HIGHLIGHTS
SUMMARY
In recent years, the possibility of geo-referenced data collection has opened doors for understanding human behaviour in urban contexts, such as pedestrian mobility (Mizzi et_al 2018), taxi services (Rodrigues et_al 2018) and bike and scooter-sharing mobility (Zhu et_al 2020). To understand cycling mobility, the authors analysed bike usage through the 320,118 self-reported unique bicycle trips in the city of Bologna within the Bella Mossa dataset. To predict bike usage in the next 10, 30 and 60-minute, the authors have prepared three datasets aggregating the number of trips by 10 . . .
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