# Transposed Variational Auto-encoder with Intrinsic Feature Learning for Traffic Forecasting

arXiv: https://arxiv.org/abs/2211.00641 · HTML: https://mlsanity.com/paper/2211.00641/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 30 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction](https://mlsanity.com/paper/1610.00081/) (2016)
- [Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes](https://mlsanity.com/paper/2203.17070/) (2022)

## Built on by (most cited first, 3 of 3)

- [Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors](https://mlsanity.com/paper/2303.07758/) (2023)
- [Foundations and Frontiers of Graph Learning Theory](https://mlsanity.com/paper/2407.03125/) (2024)
- [Decoding Defensive Coverage Responsibilities in American Football Using Factorized Attention Based Transformer Models](https://mlsanity.com/paper/2603.25901/) (2026)

## Abstract (arXiv)

In this technical report, we present our solutions to the Traffic4cast 2022 core challenge and extended challenge. In this competition, the participants are required to predict the traffic states for the future 15-minute based on the vehicle counter data in the previous hour. Compared to other competitions in the same series, this year focuses on the prediction of different data sources and sparse vertex-to-edge generalization. To address these issues, we introduce the Transposed Variational Auto-encoder (TVAE) model to reconstruct the missing data and Graph Attention Networks (GAT) to strengthen the correlations between learned representations. We further apply feature selection to learn traffic patterns from diverse but easily available data. Our solutions have ranked first in both challenges on the final leaderboard. The source code is available at urlhttps://github.com/Daftstone/Traffic4cast