Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks
Wilson Wongso, Hao Xue, Flora D. Salim

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…
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Code & Models
- CRUISEResearchGroup/Massive-STEPS-Sydneydataset· 101 dl101 dl
- CRUISEResearchGroup/Massive-STEPS-Moscowdataset· 140 dl140 dl
- CRUISEResearchGroup/Massive-STEPS-Sao-Paulodataset· 41 dl41 dl
- CRUISEResearchGroup/Massive-STEPS-Shanghaidataset· 191 dl191 dl
- CRUISEResearchGroup/Massive-STEPS-Jakartadataset· 112 dl112 dl
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Taxonomy
TopicsHuman Mobility and Location-Based Analysis · Transportation and Mobility Innovations · Data Management and Algorithms
