Modelling the complementarity and flexibility between different shared modes available in smart electric mobility hubs (eHUBS)

• Understand whether there is complementarity between shared modes in a mobility hub. • Model how people adapt when a shared mode they plan to use becomes unavailable. • Estimated several Multiple Discrete-Continuous models to capture complementarity. • Found complementarity between shared EV and shared e-bike in eHUBS. • A significant group uses other available shared modes in the hub as a replacement. eHUBS are physical locations that integrate two or more electric shared mobility modes. As they provide transport users easier access to a wide range of transport modes, multimodal behaviour is expected to be more common. However, this issue has not been addressed in previous stated preference studies on mode choices involving innovative transport modes. In this study, multimodal behaviour is explicitly addressed both in measurement and in modelling by adopting the multiple discrete–continuous (MDC) modelling framework in contrast to discrete choice models. Instead of asking transport users to indicate the most preferred alternative, they were allowed to choose more than one alternative by allocating trips between several modes. This study aims to answer two questions: 1) whether there is complementarity between the multiple shared modes offered in eHUBS and 2) how would transport users adapt when one of the shared modes that they plan to use becomes unavailable. Using stated mode choice data of non-commuting trips from transport users whose current mode is driving a private car in Manchester, UK, several models under the MDC framework were estimated including Multiple Discrete-Continuous Extreme Value (MDCEV) model, mixed MDCEV model, and the extended Multiple Discrete Continuous (eMDC) model. The results show that there is complementarity between shared electric vehicle (EV) and electric bike (e-bike) offered in the eHUBS. In addition, the research show that the flexibility between those two shared modes is stronger than assumed in the MDCEV model, and common preference heterogeneity cannot fully account for this phenomenon.

Authors

Institutions

Publication Details

Journal
Transportation Research Part A Policy and Practice
Published
2024-10-05
DOI
https://doi.org/10.1016/j.tra.2024.104279
Citations
5
Primary Topic
Transportation and Mobility Innovations
Type
article
Field-Weighted Citation Impact
1.02

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Modelling the complementarity and flexibility between different shared modes available in smart electric mobility hubs (eHUBS)

Fanchao Liao, Dilum Dissanayake, Gonçalo Homem de Almeida Correia
5 citations
Transportation Research Part A Policy and Practice
Transportation and Mobility Innovations
1.02
article

Modelling the complementarity and flexibility between different shared modes available in smart electric mobility hubs (eHUBS)

Fanchao Liao, Dilum Dissanayake, Gonçalo Homem de Almeida Correia
article en
5 citations

Abstract

• Understand whether there is complementarity between shared modes in a mobility hub. • Model how people adapt when a shared mode they plan to use becomes unavailable. • Estimated several Multiple Discrete-Continuous models to capture complementarity. • Found complementarity between shared EV and shared e-bike in eHUBS. • A significant group uses other available shared modes in the hub as a replacement. eHUBS are physical locations that integrate two or more electric shared mobility modes. As they provide transport users easier access to a wide range of transport modes, multimodal behaviour is expected to be more common. However, this issue has not been addressed in previous stated preference studies on mode choices involving innovative transport modes. In this study, multimodal behaviour is explicitly addressed both in measurement and in modelling by adopting the multiple discrete–continuous (MDC) modelling framework in contrast to discrete choice models. Instead of asking transport users to indicate the most preferred alternative, they were allowed to choose more than one alternative by allocating trips between several modes. This study aims to answer two questions: 1) whether there is complementarity between the multiple shared modes offered in eHUBS and 2) how would transport users adapt when one of the shared modes that they plan to use becomes unavailable. Using stated mode choice data of non-commuting trips from transport users whose current mode is driving a private car in Manchester, UK, several models under the MDC framework were estimated including Multiple Discrete-Continuous Extreme Value (MDCEV) model, mixed MDCEV model, and the extended Multiple Discrete Continuous (eMDC) model. The results show that there is complementarity between shared electric vehicle (EV) and electric bike (e-bike) offered in the eHUBS. In addition, the research show that the flexibility between those two shared modes is stronger than assumed in the MDCEV model, and common preference heterogeneity cannot fully account for this phenomenon.

Transportation Research Part A Policy and PracticeVol. 190
Radboud University Nijmegen (NL), University of Birmingham (GB), Delft University of Technology (NL)
Interreg North-West Europe, Interreg
Affordable and clean energy
Openalex Percentile: Top 25%
Transportation and Mobility Innovations
1.02
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.