# Pedestrian Volume Prediction Using a Diffusion Convolutional Gated Recurrent Unit Model

arXiv: https://arxiv.org/abs/2411.03360 · HTML: https://mlsanity.com/paper/2411.03360/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 47 references that are in the map.
- First posted on arXiv 5 Nov 2024.

## Builds on (most cited first, 14 of 14)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks](https://mlsanity.com/paper/1506.03099/) (2015)
- [Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting](https://mlsanity.com/paper/2007.02842/) (2020)
- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction](https://mlsanity.com/paper/2108.09091/) (2021)
- [How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey](https://mlsanity.com/paper/2005.11691/) (2020)
- [Deep Compositional Spatial Models](https://mlsanity.com/paper/1906.02840/) (2019)
- [Deep Echo State Networks with Uncertainty Quantification for Spatio-Temporal Forecasting](https://mlsanity.com/paper/1806.10728/) (2018)
- [Statistical Deep Learning for Spatial and Spatio-Temporal Data](https://mlsanity.com/paper/2206.02218/) (2022)
- [An Ensemble Quadratic Echo State Network for Nonlinear Spatio-Temporal Forecasting](https://mlsanity.com/paper/1708.05094/) (2017)
- [Neural networks for geospatial data](https://mlsanity.com/paper/2304.09157/) (2023)
- [DDP-GCN: Multi-Graph Convolutional Network for Spatiotemporal Traffic Forecasting](https://mlsanity.com/paper/1905.12256/) (2019)
- [Modeling Nonstationary and Asymmetric Multivariate Spatial Covariances via Deformations](https://mlsanity.com/paper/2004.08724/) (2020)

## Abstract (arXiv)

Effective models for analysing and predicting pedestrian flow are important to ensure the safety of both pedestrians and other road users. These tools also play a key role in optimising infrastructure design and geometry and supporting the economic utility of interconnected communities. The implementation of city-wide automatic pedestrian counting systems provides researchers with invaluable data, enabling the development and training of deep learning applications that offer better insights into traffic and crowd flows. Benefiting from real-world data provided by the City of Melbourne pedestrian counting system, this study presents a pedestrian flow prediction model, as an extension of Diffusion Convolutional Grated Recurrent Unit (DCGRU) with dynamic time warping, named DCGRU-DTW. This model captures the spatial dependencies of pedestrian flow through the diffusion process and the temporal dependency captured by Gated Recurrent Unit (GRU). Through extensive numerical experiments, we demonstrate that the proposed model outperforms the classic vector autoregressive model and the original DCGRU across multiple model accuracy metrics.