Estimating E-Scooter Traffic Flow Using Big Data to Support Planning for Micromobility

Abstract: The paper introduces a practical method for estimating e-scooter flow patterns using open datasets that track trip origins and destinations. By leveraging this data, the authors demonstrate how their models can assist cities in optimizing support for shared micromobility services. Additionally, the generated information can enhance the analysis of e-scooter trips for more precise insights.