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Title	Analyzing Geospatial Key Factors And Predicting Bike Activity In Hamburg
ID_Doc	9501
Authors	Roussel C.; Rolwes A.; Böhm K.
Year	2022
Published	Lecture Notes on Data Engineering and Communications Technologies, 143
DOI	http://dx.doi.org/10.1007/978-3-031-08017-3_2
Abstract	This paper addresses the determination of geospatial key factors, which are relevant for bike sharing stations in the city of Hamburg. They serve as an application case for limited service offers in smart cities. Our approach combines real-world empirical data with open-source data on points of interest for the determination. We apply linear regression methods in combination with an established metric for calculating the geospatial impact. On top of the determination of the geospatial key factors, our paper seeks for machine learning based approaches to predict the bike sharing activity. In our results of the analysis, we identify correlations between bike activity and geospatial factors. Moreover, our neural network provides a solid basis for predicting the activity of bike stations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	Bike sharing system; Geospatial analysis; Predictive analysis; Urban analysis

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