Hillel, Tim;
Bierlaire, Michel;
Elshafie, Mohammed;
Jin, Ying;
(2019)
Weak teachers: Assisted specification of discrete
choice models using ensemble learning.
In:
Proceedings of the 8th Symposium of the European Association for Research in Transportation (hEART 2019).
(pp. pp. 1-12).
hEART: Budapest, Hungary.
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Abstract
Mode choice modelling has almost exclusively been tackled using Discrete Choice Models (DCMs). This is in part due to their highly interpretable linear structure, which allows the model to be checked for consistency against established behavioural expectations. However, a key drawback of DCMs is that the utility functions must be specified manually in advance of fitting the model, a process that does not scale well with increasing data complexity. Machine Learning (ML) is increasingly being investigated as an alternative to DCM for modelling mode choice. Whilst ML automates the decision-making process, requiring no utility functions to be specified, it has a crucial limitation in that the resulting models are difficult to interpret and to check for behavioural consistency. In order to address the limitations of both ML and discrete choice models, we propose an assisted specification procedure, in which the aggregate structure of a fitted Ensemble Learning (EL) model is used to inform the utility functions in a DCM. The resulting models are found to have greatly improved performance over manually specified DCMs, outperforming all but the highest performing ML classifier.
| Type: | Proceedings paper |
|---|---|
| Title: | Weak teachers: Assisted specification of discrete choice models using ensemble learning |
| Event: | hEART 2019: 8th Symposium of the European Association for Research in Transportation |
| Location: | Budapest University of Technology and Economics, Hungary |
| Dates: | 4 Sep 2019 - 6 Sep 2019 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://heart2019.bme.hu/ |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10173008 |
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