# STAR: A Concise Deep Learning Framework for Citywide Human Mobility Prediction

## TL;DR

This paper introduces STAR, a simple yet effective deep learning framework using residual networks for accurate and efficient citywide human mobility prediction, outperforming existing methods in real-world benchmarks.

## Contribution

The study presents a novel single fully-convolutional residual network framework that improves prediction accuracy and efficiency for citywide human mobility forecasting.

## Key findings

- STAR outperforms state-of-the-art methods in accuracy.
- STAR uses fewer parameters and is more efficient.
- The framework is effective for both single- and multi-step predictions.

## Abstract

Human mobility forecasting in a city is of utmost importance to transportation and public safety, but with the process of urbanization and the generation of big data, intensive computing and determination of mobility pattern have become challenging. This study focuses on how to improve the accuracy and efficiency of predicting citywide human mobility via a simpler solution. A spatio-temporal mobility event prediction framework based on a single fully-convolutional residual network (STAR) is proposed. STAR is a highly simple, general and effective method for learning a single tensor representing the mobility event. Residual learning is utilized for training the deep network to derive the detailed result for scenarios of citywide prediction. Extensive benchmark evaluation results on real-world data demonstrate that STAR outperforms state-of-the-art approaches in single- and multi-step prediction while utilizing fewer parameters and achieving higher efficiency.