Improving the Service of E-bike Sharing by Demand Pattern Analysis

A Data-driven Approach

Master thesis (2021)

Authors

Z. Zhang Civil Engineering & Geosciences

Contributors

N. van Oort Transport and Planning - CEG (supervisor 1)
S.P. Hoogendoorn Transport and Planning (supervisor 1)
P.K. Krishnakumari Transport and Planning - CEG (supervisor 1)
F. Schulte Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering (supervisor 1)
Max Schalow (supervisor 1)
Chingiskhan Kazakhstan (supervisor 1)
Faculty
Civil Engineering & Geosciences
Copyright: © 2021 Ziru Zhang
Evaluation E-bike sharing Temporal clustering Demand pattern Operational strategies
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Published Date

29-11-2021

Language

English

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Faculty

Civil Engineering & Geosciences

Abstract

E-bike sharing has gradually gained popularity in recent years, while the research in this field is still quite limited. This study applies data-driven methods, mainly demand pattern analysis, to facilitate the development of operational strategies in a cost-effective and operator-friendly way. Demand pattern is analysed in an innovative spatial analytical unit, overlapping circle, which is proven to achieve more beneficial effects than the traditional units (i.e., the administrative units), and hourly clustering is conducted to derive the reallocation strategies by mitigations of imbalance in supply and demand in recurrent hourly clusters. Additionally, this work constructs several indicators to evaluate the strategies in a real-life context, taking both the operator and the users into account. The proposed methodology is applied in a case study, bondi’s e-bike sharing in The Hague with a 4-month time frame from 19-06-2021. There are 5 hourly clusters emerging via agglomerative hierarchical clustering: 1) the first peak hour (16:00-16:59); 2) the second peak hour (17:00-17:59); 3) the first transition hour (18:00-18:59); 4) the second transition hour (19:00-19:59); 5) the off peak (20:00-15:59). The corresponding reallocation strategies are then proposed to alleviate the imbalance in different periods. Additionally, adjustment in the operational areas is suggested by the supply efficiency and trip duration/distance analyses. The results prove that the operational strategies obtained from demand patterns indeed improve the service, with almost 1.5 times ridership, circa 20% decrease in vehicle idle time, compared to the baseline. and a decent monthly net retention rate at around 60%.