TITLE:
Accounting for Heterogeneity in Stop Frequency Models of Work Tours Using Latent Class Poisson Models
AUTHORS:
Babak Mirzazadeh
KEYWORDS:
Activity Based Model, Work Tour, Stop Frequency, Latent Class, Poisson Regression Model
JOURNAL NAME:
Journal of Transportation Technologies,
Vol.13 No.2,
April
11,
2023
ABSTRACT: Stop
frequency models, as one of the elements of activity based models, represent an
important part of travel behavior. Unobserved heterogeneity across the
travelers should be taken into consideration to prevent biasedness and
inconsistency in the estimated parameters in the stop frequency models.
Additionally, previous studies on the stop frequency have mostly been done in
larger metropolitan areas and less attention has been paid to the areas with
less population. This study addresses these gaps by using 2012 travel data from
a medium sized U.S. urban area using the work tour for the case study. Stop in
the work tour were classified into three groups of outbound leg, work based
subtour, and inbound leg of the commutes. Latent Class Poisson Regression
Models were used to analyze the data. The results indicate the presence of
heterogeneity across the commuters. Using latent class models significantly
improves the predictive power of the models compared to regular one class
Poisson regression models. In contrast to one class Poisson models, gender
becomes insignificant in predicting the number of tours when unobserved
heterogeneity is accounted for. The commuters are associated with increased
stops on their work based subtour when the employment density of
service-related occupations increases in their work zone, but employment
density of retail employment does not significantly contribute to the stop
making likelihood of the commuters. Additionally, an increase in the number of
work tours was associated with fewer stops on the inbound leg of the commute.
The results of this study suggest the consideration of unobserved heterogeneity
in the stop frequency models and help transportation agencies and policy makers
make better inferences from such models.