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Found 42 papers, 30 papers with code
LAG: LLM agents for Leaderboard Auto Generation on Demanding
This paper introduces Leaderboard Auto Generation (LAG), a novel and well-organized framework for automatic generation of leaderboards on a given research topic in rapidly evolving fields like Artificial Intelligence (AI).
Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances
Autonomous Driving Systems (ADSs) are revolutionizing transportation by reducing human intervention, improving operational efficiency, and enhancing safety.
CausalMob: Causal Human Mobility Prediction with LLMs-derived Human Intentions toward Public Events
In this study, we propose a causality-augmented prediction model, called CausalMob, to analyze the causal effects of public events.
Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting
To address this limitation, we reconsider the design of adaptive embeddings and propose a Principal Component Analysis (PCA) embedding approach that enables models to adapt to new scenarios without retraining.
Instruction-Tuning Llama-3-8B Excels in City-Scale Mobility Prediction
Human mobility prediction plays a critical role in applications such as disaster response, urban planning, and epidemic forecasting.
Extracting Spatiotemporal Data from Gradients with Large Language Models
To understand privacy risks in spatiotemporal federated learning, we first propose Spatiotemporal Gradient Inversion Attack (ST-GIA), a gradient attack algorithm tailored to spatiotemporal data that successfully reconstructs the original location from gradients.
Taming the Long Tail in Human Mobility Prediction
With the popularity of location-based services, human mobility prediction plays a key role in enhancing personalized navigation, optimizing recommendation systems, and facilitating urban mobility and planning.
SIMformer: Single-Layer Vanilla Transformer Can Learn Free-Space Trajectory Similarity
Free-space trajectory similarity calculation, e. g., DTW, Hausdorff, and Frechet, often incur quadratic time complexity, thus learning-based methods have been proposed to accelerate the computation.
Towards Neural Scaling Laws for Time Series Foundation Models
In this work, we examine two common TSFM architectures, encoder-only and decoder-only Transformers, and investigate their scaling behavior on both ID and OOD data.
Diffusion Models in 3D Vision: A Survey
In recent years, 3D vision has become a crucial field within computer vision, powering a wide range of applications such as autonomous driving, robotics, augmented reality, and medical imaging.
Evaluating the Generalization Ability of Spatiotemporal Model in Urban Scenario
The generalization ability of these models remains largely unexplored.
Robust Traffic Forecasting against Spatial Shift over Years
Through meticulous analysis, we attribute this decline to the models' inability to adapt to previously unobserved spatial relationships.
STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting
STGformer effectively balances the strengths of GCNs and Transformers, enabling efficient modeling of both global and local traffic patterns while maintaining a manageable computational footprint.
Non-Homophilic Graph Pre-Training and Prompt Learning
In particular, many real-world graphs are non-homophilic, not strictly or uniformly homophilic with mixing homophilic and heterophilic patterns, exhibiting varying non-homophilic characteristics across graphs and nodes.
DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs
Dynamic graph learning aims to uncover evolutionary laws in real-world systems, enabling accurate social recommendation (link prediction) or early detection of cancer cells (classification).
Enhancing Spatio-temporal Quantile Forecasting with Curriculum Learning: Lessons Learned
Furthermore, our framework incorporates a stacking fusion module to combine diverse information from three types of curriculum learning, resulting in a strong and thorough learning process.
Continuous Temporal Domain Generalization
Temporal Domain Generalization (TDG) addresses the challenge of training predictive models under temporally varying data distributions.
Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting
Spatiotemporal time series forecasting plays a key role in a wide range of real-world applications.
Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learning
Multi-modality spatio-temporal (MoST) data extends spatio-temporal (ST) data by incorporating multiple modalities, which is prevalent in monitoring systems, encompassing diverse traffic demands and air quality assessments.
Community-Invariant Graph Contrastive Learning
Graph augmentation has received great attention in recent years for graph contrastive learning (GCL) to learn well-generalized node/graph representations.
A Survey on Deep Active Learning: Recent Advances and New Frontiers
Active learning seeks to achieve strong performance with fewer training samples.
Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation
This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation.
Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting
Spatiotemporal forecasting techniques are significant for various domains such as transportation, energy, and weather.
Ranked #1 on
Traffic Prediction
on EXPY-TKY
(using extra training data)
Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection
Anomaly detection significantly enhances the robustness of cloud systems.
Revisiting Mobility Modeling with Graph: A Graph Transformer Model for Next Point-of-Interest Recommendation
Extracting distinct fine-grained features unique to each piece of information is difficult since temporal information often includes spatial information, as users tend to visit nearby POIs.
MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation
Urban time series data forecasting featuring significant contributions to sustainable development is widely studied as an essential task of the smart city.
Ranked #1 on
Traffic Prediction
on Beijing Traffic
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 #2 on
Traffic Prediction
on PeMSD7
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).
Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting
The core issue in MTS forecasting is how to effectively model complex spatial-temporal patterns.
Ranked #1 on
Time Series Forecasting
on Weather (96)
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 #2 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.
