Journal of Advanced Transportation

Graph-Based Big Data Analysis and Mining in Transportation Systems


Publishing date
01 Mar 2022
Status
Closed
Submission deadline
05 Nov 2021

Lead Editor
Guest Editors

1Newcastle University, Newcastle, UK

2Liaoning Technical University, Huludao, China

3South China University of Technology, Guangzhou, China

4Beijing Jiaotong University, Beijing, China

This issue is now closed for submissions.
More articles will be published in the near future.

Graph-Based Big Data Analysis and Mining in Transportation Systems

This issue is now closed for submissions.
More articles will be published in the near future.

Description

The rapid development of mobile and sensor technologies is leading to substantial changes in the transportation industry, with the research paradigm moving from physical mechanism-based models to data-driven models. Traffic sensors, such as loop detectors, probes, cameras on road networks, smart cards, and quick response codes in public transport, and global positioning system (GPS), Bluetooth, and Wi-Fi devices, are generating massive datasets, and providing unprecedented opportunities for the transportation industry to understand diverse travel behaviours and improve the traffic modelling process.

However, due to the nonlinearity and heterogeneity of big data, new challenges are arising in the data-driven analysis, mining, and application in the transportation industry. To overcome these challenges, graph-based machine learning is gaining increasing attention as a powerful tool. For instance, urban road networks can be directly modelled as graphs, based on which a series of neural networks and their variants can be employed for urban traffic analytics. Recently, pilot studies on traffic analysis using heterogeneous graphs were carried out. A heterogeneous graph is a graph with multiple types of nodes and links. Compared with homogeneous graphs, heterogeneous graphs have two advantages. Firstly, heterogeneous graphs are an effective tool for fusing information, that is, not only from different types of objects and their interactions but also from heterogeneous data sources. Secondly, the coexistence of multiple types of objects and relationships in heterogeneous graphs, which contain rich structural and semantic information, provides a new way to discover hidden traffic patterns in a more precise and interpretable way. Although some efforts have been made to explore graph-based methods in mining transportation data, it is still necessary to fully explore the benefits of these methods for ITS applications (e.g. traffic state analysis, traffic demand prediction, anomaly detection, traffic signal control).

The aim of this Special Issue is to collate original research and surveys on the use of graph-based models in big data analysis and mining within transportation systems. Submissions should not focus on freight or supply chain logistics.

Potential topics include but are not limited to the following:

  • Graph-based deep learning in traffic management and operation solutions
  • Graph-based analysis of the resilience of transportation systems
  • Graph-theoretic matching, classifying and assessing of transportation network
  • Data mining theory, technologies and applications in ITS based on heterogeneous graphs
  • Novel network representations and graph extraction of transportation systems
  • Security, integrity and privacy solutions for ITS using graph-based deep learning
  • Collaborative decision-making for improving the efficiency of road networks using multiple agent graphs
  • Multi-scale mobility feature analysis and prediction based on graphical modelling
  • Knowledge graph theory, technologies and applications for ITS

Articles

  • Special Issue
  • - Volume 2022
  • - Article ID 8233424
  • - Research Article

Conflict Probability Prediction and Safety Assessment of Straight-Left Traffic Flow at Signalized Intersections

Yingying Ma | Zihao Zhang | Jiabin Wu
  • Special Issue
  • - Volume 2022
  • - Article ID 4260244
  • - Research Article

Spatiotemporal DeepWalk Gated Recurrent Neural Network: A Deep Learning Framework for Traffic Learning and Forecasting

Jian Yang | Jinhong Li | ... | Fuqi Mao
  • Special Issue
  • - Volume 2022
  • - Article ID 5221362
  • - Research Article

Bi-GRCN: A Spatio-Temporal Traffic Flow Prediction Model Based on Graph Neural Network

Wenhao Jiang | Yunpeng Xiao | ... | Zheng Li
  • Special Issue
  • - Volume 2022
  • - Article ID 3843021
  • - Research Article

Research on Recommendation Algorithm of Joint Light Graph Convolution Network and DropEdge

Haicheng Qu | Jiangtao Guo | Yanji Jiang
  • Special Issue
  • - Volume 2021
  • - Article ID 9290921
  • - Research Article

Online Traffic Accident Spatial-Temporal Post-Impact Prediction Model on Highways Based on Spiking Neural Networks

Duowei Li | Jianping Wu | Depin Peng
  • Special Issue
  • - Volume 2021
  • - Article ID 2956151
  • - Research Article

MDGCN: Multiple Graph Convolutional Network Based on the Differential Calculation for Passenger Flow Forecasting in Urban Rail Transit

Chenxi Wang | Huizhen Zhang | ... | Ming Ye
  • Special Issue
  • - Volume 2021
  • - Article ID 2564211
  • - Research Article

Prediction of Road Network Traffic State Using the NARX Neural Network

Ziwen Song | Feng Sun | ... | Chenchen Li
  • Special Issue
  • - Volume 2021
  • - Article ID 9453911
  • - Research Article

Multichannel Speech Enhancement in Vehicle Environment Based on Interchannel Attention Mechanism

Xueli Shen | Zhenxing Liang | ... | Yanji Jiang
  • Special Issue
  • - Volume 2021
  • - Article ID 7649214
  • - Research Article

Graphical Optimization Method for Symmetrical Bidirectional Corridor Progression

Kai Lu | Shuyan Jiang | ... | Yinhai Wang
  • Special Issue
  • - Volume 2021
  • - Article ID 9231451
  • - Research Article

Mining Travel Time of Airport Ferry Network Based on Historical Trajectory Data

Cong Ding | Jun Bi | ... | Yi Liu
Journal of Advanced Transportation
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Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.