Collisions involving trucks are more likely to be fatal compared to those involving only passenger vehicles. These fatal collisions also account for a higher number of deaths on the road per unit distance. To combat these safety concerns, hours-of-service (HOS) laws exist with the intent of reducing fatalities where fatigue is a contributing factor. Electronic logging device (ELD) mandates are being implemented in North America to automatically track HOS and enforce compliance. Expected ELD regulations in Canada create a greater urgency for adequate truck parking when drivers approach their HOS limit.
An examination of existing literature suggests that the truck parking problem requires additional investigation to adequately compare parking supply and demand. While recent studies have identified a lack of truck parking throughout North America, one common shortfall is that the research scope is predominantly limited to public rest areas. Despite this assumption, there is evidence that truck drivers will often utilize other types of parking locations.
This presentation will discuss the development of a new truck parking classification scheme. Several important attributes of truck parking locations have been identified through an extensive literature review including ownership, legality, and accessibility. The attributes were introduced into a comprehensive classification scheme using a tree-based stratification.
The model approach discussed here is intended to be used with variables that can be easily created using geospatial techniques with shared public domain data. For example, individual property parcels are associated with truck parking violation permits to reveal where illegal parking has occurred. This type of data has become increasingly available as technology makes data storage and sharing more affordable and easier to implement.