# Incorporating dynamicity of transportation network with multi-weight traffic graph convolutional network for traffic forecasting

arXiv: https://arxiv.org/abs/1909.07105 · HTML: https://mlsanity.com/paper/1909.07105/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2019.

## Built on by (most cited first, 11 of 11)

- [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)
- [Multi-Source Urban Traffic Flow Forecasting with Drone and Loop Detector Data](https://mlsanity.com/paper/2501.03492/) (2025)
- [A Comparative Study on Basic Elements of Deep Learning Models for Spatial-Temporal Traffic Forecasting](https://mlsanity.com/paper/2111.07513/) (2021)
- [Urban Traffic Forecasting with Integrated Travel Time and Data Availability in a Conformal Graph Neural Network Framework](https://mlsanity.com/paper/2407.12238/) (2024)
- [Enhancing Graph Neural Networks in Large-scale Traffic Incident Analysis with Concurrency Hypothesis](https://mlsanity.com/paper/2411.02542/) (2024)
- [Spatio-Temporal Graph Complementary Scattering Networks](https://mlsanity.com/paper/2110.12150/) (2021)
- [A Transferable Intersection Reconstruction Network for Traffic Speed Prediction](https://mlsanity.com/paper/2207.11030/) (2022)
- [A Sparse Cross Attention-based Graph Convolution Network with Auxiliary Information Awareness for Traffic Flow Prediction](https://mlsanity.com/paper/2312.09050/) (2023)
- [Long-Horizon Traffic Forecasting via Incident-Aware Conformal Spatio-Temporal Transformers](https://mlsanity.com/paper/2603.16857/) (2026)

## Abstract (arXiv)

Traffic forecasting problem remains a challenging task in the intelligent transportation system due to its spatio-temporal complexity. Although temporal dependency has been well studied and discussed, spatial dependency is relatively less explored due to its large variations, especially in the urban environment. In this study, a novel graph convolutional network model, Multi-Weight Traffic Graph Convolutional (MW-TGC) network, is proposed and applied to two urban networks with contrasting geometric constraints. The model conducts graph convolution operations on speed data with multi-weighted adjacency matrices to combine the features, including speed limit, distance, and angle. The spatially isolated dimension reduction operation is conducted on the combined features to learn the dependencies among the features and reduce the size of the output to a computationally feasible level. The output of multi-weight graph convolution is applied to the sequence-to-sequence model with Long Short-Term Memory units to learn temporal dependencies. When applied to two urban sites, urban-core and urban-mix, MW-TGC network not only outperformed the comparative models in both sites but also reduced variance in the heterogeneous urban-mix network. We conclude that MW-TGC network can provide a robust traffic forecasting performance across the variations in spatial complexity, which can be a strong advantage in urban traffic forecasting.