scholarly journals A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting

2021 ◽  
Vol 10 (7) ◽  
pp. 485
Author(s):  
Jiandong Bai ◽  
Jiawei Zhu ◽  
Yujiao Song ◽  
Ling Zhao ◽  
Zhixiang Hou ◽  
...  
Keyword(s):  
Road Network ◽  
Temporal Dynamics ◽  
Technological Problem ◽  
Traffic Flows ◽  
Convolutional Network ◽  
Temporal Dimension ◽  
Time Points ◽  
The Road ◽  
Traffic Forecasting ◽  
Temporal Graph

Accurate real-time traffic forecasting is a core technological problem against the implementation of the intelligent transportation system. However, it remains challenging considering the complex spatial and temporal dependencies among traffic flows. In the spatial dimension, due to the connectivity of the road network, the traffic flows between linked roads are closely related. In the temporal dimension, although there exists a tendency among adjacent time points, the importance of distant time points is not necessarily less than that of recent ones, since traffic flows are also affected by external factors. In this study, an attention temporal graph convolutional network (A3T-GCN) was proposed to simultaneously capture global temporal dynamics and spatial correlations in traffic flows. The A3T-GCN model learns the short-term trend by using the gated recurrent units and learns the spatial dependence based on the topology of the road network through the graph convolutional network. Moreover, the attention mechanism was introduced to adjust the importance of different time points and assemble global temporal information to improve prediction accuracy. Experimental results in real-world datasets demonstrate the effectiveness and robustness of the proposed A3T-GCN. We observe the improvements in RMSE of 2.51–46.15% and 2.45–49.32% over baselines for the SZ-taxi and Los-loop, respectively. Meanwhile, the Accuracies are 0.95–89.91% and 0.26–10.37% higher than the baselines for the SZ-taxi and Los-loop, respectively.

scholarly journals Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting

2020 ◽  
Vol 34 (04) ◽  
pp. 3529-3536 ◽  
Author(s):  
Weiqi Chen ◽  
Ling Chen ◽  
Yu Xie ◽  
Wei Cao ◽  
Yusong Gao ◽  
...  
Keyword(s):  
Road Network ◽  
Weighted Graph ◽  
Interaction Patterns ◽  
Convolutional Network ◽  
The Road ◽  
Traffic Forecasting ◽  
Convolutional Networks ◽  
Traffic Conditions ◽  
Aggregate Information ◽  
Deep Learning Model

Traffic forecasting is of great importance to transportation management and public safety, and very challenging due to the complicated spatial-temporal dependency and essential uncertainty brought about by the road network and traffic conditions. Latest studies mainly focus on modeling the spatial dependency by utilizing graph convolutional networks (GCNs) throughout a fixed weighted graph. However, edges, i.e., the correlations between pair-wise nodes, are much more complicated and interact with each other. In this paper, we propose the Multi-Range Attentive Bicomponent GCN (MRA-BGCN), a novel deep learning model for traffic forecasting. We first build the node-wise graph according to the road network distance and the edge-wise graph according to various edge interaction patterns. Then, we implement the interactions of both nodes and edges using bicomponent graph convolution. The multi-range attention mechanism is introduced to aggregate information in different neighborhood ranges and automatically learn the importance of different ranges. Extensive experiments on two real-world road network traffic datasets, METR-LA and PEMS-BAY, show that our MRA-BGCN achieves the state-of-the-art results.

scholarly journals Global Spatial-Temporal Graph Convolutional Network for Urban Traffic Speed Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 1509 ◽  
Author(s):  
Liang Ge ◽  
Siyu Li ◽  
Yaqian Wang ◽  
Feng Chang ◽  
Kunyan Wu
Keyword(s):  
Weather Conditions ◽  
Urban Traffic ◽  
Traffic Data ◽  
Convolutional Network ◽  
Temporal Dimension ◽  
Temporal Correlations ◽  
Speed Prediction ◽  
External Component ◽  
Temporal Graph ◽  
Traffic Speed

Traffic speed prediction plays a significant role in the intelligent traffic system (ITS). However, due to the complex spatial-temporal correlations of traffic data, it is very challenging to predict traffic speed timely and accurately. The traffic speed renders not only short-term neighboring and multiple long-term periodic dependencies in the temporal dimension but also local and global dependencies in the spatial dimension. To address this problem, we propose a novel deep-learning-based model, Global Spatial-Temporal Graph Convolutional Network (GSTGCN), for urban traffic speed prediction. The model consists of three spatial-temporal components with the same structure and an external component. The three spatial-temporal components are used to model the recent, daily-periodic, and weekly-periodic spatial-temporal correlations of the traffic data, respectively. More specifically, each spatial-temporal component consists of a dynamic temporal module and a global correlated spatial module. The former contains multiple residual blocks which are stacked by dilated casual convolutions, while the latter contains a localized graph convolution and a global correlated mechanism. The external component is used to extract the effect of external factors, such as holidays and weather conditions, on the traffic speed. Experimental results on two real-world traffic datasets have demonstrated that the proposed GSTGCN outperforms the state-of-the-art baselines.

scholarly journals Feature Extraction and Representation of Urban Road Networks Based on Travel Routes

2020 ◽  
Vol 12 (22) ◽  
pp. 9621
Author(s):  
Shichen Huang ◽  
Chunfu Shao ◽  
Juan Li ◽  
Xiong Yang ◽  
Xiaoyu Zhang ◽  
...  
Keyword(s):  
Language Processing ◽  
Traffic Safety ◽  
Link Prediction ◽  
Road Network ◽  
Spatial Information ◽  
Representation Learning ◽  
Similarity Analysis ◽  
Safety Planning ◽  
Convolutional Network ◽  
The Road

Extraction of traffic features constitutes a key research direction in traffic safety planning. In previous traffic tasks, road network features are extracted manually. In contrast, Network Representation Learning aims to automatically learn low-dimensional node representations. Enlightened by feature learning in Natural Language Processing, representation learning of urban nodes is studied as a supervised task in this paper. Following this line of thinking, a deep learning framework, called StreetNode2VEC, is proposed for learning feature representations for nodes in the road network based on travel routes, and then model parameter calibration is performed. We explain the effectiveness of features from visualization, similarity analysis, and link prediction. In visualization, the features of nodes naturally present a clustered pattern, and different clusters correspond to different regions in the road network. Meanwhile, the features of nodes still retain their spatial information in similarity analysis. The proposed method StreetNode2VEC obtains a AUC score of 0.813 in link prediction, which is greater than that obtained from Graph Convolutional Network (GCN) and Node2vec. This suggests that the features of nodes can be used to effectively and credibly predict whether a link should be established between two nodes. Overall, our work provides a new way of representing road nodes in the road network, which have potential in the traffic safety planning field.

scholarly journals MODELLING TRAFFIC FLOWS USING GRAPH THEORY

2010 ◽  
Vol 2 (6) ◽  
pp. 86-89
Author(s):  
Oksana Musyt ◽  
Oksana Nadtochij ◽  
Aleksandr Stepanchiuk ◽  
Andrej Beljatynskij
Keyword(s):  
Graph Theory ◽  
Network Optimization ◽  
Road Network ◽  
Traffic Accidents ◽  
Road Transport ◽  
Traffic Flows ◽  
Urban Road ◽  
The Road ◽  
Traffic Jams ◽  
On The Road

An intensive increase in road transport, particularly individual, in recent years has led to such consequences as increased time spent on travel, the number of forced stops, traffic accidents, the occurrence of traffic jams on the road network, reducing traffic speed and a deteriorated urban road network in cities. The most effective method for solving these problems is the use of graph theory, the main characteristics of which is reliability, durability and accessibility of a free as well as loaded network. Based on their analysis the methods for network optimization are proposed.

scholarly journals DC-STGCN: Dual-Channel Based Graph Convolutional Networks for Network Traffic Forecasting

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1014
Author(s):  
Chengsheng Pan ◽  
Jiang Zhu ◽  
Zhixiang Kong ◽  
Huaifeng Shi ◽  
Wensheng Yang
Keyword(s):  
Feature Extraction ◽  
Network Traffic ◽  
Traffic Flows ◽  
Convolutional Network ◽  
Temporal Characteristics ◽  
Traffic Forecasting ◽  
Convolutional Networks ◽  
Forecasting Models ◽  
Dual Channel

Network traffic forecasting is essential for efficient network management and planning. Accurate long-term forecasting models are also essential for proactive control of upcoming congestion events. Due to the complex spatial-temporal dependencies between traffic flows, traditional time series forecasting models are often unable to fully extract the spatial-temporal characteristics between the traffic flows. To address this issue, we propose a novel dual-channel based graph convolutional network (DC-STGCN) model. The proposed model consists of two temporal components that characterize the daily and weekly correlation of the network traffic. Each of these two components contains a spatial-temporal characteristics extraction module consisting of a dual-channel graph convolutional network (DCGCN) and a gated recurrent unit (GRU). The DCGCN further consists of an adjacency feature extraction module (AGCN) and a correlation feature extraction module (PGCN) to capture the connectivity between nodes and the proximity correlation, respectively. The GRU further extracts the temporal characteristics of the traffic. The experimental results based on real network data sets show that the prediction accuracy of the DC-STGCN model overperforms the existing baseline and is capable of making long-term predictions.

scholarly journals Transport accessibility and mobility: a forecast of changes in the face of planned development of the network of expressways and motorways in Poland

2019 ◽  
Vol 26 (2) ◽  
pp. 151-176 ◽  
Author(s):  
Michał Kowalski ◽  
Szymon Wiśniewski
Keyword(s):  
European Country ◽  
Road Network ◽  
Transport Network ◽  
Transport Infrastructure ◽  
Traffic Flows ◽  
Transport Accessibility ◽  
The Road ◽  
The Face ◽  
Infrastructure Investments ◽  
Made In

The article presents a forecast of changes in the level of transport accessibility and mobility in Poland as a result of the anticipated development of the network of expressways and motorways. The progress which has been made in this respect in the last few years in Poland is unquestionable and unrepeatable by any other European country. Will the subsequent investment plans concerning the road network of the highest parameters offer equally impressive results as far as the increase in Poland’s territorial cohesion is concerned? The aim of this article is to establish in what way the planned infrastructure investments will affect the changes in transport accessibility and mobility as well as whether they will result in the changes in traffic flows directed to Warsaw and other regional centres. To achieve this, an analysis of the present and target state of the road network in Poland was conducted from the perspective of changes in accessibility, anticipated traffic flows, and mobility. For this purpose the authors used the analyses of isochrone and accumulative accessibility in ArcMap environment and research into traffic flows and their changes in the Visum software. The conducted research showed that the planned transport network might result in induced traffic through a increase in accessibility (the central variant) with the assumption that an increase in mobility would be vented in the real face of the phenomenon of motility. The fact of opening new road sections of expressways will contribute to substantial changes in the directions of traffic flows only to a slight extent, and the only transformations concern regions with already developed fast car transport infrastructure whose functionality is limited due to the lack of its cohesion in the subsequent course or lack of a developed network of expressways and motorways.

scholarly journals TRAFFIC FORECASTING ON THE CITY ROAD NETWORK TAKING INTO ACCOUNT THE CAPACITY LIMIT

2021 ◽  
Vol 2 (2) ◽  
pp. 27-33
Author(s):  
Denys Zhezherun
Keyword(s):  
Transport System ◽  
Road Network ◽  
Road Traffic ◽  
Traffic Intensity ◽  
The Road ◽  
Traffic Forecasting ◽  
Planning Decisions ◽  
Road Design ◽  
Traffic Volumes ◽  
On The Road

The purpose of the paper is to present a model of traffic forecasting on the road section based on a model of the transport system. Traffic forecasting is an integral part of the road design process, from investment to the feasibility study of working documentation. The definition of transportation and distribution of cars by sections is based on a set of interrelated factors. Full and reasonable consideration of these factors for complex road networks is possible only with the help of mathematical models and appropriate programs. The accuracy and consistency of the forecast determine the reliability of almost all the main characteristics of the projected object, from the direction of the route and the location of connection points with existing elements of the road network, ending with specific planning decisions for the road objects. Subject of research: a road traffic and a traffic intensity. Knowledge of forecast data on traffic intensity makes it possible to predict the possible mechanisms to solve the above problems. Methodology: analysis and research of methods used to predict traffic volumes. The method of extrapolation and the method of using approximating functions. Goal. The aim of the work is to compare the forecasting methods used to determine traffic on the road. It is also necessary to show the experience of traffic forecasting on the road network from a European country. Conclusion. All methods for predicting the volume and intensity of movement are short-lived, and if some achieve the desired predicted result, it is very vague and needs to be tested with complex and expensive research to determine and process the initial data. To achieve the desired results, it is necessary to apply new methods of forecasting modeling or improvement of already known ones, which would take into account the evolution of the entire transport system and its components. Determining the capacity of highways is necessary perform to identify areas with possible congestion, assessment economy and conditions of movement of vehicles, and also for a choice of methods and means to improve the traffic conditions of all road users.

scholarly journals Estimation of transport flow intensity using geoinformation systems

2021 ◽  
Vol 27 (1) ◽  
pp. 195-204
Author(s):  
Victoria Bitykova ◽  
Nikita Mozgunov
Keyword(s):  
Urban Areas ◽  
Road Network ◽  
Road Traffic ◽  
Local Level ◽  
Federal District ◽  
Transport Infrastructure ◽  
Traffic Load ◽  
Field Observations ◽  
Traffic Flows ◽  
The Road

The main discussion is about methods for assessing the intensity of traffic flows using geoinformation technologies. The intensity of traffic flows is one of the key indicators that determine the emission from transport in urban areas. In Russia, the growth in the volume and share of motor transport in pollution is increasing under the influence of an increase in the number of cars. This is most obvious examples of it are regions of the Central Federal District, but in the regional centers, under the influence of the improvement in the structure of the vehicle park, the growth of pollution is much slower, and in Moscow it has practically stabilized. At the local level, the determining factor of road traffic pollution is the change in the building density and the transport-planning structure. The collection and calculation of indicators that give an idea of the spatial differentiation of emissions from road transport is a very time-consuming stage of the study. The most common method of obtaining information on the transport and environmental situation in the city is directly field data collection. However, this method is quite time consuming for research. In conditions when the transport infrastructure is developing rapidly, the speed of field observations does not allow promptly updating information on changes in the traffic load of the road network and, as a result, assessing the current ecological situation in the territory. As an alternative to the traditional collection of information, modern sources of geoinformation data can be used. The services, originally developed to provide operational monitoring of the traffic situation and the construction of optimal routes, can also serve as a source of data for models for assessing the intensity of traffic load in environmental studies. The proposed technique has been tested at the level of districts and administrative districts of Moscow. The results obtained are compared with control field observations. The relatively low measurement error when using data from information systems is compensated by the possibility of more efficiently obtaining information about the traffic load on the sections of the road network.

scholarly journals Analysis of the Possibility to Detect Road Vehicles via Bluetooth Technology

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7281
Author(s):  
Răzvan Andrei Gheorghiu ◽  
Valentin Iordache ◽  
Angel Ciprian Cormoș
Keyword(s):  
Road Network ◽  
Road Traffic ◽  
Data Gathering ◽  
Traffic Networks ◽  
Traffic Flows ◽  
Time Data ◽  
Road Vehicles ◽  
The Road ◽  
Big Picture ◽  
Road Traffic Networks

As road traffic networks become more congested and information systems are implemented to manage traffic flows, real-time data gathering becomes increasingly important. Classic detectors are placed in one point of the network and are able to provide information only from that area. As useful as this is, it lacks the big picture of the routes the vehicles usually travel. There are applications developed to help individuals make their way into the road network, but these are no solutions that deal with the cause of traffic; rather, they counteract the effects. It becomes obvious that a proper management system, with knowledge of all the relevant aspects will better serve all travelers. The detection solution proposed in this paper is based on Bluetooth detectors. This system is able to match detected devices in the road network, filter the results, and generate a vehicle count that is proved to follow RADAR detection results.

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