Duration Of Traffic Lights Research Articles

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.

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.

ANALISA DURASI LAMPU LALU LINTAS MENGGUNAKAN METODE SIMULASI

With the growth of vehicle number every year, traffic congestion is an inevitable phenomena to be seen everywhere in the world. The congestion is sometime worsen due to the inaccurate duration of the traffic lamp. The goal of this research is to show how we could analyze the traffic condition of a junction using discrete simulation method and find the better traffic lamp duration. A busy junction which connect business district, suburb and educational district in Malang, was selected to be the object of this research. Models of existing system were developed using Arena, following data collection and observation on the object. Several scenarios based on various traffic light duration were analyzed. The best scenario found in this simulation analysis could predict the shorter vehicle queue on the junction

Critical applications in vehicular ad hoc/sensor networks

Vehicular ad hoc networks (VANETs) have emerged as a promising technology with several applications that may have a deep impact on our society. In the past few years, we have witnessed a tremendous growth in this field, with several new techniques and standards having been proposed with the objective of making it easier and safer to drive and transport passengers. Diverse applications are already available that provide information on traffic conditions, the density of vehicles, duration of traffic lights, among many other variables that allow smarter driving decisions to be taken. Moreover, drivers and passengers can download and upload this information as they travel, with the aid of the Road Side Units (RSUs), some of which are already deployed. RSUs are usually connected to the Internet, and are thus widely available to users and services.

A Real-Time Traffic Lights Scheduling Strategy

In this paper, we study the problem of real-time traffic lights scheduling in a three-dimension intersection. Here, we propose a traffic lights scheduling strategy to optimize the traffic lights sequence and duration according to traffic data detected and predicted in real-time, containing traffic volume, delays and traffic density. Simulations are carried out to compare the performance in terms of average delays, and the evaluation depicts that this strategy can obtain lower average waiting delays.

Developing a parameterized simulation platform with intelligent synthetic agents for training driver candidates

Abstract This study presents a driver Traffic Training Simulator (TTS) that utilizes intelligent synthetic actors. The movements of intelligent actors are realized using a network flow graph consisting of segment nodes. The intelligent actors in traffic are capable of such objectives as vehicles and lane following. In addition, they are capable of moving according to the traffic signs and lights. The duration of traffic lights can parametrically be determined by the simulator interface. Moreover, parametric values such as weather conditions, seasons, and sunlight can be fed to the simulator as inputs. Similarly, the type of the driver’s vehicle and other intelligent vehicles and their numbers can also be parametrically determined. The driver is able to drive in the heavy and light traffic conditions. Currently we are focusing on incorporating the hardware components into the system. Following the tests for the candidates with the system is expected to take place in driver training schools.

Modeling urban traffic: A cellular automata approach

In this article we introduce a new cellular automata approach to construct an urban traffic mobility model. Based on the developed model, characteristics of global traffic patterns in urban areas are studied. Our results show that different control mechanisms used at intersections such as cycle duration, green split, and coordination of traffic lights have a significant effect on intervehicle spacing distribution and traffic dynamics. These findings provide important insights into the network connectivity behavior of urban traffic, which are essential for designing appropriate routing protocols for vehicular ad hoc networks in urban scenarios.

Traffic noise and traffic light control

An optimal control problem of traffic light duration is considered. The traffic noise level is introduced as a state variable in a dynamical optimization problem. A closed loop control system is designed which influences the green duration of the lights according to the equivalent noise level. Real time considerations lead to sub-optimal control implementation. This control policy decreases the noise levels at intensive traffic intersections. The traffic lights adapt their duration according to the noise pollution. Simulation and experimental results are discussed.