Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks

TL;DR

Massive-STEPS provides a large, diverse, and recent POI check-in dataset across 15 cities, enabling more accurate and reproducible human mobility modeling and benchmarking.

Contribution

The paper introduces Massive-STEPS, a new extensive dataset with semantic POI data from 15 cities, addressing previous data limitations and supporting diverse modeling approaches.

Findings

Benchmarking reveals varying model performances across cities.

Recent data improves the relevance of mobility models.

Semantic enrichment enhances POI trajectory understanding.

Abstract

Understanding human mobility through Point-of-Interest (POI) trajectory modeling is increasingly important for applications such as urban planning, personalized services, and generative agent simulation. However, progress in this field is hindered by two key challenges: the over-reliance on older datasets from 2012-2013 and the lack of reproducible, city-level check-in datasets that reflect diverse global regions. To address these gaps, we present Massive-STEPS (Massive Semantic Trajectories for Understanding POI Check-ins), a large-scale, publicly available benchmark dataset built upon the Semantic Trails dataset and enriched with semantic POI metadata. Massive-STEPS spans 15 geographically and culturally diverse cities and features more recent (2017-2018) and longer-duration (24 months) check-in data than prior datasets. We benchmarked a wide range of POI models on Massive-STEPS using both supervised and zero-shot approaches, and evaluated their performance across multiple urban contexts. By releasing Massive-STEPS, we aim to facilitate reproducible and equitable research in human mobility and POI trajectory modeling. The dataset and benchmarking code are available at: https://github.com/cruiseresearchgroup/Massive-STEPS.