Hazard Duration Model with Panel Data for Daily Car Travel Distance: A Toyota City Case Study

Abstract

The limitation of battery size for electric vehicles has driven researchers to study driving distance. Trip patterns and traveler preferences in terms of distance are affected by multiple variables. This study, using socioeconomics, weather conditions, and vehicle characteristics as covariates, compares lognormal, log-logistic, and Weibull distribution assumptions on daily car travel distances with a parametric hazard model for both pooled and panel regression. The results reveal that the log-logistic distribution performed best for both the pooled and panel models, and the inclusion of heterogeneity by the panel model improves the model. The results suggest that the travel distances achieved by people in Toyota City, Japan, is highly dependent on the weather conditions, specifically the precipitation and wind speed. Socioeconomic indicators, such as age and gender, and vehicle characteristics, such as engine size and vehicle price, also significantly affect the car travel distance.