Work

Using Ridership Profile Clustering and Geographically Weighted Regression to Investigate Spatio-Temporal Patterns of Shared E-scooter Ridership in Christchurch, New Zealand

Year: 2025

Type: conference-paper

Abstract: This study presents the street-segment analysis of shared e-scooter ridership using real trip count data, combining Dynamic Time Warping clustering and Geographically Weighted Poisson Regression to reveal distinct patterns and spatial influencing factors of ridership in Christchurch, New Zealand.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.16868797

Authors: Leung, Wai Chi Goldie, Sila-Nowicka, Katarzyna, Conrow, Lindsey, McKenzie, Grant, Kingham, Simon, da Silva Brum Bastos, Vanessa

Institutions: University of Canterbury, University of Auckland, McGill University

Language: English

Topic: Urban Transport and Accessibility

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDG: Sustainable cities and communities

Open Access status: green

OpenAlex ID: W6930943616

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