Nonlinear gating control for urban road traffic network using the network fundamental diagram

2014 ◽  
Vol 49 (5) ◽  
pp. 597-615 ◽  
Author(s):  
Alfréd Csikós ◽  
Tamás Tettamanti ◽  
István Varga
Keyword(s):  
Road Traffic ◽  
Traffic Network ◽  
Fundamental Diagram ◽  
Urban Road ◽  
Network Fundamental Diagram

scholarly journals Analyzing railroad congestion in a dense urban network through the use of a road traffic network fundamental diagram concept

2015 ◽  
Vol 7 (3) ◽  
pp. 355-367 ◽  
Author(s):  
Pierre-Antoine Cuniasse ◽  
Christine Buisson ◽  
Joaquin Rodriguez ◽  
Emmanuel Teboul ◽  
David de Almeida
Keyword(s):  
Road Traffic ◽  
Traffic Network ◽  
Fundamental Diagram ◽  
Urban Network ◽  
Network Fundamental Diagram

scholarly journals PATTERN RECOGNITION BASED SPEED FORECASTING METHODOLOGY FOR URBAN TRAFFIC NETWORK

Transport ◽  
2018 ◽  
Vol 33 (4) ◽  
pp. 959-970 ◽  
Author(s):  
Tamás Tettamanti ◽  
Alfréd Csikós ◽  
Krisztián Balázs Kis ◽  
Zsolt János Viharos ◽  
István Varga
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Road Traffic ◽  
Urban Traffic ◽  
Traffic Network ◽  
Data Handling ◽  
Urban Road ◽  
Traffic Sensors ◽  
Network Training ◽  
Artificial Neural Network Ann

A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic.

Determination of key nodes in urban road traffic network

2014 ◽  
Author(s):  
Zhao Tian ◽  
Limin Jia ◽  
Honghui Dong ◽  
Zundong Zhang ◽  
Yanfang Yang ◽  
...  
Keyword(s):  
Road Traffic ◽  
Traffic Network ◽  
Urban Road ◽  
Key Nodes

scholarly journals Golden Ratio Genetic Algorithm Based Approach for Modelling and Analysis of the Capacity Expansion of Urban Road Traffic Network

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Lun Zhang ◽  
Meng Zhang ◽  
Wenchen Yang ◽  
Decun Dong
Keyword(s):  
Genetic Algorithm ◽  
Road Network ◽  
Road Traffic ◽  
Golden Ratio ◽  
Hybrid Genetic Algorithm ◽  
Capacity Expansion ◽  
Network Capacity ◽  
Traffic Network ◽  
Effective Measure ◽  
Urban Road

This paper presents the modelling and analysis of the capacity expansion of urban road traffic network (ICURTN). Thebilevel programming model is first employed to model the ICURTN, in which the utility of the entire network is maximized with the optimal utility of travelers’ route choice. Then, an improved hybrid genetic algorithm integrated with golden ratio (HGAGR) is developed to enhance the local search of simple genetic algorithms, and the proposed capacity expansion model is solved by the combination of the HGAGR and the Frank-Wolfe algorithm. Taking the traditional one-way network and bidirectional network as the study case, three numerical calculations are conducted to validate the presented model and algorithm, and the primary influencing factors on extended capacity model are analyzed. The calculation results indicate that capacity expansion of road network is an effective measure to enlarge the capacity of urban road network, especially on the condition of limited construction budget; the average computation time of the HGAGR is 122 seconds, which meets the real-time demand in the evaluation of the road network capacity.

scholarly journals MACROSCOPIC MODELING AND CONTROL OF EMISSION IN URBAN ROAD TRAFFIC NETWORKS

Transport ◽  
2015 ◽  
Vol 30 (2) ◽  
pp. 152-161 ◽  
Author(s):  
Alfréd Csikós ◽  
Tamás Tettamanti ◽  
István Varga
Keyword(s):  
Road Traffic ◽  
Traffic Emissions ◽  
Emission Model ◽  
Traffic Networks ◽  
Fundamental Diagram ◽  
Traffic Emission ◽  
Urban Network ◽  
Modeling And Control ◽  
Urban Road ◽  
Road Traffic Networks

This work suggests a framework for modeling the traffic emissions in urban road traffic networks that are described by the Network Fundamental Diagram (NFD) concept. Traffic emission is formalized in finite spatiotemporal windows as a function of aggregated traffic variables, i.e. Total Travel Distances (TTDs) in the network and network average speed. The framework is extended for the size of an urban network during a signal cycle – the size of a window in which the network aggregated parameters are modeled in the NFD concept. Simulations have been carried out for model accuracy analysis, using the microscopic Versit+Micro model as reference. By applying the macroscopic emission model function and the traffic modeling relationships, the control objective for pollution reduction has also been formalized. Basically, multi-criteria control design has been introduced for two criteria: maximization of the TTD and minimization of traffic emissions within the network.

Study on Pinning Control Methods of Urban Road Traffic Network

CICTP 2019 ◽  
2019 ◽  
Author(s):  
Lili Zheng ◽  
Yibin Zhang ◽  
Tongqiang Ding ◽  
Junming Hu ◽  
Xue Xiao ◽  
...  
Keyword(s):  
Road Traffic ◽  
Pinning Control ◽  
Traffic Network ◽  
Control Methods ◽  
Urban Road

scholarly journals A New Cellular Automaton Model for Urban Two-Way Road Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Junqing Shi ◽  
Lin Cheng ◽  
Jiancheng Long ◽  
Yuanlin Liu
Keyword(s):  
Cellular Automaton ◽  
Road Traffic ◽  
Vehicle Speed ◽  
Flow State ◽  
Fundamental Diagram ◽  
Network Systems ◽  
Traffic Dynamics ◽  
Ca Model ◽  
Additional Rule ◽  
Network Fundamental Diagram

A new cellular automaton (CA) model is proposed to simulate traffic dynamics in urban two-way road network systems. The NaSch rule is adopted to represent vehicle movements on road sections. Two novel rules are proposed to move the vehicles in intersection areas, and an additional rule is developed to avoid the “gridlock” phenomenon. Simulation results show that the network fundamental diagram is very similar to that of road traffic flow. We found that the randomization probability and the maximum vehicle speed have significant impact on network traffic mobility for free-flow state. Their effect may be weak when the network is congested.

Key links identification for urban road traffic network based on temporal-spatial distribution of traffic congestion

2019 ◽  
Vol 33 (25) ◽  
pp. 1950307
Author(s):  
Zhao Tian ◽  
Wei She ◽  
Shuang Li ◽  
You-Wei Wang ◽  
Wei Liu ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Road Traffic ◽  
Computing Time ◽  
Simulated Data ◽  
Time Interval ◽  
Traffic Network ◽  
Urban Road ◽  
Interval Coverage

Traffic congestion is now nearly ubiquitous in many urban areas. The improvement of road infrastructure is an effective way to ease traffic congestion, especially the key road links. So, it is a fundamental and important step to identify the key link for improving transportation efficiency. However, most approaches in the current literature use simulated data and need many assumption conditions. The result shows the low comprehensibility and the bad exactitude. This paper provides a new identification method of key links for urban road traffic network (URTN) based on temporal-spatial distribution of traffic congestion. The method involves identifying congestion state, computing time distribution of congestion state and determining key road link. By the cluster analysis of the history field data of URTN, the threshold to determine the traffic congestion of each link can be obtained. Then the time-interval of the traffic congestion can be computed by median filtering. At last, the time-interval coverage is defined and used to determine the target road link whether it is a key road link or not. The method is validated by a real-world case (Beijing road traffic network, BRTN). The result shows the feasibility and accuracy.

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