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.
- Improving Public Transport through Machine Learning Influence Flow Analysis (MIFA): Southern England Bus Case Study
- Benjamin Lee - Singapore Management UniversityWolfgang Garn - University of Surrey, Surrey Business SchoolMasoud Fakhimi - University of Surrey, Surrey Business SchoolNicholas F Ryman-Tubb - University of Surrey, Surrey Business School
- Public transport
- springer nature link; HEIDELBERG
- 41
- 10/04/2025
- 06/12/2024
- Surrey County Council: 9935 Innovate UKUniversity of Surrey
First and foremost, we would like to acknowledge the importance of the Knowledge Transfer Partnership (Partnership number: 9935) between Surrey County Council, Innovate UK and the University of Surrey, which allowed the collection of data fundamental to this study as detailed in Pape et al. (2017). We want to thank the anonymous reviewers for their feedback and for sharing ideas for future work.
- 99950965802346; WOS:001463317800001
- Surrey Business School
- English
- Journal article
- Data is available upon reasonable request from the authors.