Citation

Imani AF, Ghafghazi G, Eluru N, Pinjari AR. Transp. Lett. 2014; 6(1): 1-13.

Copyright

(Copyright © 2014, Maney Publishing, Publisher Informa - Taylor and Francis Group)

DOI

10.1179/1942787513Y.0000000001

PMID

unavailable

Abstract

There is increasing recognition that the choice of vehicle type and usage decisions form a crucial element in understanding transport-related greenhouse gas (GHG) emissions. The traditional approach to examining these dimensions is focused on vehicle fleet purchase decisions (number and type) and annual usage. These studies aggregate the overall vehicle usage as an annual decision, whereas in reality households face the choice of vehicle type for out-of-home activity participation on a daily basis. With the recent emphasis on activity-travel models, there is growing recognition that daily vehicle usage choice is affected by activity purpose choice and the travel group for the activity. This study develops a representative framework to examine daily vehicle type and usage decisions while incorporating the influence of activity type and accompaniment type choices. In the authors' approach, travel mode choice by considering the various travel mode alternatives (transit, walking/bicycling) and replacing the private vehicle alternative with various vehicle type options that are available to individuals is included. Thus the three choice dimensions, travel mode that implicitly considers vehicle type, activity purpose, and accompaniment type, are jointly analyzed by generating combination alternatives. The mixed multiple-discrete continuous extreme value (MMDCEV) framework provides an elegant modeling approach to study these choices. In the authors' study, the discrete component is formed as a combination of travel mode, activity purpose, and accompaniment type while mileage for each combination provides the continuous component. The proposed model approach is empirically tested for workers and non-workers in New York region using the National Household Travel Survey (NHTS) 2009. The model results provide insights on how socio-demographics, location, and temporal attributes influence daily vehicle type and usage, activity type, and accompaniment type decisions.

Language: en