Publication Type

Journal Article

Version

publishedVersion

Publication Date

4-2025

Abstract

Public transport (PT) is crucial for enhancing the quality of life and enabling sustainable urban development. As part of the UK Transport Investment Strategy, increasing PT usage is critical to achieving efficient and sustainable mobility. This paper introduces Machine Learning Influence Flow Analysis (MIFA), a novel framework for identifying the key influencers of PT usage. Using survey data from bus passengers in Southern England, we evaluate machine learning models. Subsequently, MIFA uncovers that easy payments, e-ticketing, and mobile applications can substantially improve the PT service. MIFA’s implementation demonstrates that strength and importance lead to specific insights into how service characteristics impact user decisions. Practical implications include deploying smart ticketing systems and contactless payments to streamline bus usage. Our results suggest that these strategies can enable bus operators to allocate resources more effectively, leading to increased ridership and enhanced user satisfaction.

Keywords

Public transport, Bus services, Machine learning, Key influencers

Discipline

Accounting | Numerical Analysis and Scientific Computing | Transportation

Publication

Public Transport

First Page

1

Last Page

41

ISSN

1866-749X

Identifier

10.1007/s12469-024-00387-2

Copyright Owner and License

Authors-CC-BY

Creative Commons License

Creative Commons Attribution 3.0 License
This work is licensed under a Creative Commons Attribution 3.0 License.

Additional URL

https://doi.org/10.1007/s12469-024-00387-2

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