# PCNN: Deep Convolutional Networks for Short-term Traffic Congestion Prediction

arXiv: https://arxiv.org/abs/2003.07033 · HTML: https://mlsanity.com/paper/2003.07033/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2020.

## Built on by (most cited first, 12 of 12)

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- [Automating Traffic Model Enhancement with AI Research Agent](https://mlsanity.com/paper/2409.16876/) (2024)
- [Interpretable Cascading Mixture-of-Experts for Urban Traffic Congestion Prediction](https://mlsanity.com/paper/2406.12923/) (2024)
- [Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flow](https://mlsanity.com/paper/2107.13226/) (2021)
- [GridTuner: Reinvestigate Grid Size Selection for Spatiotemporal Prediction Models [Technical Report]](https://mlsanity.com/paper/2201.03244/) (2022)
- [A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas](https://mlsanity.com/paper/2302.04018/) (2023)
- [PSTN: Periodic Spatial-temporal Deep Neural Network for Traffic Condition Prediction](https://mlsanity.com/paper/2108.02424/) (2021)
- [A Comparative Study on Basic Elements of Deep Learning Models for Spatial-Temporal Traffic Forecasting](https://mlsanity.com/paper/2111.07513/) (2021)
- [Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models](https://mlsanity.com/paper/2303.02669/) (2023)
- [CVVLSNet: Vehicle Location and Speed Estimation Using Partial Connected Vehicle Trajectory Data](https://mlsanity.com/paper/2410.00132/) (2024)
- [Neighbor-embedded Graph Neural Network-based Crowd Delivery Traffic Management in Smart City](https://mlsanity.com/paper/2608.22555/) (2026)

## Abstract (arXiv)

Traffic problems have seriously affected people's life quality and urban development, and forecasting the short-term traffic congestion is of great importance to both individuals and governments. However, understanding and modeling the traffic conditions can be extremely difficult, and our observations from real traffic data reveal that (1) similar traffic congestion patterns exist in the neighboring time slots and on consecutive workdays; (2) the levels of traffic congestion have clear multiscale properties. To capture these characteristics, we propose a novel method named PCNN based on deep Convolutional Neural Network, modeling Periodic traffic data for short-term traffic congestion prediction. PCNN has two pivotal procedures: time series folding and multi-grained learning. It first temporally folds the time series and constructs a two-dimensional matrix as the network input, such that both the real-time traffic conditions and past traffic patterns are well considered; then with a series of convolutions over the input matrix, it is able to model the local temporal dependency and multiscale traffic patterns. In particular, the global trend of congestion can be addressed at the macroscale; whereas more details and variations of the congestion can be captured at the microscale. Experimental results on a real-world urban traffic dataset confirm that folding time series data into a two-dimensional matrix is effective and PCNN outperforms the baselines significantly for the task of short-term congestion prediction.