scholarly journals Predicting traffic flow evolution on degraded road networks

2021 ◽  
Author(s):  
Z. Shen
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Information Quality ◽  
Moving Average ◽  
Stable State ◽  
Threshold Value ◽  
Quality Parameters ◽  
Information Environment ◽  
The Road ◽  
Flow Evolution

Abstract In order to investigate the influence of the forecast information provided by ATIS on the traffic flow evolution of the degraded road network, a weighted moving average based travel time forecasting method is designed, and the path update rule is established in the forecast information environment. The proposed route update rule is used to analyze the effects of prediction information dependency parameters, information quality parameters, weight parameters and network degradation degree on traffic flow evolution for a small test network. The results show that: 1) there is a threshold value for the dependence of travelers on the predicted information in the information environment, when the dependence is less than this value, the traffic flow quickly evolves to a stable state, and when the dependence is greater than this value, the network traffic flow will oscillate; 2) there is a situation where the higher the quality of the predicted information provided by the information system, the worse the traffic flow evolution; 3) for a particular degraded network, there is a certain optimal combination of weights that can be used to guide the release of forecast information of the network; 4) the smaller the degradation of the road network, the greater the role played by the forecast information.

scholarly journals Dynamic Assessment of Road Network Vulnerability Based on Cell Transmission Model

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yu Sun ◽  
Binglei Xie ◽  
Shan Wang ◽  
Dazhuang Wu
Keyword(s):  
Traffic Flow ◽  
Vulnerability Assessment ◽  
Road Network ◽  
Path Selection ◽  
Transmission Model ◽  
Network Vulnerability ◽  
Cell Transmission Model ◽  
The Road ◽  
Cell Transmission ◽  
Flow Evolution

The road network maintaining stability is critical for guaranteeing urban traffic function. Therefore, the vulnerable links need to be identified accurately. Previous vulnerability research under static condition compared the operating states of the old equilibrium before the event and the new equilibrium after the event to assess vulnerability ignoring the dynamic variation process. Does road network vulnerability change over time? This paper combines the vulnerability assessment with the traffic flow evolution process, exploring the road network vulnerability evaluation from the perspective of time dimension. More accurate identification and evaluation of vulnerable nodes and links can help to strengthen the ability of road network resisting disturbances. A modified dynamic traffic assignment (DTA) model is established for dynamic path selection (reselect the shortest path at the end of each link) based on the dynamic user optimal (DUO) principle. A modified cell transmission model is established to simulate the traffic flow evolution processes. The cumulative and time-varying index of vulnerability assessment is established from the viewpoint of traveler’s time loss. Then the road network vulnerability assessment combined the traffic flow model with the vulnerability index. The road network vulnerability assessment of Bao’an Central District of Shenzhen, China, reveals that road network vulnerability does contain a dynamic process, and vulnerable links in each phase can be exactly identified by the model. Results showed that the road network would have a large vulnerability during the disordered phase when the main road fails. Therefore, prioritizing the smooth flow of main roads can weaken the impact of road network vulnerability exposure.

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 SCALE-FREE NETWORK THEORY IN STUDYING THE STRUCTURE OF THE ROAD NETWORK IN POLAND

2014 ◽  
Vol 26 (3) ◽  
pp. 235-242 ◽  
Author(s):  
Katarzyna KOCUR-BERA
Keyword(s):  
Statistical Analysis ◽  
Traffic Flow ◽  
Network Theory ◽  
Road Network ◽  
Scale Free Network ◽  
Scale Free ◽  
The Road ◽  
Normal Character ◽  
Free Network ◽  
The Relationship

This paper discusses the issue of statistical analysis of traffic flow in different regions of Poland. Such analysis allows us to identify “valuable (sensitive) areas” whose damage or blockage may provoke considerable disturbances or even a stoppage of traffic flow in the examined road network. The results of the studies indicate that the road network in Poland has the properties of a scale-free network. The distribution of the examined variables does not have a normal character, whereas the relationship between the number of nodes and the number of connections is a power-law feature. 

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.

Organization of non-traffic jam traffic at an intersection when cycle time and roadway width are limited

2020 ◽  
Keyword(s):  
Traffic Flow ◽  
Road Safety ◽  
Traffic Management ◽  
Road Network ◽  
Traffic Light ◽  
Maximum Capacity ◽  
Traffic Jam ◽  
The Road ◽  
Traffic Management System ◽  
Stop Line

In General, two problems need to be solved in the traffic management system: road safety and capacity. In this paper, it is proposed to use a calculated way to optimize the cycle of a traffic light object in order to ensure the maximum capacity of the node of the road network. The calculation method is based to determining the optimal ratio of the number of lanes intended for vehicle traffic and the duration of a cycle of the traffic light object. Keywords capacity, street and road network, traffic flow, stop line, width of the roadway

scholarly journals The Prediction of Multistep Traffic Flow Based on AST-GCN-LSTM

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fan Hou ◽  
Yue Zhang ◽  
Xinli Fu ◽  
Lele Jiao ◽  
Wen Zheng
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Road Network ◽  
Short Term Memory ◽  
The Road ◽  
Traffic Flow Prediction ◽  
Point Of Interest ◽  
Flow Prediction ◽  
Long Short Term Memory ◽  
On The Road

Aiming at the traffic flow prediction problem of the traffic network, this paper proposes a multistep traffic flow prediction model based on attention-based spatial-temporal-graph neural network-long short-term memory neural network (AST-GCN-LSTM). The model can capture the complex spatial dependence of road nodes on the road network and use LSGC (local spectrogram convolution) to capture spatial correlation features from the K-order local neighbors of the road segment nodes in the road network. It is more accurate to extract the information of neighbor nodes by replacing the single-hop neighborhood matrix with K-order local neighborhoods to expand the receptive field of graph convolution. The high-order neighborhood of road nodes is also fully considered instead of only extracting features from first-order neighbor nodes. In addition, an external attribute enhancement unit is designed to extract external factors (weather, point of interest, time, etc.) that affect traffic flow in order to improve the accuracy of the model’s traffic flow prediction. The experimental results show that when considering the static, dynamic, and static and dynamic combination, the model has excellent performance: RMSE (4.0406, 4.0362, 4.0234), MAE (2.7184, 2.7044, 2.7030), accuracy (0.7132, 0.7190, 0.7223).

scholarly journals Traffic Flow Prediction Model Based on Drivers’ Cognition of Road Network

2020 ◽  
Vol 24 (7) ◽  
pp. 900-907
Author(s):  
Songjiang Li ◽  
◽  
Wen An ◽  
Peng Wang
Keyword(s):  
Prediction Model ◽  
Traffic Flow ◽  
Road Network ◽  
Probability Model ◽  
Prediction Method ◽  
Path Selection ◽  
Peak Period ◽  
The Road ◽  
Traffic Flow Prediction ◽  
Flow Prediction

The traditional traffic flow prediction method is based on data modeling, when emergencies occur, it is impossible to accurately analyze the changes in traffic characteristics. This paper proposes a traffic flow prediction model (BAT-GCN) which is based on drivers’ cognition of the road network. Firstly, drivers can judge the capacity of different paths by analyzing the travel time in the road network, which bases on the drivers’ cognition of road network space. Secondly, under the condition that the known road information is obtained, people through game decision-making for different road sections to establish the probability model of path selection; Finally, drivers obtain the probability distribution of different paths in the regional road network and build the prediction model by combining the spatiotemporal directed graph convolution neural network. The experimental results show that the BAT-GCN model reduces the prediction error compared with other baseline models in the peak period.

Fractal Characteristics of Mountain Cities' Traffic Flow Based on EMD and Multifractal

2013 ◽  
Vol 831 ◽  
pp. 430-434
Author(s):  
Ling Zhao ◽  
Juan Cao ◽  
Bo Mi
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Road Traffic ◽  
Multifractal Spectrum ◽  
Health State ◽  
Traffic Data ◽  
Time Data ◽  
Flow Signal ◽  
The Road ◽  
Fractal Characteristics

Aim at the characteristics of the mountain cities road traffic network, the short-time data signals in the congestion state of the road network traffic is analyzed. Fractal characteristics of traffic data signal is in research based on the self-similarity of the traffic data signals. The non-stationary property of the traffic flow signal in the congestion state is known through the calculation of the multifractal spectrum of the traffic flow signal based on EMD. The experimental results show the feasibility of the method, which also can provide theoretical support for the traffic flow control of the mountain city road network in the sub-health state.

ORGANIZATION FEATURES OF THE TRAFFIC FLOW NATURAL RESEARCHES WITH USING OF AUTOMATIC FIXING MEANS

2016 ◽  
Vol 3 (1) ◽  
pp. 273-277
Author(s):  
Митюгин ◽  
V. Mityugin ◽  
Пышный ◽  
V. Pyshnyy
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Moscow Region ◽  
The Road

The article describes the organization and carrying out traffic flow natural researches method with using automatic video fixing means on the example of individual sections of the road network of the Serpukhov district, Moscow region. Also in the article, a list of necessary equipment and software

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