Evaluating Electric Micro-Mobility Related Mode Choice Stated Preferences: A Latent Class Choice Approach

This paper examines mode choice behaviour regarding Electric Micro-Mobility (EMM) among car and public transport users using a Latent Class Choice Modelling (LCCM) approach. Utilizing stated preference survey data from 1,671 Brisbane residents, the study identifies distinct user segments with heterogeneous sensitivities to EMM attributes. Results reveal significant potential for EMM adoption, notably among segments inclined toward multimodal trips or personal micromobility, driven primarily by weather conditions, travel time, and cost. Conversely, resistant segments characterized by demographic factors such as age, gender, income, and lack of EMM experience highlight challenges in shifting mode choices. Further analyses, including class profiles, elasticities, and geographical distribution, are conducted to generate comprehensive insights that inform policy decisions. These insights support targeted investments in micromobility infrastructure and integrated pricing strategies, thereby enhancing EMM adoption and promoting sustainable urban mobility.

Publication Details

Journal
ArXiv.org
Published
2025-04-01
Primary Topic
Socioeconomic Development in MENA
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article
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article

Evaluating Electric Micro-Mobility Related Mode Choice Stated Preferences: A Latent Class Choice Approach

ArXiv.org
Socioeconomic Development in MENA
article

Evaluating Electric Micro-Mobility Related Mode Choice Stated Preferences: A Latent Class Choice Approach

article en

Abstract

This paper examines mode choice behaviour regarding Electric Micro-Mobility (EMM) among car and public transport users using a Latent Class Choice Modelling (LCCM) approach. Utilizing stated preference survey data from 1,671 Brisbane residents, the study identifies distinct user segments with heterogeneous sensitivities to EMM attributes. Results reveal significant potential for EMM adoption, notably among segments inclined toward multimodal trips or personal micromobility, driven primarily by weather conditions, travel time, and cost. Conversely, resistant segments characterized by demographic factors such as age, gender, income, and lack of EMM experience highlight challenges in shifting mode choices. Further analyses, including class profiles, elasticities, and geographical distribution, are conducted to generate comprehensive insights that inform policy decisions. These insights support targeted investments in micromobility infrastructure and integrated pricing strategies, thereby enhancing EMM adoption and promoting sustainable urban mobility.

ArXiv.org
Sustainable cities and communities
Openalex Percentile: Top 40%
Socioeconomic Development in MENA
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