Influence ranking of road segments in urban road traffic networks

Computing ◽  
2020 ◽  
Vol 102 (11) ◽  
pp. 2333-2360
Author(s):  
Tarique Anwar ◽  
Chengfei Liu ◽  
Hai L. Vu ◽  
Md. Saiful Islam ◽  
Dongjin Yu ◽  
...  
Keyword(s):  
Road Traffic ◽  
Traffic Networks ◽  
Urban Road ◽  
Road Segments ◽  
Road Traffic Networks

Cascading Failure Mitigation Strategy for Urban Road Traffic Networks

2021 ◽  
pp. 170-180
Author(s):  
Xudan Song ◽  
Jing Wang ◽  
Sijia Liu ◽  
Xiaohan Cui ◽  
Rong-rong Yin
Keyword(s):  
Road Traffic ◽  
Mitigation Strategy ◽  
Cascading Failure ◽  
Traffic Networks ◽  
Urban Road ◽  
Failure Mitigation ◽  
Road Traffic Networks

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.

scholarly journals Representation of Traffic Congestion Data for Urban Road Traffic Networks Based on Pooling Operations

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 84 ◽  
Author(s):  
Sen Zhang ◽  
Shaobo Li ◽  
Xiang Li ◽  
Yong Yao
Keyword(s):  
Traffic Congestion ◽  
Large Scale ◽  
Short Term Memory ◽  
Road Traffic ◽  
Traffic Networks ◽  
Urban Road ◽  
Congestion Prediction ◽  
Cost Efficient ◽  
Computational Resources ◽  
Road Traffic Networks

In order to improve the efficiency of transportation networks, it is critical to forecast traffic congestion. Large-scale traffic congestion data have become available and accessible, yet they need to be properly represented in order to avoid overfitting, reduce the requirements of computational resources, and be utilized effectively by various methodologies and models. Inspired by pooling operations in deep learning, we propose a representation framework for traffic congestion data in urban road traffic networks. This framework consists of grid-based partition of urban road traffic networks and a pooling operation to reduce multiple values into an aggregated one. We also propose using a pooling operation to calculate the maximum value in each grid (MAV). Raw snapshots of traffic congestion maps are transformed and represented as a series of matrices which are used as inputs to a spatiotemporal congestion prediction network (STCN) to evaluate the effectiveness of representation when predicting traffic congestion. STCN combines convolutional neural networks (CNNs) and long short-term memory neural network (LSTMs) for their spatiotemporal capability. CNNs can extract spatial features and dependencies of traffic congestion between roads, and LSTMs can learn their temporal evolution patterns and correlations. An empirical experiment on an urban road traffic network shows that when incorporated into our proposed representation framework, MAV outperforms other pooling operations in the effectiveness of the representation of traffic congestion data for traffic congestion prediction, and that the framework is cost-efficient in terms of computational resources.

Robust Control for Urban Road Traffic Networks

2014 ◽  
Vol 15 (1) ◽  
pp. 385-398 ◽  
Author(s):  
Tamas Tettamanti ◽  
Tamas Luspay ◽  
Balazs Kulcsar ◽  
Tamas Peni ◽  
Istvan Varga
Keyword(s):  
Robust Control ◽  
Road Traffic ◽  
Traffic Networks ◽  
Urban Road ◽  
Road Traffic Networks
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