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An Early Stopping Bayesian Data Assimilation Approach for improved Mixed Multinomial Logit transferability

Xie, Shanshan; Hillel, Tim; Jin, Ying; (2021) An Early Stopping Bayesian Data Assimilation Approach for improved Mixed Multinomial Logit transferability. In: Proceedings of the 9th Symposium of the European Association for Research in Transportation. European Association for Research in Transportation: Lyon, France. Green open access

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Abstract

Mixed Multinomial Logit (MMNL) models can provide valuable insights into inter and intra-individual heterogeneity in transportation choice modelling. However, the high computational and data requirements for MMNL models has limited the application of MMNL models in practice. These requirements are particularly problematic when investigating the behaviour of specific population sub-groups or market segments, where a modeller may want to estimate separate models for a number of similar contexts, each with low data availability. The same challenges arise when adapting one model to a new location or time period. To overcome these barriers, we establish a new Early Stopping Bayesian Data Assimilation (ESBDA) approach which updates a previously estimated MMNL on a new data sample or subsample through iterative Bayesian inference. This approach therefore enables an existing model from one context to be transferred to a new context with lower data availability. The ESBDA approach is benchmarked against two reference estimators: (i) a standard Bayesian estimator (MMNL); and (ii) a Bayesian Data Assimilation (BDA) estimator without early stopping. The results show that the proposed ESBDA approach can effectively overcome over-fitting and non-convergence. ESBDA models outperform the models estimated by the reference estimators in terms of behavioural consistency of parameter estimates and the out-of-sample predictive performance of the model. Even when using few collected data, ESBDA can still produce suitable and stable MMNL model with parameter estimates consistent with established behavioural theory.

Type: Proceedings paper
Title: An Early Stopping Bayesian Data Assimilation Approach for improved Mixed Multinomial Logit transferability
Event: hEART 2020: 9th Symposium of the European Association for Research in Transportation
Location: Université de Lyon, France (online)
Dates: 3 Feb 2021 - 4 Feb 2021
Open access status: An open access version is available from UCL Discovery
Publisher version: https://transp-or.epfl.ch/heart/2020.php
Language: English
Additional information: This version is the author accepted manuscript. 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/10173009
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