Estimation and validation of Hybrid choice models to identify which factors could affect the choice of the bicycle

The bicycle is one of the most sustainable, environmentally friendly and healthy forms of transport. Many government agencies and public health organizations have explicitly advocated more bicycling as a way to improve individual health as well as reduce air pollution, carbon emissions, congestion, noise, traffic dangers, and other harmful impacts of car use. Nevertheless it is not clear which measures are the most effective and should be given priority in designing and implementing a pro-bicycle policy package. Given the growing consensus on the benefits of bicycling, the important question for researchers is how to increase bicycling. Many authors as Pucher et al. , 2010; Heinen et al. , 2010, Fernandez-Heredia et al. , 2014, claim that promotion of bicycle use requires an in-depth knowledge of the factors underlying the propensity to cycle and also of the structural and psychosocial barriers that might inhibit bicycle use. Few works, (Kamargianni and Polydoropoulou, 2013; Habib et al. , 2014: Maldonado-Hinarejos et al. , 2014; Motoaki and Daziano, 2015) have studied the factors affecting the choice of bicycle using an Integrated Choice and Latent Variable (ICLV) where the bicycle is one of the modes available in the choice set. However, following the psychological theory, it is important to explore first if the bicycle is considered as alternative mode of transport. This is in particular important when bicycle is mostly related to leisure and recreational activities. In this work we used Hybrid Choice Models modeling to study the choice of cycling (for any purpose) vs. the choice of not cycling at all. This approach allowed us to firstly detect the determinant keys of the choice of cycling/not cycling and secondly a specific intervention policy for travel behavior change, in support of an increase in bicycle use, which relies on a combination of hard and soft measures. The data used in this study were collected between 2014 and 2016 in a survey, named “BikeILikeYou”, carried out among the employees of the RAS and municipal authorities. The survey had the objective to measure factors and barriers related to bicycle use, along with socio-demographic characteristics and the psychological aspects underlying individual’s behavior related to cycling ( e.g. attitudes, belief, perceived behavior control, etc. ) according to the Theory of Planned Behavior formulated by Ajzen, (1991). 4,691 individuals answered the questionnaire but 1,939 were incomplete, so the final sample used for estimation consisted of 2,752 observations. The model specification includes 3 latent variables related to (1) beliefs about bicycle as a mean of transport, (2) Perceived Behavior Control depending on the contextual factors (perceived safety, integration with other travel modes, etc. ) and (3) Intention of using the bike or starting to cycle were considered, based on the information collected in a survey. The items used as indicators for each LV were identified through Principal Component Analysis and a Factor Analysis. First results indicated that a more positive belief about bicycle as a mean of transport corresponds to a higher probability to cycle. At the same time, the greater is the intention to cycle, the higher probability to cycle. It seems also that the intention weights more than beliefs in the choice (different magnitude of parameters estimated). Models results highlight that, beside the individual characteristics (young individuals, males, without children in the household are more willing to cycle), the existence of latent aspects as belief (depending in particular on the level of education among all the socioeconomic characteristics) and intention (depending on household characteristics, income and car ownership) significantly affect the choice to use the bicycle. Further, we also contributed to state of the art, validating the model results. While there is a vast literature on HCM, the key issue of validating these models has been vastly neglected. Mabit et al. (2015) is the only published paper we are aware of that validate a HCM using a holdout sample. Our paper then contributes to this very short literature discussing the validation of the HCM. The validation approach was conducted holding out 20% (550 observations) and estimating the HCMs on the remaining 80% of the sample (2202 observations). It was also checked that the hold out sample presented the same distribution of the characteristics as the sample used for estimation. We analyzed several socio-economic characteristics and for all of them the distribution in the hold out sample was not statistically significantly different from the estimation sample. Results from the validation indicated that the ratio between the parameters estimated in the whole sample model and holdout sample model is not significantly different from 1 in most of the parameters. References: Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211. Habib, K. N., Mann, J., Mahmoud, M., & Weiss, A. (2014). Synopsis of bicycle demand in the City of Toronto: Investigating the effects of perception, consciousness and comfortability on the purpose of biking and bike ownership. Transportation Research A 70, 67-80. Heinen, E., van Wee, B., & Maat, K. (2010). Commuting by bicycle: an overview of the literature. Transport Reviews, 30(1), 59-96. Kamargianni, M., & Polydoropoulou, A. (2013). Hybrid choice model to investigate effects of teenagers' attitudes toward walking and cycling on mode choice behavior. Transportation Research Record: Journal of the Transportation Research Board 2382, 151-161. Maldonado-Hinarejos, R., Sivakumar, A., & Polak, J. W. (2014). Exploring the role of individual attitudes and perceptions in predicting the demand for cycling: a hybrid choice modelling approach. Transportation, 41(6), 1287-1304. Motoaki, Y., & Daziano, R. A. (2015). A hybrid-choice latent-class model for the analysis of the effects of weather on cycling demand. Transportation Research A 75, 217-230.

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