Journal
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Volume 22, Issue 4, Pages 2123-2132Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2020.3040904
Keywords
Data mining; Autonomous vehicles; Resource management; Predictive models; Frequency measurement; Standards; Social networking (online); Topic modelling; latent dirichlet allocation; supervised latent dirichlet allocation; Likert scales; open-ended questions; travel behaviour research
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Funding
- Short-Term Scientific Mission from COST Action of the European Cooperation for Science and Technology [TU1305]
- Fundacao para a Ciencia e a Tecnologia (FCT, IP), Portugal [PD/BD/129394/2017]
- Fundação para a Ciência e a Tecnologia [PD/BD/129394/2017] Funding Source: FCT
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The research explores the use of topic modeling techniques to extract information from open-ended responses, compared to closed-ended responses, finding that open-ended responses yield better information extraction results, especially when combined with both unsupervised and supervised methods. Open-ended questions can be used to measure attitudes, while topic modeling can be used to extract information.
For practical reasons, surveys that aim for a large number of respondents tend to restrict themselves to closed-ended responses. Despite potentially bringing richer insights, the use of open-ended questions poses great challenges in terms of extracting useful information while significantly increasing the analysis time. Nevertheless, automatic text analysis techniques speed up the analysis of open-ended responses. In this research, we explore the potential to use techniques in topic modelling [Latent Dirichlet Allocation (LDA) and Supervised LDA (sLDA)] to extract information from open-ended responses. This is compared to the information obtained from closed-ended responses, accomplished using a questionnaire that measures the intention to use shared autonomous vehicles (SAVs). Two versions of the questionnaire- Ver_OE and Ver_Lk were used, with open-ended and Likert scales measuring the same attitudes in the alternative versions. Factors were extracted for closed-ended questions. For questions common to both versions of the questionnaire, respondents answering Ver_OE had a higher positive attitude towards autonomous vehicles. These attitudinal questions were placed after the open-ended questions. When evaluating the performance of the models that predict the intention to use SAVs, models estimated using Ver_OE performed better. This increased further with the inclusion of the information extracted from the open-ended responses using both, the unsupervised (LDA) and supervised (sLDA) methods. No improvement was observed in the model for Ver_Lk. These indicate the potential for the use of open-ended questions to measure attitudes and topic modelling to extract information from these responses.
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