STContext: A Multifaceted Dataset for Developing Context-aware Spatio-temporal Crowd Mobility Prediction Models

TL;DR

This paper introduces STContext, a comprehensive dataset with diverse contextual features for developing and benchmarking context-aware spatio-temporal crowd mobility prediction models in smart cities.

Contribution

The paper presents STContext, a multifaceted dataset with nine spatio-temporal datasets and ten contextual features, along with a unified workflow for integrating context into deep prediction models.

Findings

Effective context modeling guidelines derived from extensive experiments.

Insights into the impact of various contextual features on prediction accuracy.

Open-source availability of the STContext dataset for future research.

Abstract

In smart cities, context-aware spatio-temporal crowd flow prediction (STCFP) models leverage contextual features (e.g., weather) to identify unusual crowd mobility patterns and enhance prediction accuracy. However, the best practice for incorporating contextual features remains unclear due to inconsistent usage of contextual features in different papers. Developing a multifaceted dataset with rich types of contextual features and STCFP scenarios is crucial for establishing a principled context modeling paradigm. Existing open crowd flow datasets lack an adequate range of contextual features, which poses an urgent requirement to build a multifaceted dataset to fill these research gaps. To this end, we create STContext, a multifaceted dataset for developing context-aware STCFP models. Specifically, STContext provides nine spatio-temporal datasets across five STCFP scenarios and includes ten contextual features, including weather, air quality index, holidays, points of interest, road networks, etc. Besides, we propose a unified workflow for incorporating contextual features into deep STCFP methods, with steps including feature transformation, dependency modeling, representation fusion, and training strategies. Through extensive experiments, we have obtained several useful guidelines for effective context modeling and insights for future research. The STContext is open-sourced at https://github.com/Liyue-Chen/STContext.