Big data, big bias? On factors shaping transit and shared micromobility integration

Document Type

Journal Article

Publication Date

2025

Subject Area

place - urban, place - north america, planning - integration, planning - surveys, planning - methods, ridership - modelling

Keywords

E-scooter, Micromobility, Public transit, First mile and last mile, Data bias

Abstract

Understanding what drives shared micromobility use as a first-/last-mile (FM/LM) connection to transit is vital for enhancing multimodal travel. However, reliable data on transit-connecting micromobility trips are limited. Researchers often use buffer-based methods to infer FM/LM trips from large datasets, but such methods may cause bias in study results. This study uses a novel dataset with thousands of user-reported transit-connecting e-scooter trips to build a ground-truth model. Results show that more FM/LM trips are associated with higher transit-stop and road density, lower residential and intersection density, CBD proximity, greater employment rate, and lower shares of Black residents. Also, we find that using transit-connecting trips inferred from buffer-based methods unavoidably lead to biased model outputs. These biases stem from heterogeneity in station characteristics and transit-connecting trip patterns. We highlight trade-offs in inference accuracy and offer guidance for the use of big data in future research on transit and micromobility integration.

Rights

Permission to publish the abstract has been given by Elsevier, copyright remains with them.

Comments

Transportation Research Part D Home Page:

http://www.sciencedirect.com/science/journal/13619209

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