for the traffic lights that exploits the reaction of the drivers in order to divert them to a different route to further increase the performances. These claims are supported by extensive numerical simulations.

Implementation of Multi Level Traffic Controlling System using IoT

Abstract: In civic areas, traffic system is one of the significant pointers to show the growth and progress of a megacity and it also influences the quality of life of people living in metropolitan metropolises. In recent times, there's a significant increase in operation of road vehicles which is getting challenge for existing transportation system. The presently stationed traffic system isn't grounded on the traffic position and a predefined time is allocated for traffic signals at every road crossing which results in traffic congestion and situation becomes worst in the peak business hours. When traffic demand is great enough that the commerce between vehicles slows the speed of the traffic sluice, this results in some traffic. This high position business traffic contributes in the pollution by the emigration of CO 2 and several other adulterants in air. Also, it also causes tripling of the energy consumption and accordingly put adverse effects on the economy as well. To address the above problem, this paper presents the development of congestion level based dynamic traffic control system using IoT. It regulates the traffic lights duration grounded on the real time position of the traffic measured at the road crossings by using IR detectors. The development of this design is divided in three phases i.e., simulation and sense/logic development, development of IoT grounded system and eventually hardware implementation. In first phase the simulations are done in Proteus and results are presented in four cases i.e., normal routine, low position traffic, medium position traffic and high position traffic. In alternate phase the IoT grounded system is developed by making the communication link between the end bumps and the gateway over the internet. Eventually, the real- time prototype is enforced. In addition we're adding the features like Emergency Vehicle Traffic Clearance i.e., Ambulance or VIP Vehicles, Air Pollution discovery, Theft Vehicle Identification Point and Automatic parking. Keywords: Arduino Uno, IR sensor, LCD display, RF transmitter, Thing view App, RFID EM Module

Building a smart traffic light system based on Internet of Things using ‐calculus

AbstractTraffic is very terrible in most big cities. These cities need many traffic police to control traffic congestion. They can change the duration of traffic lights to reduce levels of congestion of corners. However, the time changed by them is determined just only the feeling of themselves. Making decisions suitable for current congestion at that moment is not trivial. The resistant questions are how to define the logical time of traffic lights or how is an effect of this corner to the consecutive one. Maybe we need to increase the time of the green light at the next corner because of the traffic jam at this corner. The link between two corners is interaction inside concurrent processes. Besides, some exceptional cases also need to be handled, for instance, ambulances or fire trucks. The green light needs to be turned on when they come, and turn back the red light state later. This article proposes a tentative approach that uses context types, including locations, priorities of cars, velocity, and remaining time (the car comes to the corner) to determine traffic light periods. The context‐aware is applied in the infrastructure of the Internet of Things when transferring information to the corner. Maybe, in the future, the other systems such as Arduino, Raspberry pi, or other embedded systems will be joined in the designs as acting part (turn the traffic lights on/off ) and communicating role (connecting to the Internet via wifi) to control the duration of the traffic lights.

Adaptive Optimization of Traffic Signal Timing via Deep Reinforcement Learning

With rapid development of the urbanization, how to improve the traffic lights efficiency has become an urgent issue. The traditional traffic light control is a method that calculates a series of corresponding timing parameters by optimizing the cycle length. However, fixing sequence and duration of traffic lights is inefficient for dynamic traffic flow regulation. In order to solve the above problem, this study proposes a traffic light timing optimization scheme based on deep reinforcement learning (DRL). In this scheme, the traffic lights can output an appropriate phase according to the traffic flow state of each direction at the intersection and dynamically adjust the phase length. Specifically, we first adopt Proximal Policy Optimization (PPO) to improve the convergence speed of the model. Then, we elaborate the design of state, action, and reward, with the vehicle state defined by Discrete Traffic State Encoding (DTSE) method. Finally, we conduct experiments on real traffic data via the traffic simulation platform SUMO. The results show that, compared to the traditional timing control, the proposed scheme can effectively reduce the waiting time of vehicles and queue length in various traffic flow modes.

Multi-factor Comprehensive Prediction of Delay Time through Congested Road Sections

The navigation software uses the positioning system to determine the traffic conditions of the road sections in advance, so as to predict the travel time of the road sections. However, in the case of traffic congestion, the accuracy of its prediction time is low. After empirical analysis, this paper establishes a multi-factor synthesis by studying 7 factors: traffic flow, number of stops, traffic light duration, road network density, average speed, road area, and number of intersections the prediction function achieves the purpose of accurately predicting the transit time of congested road sections. The gray correlation coefficients of the seven factors obtained from the gray correlation analysis are: 0.9827, 0.9679, 0.6747, 0.8030, 0.9445, 0.8759, 0.4328. The correlation coefficients of traffic volume, number of stops, average speed, and road congestion delay time were all about 95%, which were the main influencing factors of the study. The prediction needs to be based on functions. This paper fits the main influencing factors to the delay time of congested roads. It is found that the delay time varies parabolically with the traffic flow and the number of stops, and linearly with the average speed. Because the three impact factors have different weights on the delay time of congested roads, demand takes the weight of each factor. Therefore, the gray correlation coefficients occupied by the main influencing factors are normalized to obtain the weights of three of 0.340, 0.334, and 0.326. The weighted fitting function is subjected to nonlinear summation processing to obtain a multi-factor comprehensive prediction function. By comparing the original data with the fitting data and calculating the accuracy of the fitting function, it is found that the accuracy of each fitting function is close to 0, the residual error, the relative error is small, and the accuracy is high.

BIG MOBILITY DATA ANALYTICS FOR TRAFFIC MONITORING AND CONTROL

With the overpopulation of large cities, the problems with citizens’ mobility, transport inefficiency, traffic congestions and environmental pollution caused by the heavy traffic require advanced ITS solutions to be overcome. Recent advances and wide proliferation of mobile and Internet of Things (IoT) devices, carried by people, built in vehicles and integrated in a road infrastructure, enable collection of large scale data related to mobility and traffic in smart cities, still with a limited use in real world applications. In this paper, we propose the traffic monitoring, control and adaptation platform, named TrafficSense, based on Big Mobility Data processing and analytics. It provides a continuous monitoring of a traffic situation and detection of important traffic parameters, conditions and events, such as travel times along the street segments and traffic congestions in real time. Upon detecting a traffic congestion on an intersection, the TrafficSense application leverages the feedback control loop mechanism to provide a traffic adaptation based on the dynamic configuration of traffic lights duration in order to increase the traffic flows in critical directions at the intersections. We tested and evaluated the developed application on the distributed cloud computing infrastructure. By varying the streaming workload and the cluster parameters we show the feasibility and applicability of our approach and the platform.

Fuzzy Inference Enabled Deep Reinforcement Learning-Based Traffic Light Control for Intelligent Transportation System

Intelligent Transportation System (ITS) has been emerged an important component and widely adopted for the smart city as it overcomes the limitations of the traditional transportation system. Existing fixed traffic light control systems split the traffic light signal into fixed duration and run in an inefficient way, therefore, it suffers from many weaknesses such as long waiting time, waste of fuel and increase in carbon emission. To tackle these issues and increase efficiency of the traffic light control system, in this work, a Dynamic and Intelligent Traffic Light Control System (DITLCS) is proposed which takes real-time traffic information as the input and dynamically adjusts the traffic light duration. Further, the proposed DITLCS runs in three modes namely Fair Mode (FM), Priority Mode (PM) and Emergency Mode (EM) where all the vehicles are considered with equal priority, vehicles of different categories are given different level of priority and emergency vehicles are given at most priority respectively. Furthermore, a deep reinforcement learning model is also proposed to switch the traffic lights in different phases (Red, Green and Yellow), and fuzzy inference system selects one mode among three modes i.e., FM, PM and EM according to the traffic information. We have evaluated DITLCS via realistic simulation on Gwalior city map of India using an open-source simulator i.e., Simulation of Urban MObility (SUMO). The simulation results prove the efficiency of DITLCS in comparison to other state of the art algorithms on various performance parameters.

Software modeling application for verification of transportation planning engineering hypotheses

The organization of traffic is a complex of engineering and organizational arrangements aimed at maximizing the use of traffic flows opportunities represented by the road geometric parameters and condition. The article deals with an example of using software modeling for creating a digital twin of a complex intersection in Saratov in order to verify the engineering hypotheses concerning the effective transportation planning. The current traffic situation in the PTV VISSIM program is simulated. The transportation planning engineering hypothesis proposed and simulated the solution options of changing the working traffic light duration at the intersection for verifying and widening the carriageway by adding a lane. The analysis of two models options allowed determining the most effective transportation planning measure in a given transport hub of the city with a high level of traffic delays, which is actually the duration change of the working traffic light at the intersection. The software modeling application is confirmed for creating a digital twin complex intersection in Saratov.

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.

Enhancing pedestrian safety, walkability and traffic flow with fuzzy logic

The continuous congestion in the Arabian Gulf Road, located in Kuwait, contributes to air pollution in the area and causes discomfort for both drivers and pedestrians. The objective of this work is to enhance walkability and safety of pedestrians in the Gulf road while facilitating traffic flow. The study had been conducted for the road and surrounding area staring from the Society of Engineers till the British Embassy. Two methods were established to enhance walkability at each intersection. The first method used Synchro software to improve the traffic condition, lessen the delay time, and add pedestrian phase for each intersection. In the second method, fuzzy logic code was scripted using MATLAB to adjust traffic lights duration, for creating an adaptive traffic system. The best-established solution for traffic phasing at the Engineering Society intersection was mitigation scenario one, which contributed to decreasing the delay time by 63.82%, reaching only 5 min of delay. As for the British embassy intersection, the delay had been reduced by 11.82% using mitigation scenario 2. Several adjustments had been implemented in the study area that included replacing the current parking space with a wide green area, adding underground parking, and designating a particular lane for bicycles. The green area was provided with a shaded pathway using photovoltaic panels, jugging pathway, retail shops, and playing grounds to encourage walkability and reduce dependence on vehicles. A LEED-certified restaurant model had been designed that scored a gold certificate. Two additional restaurants were proposed in the area and a pond to attract more visitors.

Signal Light Ptimization based on PTV Vissim Software

This paper conducts on-the-spot investigation on the traffic flow of each entry and exit road during the early peak period of the case intersection, and uses the data obtained from the survey to make accurate calculations to obtain the capacity and delay time of the current intersection, and then use PTV Vissim. The simulation software simulates the real-time road condition reduction simulation of this intersection.Finally, based on the results of the PTV Vissim simulation software, a more reasonable optimization plan for the traffic light duration configuration of the intersection is proposed, which can make the interlaced area smooth, fastest, and maximize travel efficiency. A new signal optimization solution based on PTV Vissim software can reduce the delay time of the intersection and improve its traffic capacity.

Optimizing the traffic control system of Sultan Agung street Yogyakarta using fuzzy logic controller

The traffic control system is a system that functions to regulate and control traffic flow at crossroads, pedestrian crossings (zebra crossing) and other traffic flows. A good traffic control system is a system that is able to adjust the duration of the traffic lights with the density of traffic flow automatically. In big cities like Yogyakarta, traffic congestion is one of the most common problems. It is caused by the traffic lights that use a conventional regulation system. This study aims to discuss the establishment of a traffic light regulation system on Sultan Agung street and surrounding areas where there are nearby traffic lights and are interrelated with one another. Determining the duration of the green light is calculated using the fuzzy logic controller method with 3 input variables namely the number of cars, the number of motorcycles, and the length of the queue. The results of the traffic light regulation system using fuzzy logic controllers are more effective than conventional system settings. It is because the system is able to adjust the duration of the traffic lights with traffic conditions at that time, so the duration of traffic light is varied for each traffic conditions.

Max-plus algebraic modeling of three crossroad traffic queue systems with one underpass

This research aims to model traffic flow queuing system of three crossroads, namely the Monjali, Kentungan, and Gejayan crossroads in Yogyakarta with one underpass at the Kentungan crossroad using max-plus algebra. This research is based on data of traffic flow density, especially cars and traffic light duration data on the three crossroads. Then, a crossing condition scheme is created and represents the direction of car movement especially in the direction from Monjali to Gejayan. Then the model is compiled which includes the waiting time of cars when the red traffic light is on, the time cars leave the crossroad during the green traffic light is on, and the travel time of cars between crossroad with max-plus algebra, so that the total vehicle time can be determined from Monjali to the Gejayan crossroad . The results show that the queueing system can be modelled using max-plus algebra. Furthermore, it can be determined the waiting time, the time to leave crossroad, and the total time needed to cross from the Monjali crossroad to exit the Gejayan crossroad. The resulting model can reduce congestion on the lines of the intersection of Monjali, Kentungan and Gejayan.