Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

TL;DR

This paper introduces ST-ResNet, a deep residual neural network model that effectively forecasts citywide crowd flows by capturing complex spatio-temporal patterns and external factors, outperforming existing methods.

Contribution

The paper presents a novel end-to-end deep learning framework that models temporal properties and spatial dependencies of crowd flows using residual networks, incorporating external factors for improved accuracy.

Findings

ST-ResNet outperforms six existing methods in predicting crowd flows.

The model effectively captures temporal closeness, period, and trend.

External factors like weather improve prediction accuracy.

Abstract

Forecasting the flow of crowds is of great importance to traffic management and public safety, yet a very challenging task affected by many complex factors, such as inter-region traffic, events and weather. In this paper, we propose a deep-learning-based approach, called ST-ResNet, to collectively forecast the in-flow and out-flow of crowds in each and every region through 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 framework of the residual neural networks to model the temporal closeness, period, and trend properties of the crowd traffic, respectively. For each property, we design a branch of residual convolutional units, each of which models the spatial properties of the 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 evaluate ST-ResNet based on two types of crowd flows in Beijing and NYC, finding that its performance exceeds six well-know methods.