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Found 16 papers, 13 papers with code
STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting
With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge.
Ranked #1 on
Traffic Prediction
on PeMS08
Learning Gaussian Mixture Representations for Tensor Time Series Forecasting
Tensor time series (TTS) data, a generalization of one-dimensional time series on a high-dimensional space, is ubiquitous in real-world scenarios, especially in monitoring systems involving multi-source spatio-temporal data (e. g., transportation demands and air pollutants).
Learning Structured Components: Towards Modular and Interpretable Multivariate Time Series Forecasting
SCNN works with a pre-defined generative process of MTS, which arithmetically characterizes the latent structure of the spatial-temporal patterns.
MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal Modeling
Spatio-temporal modeling as a canonical task of multivariate time series forecasting has been a significant research topic in AI community.
Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout
Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area.
Spatio-Temporal Meta-Graph Learning for Traffic Forecasting
Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community.
Ranked #1 on
Traffic Prediction
on EXPY-TKY
Online Trajectory Prediction for Metropolitan Scale Mobility Digital Twin
In the first stage, to encode the daily variation of human mobility at a metropolitan level, we automatically extract citywide mobility trends as crowd contexts and predict long-term and long-distance movements at a coarse level.
mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations
The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability to couple with supercomputers and edge devices through high-performance networks.
Event-Aware Multimodal Mobility Nowcasting
As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios where societal events drive mobility behavior deviated from the normality.
Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction
Forecasting incident occurrences (e. g. crime, EMS, traffic accident) is a crucial task for emergency service providers and transportation agencies in performing response time optimization and dynamic fleet management.
A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecasting
Therefore, there are two fundamental views which can be used to analyze MTS data, namely the spatial view and the temporal view.
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, car navigation systems, and traffic sensors.
DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction
Based on this idea, a series of methods have been proposed to address grid-based prediction for citywide crowd and traffic.
Forecasting Ambulance Demand with Profiled Human Mobility via Heterogeneous Multi-Graph Neural Networks
Forecasting regional ambulance demand plays a fundamental part in dynamic fleet allocation and redeployment.
VLUC: An Empirical Benchmark for Video-Like Urban Computing on Citywide Crowd and Traffic Prediction
In this study, we publish a new aggregated human mobility dataset generated from a real-world smartphone application and build a standard benchmark for such kind of video-like urban computing with this new dataset and the existing open datasets.
DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events
Therefore in this study, we aim to extract the “deep” trend only from the current momentary observations and generate an accurate prediction for the trend in the short future, which is considered to be an effective way to deal with the event situations.
