Attitudes on Autonomous Vehicle Adoption using Interpretable Gradient Boosting Machine
This article applies machine learning (ML) to develop a choice model on three choice alternatives related to autonomous vehicles (AV): regular vehicle (REG), private AV (PAV), and shared AV (SAV). The learned model is used to examine users’ preferences and behaviors on AV uptake by car commuters. Specifically, this study applies gradient boosting machine (GBM) to stated preference (SP) survey data (i.e., panel data). GBM notably possesses more interpretable features than other ML methods as well as high predictive performance for panel data. The prediction performance of GBM is evaluated by conducting a 5-fold cross-validation and shows around 80% accuracy. To interpret users’ behaviors, variable importance (VI) and partial dependence (PD) were measured. The results of VI indicate that trip cost, purchase cost, and subscription cost are the most influential variables in selecting an alternative. Moreover, the attitudinal variables Pro-AV Sentiment and Environmental Concern are also shown to be significant. The article also examines the sensitivity of choice by using the PD of the log-odds on selected important factors. The results inform both the modeling of transportation technology uptake and the configuration and interpretation of GBM that can be applied for policy analysis.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
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Supplemental Notes:
- The Standing Committee on Transportation Demand Forecasting (ADB40) peer-reviewed this paper (19-02893). © National Academy of Sciences: Transportation Research Board 2019.
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Authors:
- Lee, Dongwoo
- Mulrow, John
- Haboucha, Chana Joanne
- Derrible, Sybil
- Shiftan, Yoram
- Publication Date: 2019-11
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 865-878
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Serial:
- Transportation Research Record: Journal of the Transportation Research Board
- Volume: 2673
- Issue Number: 11
- Publisher: Sage Publications, Incorporated
- ISSN: 0361-1981
- EISSN: 2169-4052
- Serial URL: http://journals.sagepub.com/home/trr
Subject/Index Terms
- TRT Terms: Attitudes; Autonomous vehicles; Behavior; Commuters; Consumer preferences; Data analysis; Decision making; Machine learning
- Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;
Filing Info
- Accession Number: 01710447
- Record Type: Publication
- Files: TRIS, TRB, ATRI
- Created Date: Jul 8 2019 11:43AM