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A survey on spatio-temporal series prediction with deep learning: taxonomy, applications, and future directions
F Sun, W Hao, A Zou, Q Shen - Neural Computing and Applications, 2024 - Springer
With the rapid development of data acquisition and storage technology, spatio-temporal (ST)
data in various fields are growing explosively, so many ST prediction methods have …
data in various fields are growing explosively, so many ST prediction methods have …
[HTML][HTML] Sparse trip demand prediction for shared E-scooter using spatio-temporal graph neural networks
The shared electric scooter (E-scooter) is an emerging micro-mobility mode in sustainable
cities. Accurate hourly trip demand prediction is critical for effective service maintenance, but …
cities. Accurate hourly trip demand prediction is critical for effective service maintenance, but …
Spatiotemporal forecasting using multi-graph neural network assisted dual domain transformer for wind power
Accurate prediction of wind power generation is crucial for operational and maintenance
decision in wind farms. With the increasing scale and capacity of turbines, incorporating both …
decision in wind farms. With the increasing scale and capacity of turbines, incorporating both …
Wildfire evacuation decision modeling using GPS data
The threat of wildfires is increasing at an alarming rate due to climate change and the
expansion of the wildland–urban interface. It is critical to improve understanding of people's …
expansion of the wildland–urban interface. It is critical to improve understanding of people's …
Exploring spatial heterogeneity of e-scooter's relationship with ridesourcing using explainable machine learning
The expansion of e-scooter sharing system has introduced several novel interactions within
the existing transportation system. However, few studies have explored how spatial contexts …
the existing transportation system. However, few studies have explored how spatial contexts …
Analyzing spatial heterogeneity of ridesourcing usage determinants using explainable machine learning
There is a pressing need to study spatial heterogeneity of ridesourcing usage determinants
to develop better-targeted transportation and land use policies. This study incorporates …
to develop better-targeted transportation and land use policies. This study incorporates …
[HTML][HTML] Meta-analysis of shared micromobility ridership determinants
Shared micromobility (SμM)—shared e-scooters and (e-) bikes—offer moderate-speed,
space-efficient, and carbon-light mobility, promoting environmental sustainability and …
space-efficient, and carbon-light mobility, promoting environmental sustainability and …
[HTML][HTML] Situational-aware multi-graph convolutional recurrent network (SA-MGCRN) for travel demand forecasting during wildfires
Natural hazards, such as wildfires, pose a significant threat to communities worldwide. Real-
time forecasting of travel demand during wildfire evacuations is crucial for emergency …
time forecasting of travel demand during wildfire evacuations is crucial for emergency …
Naturalistic e-scooter maneuver recognition with federated contrastive rider interaction learning
Smart micromobility, particularly the electric (e)-scooters, has emerged as an important
ubiquitous mobility option that has proliferated within and across many cities in North …
ubiquitous mobility option that has proliferated within and across many cities in North …
ICN: Interactive convolutional network for forecasting travel demand of shared micromobility
Accurate shared micromobility demand predictions are essential for transportation planning
and management. Although deep learning methods provide robust mechanisms to tackle …
and management. Although deep learning methods provide robust mechanisms to tackle …