ABSTRACT
Count data consist of non-negative integer values and are encountered frequently in the modeling of transportation-related phenomena. Examples of count data variables in transportation include the number of driver route changes per week, the number of trip departure changes per week, drivers’ frequency of use of intelligent transportation systems (ITS) technologies over some time period, number of vehicles waiting in a queue, and the number of accidents observed on road segments per year. Numerous other examples are found in driver decision making, which are described in Mannering (1998). A common mistake is to model count data as continuous data by applying standard least squares regression. This is not correct because regression models yield predicted values that are non-integers and can also predict values that are negative, both of which are inconsistent with count data. These limitations make standard regression analysis inappropriate for modeling count data without modifying dependent variables.
