[KDD'2024] "UrbanGPT: Spatio-Temporal Large Language Models"

This project is a collection of recent research in areas such as new infrastructure and urban computing, including white papers, academic papers, AI lab and dataset etc.

A collection of research on spatio-temporal data mining

A professional list on Multi-modal Data Fusion Models and Key Datasets for Urban Computing.

"OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction"

[ICDE'2023] When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks

[NeurIPS'2023] "GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks"

[ICML'2024] "FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction"

[WWW'2023] "AutoST: Automated Spatio-Temporal Graph Contrastive Learning"

The Tombolo Digital Connector enables users to combine different sources of data in a transparent and reproducible way.

[ICML'2023] "GraphST: Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation"

[CIKM 2022] Source codes of CIKM2022 Full Paper "Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities"

Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction

[EMNLP 2024 Industry Track & KDD UrbComp 2024 Best Paper Award] ITINERA: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning

[CIKM'2023] "CL4ST: Spatio-Temporal Meta Contrastive Learning"

ST-SiameseNet (KDD'20)

[CIKM'2024] "EasyST: A Simple Framework for Spatio-Temporal Prediction"

OpenAI-gym-like Reinforcement Learning environment for Dispatching of Mobile Chargers with SUMO. Compatible with Gym and popular RL libraries such as stable-baselines3.