scholarly journals Identification of Critical Links in Urban Road Network considering Cascading Failures

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yulin Tian ◽  
Xiaoming Liu ◽  
Zhen Li ◽  
Shaohu Tang ◽  
Chunlin Shang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Failure Probability ◽  
Calculation Method ◽  
Road Network ◽  
Probability Model ◽  
Real Data ◽  
Sequence Diagram ◽  
Cascading Failures ◽  
Urban Road ◽  
And Control

The identification of critical road links is greatly important to the management and control of the transportation system. Existing works fail to fully consider the influence of the distribution of traffic flow and its dynamic characteristics on critical road link identification. In this paper, we propose a criticality calculation method for urban road networks considering the effect of cascading failures which models the distribution change of traffic flow after a specific road link failed. Firstly, a sequence diagram calculation method is proposed to model how the traffic failure on one road link propagates to related links. Secondly, the diagram of the cascade failure sequence is divided into different stages according to the consistency of the objective function. The influence value of each stage is computed for the target road link. Finally, the failure probability model and the importance indicator are proposed to calculate the criticality for each road link. We evaluate our critical road link identification method on both simulated and read scenes. In our simulation, we achieve 90.6% and 91.7% for the accuracy on two key metrics, respectively, i.e., the length of failure road link and the total parking delay, which proves the feasibility of our method. Our method also achieves reasonable conclusions on real data and helps to find the critical road links.

Cascade defense in urban road network by inserting modular topologies

Kybernetes ◽  
2014 ◽  
Vol 43 (5) ◽  
pp. 750-763 ◽  
Author(s):  
Zhiyun Zou ◽  
Jinlong Wu ◽  
Jianzhi Gao ◽  
Xuecai Xu
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Random Network ◽  
Small World ◽  
User Equilibrium ◽  
Cascading Failures ◽  
Urban Road Network ◽  
Content Type ◽  
Urban Road ◽  
Critical Components

Purpose – By aiming at defending cascade failures effectively, the purpose of this paper is to present a strategy of inserting modular topologies into urban road network through reducing the burdens of critical components with too much traffic flow. Design/methodology/approach – Each module is considered as a small-world random network, which is inserted into the initial Barabási-Albert scale-free network. Based on the user-equilibrium assignment, the strategy searches for remote nodes with low betweeness and flow in the network, and sets these nodes to be connected with the modular topologies. In this sense, the inserted modules are supposed to attach to the nodes with lower intensity of shorter path, and avoid bringing more impact to the nodes with higher betweeness and traffic flow. By using efficiency as the measurement of cascading failures, the performance of the networks generated through the strategy is tested. Findings – The results show that the performance of the strategy is sensitive to the average degree of the inserted modular, and the modular size with a better effect on reducing the size of cascading failures or delaying the time of breakdown, while the other factors (e.g. the rewired probability) present few differences among various values. Meanwhile, it is found that the importance-based attachment mechanism has a better effect on preventing the cascading failures, especially delaying the step time of the larger reduction. Practical implications – The strategy aims at alleviating the burdens in critical components to prevent the cascading failures of the network, and provides practical guidance on the decision of the urban road network evolving process. Originality/value – An effective strategy for cascade defense in urban road network is proposed in this paper.

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

2022 ◽  
Vol 13 (2) ◽  
pp. 1-25
Author(s):  
Bin Lu ◽  
Xiaoying Gan ◽  
Haiming Jin ◽  
Luoyi Fu ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Large Scale ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Propagation Mechanism ◽  
Urban Road ◽  
The Road ◽  
Traffic Flow Forecasting ◽  
Gating Mechanism

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

scholarly journals Effects of static bottlenecks on traffic flow in urban road network

2020 ◽  
Vol 12 (3) ◽  
pp. 1-15
Author(s):  
John N.P. Mahona ◽  
Cuthbert F. Mhilu ◽  
Joseph Kihedu ◽  
Hannibal Bwire
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Traffic Density ◽  
Urban Road Network ◽  
Peak Period ◽  
Flow Models ◽  
Urban Road ◽  
Stop And Go ◽  
Traffic Flow Models ◽  
Methodological Aspects

Existing traffic flow models do not consider the effects of road static bottlenecks on traffic flow. In this paper, a modified macroscopic continuum  model for traffic flow on urban road network with static bottlenecks is presented. The model takes into account the fluctuations of traffic flow considering static bottlenecks during the morning peak period. The model results show that existence of static road bottlenecks with various configurations cause traffic flow instabilities. This phenomenon lead into stop-and-go traffic flow conditions under the moderate density and reduction of the traffic system’s efficiency. Furthermore, results show that an increase in traffic density is accompanied by a significant decrease of speed which adversely influences performance of roadway and decrease the traffic system’s efficiency and thus resulting to the occurrence of congestions. The methodological aspects of the study and results will enable traffic engineers and planners to assess and improve existing urbanroad networks. Keywords: Traffic flow, Bottlenecks, stability, Stop-and-go traffic, System’s efficiency, Congestion.

Study on Urban Intersection Traffic Flow Forecasting and Traffic Signal Control

2011 ◽  
Vol 135-136 ◽  
pp. 969-974 ◽  
Author(s):  
Yong Feng Ju ◽  
Xiao Wei Wei
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Control ◽  
Road Network ◽  
Intelligent Transportation ◽  
Traffic Signal Control ◽  
Time Data ◽  
Urban Road ◽  
Traffic Flow Forecasting ◽  
Short Time

Short-traffic flow forecasting is an important part of ITS, and its accuracy and real-time is directly related to the effect of traffic control and traffic induce. Gathering and analyzing the real-time data of urban road network ,short-time traffic flow forecasting could estimate the state of traffic flow for a few minutes in future and provide support to intelligent transportation control, so it is one of the important premise for ITS.

A MODEL FOR THE STUDY OF TRAFFIC FLOW ON THE UKRAINIAN CITIES ROAD NETWORK

2019 ◽  
pp. 30-33
Author(s):  
Liydmila Nagrebelna
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Operating Conditions ◽  
Negative Effects ◽  
Efficient Operation ◽  
Urban Road ◽  
The Road ◽  
Effective Functioning ◽  
Organization And Management ◽  
The City

The problems of efficient functioning of the city road network are outlined. The method by which it is possible to improve the functioning of the street-road network of Ukrainian cities is presented. Improving the efficiency of the urban road network is to use all the resources of this network to create the necessary languages for its reliable and efficient operation and reduce the negative effects of motorization. It is proved that in order to ensure the effective functioning of the road network it is necessary to carry out a set of measures for the organization and management of traffic. The purpose of this article is to identify factors that affect the deterioration of operating conditions; identify the conditions for the effective functioning of the road network; the choice of a model for the effective functioning of the street-road network of Ukrainian cities is grounded. Because the management impact on traffic flow can be estimated on the basis of the developed models. Keywords: road network, efficient operation, methods, conditions.

scholarly journals Investigating the Effect of Traffic Flow on Pollution, Noise for Urban Road Network

2022 ◽  
Vol 961 (1) ◽  
pp. 012067
Author(s):  
Ali I Mansour ◽  
Hamid A Aljamil
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Vehicle Emissions ◽  
Solid Particles ◽  
Sulfur Oxides ◽  
Urban Road ◽  
Unburned Hydrocarbons ◽  
Carbon Dioxide Co2 ◽  
Atmospheric Phenomena

Abstract Congestion has a significant impact on the environment. It’s the predominant source of pollution, as noise and air pollution. The sound produced by vehicles as well as horns creates the worst possible environment. High motorized traffic flow nowadays is the major contributor to rising externalities, vehicle emissions, and other pollutants that impact the environment and the atmosphere, which result in negative atmospheric phenomena, global warming, and climate change. Vehicle emissions cause numerous vulnerabilities, so a serious consequence may arise in the long term, both regional and global. This study investigated Noise and pollution for different roads in the different cities based on field data at peak periods of traffic flow, shows that the major pollutants that are emitted from engines are: nitrogen oxides (NOX), carbon monoxide (CO), unburned hydrocarbons (CxHy), sulfur oxides (SOX), solid particles, including aerosols, as well as carbon dioxide (CO2).

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