# Enhancing crowd flow prediction in various spatial and temporal granularities

arXiv: https://arxiv.org/abs/2203.07372 · HTML: https://mlsanity.com/paper/2203.07372/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 12 Mar 2022.

## Builds on (most cited first, 11 of 11)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017)
- [Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction](https://mlsanity.com/paper/1802.08714/) (2018)
- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information](https://mlsanity.com/paper/2012.00489/) (2020)
- [Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks](https://mlsanity.com/paper/1701.02543/) (2017)
- [Modeling International Mobility using Roaming Cell Phone Traces during COVID-19 Pandemic](https://mlsanity.com/paper/2201.02470/) (2022)
- [Leveraging Mobile Phone Data for Migration Flows](https://mlsanity.com/paper/2105.14956/) (2021)

## Built on by (most cited first, 1 of 1)

- [TS-Mob: Social and Geographical-Aware Time Series Foundation-Model Framework for Human Mobility Prediction](https://mlsanity.com/paper/2507.00945/) (2025)

## Abstract (arXiv)

Thanks to the diffusion of the Internet of Things, nowadays it is possible to sense human mobility almost in real time using unconventional methods (e.g., number of bikes in a bike station). Due to the diffusion of such technologies, the last years have witnessed a significant growth of human mobility studies, motivated by their importance in a wide range of applications, from traffic management to public security and computational epidemiology. A mobility task that is becoming prominent is crowd flow prediction, i.e., forecasting aggregated incoming and outgoing flows in the locations of a geographic region. Although several deep learning approaches have been proposed to solve this problem, their usage is limited to specific types of spatial tessellations and cannot provide sufficient explanations of their predictions. We propose CrowdNet, a solution to crowd flow prediction based on graph convolutional networks. Compared with state-of-the-art solutions, CrowdNet can be used with regions of irregular shapes and provide meaningful explanations of the predicted crowd flows. We conduct experiments on public data varying the spatio-temporal granularity of crowd flows to show the superiority of our model with respect to existing methods, and we investigate CrowdNet's reliability to missing or noisy input data. Our model is a step forward in the design of reliable deep learning models to predict and explain human displacements in urban environments.