Mobility Networked Time-Series Forecasting Benchmark Datasets

Jihye Na, Youngeun Nam, Susik Yoon, Hwanjun Song, Byung Suk Lee, Jae-Gil Lee

Keywords: mobility, networked time series, time series, origin-destination, forecasting, prediction, transportation, epidemic

TL;DR: This paper proposes a diverse and explainable dataset collection for mobility networked time-series forecasting.

Abstract: Human mobility is crucial for urban planning (e.g., public transportation) and epidemic response strategies. However, existing research often neglects integrating comprehensive perspectives on spatial dynamics, temporal trends, and other contextual views due to the limitations of existing mobility datasets. To bridge this gap, we introduce **MOBINS** (**MOBI**lity **N**etworked time **S**eries), a novel dataset collection designed for networked time-series forecasting of dynamic human movements. **MOBINS** features diverse and explainable datasets that capture various mobility patterns across different transportation modes in four cities and two countries and cover both transportation and epidemic domains at the administrative area level. Our experiments with nine baseline methods reveal the significant impact of different model backbones on the proposed six datasets.

Primary Area: datasets and benchmarks

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Submission Number: 8261