The analysis of influences of attitudes on mode choice under highly unbalanced mode share patterns.

Abstract

The aim of the study is to examine the potential effect of attitudes towards physical activity on bus utility in the context of a rural area where studies have shown that the level and opportunity for physical activity are generally low. The need to analyze attitudes remains a strong motivation for the application of integrated choice and latent variable models. As such, we integrated attitudes towards physical activity with two specific attitudes towards bus and car use in a binary logit mode choice model between car and bus, looking at the area of Asuke, Japan. As we found data separation due to highly unbalanced mode shares in the input data, the choice models were estimated with the likelihood function penalized. While only a few parameters were found to be significant, arguably as a result of the unbalanced mode share pattern, attitude variables were almost unaffected by the data separation phenomenon. Whereas maximum likelihood estimates do not exist in the presence of data separation, the employed penalized maximum likelihood estimator was demonstrated to be a solution to this problem. Thus, we suggest that checking for data separation in the case of highly unbalanced mode share patterns is important and if data separation exits, penalizing the likelihood functions can be a solution rather than excluding out some irrelevant variables to avoid data separation. Overall, we found that attitude towards physical activity had a significant effect on bus utility, suggesting that policymakers could use this factor to connect transport and health policies.