Deep learning for spatio-temporal data mining: A survey
With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
A survey on deep learning for human mobility
The study of human mobility is crucial due to its impact on several aspects of our society,
such as disease spreading, urban planning, well-being, pollution, and more. The …
such as disease spreading, urban planning, well-being, pollution, and more. The …
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 …
A hybrid deep learning model with attention-based conv-LSTM networks for short-term traffic flow prediction
Accurate short-time traffic flow prediction has gained gradually increasing importance for
traffic plan and management with the deployment of intelligent transportation systems (ITSs) …
traffic plan and management with the deployment of intelligent transportation systems (ITSs) …
Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction
Traffic prediction has drawn increasing attention in AI research field due to the increasing
availability of large-scale traffic data and its importance in the real world. For example, an …
availability of large-scale traffic data and its importance in the real world. For example, an …
Deep learning on traffic prediction: Methods, analysis, and future directions
X Yin, G Wu, J Wei, Y Shen, H Qi… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …
prediction can assist route planing, guide vehicle dispatching, and mitigate traffic …
Dl-traff: Survey and benchmark of deep learning models for urban traffic prediction
Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical
Systems) technologies, big spatiotemporal data are being generated from mobile phones …
Systems) technologies, big spatiotemporal data are being generated from mobile phones …
Deepstn+: Context-aware spatial-temporal neural network for crowd flow prediction in metropolis
Crowd flow prediction is of great importance in a wide range of applications from urban
planning, traffic control to public safety. It aims to predict the inflow (the traffic of crowds …
planning, traffic control to public safety. It aims to predict the inflow (the traffic of crowds …
Learning from multiple cities: A meta-learning approach for spatial-temporal prediction
Spatial-temporal prediction is a fundamental problem for constructing smart city, which is
useful for tasks such as traffic control, taxi dispatching, and environment policy making. Due …
useful for tasks such as traffic control, taxi dispatching, and environment policy making. Due …
Meta graph transformer: A novel framework for spatial–temporal traffic prediction
Accurate traffic prediction is critical for enhancing the performance of intelligent
transportation systems. The key challenge to this task is how to properly model the complex …
transportation systems. The key challenge to this task is how to properly model the complex …