Count Data Models for Trip Generation

Abstract

Count data models are established to overcome the shortcoming of linear regression model used for trip generation in conventional four step travel demand forecasting. It should be checked if there are overdispersion and excess zero responses in count data to forecast the generation of trips. The forecasted values should also be non-negative ones. The study applies to nonhome based trips at household level to perform efficient analysis on count data. The Poisson model with an assumption of equidispersion has frequently been used to analyze count data. However, if the variance of data is greater than the mean, the Poisson model tends to underestimate errors, resulting in problem in reliability. Excess zeros in data result in heterogeneity leading to biased coefficient estimates for the models. The negative binomial model and the modified count data models are established to consider overdispersion and heterogeneity to improve the reliability. The optimal model is chosen through Vuong test. Model reliability is also checked by likelihood test and accuracy of estimated value of model by Theil inequality coefficient. Finally, sensitivity analysis is performed to know the change of nonhome based trips depending on the change in socio-economic characteristics.