An extended signal control strategy for urban network traffic flow

2016 ◽  
Vol 445 ◽  
pp. 117-127 ◽  
Author(s):  
Fei Yan ◽  
Fuli Tian ◽  
Zhongke Shi
Keyword(s):  
Traffic Flow ◽  
Control Strategy ◽  
Network Traffic ◽  
Signal Control ◽  
Urban Network

Dynamics of Urban Network Traffic flow during a Large-Scale Evacuation

2014 ◽  
Vol 2422 (1) ◽  
pp. 21-33 ◽  
Author(s):  
Ali Zockaie ◽  
Hani S. Mahmassani ◽  
Meead Saberi ◽  
Ömer Verbas
Keyword(s):  
Traffic Flow ◽  
Network Traffic ◽  
Large Scale ◽  
Urban Network

scholarly journals Subdivision of Urban Traffic Area Based on the Combination of Static Zoning and Dynamic Zoning

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Jing Luo ◽  
Qingnian Zhang
Keyword(s):  
Decision Making ◽  
Traffic Flow ◽  
Control Strategy ◽  
Road Network ◽  
Model Verification ◽  
Signal Control ◽  
The Road ◽  
Traffic Area ◽  
Center Point ◽  
Clustering Center

In this paper, the traffic area of subzone division in urban road network is studied and a subzone division method based on the combination of static partition and dynamic partition is proposed. The static partition is carried out for the road network when the traffic flow is in a noncongested state, so as to provide the decision-making basis for the traffic green wave signal control strategy. At the same time, aiming at the road network when the traffic flow is congested, the dynamic partition is carried out on the basis of static partition to provide the decision-making basis for the traffic maximum flow signal control strategy. In view of the fact that it is difficult to determine the clustering center point during the initial division, this method proposes to determine the clustering center point according to the value of nodes of betweenness centrality. In order to solve the problem that it is difficult to collect traffic data, a method for estimating traffic flow density is proposed. In order to solve the problem of normalization of different probability distribution among various parameters, Mahalanobis distance is used as the fusion index of subzone division. Model verification shows that the method is feasible and effective.

scholarly journals The Impact of Traffic Crashes on Urban Network Traffic Flow

2019 ◽  
Vol 11 (14) ◽  
pp. 3956 ◽  
Author(s):  
Junwei Zeng ◽  
Yongsheng Qian ◽  
Bingbing Wang ◽  
Tingjuan Wang ◽  
Xuting Wei
Keyword(s):  
Traffic Flow ◽  
Network Traffic ◽  
Network Flow ◽  
Time Distribution ◽  
Urban Traffic ◽  
Extended Model ◽  
Traffic Network ◽  
Traffic Crashes ◽  
Urban Network ◽  
The Impact

This paper aims to investigate the impact of occasional traffic crashes on the urban traffic network flow. Toward this purpose, an extended model of coupled Nagel–Schreckenberg (NaSch) and Biham–Middleton–Levine (BML) models is presented. This extended model not only improves the initial conditions of the coupled models, but also gives the definition of traffic crashes and their spatial/time distribution. Further, we simulated the impact of the number of traffic crashes, their time distribution, and their spatial distribution on urban network traffic flow. This research contributes to the comprehensive understanding of the operational state of urban network traffic flow after traffic crashes, towards mastering the causes and propagation rules of traffic congestion. This work also a theoretical guidance value for the optimization of urban traffic network flow and the prevention and release of traffic crashes.

Evaluation of Signal Control Strategy Based on Hardware-in-the-Loop

2014 ◽  
Vol 505-506 ◽  
pp. 1122-1126
Author(s):  
Xiao Hua Zhao ◽  
Chen Chen ◽  
Jian Rong
Keyword(s):  
Travel Time ◽  
Traffic Flow ◽  
Virtual Environment ◽  
Control Strategy ◽  
Control Strategies ◽  
Simulation Software ◽  
Signal Control ◽  
Hardware In The Loop ◽  
Microscopic Traffic Simulation ◽  
Real Signal

To evaluate the performance of different signal control strategies in intersection, based on the Hardware-In-The-Loop (HITL) simulation technology, a HITL system was established to perform experiment. In the system, microscopic traffic simulation software VISSIM created a virtual environment, in which the traffic flow can be controlled by the real signal controller. One type of intersection and four degrees of traffic volume were designed in the simulation program and three control strategies were set in the signal controller. Twelve simulations were performed in the system. The analysis of travel time and queuing length indicates that different strategies has remarkable influence on travel time, but no significant effect on queuing length.

Traffic Flow Data Collection and Signal Control System Based on Internet of Things and Cloud Computing

2013 ◽  
Vol 846-847 ◽  
pp. 1608-1611 ◽  
Author(s):  
Hui Jie Ding
Keyword(s):  
Cloud Computing ◽  
Control System ◽  
Data Collection ◽  
Internet Of Things ◽  
Traffic Flow ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Flow Data ◽  
Traffic Jam ◽  
Sensing Data

As more and more cars are in service, the traffic jam becomes a serious problem in our society. At the same time, more and more sensors make the cars more and more intelligent, and this promotes the development of Internet of things. Real time monitoring the cars will produce massive sensing data, the Cloud computing gives us a good manner to solve this problem. In this paper, we propose a traffic flow data collection and traffic signal control system based on Internet of things and the Cloud computing. The proposed system contains two main parts, sensing data collection and traffic status control subsystem.

An Improved Stacked Auto-Encoder for Network Traffic Flow Classification

IEEE Network ◽  
2018 ◽  
Vol 32 (6) ◽  
pp. 22-27 ◽  
Author(s):  
Peng Li ◽  
Zhikui Chen ◽  
Laurence T. Yang ◽  
Jing Gao ◽  
Qingchen Zhang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Network Traffic ◽  
Flow Classification
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