Utilization and Cost Estimation Models for Highway Fleet Equipment. Issue 12 (December 2021)

• Record Type: Journal Article
• Authors: Tajalli, Mehrdad; Mirheli, Amir; Hajbabaie, Ali; Hajibabai, Leila
• Is Part Of: Transportation research record. Volume 2675:Issue 12(2021)
• Page Start: 1172
• Page End: 1186
• Publication Date: 2021-12
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.1177/03611981211032215
• Languages: English
• ISSNs: 0361-1981

Abstract

Highway agencies need to manage the utilization of their highway equipment assets to reduce fleet management costs, balance equipment use, and provide the required services. Predictive equipment utilization and operational cost models are required for optimal management; however, there are no widely accepted models for this purpose. Although the utilization data is collected by state DOTs, the literature does not show any specific statistical model to predict equipment utilization as a function of contributing factors such as asset age, fleet size, costs, and demand for service. This study will bridge this gap and develop a predictive model to estimate the utilization of fleet equipment. The main objective of this paper is to develop a set of predictive models to estimate the annual utilization of seven non-stationary highway equipment types based on several explanatory variables including their annual fuel cost, downtime hours, age, and weight. Furthermore, another set of models are fit to predict the annual operational cost for these equipment types based on the most important contributing factors. The prediction models are developed after a nationwide data collection. Several years of collected data from seven states are processed and used for model development. This research has identified annual mileage as an appropriate and widely used utilization metric. Various model structures to predict annual mileage are considered. The logarithmic function of annual mileage has provided the most appropriate structure. The final annual mileage predictive models have R-squared values that are between 0.65 and 0.89, which indicates a good fit for all models. The models are validated by performing several statistical tests and they have satisfied all required assumptions of regression analysis. The result of modeling and statistical analysis showed that the proposed models accurately estimated the utilization and operational cost for highway equipment assets.