Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis
The volume and availability of data in the Intelligent Transportation System (ITS) result in the
need for data-driven approaches. Big Data algorithms are applied to further enhance the …
need for data-driven approaches. Big Data algorithms are applied to further enhance the …
Road traffic forecasting: Recent advances and new challenges
Due to its paramount relevance in transport planning and logistics, road traffic forecasting
has been a subject of active research within the engineering community for more than 40 …
has been a subject of active research within the engineering community for more than 40 …
Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction
The prediction of crowd flows is an important urban computing issue whose purpose is to
predict the future number of incoming and outgoing people in regions. Measuring the …
predict the future number of incoming and outgoing people in regions. Measuring the …
Spatial-temporal graph ode networks for traffic flow forecasting
Spatial-temporal forecasting has attracted tremendous attention in a wide range of
applications, and traffic flow prediction is a canonical and typical example. The complex and …
applications, and traffic flow prediction is a canonical and typical example. The complex and …
Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting
Accurate traffic forecasting is critical in improving safety, stability, and efficiency of intelligent
transportation systems. Despite years of studies, accurate traffic prediction still faces the …
transportation systems. Despite years of studies, accurate traffic prediction still faces the …
Attention based spatial-temporal graph convolutional networks for traffic flow forecasting
Forecasting the traffic flows is a critical issue for researchers and practitioners in the field of
transportation. However, it is very challenging since the traffic flows usually show high …
transportation. However, it is very challenging since the traffic flows usually show high …
T-GCN: A temporal graph convolutional network for traffic prediction
Accurate and real-time traffic forecasting plays an important role in the intelligent traffic
system and is of great significance for urban traffic planning, traffic management, and traffic …
system and is of great significance for urban traffic planning, traffic management, and traffic …
Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks
For intelligent transportation systems (ITS), predicting urban traffic crowd flows is of great
importance. However, it is challenging to represent various complex spatial relationships …
importance. However, it is challenging to represent various complex spatial relationships …
Predicting urban region heat via learning arrive-stay-leave behaviors of private cars
Urban region heat refers to the extent of which people congregate in various regions when
they travel to and stay in a specified place. Predicting urban region heat facilitates broad …
they travel to and stay in a specified place. Predicting urban region heat facilitates broad …
A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing
Accurate and timely predicting citywide traffic crowd flows precisely is crucial for public
safety and traffic management in smart cities. Nevertheless, its crucial challenge lies in how …
safety and traffic management in smart cities. Nevertheless, its crucial challenge lies in how …