Deep time series models: A comprehensive survey and benchmark
Time series, characterized by a sequence of data points arranged in a discrete-time order,
are ubiquitous in real-world applications. Different from other modalities, time series present …
are ubiquitous in real-world applications. Different from other modalities, time series present …
[HTML][HTML] Utilizing machine learning on freight transportation and logistics applications: A review
This review article explores and locates the current state-of-the-art related to application
areas from freight transportation, supply chain and logistics that focuses on arrival time …
areas from freight transportation, supply chain and logistics that focuses on arrival time …
Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Largest: A benchmark dataset for large-scale traffic forecasting
Road traffic forecasting plays a critical role in smart city initiatives and has experienced
significant advancements thanks to the power of deep learning in capturing non-linear …
significant advancements thanks to the power of deep learning in capturing non-linear …
Decoupled dynamic spatial-temporal graph neural network for traffic forecasting
We all depend on mobility, and vehicular transportation affects the daily lives of most of us.
Thus, the ability to forecast the state of traffic in a road network is an important functionality …
Thus, the ability to forecast the state of traffic in a road network is an important functionality …
Spatio-temporal meta-graph learning for traffic forecasting
Traffic forecasting as a canonical task of multivariate time series forecasting has been a
significant research topic in AI community. To address the spatio-temporal heterogeneity …
significant research topic in AI community. To address the spatio-temporal heterogeneity …
Exploring progress in multivariate time series forecasting: Comprehensive benchmarking and heterogeneity analysis
Multivariate Time Series (MTS) analysis is crucial to understanding and managing complex
systems, such as traffic and energy systems, and a variety of approaches to MTS forecasting …
systems, such as traffic and energy systems, and a variety of approaches to MTS forecasting …
A hybrid-convolution spatial–temporal recurrent network for traffic flow prediction
X Zhang, S Wen, L Yan, J Feng, Y **a - The Computer Journal, 2024 - academic.oup.com
Accurate traffic flow prediction is valuable for satisfying citizens' travel needs and alleviating
urban traffic pressure. However, it is highly challenging due to the complexity of the urban …
urban traffic pressure. However, it is highly challenging due to the complexity of the urban …
Spatial-temporal aware inductive graph neural network for C-ITS data recovery
With the prevalence of Intelligent Transportation Systems (ITS), massive sensors are
deployed on roadside, vehicles, and infrastructures. One key challenge is imputing several …
deployed on roadside, vehicles, and infrastructures. One key challenge is imputing several …
A decomposition dynamic graph convolutional recurrent network for traffic forecasting
Our daily lives are greatly impacted by traffic conditions, making it essential to have accurate
predictions of traffic flow within a road network. Traffic signals used for forecasting are …
predictions of traffic flow within a road network. Traffic signals used for forecasting are …