Travel mode choice prediction: developing new techniques to prioritize variables and interpret black-box machine learning techniques
Hamed Naseri,
E.O.D. Waygood,
Zachary Patterson,
Meredith Alousi-Jones and
Bobin Wang
Transportation Planning and Technology, 2025, vol. 48, issue 3, 582-605
Abstract:
Travel Mode Choice (TMC) prediction is vital for forecasting travel demand and transportation planning. To be helpful for those purposes, one needs to know with high accuracy what influences choices and how. For accuracy, Machine Learning (ML) classification techniques often produce results with higher accuracy than traditional methods. However, many ML techniques are black-box tools, making them less useful for planning. To this end, two new approaches were proposed to interpret the results of ML techniques and investigate the influence of different variables on TMC. The results suggested that ensemble learning techniques outperform other prediction methods. Adding accessibility, geographic, and land-use variables to the conventional TMC prediction models could improve their performance. The most important parameters for TMC were found to be: trip distance, availability of a transit pass and availability of a driver’s license. Their respective influences on the different modes are demonstrated using the novel method mentioned above.
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/03081060.2024.2411611 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:transp:v:48:y:2025:i:3:p:582-605
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GTPT20
DOI: 10.1080/03081060.2024.2411611
Access Statistics for this article
Transportation Planning and Technology is currently edited by Dr. David Gillingwater
More articles in Transportation Planning and Technology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().