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 …
Towards automated urban planning: When generative and chatgpt-like ai meets urban planning
The two fields of urban planning and artificial intelligence (AI) arose and developed
separately. However, there is now cross-pollination and increasing interest in both fields to …
separately. However, there is now cross-pollination and increasing interest in both fields to …
Lbsn2vec++: Heterogeneous hypergraph embedding for location-based social networks
Location-Based Social Networks (LBSNs) have been widely used as a primary data source
for studying the impact of mobility and social relationships on each other. Traditional …
for studying the impact of mobility and social relationships on each other. Traditional …
Deep generative model for periodic graphs
Periodic graphs are graphs consisting of repetitive local structures, such as crystal nets and
polygon mesh. Their generative modeling has great potential in real-world applications such …
polygon mesh. Their generative modeling has great potential in real-world applications such …
Adversarial substructured representation learning for mobile user profiling
Mobile user profiles are a summary of characteristics of user-specific mobile activities.
Mobile user profiling is to extract a user's interest and behavioral patterns from mobile …
Mobile user profiling is to extract a user's interest and behavioral patterns from mobile …
Unifying inter-region autocorrelation and intra-region structures for spatial embedding via collective adversarial learning
Unsupervised spatial representation learning aims to automatically identify effective features
of geographic entities (ie, regions) from unlabeled yet structural geographical data. Existing …
of geographic entities (ie, regions) from unlabeled yet structural geographical data. Existing …
Urban2vec: Incorporating street view imagery and pois for multi-modal urban neighborhood embedding
Understanding intrinsic patterns and predicting spatiotemporal characteristics of cities
require a comprehensive representation of urban neighborhoods. Existing works relied on …
require a comprehensive representation of urban neighborhoods. Existing works relied on …
Resolving urban mobility networks from individual travel graphs using massive-scale mobile phone tracking data
Human movements and interactions with cities are characterized by urban mobility
networks. Many studies that address urban mobility are inspired by complex networks. The …
networks. Many studies that address urban mobility are inspired by complex networks. The …
Joint representation learning for multi-modal transportation recommendation
Multi-modal transportation recommendation has a goal of recommending a travel plan which
considers various transportation modes, such as walking, cycling, automobile, and public …
considers various transportation modes, such as walking, cycling, automobile, and public …
You are how you drive: Peer and temporal-aware representation learning for driving behavior analysis
Driving is a complex activity that requires multi-level skilled operations (eg, acceleration,
braking, turning). Analyzing driving behavior can help us assess driver performances …
braking, turning). Analyzing driving behavior can help us assess driver performances …