A data-driven systematic approach for identifying and classifying long-haul truck parking locations

Abstract

Heavy Commercial Vehicle (HCV) parking is an important issue for commercial drivers due to concerns regarding parking supply, hours-of-service (HOS) laws, and electronic logging device (ELD) mandates. HOS laws are intended to reduce driver fatigue while ELD mandates ensure strict HOS compliance. Unfortunately, inadequate HCV parking availability may force drivers to make an undesirable choice: park illegally in a potentially unsafe location or drive longer than the law permits and face fines for HOS violations. These outcomes occur frequently throughout Canada and the United States, leading to safety and financial concerns for government jurisdictions and the broader public.Despite the importance of this topic, we lacked an exhaustive classification scheme for the systematic analysis of truck parking in a given area. Many studies have considered only public rest areas and likely underestimated the actual amount of viable HCV parking supply. Without accurate data on HCV parking locations and deficiencies, jurisdictions are unable to make optimal decisions.This paper developed a set of categories that can be used to classify locations used by HCV drivers for rest breaks on long journeys. The categories are based on a thorough literature review and detailed GPS data obtained for our study area in Peel Region, Ontario, Canada. The categories are exhaustive in that they include all HCV parking locations, both legal and unauthorized. The data-driven systematic approach contributes towards a better understanding of HCV parking supply for long-haul truck trips.