# DDP-GCN: Multi-Graph Convolutional Network for Spatiotemporal Traffic Forecasting

arXiv: https://arxiv.org/abs/1905.12256 · HTML: https://mlsanity.com/paper/1905.12256/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2019.

## Built on by (most cited first, 10 of 10)

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities](https://mlsanity.com/paper/2305.19591/) (2023)
- [PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecasting](https://mlsanity.com/paper/2202.08982/) (2022)
- [Time Series Foundation Models as Strong Baselines in Transportation Forecasting: A Large-Scale Benchmark Analysis](https://mlsanity.com/paper/2602.24238/) (2026)
- [On the importance of stationarity, strong baselines and benchmarks in transport prediction problems](https://mlsanity.com/paper/2203.02954/) (2022)
- [Structured Time Series Prediction without Structural Prior](https://mlsanity.com/paper/2202.03539/) (2022)
- [Establishing a real-time traffic alarm in the city of Valencia with Deep Learning](https://mlsanity.com/paper/2309.02010/) (2023)
- [A Sparse Cross Attention-based Graph Convolution Network with Auxiliary Information Awareness for Traffic Flow Prediction](https://mlsanity.com/paper/2312.09050/) (2023)
- [Pedestrian Volume Prediction Using a Diffusion Convolutional Gated Recurrent Unit Model](https://mlsanity.com/paper/2411.03360/) (2024)
- [Electric Vehicle Charging Station Location Selection using Geospatial Artificial Intelligence (GeoAI)](https://mlsanity.com/paper/2609.30417/) (2026)

## Abstract (arXiv)

Traffic speed forecasting is one of the core problems in transportation systems. For a more accurate prediction, recent studies started using not only the temporal speed patterns but also the spatial information on the road network through the graph convolutional networks. Even though the road network is highly complex due to its non-Euclidean and directional characteristics, previous approaches mainly focused on modeling the spatial dependencies using the distance only. In this paper, we identify two essential spatial dependencies in traffic forecasting in addition to distance, direction and positional relationship, for designing basic graph elements as the fundamental building blocks. Using the building blocks, we suggest DDP-GCN (Distance, Direction, and Positional relationship Graph Convolutional Network) to incorporate the three spatial relationships into deep neural networks. We evaluate the proposed model with two large-scale real-world datasets, and find positive improvements for long-term forecasting in highly complex urban networks. The improvement can be larger for commute hours, but it can be also limited for short-term forecasting.