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.
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.
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.
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.
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.
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.
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.
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.
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.
citing papers explorer
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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.