scholarly journals Multiagent Based Decentralized Traffic Light Control for Large Urban Transportation System

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yang Xu ◽  
Yulin Zhang ◽  
Ming Liu
Keyword(s):  
Traffic Control ◽  
Large Scale ◽  
Urban Transportation ◽  
Transportation System ◽  
Transportation Systems ◽  
Communication Overhead ◽  
Traffic Light ◽  
Traffic Lights ◽  
Joint Activities ◽  
Green Transportation

Intelligent traffic control is an important issue of the modern transportation system. However, in large-scale urban transportation systems, traditional centralized coordination methods suffer bottlenecks in both communication and computation. Decentralized control is hard if there is very limited observation to the whole network as evidences to support joint traffic coordination decisions. In this paper, we proposed a novel decentralized, multiagent based approach for massive traffic lights coordination to promote the large-scale green transportation. Considering that only the traffic from the adjacent intersections may affect the state of a given intersection one time ahead, the key of our approach is using the observations of a local intersection and its neighbors as evidences to support the traffic light coordination decisions. Therefore, we can model the interactions as decentralized agents coordinating with a decision theoretical model. Within a local intersection, constraint optimizing agents are designed to efficiently search for joint activities of the lights. Since this approach involves only local intersection cooperation, it is well scalable and easily implemented with small communication overhead. In the last section, we present our software design on this approach and based on our simulation, this approach is feasible to a large urban transportation system.

scholarly journals Towards Personal Virtual Traffic Lights

Information ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 32 ◽  
Author(s):  
Vanessa Martins ◽  
João Rufino ◽  
Luis Silva ◽  
João Almeida ◽  
Bruno Miguel Fernandes Silva ◽  
...  
Keyword(s):  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Communication Systems ◽  
Field Tests ◽  
Transportation Systems ◽  
Smart Devices ◽  
Vehicular Communication ◽  
Traffic Light ◽  
Correct Operation ◽  
Traffic Lights

Traffic control management at intersections, a challenging and complex field of study, aims to strike a balance between safety and efficient traffic control. Nowadays, traffic control at intersections is typically done by traffic light systems which are not optimal and exhibit several drawbacks, such as poor efficiency and real-time adaptability. With the advent of Intelligent Transportation Systems (ITS), vehicles are being equipped with state-of-the-art technology, enabling cooperative decision-making which will certainly overwhelm the available traffic control systems. This solution strongly penalizes users without such capabilities, namely pedestrians, cyclists, and other legacy vehicles. Therefore, in this work, a prototype based on an alternative technology to the standard vehicular communications, Bluetooth Low Energy (BLE), is presented. The proposed framework aims to integrate legacy and modern vehicular communication systems into a cohesive management system. In this framework, the movements of users at intersections are managed by a centralized controller which, through the use of networked retransmitters deployed at intersections, broadcasts alerts and virtual light signalization orders. Users receive the aforementioned information on their own smart devices, discarding the need for dedicated light signalization infrastructures. Field tests, carried out with a real-world implementation, validate the correct operation of the proposed framework.

Fog-based Intelligent Transportation System for Traffic Light Optimization

2021 ◽  
Vol 58 (S) ◽  
pp. 29-35
Author(s):  
Muhammad Rusyadi Ramli ◽  
Riesa Krisna Astuti Sakir ◽  
Dong-Seong Kim
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Accidents ◽  
Intelligent Transportation System ◽  
Optimization Problems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Traffic Light ◽  
Traffic Condition ◽  
Dilemma Zone ◽  
Traffic Lights

This paper presents fog-based intelligent transportation systems (ITS) architecture for traffic light optimization. Specifically, each intersection consists of traffic lights equipped with a fog node. The roadside unit (RSU) node is deployed to monitor the traffic condition and transmit it to the fog node. The traffic light center (TLC) is used to collect the traffic condition from the fog nodes of all intersections. In this work, two traffic light optimization problems are addressed where each problem will be processed either on fog node or TLC according to their requirements. First, the high latency for the vehicle to decide the dilemma zone is addressed. In the dilemma zone, the vehicle may hesitate whether to accelerate or decelerate that can lead to traffic accidents if the decision is not taken quickly. This first problem is processed on the fog node since it requires a real-time process to accomplish. Second, the proposed architecture aims each intersection aware of its adjacent traffic condition. Thus, the TLC is used to estimate the total incoming number of vehicles based on the gathered information from all fog nodes of each intersection. The results show that the proposed fog-based ITS architecture has better performance in terms of network latency compared to the existing solution in which relies only on TLC.

scholarly journals Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6218
Author(s):  
Rodrigo Carvalho Barbosa ◽  
Muhammad Shoaib Ayub ◽  
Renata Lopes Rosa ◽  
Demóstenes Zegarra Rodríguez ◽  
Lunchakorn Wuttisittikulkij
Keyword(s):  
Deep Learning ◽  
Traffic Control ◽  
Detection System ◽  
Lightweight Design ◽  
Vehicle Detection ◽  
Activation Function ◽  
Urban Traffic ◽  
Vehicle Classification ◽  
Traffic Light ◽  
Traffic Lights

Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on YOLOV3, (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution.

scholarly journals Bi-Level Optimization in a Transport Network

2015 ◽  
Vol 15 (5) ◽  
pp. 37-49 ◽  
Author(s):  
Todor Stoilov ◽  
Krasimira Stoilova ◽  
Markos Papageorgiou ◽  
Ioannis Papamichail
Keyword(s):  
Traffic Control ◽  
Optimization Problem ◽  
Transportation Network ◽  
Urban Transportation ◽  
Light Cycle ◽  
Constant Parameter ◽  
Optimal Solutions ◽  
Traffic Light ◽  
Control Space ◽  
Store And Forward

Abstract This paper applies a bi-level formalism for the optimal control of an urban transportation network. The well known store-and-forward model in traffic control is utilized in order to increase the control space of the optimization problem. Mainly, the store-and-forward models apply the split as a control argument, assuming the traffic light cycle as a constant parameter. The paper shows that by using a bi-level formalism the control problem can be defined within increased control space comprising both the split and the cycle. Both are found as optimal solutions of a bi-level optimization problem.

scholarly journals Bi-Level Optimization Model for Urban Traffic Control

2021 ◽  
Vol 21 (3) ◽  
pp. 108-126
Author(s):  
Krasimira Stoilova ◽  
Todor Stoilov ◽  
Stanislav Dimitrov
Keyword(s):  
Traffic Control ◽  
Optimization Model ◽  
Dynamical Behavior ◽  
Control Variable ◽  
Green Light ◽  
Urban Traffic ◽  
Traffic Light ◽  
Urban Network ◽  
Traffic Lights ◽  
Urban Traffic Control

Abstract The urban traffic control optimization is a complex problem because of the interconnections among the junctions and the dynamical behavior of the traffic flows. Optimization with one control variable in the literature is presented. In this research optimization model consisting of two control variables is developed. Hierarchical bi-level methodology is proposed for realization of integrated optimal control. The urban traffic management is implemented by simultaneously control of traffic light cycles and green light durations of the traffic lights of urban network of crossroads.

Fruitful Synergy Model of Artificial Intelligence and Internet of Thing for Smart Transportation System

2020 ◽  
Vol 4 (1) ◽  
pp. 43-57 ◽  
Author(s):  
Vikram Puri ◽  
Chung Van Le ◽  
Raghvendra Kumar ◽  
Sandeep Singh Jagdev
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Urban Transportation ◽  
Fall Detection ◽  
Transportation System ◽  
Transportation Systems ◽  
Support Vector ◽  
Smart Transportation ◽  
Artificial Neural Network Ann

In urban transportation systems, bicycle sharing systems are majorly deployed in major cities of both developed and developing countries. The recent boom of bicycle sharing system along with its upgraded technology have opened new opportunities towards urban transportation system. With the enlargement of intelligent transportation systems (ITS's), smart bicycle sharing schemes are more popular to smart cities as a green transportation mode. In this article, the Internet of Things (IoT) and artificial intelligence-based monitoring devices have been proposed for the bicycles. This system contains a harmful exhaust gas sensor, wireless module, and a GPS receiver and camera that are capable to send data with time and date stamping. In addition, sensor also integrated on the bicycle for the fall detection. An artificial neural network (ANN) and support vector machine (SVM) applied to the data collected at central server is designed to analyze the root mean square error (RMSE), and coefficient of correlation (R2). Result shows that ANN performance is better when compared to SVM.

Employing Traffic Lights as Road Side Units for Road Safety Information Broadcast

2015 ◽  
pp. 143-159
Author(s):  
Navin Kumar ◽  
Luis Nero Alves ◽  
Rui L. Aguiar
Keyword(s):  
Traffic Safety ◽  
Intelligent Transportation Systems ◽  
Safety Information ◽  
Visible Light Communication ◽  
Transportation Systems ◽  
Vehicular Communication ◽  
Traffic Light ◽  
Traffic Lights ◽  
The Road ◽  
Smooth Flow

There is great concern over growing road accidents and associated fatalities. In order to reduce accidents, improve congestion and offer smooth flow of traffic, several measures, such as providing intelligence to transport, providing communication infrastructure along the road, and vehicular communication, are being undertaken. Traffic safety information broadcast from traffic lights using Visible Light Communication (VLC) is a new cost effective technology which assists drivers in taking necessary safety measures. This chapter presents the VLC broadcast system considering LED-based traffic lights. It discusses the integration of traffic light Roadside Units (RSUs) with upcoming Intelligent Transportation Systems (ITS) architecture. Some of the offered services using this technology in vehicular environment together with future directions and challenges are discussed. A prototype demonstrator of the designed VLC systems is also presented.

Application research of urban subway traffic mode based on behavior entropy in the background of big data

2021 ◽  
pp. 1-14
Author(s):  
Wanxin Hu ◽  
Fen Cheng
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Rapid Development ◽  
Urban Transportation ◽  
Transportation System ◽  
Urban Traffic ◽  
Time Optimization ◽  
The City

With the development of society and the Internet and the advent of the cloud era, people began to pay attention to big data. The background of big data brings opportunities and challenges to the research of urban intelligent transportation networks. Urban transportation system is one of the important foundations for maintaining urban operation. The rapid development of the city has brought tremendous pressure on the traffic, and the congestion of urban traffic has restricted the healthy development of the city. Therefore, how to improve the urban transportation network model and improve transportation and transportation has become an urgent problem to be solved in urban development. Specific patterns hidden in large-scale crowd movements can be studied through transportation networks such as subway networks to explore urban subway transportation modes to support corresponding decisions in urban planning, transportation planning, public health, social networks, and so on. Research on urban subway traffic patterns is crucial. At the same time, a correct understanding of the behavior patterns and laws of residents’ travel is a key factor in solving urban traffic problems. Therefore, this paper takes the metro operation big data as the background, takes the passenger travel behavior in the urban subway transportation system as the research object, uses the behavior entropy to measure the human behavior, and actively explores the urban subway traffic mode based on the metro passenger behavior entropy in the context of big data. At the same time, the congestion degree of the subway station is analyzed, and the redundancy time optimization model of the subway train stop is established to improve the efficiency of the subway operation, so as to provide important and objective data and theoretical support for the traveler, planner and decision maker. Compared to the operation graph without redundant time, the total travel time optimization effect of passengers is 7.74%, and the waiting time optimization effect of passengers is 6.583%.

scholarly journals Smart Cities and Data Analytics for Intelligent Transportation Systems: An Analytical Model for Scheduling Phases and Traffic Lights at Signalized Intersections

2021 ◽  
Vol 11 (15) ◽  
pp. 6816
Author(s):  
Fatih Gunes ◽  
Selim Bayrakli ◽  
Abdul Halim Zaim
Keyword(s):  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Transportation Systems ◽  
Signalized Intersections ◽  
Effective Control ◽  
Main Role ◽  
Smart Transportation ◽  
Traffic Lights ◽  
Service Rates

This paper is intended to improve the performance of signalized intersections, one of the most important systems of traffic control explained within the scope of smart transportation systems. These structures, which have the main role in ensuring the order and flow of traffic, are alternative systems depending on the different methods and techniques used. Determining the operation principles of these systems requires an extremely careful and planned study, considering their important role. Performance outputs obtained from the queue analyses made in previous studies formed the input of this study. The most important techniques are used in the effective control of intersections, such as signal timing: in particular, the use of effective green time and order of the transitions between phases. In this research, a traffic-sensitive signalized intersection control system was designed with the suggested methods against these two problems. The sample intersections were selected from three cities with the highest population density as the case study area. In the analysis of the performance of the connection arms of the selected intersections, flow intensity data were taken into consideration, as well as the arrival and service rates. Based on this, the outputs of the two proposed models regarding the use of phase transitions and effective green durations were compared with two other adaptive control systems and their positive results were shared. The results showed that signalized intersections, operating with a well-planned and correctly chosen technique, better regulate density and queuing.

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