Exploring the relationship between vehicle type choice and distance traveled: a latent segmentation approach

Abstract

In the context of vehicle usage decisions, there are two important choice dimensions namely, the choice of vehicle from household fleet that will be utilized for trips and second, the distance traveled to pursue the planned activities. There are interrelationships between these two choice dimensions with one dimension potentially influencing the other. The direction of the interrelationship has important implications for transportation planning and policy analyses. In an effort to explore the interrelationships, a latent segmentation-based modeling approach is proposed in this paper. The approach allows for exploring alternative interrelationship structures between choice dimensions in the same modeling framework. The methodology is demonstrated using data from the latest wave of the National Household Travel Survey (NHTS) in the United States. The results show the need for accommodating alternative structures between choice dimensions to accurately describe the vehicle usage decision processes exhibited by individuals.

Keywords:

• Latent segmentation models
• short-term vehicle usage decisions
• vehicle type choice
• distance traveled

Acknowledgements

The authors acknowledge the valuable feedback from four anonymous reviewers on an earlier version of the paper.

Notes

1. The vehicle type variable used in the research was created by consolidating the VEHTYPE (defined as ‘Vehicle type’ in the NHTS 2009). The vehicle type variable consists of four categories namely auto, van, SUV, Truck. As can be seen, the categorization is coarse and no distinction was drawn further for a specific vehicle type based on body type or fuel type. For example, one could have further classified auto based on body type as sub-compact, compact, mid-size, and luxury among others. Similarly, one could have classified auto based on fuel type as motor gas, diesel, natural gas, and electricity. The choice of coarse categorization was in part driven by the sample size requirements for each vehicle type category to obtain plausible model estimation results.

2. A variety of vehicle attributes including vehicle age, fuel type, and mileage were explored.