CausalPOI proposes a spatio-temporal graph causal learning method for cold-start POI check-in forecasting that builds functional interaction graphs and treatment-control pairs to outperform baselines on SafeGraph data.
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Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
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abstract
Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain. Traffic forecasting is one canonical example of such learning task. The task is challenging due to (1) complex spatial dependency on road networks, (2) non-linear temporal dynamics with changing road conditions and (3) inherent difficulty of long-term forecasting. To address these challenges, we propose to model the traffic flow as a diffusion process on a directed graph and introduce Diffusion Convolutional Recurrent Neural Network (DCRNN), a deep learning framework for traffic forecasting that incorporates both spatial and temporal dependency in the traffic flow. Specifically, DCRNN captures the spatial dependency using bidirectional random walks on the graph, and the temporal dependency using the encoder-decoder architecture with scheduled sampling. We evaluate the framework on two real-world large scale road network traffic datasets and observe consistent improvement of 12% - 15% over state-of-the-art baselines.
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representative citing papers
Bridge augments a graph neural network backbone with time-aware retrieval from a memory of region-time windows to improve cold-start and cross-city urban delivery demand forecasting.
OSM+ is a new open billion-vertex worldwide road network graph dataset derived from OpenStreetMap, accompanied by 31-city traffic prediction and six-city policy control benchmarks.
SpatialEpiBench shows adjacency-informed models with epidemic priors underperform a last-value baseline across 11 datasets from 1 day to 1 month ahead, identifying failures in outbreak anticipation, sparsity handling, and geographic adjacency utility.
AirQualityBench is a realistic global benchmark using hourly data from 3720 stations across 2021-2025 for six pollutants, preserving native missingness masks and evaluating on inverse-transformed physical scales.
TRIP-Evaluate is a new open multimodal benchmark with 837 text, image, and point-cloud items organized by a role-task-knowledge taxonomy to evaluate large models on transportation workflows.
MobiWM is a multimodal world model for mobile networks that learns state-action dynamics to enable unlimited-horizon counterfactual traffic simulations and optimization.
SCOT uses Sinkhorn entropic optimal transport to learn explicit soft correspondences between unequal region sets for multi-source cross-city transfer, adding contrastive sharpening and cycle reconstruction for stability and a prototype hub for multi-source alignment.
Characterizes an estimation-prediction tradeoff in binary logistic models for causal probabilistic temporal graphs and proposes a framework to jointly evaluate temporal link prediction with causal parameter recovery via Cramér-Rao bounds.
GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
INDEQS is a graph-informed NCDE variant that separates inner hidden-state mixing from outer vector-field mixing and reports lower MAE than uninformed NCDEs on synthetic advection data and real river/traffic tasks when the graph is known.
Xcientist externalizes research synthesis and validation in AI scientists via contract-governed artifacts to maintain traceable trajectories and avoid claim drift across three domains.
STRP is a granularity-aware model that predicts fine-grained spatio-temporal traffic from coarse inputs via tree convolution and inverse dilated convolution, outperforming baselines on six datasets in window-based and duration-based settings.
STaT is a Symbolic-Temporal-Textual Alignment model that integrates three modalities to reduce shape distortion in non-stationary time series forecasting, reporting up to 8.9% gains in magnitude metrics and 8.5% less distortion on eight benchmarks.
GL-LFGNN applies Liang-Kleeman causal information flow within a global-local dual-branch GNN architecture to reach 86.17% arousal and 86.71% valence accuracy on the MEEG dataset using only 37K parameters.
UniSTOK improves inductive spatio-temporal kriging under incomplete observations by reliability-guided signal regulation and residual bias calibration.
Neural CDEs serve as correctors that reduce error accumulation in multi-step forecasts from learned time-series models across synthetic, physics, and real-world data.
ST-PT turns transformers into explicit factor graphs for time series, enabling structural injection of symbolic priors, per-sample conditional generation, and principled latent autoregressive forecasting via MFVI iterations.
CAARL decomposes co-evolving time series into autoregressive segments, builds a temporal dependency graph, serializes it into a narrative, and uses LLMs for interpretable forecasting via chain-of-thought reasoning.
GAMMA-Net combines Graph Attention Networks and multi-axis Mamba to outperform prior models in long-horizon traffic forecasting, with up to 16.25% lower MAE on benchmarks like METR-LA and PEMS datasets.
Graph networks unify graph-based neural methods into a general framework with strong relational inductive biases to support combinatorial generalization and structured reasoning in AI.
NetCause applies counterfactual simulation on graph-temporal models of network incidents to rank root causes, showing 16.1% accuracy gain over a rule-based baseline on 31 labeled cases from production data.
GraMO couples graph interactions and temporal state updates in one linear recurrence with input-dependent coefficients to simulate N-body, motion, and robotics systems with lower long-horizon error than prior GNN or SSM approaches.
A plug-and-play learnable Tweedie output head attached to ST-GNN backbones improves RMSE on sparse maritime traffic data by predicting means and learning node-level variance powers via closed-form unit deviance optimization.
citing papers explorer
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CausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting
CausalPOI proposes a spatio-temporal graph causal learning method for cold-start POI check-in forecasting that builds functional interaction graphs and treatment-control pairs to outperform baselines on SafeGraph data.
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Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand
Bridge augments a graph neural network backbone with time-aware retrieval from a memory of region-time windows to improve cold-start and cross-city urban delivery demand forecasting.
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OSM+: Billion-Level OpenStreetMap Dataset for City-wide Experiments
OSM+ is a new open billion-vertex worldwide road network graph dataset derived from OpenStreetMap, accompanied by 31-city traffic prediction and six-city policy control benchmarks.
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SpatialEpiBench: Benchmarking Spatial Information and Epidemic Priors in Forecasting
SpatialEpiBench shows adjacency-informed models with epidemic priors underperform a last-value baseline across 11 datasets from 1 day to 1 month ahead, identifying failures in outbreak anticipation, sparsity handling, and geographic adjacency utility.
-
AirQualityBench: A Realistic Evaluation Benchmark for Global Air Quality Forecasting
AirQualityBench is a realistic global benchmark using hourly data from 3720 stations across 2021-2025 for six pollutants, preserving native missingness masks and evaluating on inverse-transformed physical scales.
-
TRIP-Evaluate: An Open Multimodal Benchmark for Evaluating Large Models in Transportation
TRIP-Evaluate is a new open multimodal benchmark with 837 text, image, and point-cloud items organized by a role-task-knowledge taxonomy to evaluate large models on transportation workflows.
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Beyond Static Forecasting: Unleashing the Power of World Models for Mobile Traffic Extrapolation
MobiWM is a multimodal world model for mobile networks that learns state-action dynamics to enable unlimited-horizon counterfactual traffic simulations and optimization.
-
SCOT: Multi-Source Cross-City Transfer with Optimal-Transport Soft-Correspondence Objective
SCOT uses Sinkhorn entropic optimal transport to learn explicit soft correspondences between unequal region sets for multi-source cross-city transfer, adding contrastive sharpening and cycle reconstruction for stability and a prototype hub for multi-source alignment.
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Estimation--Prediction Tradeoff in Causal Probabilistic Temporal Graphs
Characterizes an estimation-prediction tradeoff in binary logistic models for causal probabilistic temporal graphs and proposes a framework to jointly evaluate temporal link prediction with causal parameter recovery via Cramér-Rao bounds.
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Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation
GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
-
INDEQS: Informed Neural controlled Differential EQuationS
INDEQS is a graph-informed NCDE variant that separates inner hidden-state mixing from outer vector-field mixing and reports lower MAE than uninformed NCDEs on synthetic advection data and real river/traffic tasks when the graph is known.
-
Externalizing Research Synthesis and Validation in AI Scientists through a Research Harness
Xcientist externalizes research synthesis and validation in AI scientists via contract-governed artifacts to maintain traceable trajectories and avoid claim drift across three domains.
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From Coarse to Fine: Managing Temporal Granularity in Spatio-Temporal Data for Fine-Grained Traffic Prediction
STRP is a granularity-aware model that predicts fine-grained spatio-temporal traffic from coarse inputs via tree convolution and inverse dilated convolution, outperforming baselines on six datasets in window-based and duration-based settings.
-
STaT: Resolving Shape Distortion in Non-Stationary Time Series via Tri-Modal Synergy
STaT is a Symbolic-Temporal-Textual Alignment model that integrates three modalities to reduce shape distortion in non-stationary time series forecasting, reporting up to 8.9% gains in magnitude metrics and 8.5% less distortion on eight benchmarks.
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GL-LFGNN:A Global-Local Dual-branch Causal Graph Neural Network Based on Liang-Kleeman Information Flow for EEG Emotion Recognition
GL-LFGNN applies Liang-Kleeman causal information flow within a global-local dual-branch GNN architecture to reach 86.17% arousal and 86.71% valence accuracy on the MEEG dataset using only 37K parameters.
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Uniform Inductive Spatio-Temporal Kriging
UniSTOK improves inductive spatio-temporal kriging under incomplete observations by reliability-guided signal regulation and residual bias calibration.
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Neural CDEs as Correctors for Learned Time Series Models
Neural CDEs serve as correctors that reduce error accumulation in multi-step forecasts from learned time-series models across synthetic, physics, and real-world data.
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Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework
ST-PT turns transformers into explicit factor graphs for time series, enabling structural injection of symbolic priors, per-sample conditional generation, and principled latent autoregressive forecasting via MFVI iterations.
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CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting
CAARL decomposes co-evolving time series into autoregressive segments, builds a temporal dependency graph, serializes it into a narrative, and uses LLMs for interpretable forecasting via chain-of-thought reasoning.
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GAMMA-Net: Adaptive Long-Horizon Traffic Spatio-Temporal Forecasting Model based on Interleaved Graph Attention and Multi-Axis Mamba
GAMMA-Net combines Graph Attention Networks and multi-axis Mamba to outperform prior models in long-horizon traffic forecasting, with up to 16.25% lower MAE on benchmarks like METR-LA and PEMS datasets.
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Relational inductive biases, deep learning, and graph networks
Graph networks unify graph-based neural methods into a general framework with strong relational inductive biases to support combinatorial generalization and structured reasoning in AI.
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NetCause: Counterfactual Learning for Root Cause Analysis in Large-Scale Networks
NetCause applies counterfactual simulation on graph-temporal models of network incidents to rank root causes, showing 16.1% accuracy gain over a rule-based baseline on 31 labeled cases from production data.
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Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems
GraMO couples graph interactions and temporal state updates in one linear recurrence with input-dependent coefficients to simulate N-body, motion, and robotics systems with lower long-horizon error than prior GNN or SSM approaches.
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Vessel Traffic Flow Prediction on Sparse Data via Spatio-Temporal Graph Neural Networks with a Learnable Tweedie Head
A plug-and-play learnable Tweedie output head attached to ST-GNN backbones improves RMSE on sparse maritime traffic data by predicting means and learning node-level variance powers via closed-form unit deviance optimization.
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Graph-Conditioned Mixture of Graph Neural Network Experts for Traffic Forecasting
GC-MoE improves MAE on four traffic forecasting benchmarks by routing nodes to combinations of frozen spatio-temporal GNN experts via a graph-conditioned lightweight router, training only ~17K parameters atop 1.5M frozen weights.
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Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty
IDEAL is a selective dual ambulance dispatch framework that learns context-specific travel times via weakly supervised bilevel networks and models uncertainty with Burg-divergence perturbations to achieve better response-time and resource trade-offs than region-based or map-based baselines.
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A Global-Local Graph Attention Network for Traffic Forecasting
GLGAT uses global-local graph attention with pairwise encoding and event-based adjacency to capture spatio-temporal traffic correlations and reports competitive results on two real-world datasets.
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MoveOD: Synthesizing Origin-Destination Commute Distribution from U.S. Census Data
MOVEOD synthesizes fine-grained commuter OD flows for any US county by reconciling ACS departure times, LODES residence-work flows, and OSM data via constrained sampling and integer programming.
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TSNN: A Non-parametric and Interpretable Framework for Traffic Time Series Forecasting
TSNN matches time series entries to a training-derived memory bank to forecast traffic without any trainable parameters and achieves competitive accuracy on four real-world datasets.
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Efficient Prompt Learning for Traffic Forecasting
SimpleST is a model-agnostic prompt tuning framework that lets pre-trained spatio-temporal GNNs adapt to distribution shifts in traffic data while keeping all original model weights fixed.
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FDN: Interpretable Spatiotemporal Forecasting with Future Decomposition Networks
FDN is a neural forecasting architecture that decomposes future predictions via classification to yield interpretable latent patterns alongside SOTA-level accuracy at reduced memory and runtime cost.
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EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction
EnergyMamba improves energy consumption prediction accuracy by about 5% and uncertainty quantification by about 6% over 15 baselines on four real-world US datasets by combining graph-enhanced Mamba with adaptive sequential conformalized quantile regression.
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Missing Pattern Recognized Diffusion Imputation Model for Missing Not At Random
PRDIM is a diffusion model using a pattern recognizer to impute MNAR missing data by maximizing joint likelihood of observed values and missing mask via EM.
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Parameter-Efficient Quantum-Inspired Fast Weight Programmers for Traffic-Matrix Forecasting
Gated QKAN fast-weight programmer achieves lowest pooled RMSE on Abilene TM forecasting while using 22.4% of a larger LSTM's parameters and outperforming classical G-FWP.
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Global-local Spatial-temporal Aware Graph Attention Network for Network Traffic Forecasting
GLSTaGAT is a spatial-temporal graph attention network using data-driven fusion graphs, global-local blocks, node normalization, and a transformer encoder to outperform baselines on real-world network traffic datasets.