# Long-Horizon Traffic Forecasting via Incident-Aware Conformal Spatio-Temporal Transformers

arXiv: https://arxiv.org/abs/2603.16857 · HTML: https://mlsanity.com/paper/2603.16857/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 97 references that are in the map.
- First posted on arXiv 17 Mar 2026.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks](https://mlsanity.com/paper/1703.07015/) (2017)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Conformalized Quantile Regression](https://mlsanity.com/paper/1905.03222/) (2019)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction](https://mlsanity.com/paper/1811.05320/) (2018)
- [Adaptive Conformal Inference Under Distribution Shift](https://mlsanity.com/paper/2106.00170/) (2021)
- [A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction](https://mlsanity.com/paper/1704.02971/) (2017)
- [Spatial-Temporal Transformer Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2001.02908/) (2020)
- [Adaptive Conformal Predictions for Time Series](https://mlsanity.com/paper/2202.07282/) (2022)
- [Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting](https://mlsanity.com/paper/1802.07007/) (2018)
- [Learning dynamic and hierarchical traffic spatiotemporal features with Transformer](https://mlsanity.com/paper/2104.05163/) (2021)
- [HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecasting](https://mlsanity.com/paper/2307.14596/) (2023)
- [Incorporating dynamicity of transportation network with multi-weight traffic graph convolutional network for traffic forecasting](https://mlsanity.com/paper/1909.07105/) (2019)
- [FPTN: Fast Pure Transformer Network for Traffic Flow Forecasting](https://mlsanity.com/paper/2303.07685/) (2023)
- [SpikeSTAG: Spatial-Temporal Forecasting via GNN-SNN Collaboration](https://mlsanity.com/paper/2508.02069/) (2025)
- [Travel Time and Weather-Aware Traffic Forecasting in a Conformal Graph Neural Network Framework](https://mlsanity.com/paper/2509.12043/) (2025)
- [Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting](https://mlsanity.com/paper/2209.13123/) (2022)
- [TrafFormer: A Transformer Model for Predicting Long-term Traffic](https://mlsanity.com/paper/2302.12388/) (2023)

## Abstract (arXiv)

Reliable multi-horizon traffic forecasting is challenging because network conditions are stochastic, incident disruptions are intermittent, and effective spatial dependencies vary across time-of-day patterns. This study is conducted on the Ohio Department of Transportation (ODOT) traffic count data and corresponding ODOT crash records. This work utilizes a Spatio-Temporal Transformer (STT) model with Adaptive Conformal Prediction (ACP) to produce multi-horizon forecasts with calibrated uncertainty. We propose a piecewise Coefficient of Variation (CV) strategy that models hour-to-hour traveltime variability using a log-normal distribution, enabling the construction of a per-hour dynamic adjacency matrix. We further perturb edge weights using incident-related severity signals derived from the ODOT crash dataset that comprises incident clearance time, weather conditions, speed violations, work zones, and roadway functional class, to capture localized disruptions and peak/off-peak transitions. This dynamic graph construction replaces a fixed-CV assumption and better represents changing traffic conditions within the forecast window. For validation, we generate extended trips via multi-hour loop runs on the Columbus, Ohio, network in SUMO simulations and apply a Monte Carlo simulation to obtain travel-time distributions for a Vehicle Under Test (VUT). Experiments demonstrate improved long-horizon accuracy and well-calibrated prediction intervals compared to other baseline methods.