UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

A Random Effect Bayesian Neural Network (RE-BNN) for travel mode choice analysis across multiple regions

Xia, Y; Chen, H; Zimmermann, R; (2023) A Random Effect Bayesian Neural Network (RE-BNN) for travel mode choice analysis across multiple regions. Travel Behaviour and Society , 30 pp. 118-134. 10.1016/j.tbs.2022.08.011. (In press). Green open access

[thumbnail of 1-s2.0-S2214367X22000989-main.pdf]
Preview
Text
1-s2.0-S2214367X22000989-main.pdf - Published Version

Download (4MB) | Preview

Abstract

Travel mode choice modelling plays a critical role in predicting passengers’ travel demand and planning local transportation systems. Researchers commonly adopt classical Random Utility Models to analyse individual decision-making based on the utility theory. Recently, with an increasing interest in applying Machine Learning techniques, a number of studies have used these methods for modelling travel mode preferences for their excellent predictive power. However, none of these studies proposes machine learning models that investigate the regional heterogeneity of travel behaviours. To address this gap, this study develops a Random Effect-Bayesian Neural Network (RE-BNN) framework to predict and explain travel mode choice across multiple regions by combining the Random Effect (RE) model and the Bayesian Neural Networks (BNN). The results show that this model outperforms the plain Deep Neural Network (DNN) regarding prediction accuracy and is more robust across different datasets. In addition, in terms of interpretation, the capability of RE-BNN to learn the travel behaviours across regions has been demonstrated by offset utilities, choice probability functions and local travel mode shares.

Type: Article
Title: A Random Effect Bayesian Neural Network (RE-BNN) for travel mode choice analysis across multiple regions
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.tbs.2022.08.011
Publisher version: https://doi.org/10.1016/j.tbs.2022.08.011
Language: English
Additional information: © 2022 The Author(s). Published by Elsevier Ltd on behalf of Hong Kong Society for Transportation Studies. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Bayesian neural network, Travel mode choice modelling, Regional behaviour comparison, Random effect model
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10156709
Downloads since deposit
61Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item