Comparison of Probit and Logit Modeling Approaches in Prioritizing Taxi Hailing Attributes: Best-Worst Scaling-Case 1

Document Type : Original Article

Authors
Mohsen Makaremi Sharifi 1
AmirAbbas Rassafi 2
1 Ph.D., Department of Transportation Planning, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran.
2 Professor, Department of Transportation Planning, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran.
10.22034/road.2024.456346.2281
Abstract
The expansion of the internet and development in the business world have led to the emergence of new businesses, including taxi hailing. The purpose of this study is to examine these attributes (10 attributes are selected here) and prioritize them. In this study, unlike the traditional approach of dealing with discrete choice models, which focuses on choosing the best (most important) alternative among alternatives, the role of the worst (least important) alternative is also considered. The probit model is employed for modeling, and its results are compared with the logit model.The results of this research show that although the coefficients of the features in the two models are not the same, and the performance evaluation criteria show the superiority of the probit model over the logit model, but the overall result in the rating of the features is not different.The results of this research indicate that although the coefficients of attributes in the two modeling approaches are not the same, and evaluation criteria demonstrate the superior performance of the probit model over the logit model, the overall conclusions regarding the ranking of attributes do not differ between them.When dealing with taxi hailing services, issues related to security and confidence are more important. In other words, users pay the most attention to aspects that bring about a sense of security and mental satisfaction when choosing taxi hailing services.Furthermore, this study indicates that issues such as compliance with health cares and social distancing are among the least important attributes.
Keywords
Probit
Logit
BWS-case1
Taxi Hailing Attributes
-Ahmad, H., & Halim, H. (2017). Determining sample size for research activities. Selangor Business Review, 20-34.
-Aizaki, H., & Fogarty, J. (2023). R packages and tutorial for case 1 best–worst scaling. Journal of Choice Modelling, 46, Article Number: 100394.
-Akbari, M., Amiri, N. S., Zúñiga, M. Á., Padash, H., & Shakiba, H. (2020). Evidence for Acceptance of Ride-Hailing Services in Iran. Transportation Research Record, 2674(11), 289-303.
-Alemi, F., Circella, G., Handy, S., & Mokhtarian, P. (2018). What influences travelers to use Uber? Exploring the factors affecting the adoption of on-demand ride services in California [Article]. Travel Behaviour and Society, 13, 88-104.
-Assele, S. Y., Meulders, M., & Vandebroek, M. (2023). Sample size selection for discrete choice experiments using design features. Journal of Choice Modelling, 49, 100436.
-Burton, N., Burton, M., Rigby, D., Sutherland, C. A., & Rhodes, G. (2019). Best-worst scaling improves measurement of first impressions. Cognitive research: principles and implications, 4(Article number: 36), 1-10.
-Chen, Y., Mahmassani, H. S., & Frei, A. (2018). Incorporating social media in travel and activity choice models: conceptual framework and exploratory analysis. International Journal of Urban Sciences, 22(2), 180-200.
-Choe, C., Jung, S., & Oaxaca, R. L. (2020). Identification and decompositions in probit and logit models. Empirical Economics, 59, 1479-1492.
-Dow, C. R., Nguyen, D. B., Wang, S. C., Hwang, S. F., & Tsai, M. F. (2016). A Geo-Aware Taxi Carrying Management System by Using Location Based Services and Zone Queuing Techniques on Internet of Things [Article]. Mobile Information Systems, 2016, Article ID 9817374, Article 9817374.
-Flynn, T. N., Louviere, J. J., Peters, T. J., & Coast, J. (2007). Best–worst scaling: What it can do for health care research and how to do it. Journal of Health Economics, 26(1), 171-189.
-Guo, Q., & Shen, J. (2019). An Empirical Comparison Between Discrete Choice Experiment and Best-worst Scaling: A Case Study of Mobile Payment Choice. Research Institute for Economics and Business Administration, Kobe University.
-Hollis, G., & Westbury, C. (2018). When is best-worst best? A comparison of best-worst scaling, numeric estimation, and rating scales for collection of semantic norms. Behavior Research Methods, 50, 115 - 133.
-Jing, P., Chen, Y., Wang, X., Pan, K., & Yuan, D. (2021). Evaluating the effectiveness of Didi ride-hailing security measures: An integration model. Transportation Research Part F: Traffic Psychology and Behaviour, 76, 139-166.
-Koob, C. (2023). Consumers’ Preferences for Digital Corporate Content on Company Websites: A Best–Worst Scaling Analysis. Journal of Theoretical and Applied Electronic Commerce Research, 18(3), 1301-1319.
-Lancsar, E., Fiebig, D. G., & Hole, A. R. (2017). Discrete Choice Experiments: A Guide to Model Specification, Estimation and Software. PharmacoEconomics, 35(7), 697-716.
-Li, H.-r. (2016). Taxi Positioning in the New Age of Internet and Industrial Development Research. Procedia Engineering, 137, 811-816.
-Louviere, J., Lings, I., Islam, T., Gudergan, S., & Flynn, T. (2013). An introduction to the application of (case 1) best–worst scaling in marketing research. International journal of research in marketing, 30(3), 292-303.
-Louviere, J. J., Flynn, T. N., & Marley, A. A. J. (2015). Best-worst scaling: Theory, methods and applications. Cambridge University Press.
-[Record #11 is using a reference type undefined in this output style.]
-Mjahed, L. B., Mittal, A., Elfar, A., Mahmassani, H. S., & Chen, Y. (2017). Exploring the role of social media platforms in informing trip planning: Case of Yelp.com [Article]. Transportation Research Record, 2666, 1-9.
-Thompson, S. K. (1987). Sample Size for Estimating Multinomial Proportions. The American Statistician, 41(1), 42-46.
-Walaza, S., Onderwater, P., & Zuidgeest, M. (2023). Best-worst scaling approach to measure public transport user quality perceptions and preferences in cape town.
-White, M. H. (2021). bwsTools: An R package for case 1 best-worst scaling. Journal of Choice Modelling, 39, 100289.
-Yi, C., & Xia, L. (2015). A Forecasting model of trip distribution for vacant taxis with taxi-hailing Apps [J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 39(1), 51-54.