A systematic review of machine learning classification methodologies for modelling passenger mode choice
| Autor: | Mohammed Z. E. B. Elshafie, Ying Jin, Michel Bierlaire, Tim Hillel |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Estimation
050210 logistics & transportation Computer science 030503 health policy & services 05 social sciences Data science Abstract machine 03 medical and health sciences Statistical classification Systematic review Data extraction Modeling and Simulation 0502 economics and business Relevance (information retrieval) Statistics Probability and Uncertainty 0305 other medical science Mode choice Choice modelling |
| Zdroj: | Journal of Choice Modelling. 38:100221 |
| ISSN: | 1755-5345 |
| DOI: | 10.1016/j.jocm.2020.100221 |
| Popis: | Machine Learning (ML) approaches are increasingly being investigated as an alternative to Random Utility Models (RUMs) for modelling passenger mode choice. These approaches have the potential to provide valuable insights into choice modelling research questions. However, the research and the methodologies used are fragmented. Whilst systematic reviews on RUMs for mode choice prediction have long existed and the methods have been well scrutinised for mode choice prediction, the same is not true for ML models. To address this need, this paper conducts a systematic review of ML methodologies for modelling passenger mode choice. The review analyses the methodologies employed within each study to (a) establish the state-of-research frameworks for ML mode choice modelling and (b) identify and quantify the prevalence of methodological limitations in previous studies. A comprehensive search methodology across the three largest online publication databases is used to identify 574 unique records. These are screened for relevance, leaving 70 peer-reviewed articles containing 73 primary studies for data extraction. The studies are reviewed in detail to extract 17 attributes covering five research questions, concerning (i) classification techniques, (ii) datasets, (iii) performance estimation, (iv) hyper-parameter selection, and (v) model analysis. The review identifies ten common methodological limitations. Five are determined to be methodological pitfalls, which are likely to introduce bias in the estimation of model performance. The remaining five are identified as areas for improvement, which may limit the achieved performance of the models considered. A further six general limitations are identified, which highlight gaps in knowledge for future work. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect