# Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks

arXiv: https://arxiv.org/abs/1701.02543 · HTML: https://mlsanity.com/paper/1701.02543/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2017.

## Built on by (most cited first, 30 of 34)

- [Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook](https://mlsanity.com/paper/2402.19348/) (2024)
- [Building Transportation Foundation Model via Generative Graph Transformer](https://mlsanity.com/paper/2305.14826/) (2023)
- [Spatiotemporal Data Mining: A Survey on Challenges and Open Problems](https://mlsanity.com/paper/2103.17128/) (2021)
- [ST-ExpertNet: A Deep Expert Framework for Traffic Prediction](https://mlsanity.com/paper/2205.07851/) (2022)
- [SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2406.12282/) (2024)
- [Continual Traffic Forecasting via Mixture of Experts](https://mlsanity.com/paper/2406.03140/) (2024)
- [PredBench: Benchmarking Spatio-Temporal Prediction across Diverse Disciplines](https://mlsanity.com/paper/2407.08418/) (2024)
- [AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting](https://mlsanity.com/paper/2101.12465/) (2021)
- [Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flow](https://mlsanity.com/paper/2107.13226/) (2021)
- [S-HR-VQVAE: Sequential Hierarchical Residual Learning Vector Quantized Variational Autoencoder for Video Prediction](https://mlsanity.com/paper/2307.06701/) (2023)
- [A Correlation Information-based Spatiotemporal Network for Traffic Flow Forecasting](https://mlsanity.com/paper/2205.10365/) (2022)
- [UniSTD: Towards Unified Spatio-Temporal Learning across Diverse Disciplines](https://mlsanity.com/paper/2503.20748/) (2025)
- [DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for Traffic Forecasting](https://mlsanity.com/paper/2307.00518/) (2023)
- [A Gravity-informed Spatiotemporal Transformer for Human Activity Intensity Prediction](https://mlsanity.com/paper/2506.13678/) (2025)
- [Enhancing crowd flow prediction in various spatial and temporal granularities](https://mlsanity.com/paper/2203.07372/) (2022)
- [Adapting to Skew: Imputing Spatiotemporal Urban Data with 3D Partial Convolutions and Biased Masking](https://mlsanity.com/paper/2301.04233/) (2023)
- [Understanding Pedestrian Movement Using Urban Sensing Technologies: The Promise of Audio-based Sensors](https://mlsanity.com/paper/2406.09998/) (2024)
- [Spatiotemporal Forecasting of Traffic Flow using Wavelet-based Temporal Attention](https://mlsanity.com/paper/2407.04440/) (2024)
- [STLight: a Fully Convolutional Approach for Efficient Predictive Learning by Spatio-Temporal joint Processing](https://mlsanity.com/paper/2411.10198/) (2024)
- [FRTP: Federating Route Search Records to Enhance Long-term Traffic Prediction](https://mlsanity.com/paper/2412.17373/) (2024)
- [MoDE-Boost: Boosting Shared Mobility Demand with Edge-Ready Prediction Models](https://mlsanity.com/paper/2602.16573/) (2026)
- [Listening to the city, attentively: A Spatio-Temporal Attention Boosted Autoencoder for the Short-Term Flow Prediction Problem](https://mlsanity.com/paper/2103.00983/) (2021)
- [Bayesian Graph Convolutional Network for Traffic Prediction](https://mlsanity.com/paper/2104.00488/) (2021)
- [GACAN: Graph Attention-Convolution-Attention Networks for Traffic Forecasting Based on Multi-granularity Time Series](https://mlsanity.com/paper/2110.14331/) (2021)
- [Random Graph-Based Neuromorphic Learning with a Layer-Weaken Structure](https://mlsanity.com/paper/2111.08888/) (2021)
- [Predicting Parking Lot Availability by Graph-to-Sequence Model: A Case Study with SmartSantander](https://mlsanity.com/paper/2206.10160/) (2022)
- [Multi-graph Spatio-temporal Graph Convolutional Network for Traffic Flow Prediction](https://mlsanity.com/paper/2308.05601/) (2023)
- [Temporal Graph Learning Recurrent Neural Network for Traffic Forecasting](https://mlsanity.com/paper/2406.02726/) (2024)
- [DG-STMTL: A Novel Graph Convolutional Network for Multi-Task Spatio-Temporal Traffic Forecasting](https://mlsanity.com/paper/2504.07822/) (2025)
- [Lightning Prediction under Uncertainty: DeepLight with Hazy Loss](https://mlsanity.com/paper/2508.07428/) (2025)

## Abstract (arXiv)

Forecasting the flow of crowds is of great importance to traffic management and public safety, and very challenging as it is affected by many complex factors, including spatial dependencies (nearby and distant), temporal dependencies (closeness, period, trend), and external conditions (e.g., weather and events). We propose a deep-learning-based approach, called ST-ResNet, to collectively forecast two types of crowd flows (i.e. inflow and outflow) in each and every region of a city. We design an end-to-end structure of ST-ResNet based on unique properties of spatio-temporal data. More specifically, we employ the residual neural network framework to model the temporal closeness, period, and trend properties of crowd traffic. For each property, we design a branch of residual convolutional units, each of which models the spatial properties of crowd traffic. ST-ResNet learns to dynamically aggregate the output of the three residual neural networks based on data, assigning different weights to different branches and regions. The aggregation is further combined with external factors, such as weather and day of the week, to predict the final traffic of crowds in each and every region. We have developed a real-time system based on Microsoft Azure Cloud, called UrbanFlow, providing the crowd flow monitoring and forecasting in Guiyang City of China. In addition, we present an extensive experimental evaluation using two types of crowd flows in Beijing and New York City (NYC), where ST-ResNet outperforms nine well-known baselines.