Comparison of Overnight Truck Parking Counts with GPS-Derived Counts for Truck Parking Facility Utilization Analysis

Abstract

Increased demand for truck parking resulting from hours-of-service regulations and growing truck volumes, coupled with limited supply of parking facilities, is concerning for transportation agencies and industry stakeholders. To monitor truck parking congestion, the Arkansas Department of Transportation (ARDOT) conducts an annual observational survey of truck parking facilities. As a result of survey methodology, it cannot capture patterns of diurnal and seasonal use, arrival times, and duration. Truck Global Positioning System (GPS) data provide an apt alternative for monitoring parking facility utilization. The issue is that most truck GPS datasets represent a sample of the truck population and the representativeness of that sample may differ by application. Currently no method exists to accurately expand a GPS sample to reflect population-level truck parking facility utilization. This paper leverages the ARDOT study to estimate GPS “expansion factors” by parking facility type and defines two expansion factors: (1) the ratio of trucks parked derived from the GPS sample to those observed during the Overnight Study, and (2) the ratio of truck volume derived from the GPS sample to total truck volume measured on the nearest roadway. Varied expansion factors are found for public, private commercial (e.g., restaurant, retail store, etc.), and private truck stop facilities. Comparatively, the expansion factor based on roadway truck volumes was at least twice as high as that derived from the Overnight Study. Considering this, the method to determine expansion factors has significant implications on the estimated magnitudes of parking facility congestion, and thus will have consequences for investment prioritization.

Truck Parking Usage Patterns by Facility Amenity Availability

“…The input feature measuring usage, Facility Usage Ratio , was measured as the ratio of parked trucks counted from the GPS sample to the total capacity of the facility. To estimate total usage of the facility by time of day, sample expansion factors estimated in prior work were applied ( 32 ). The expansion factors were defined by comparing truck GPS data with those gathered from the manual counting of parked trucks during an annual observational Overnight Truck Parking Study conducted by ARDOT in 2016.…”

Where Truck Drivers Stop – Application of Vehicle Tracking Data for the Identification of Rest Locations and Driving Patterns

“…Truck parking studies in North America and Europe usually investigate the use and occupancy rates of rest areas; many of them indicate that the number of designated parking places are insufficient and that it is often difficult for drivers to find a place to rest in compliance with pertinent regulations [8][9][10]. In spite of the relevance of understanding patterns in driving and rest times, along with the use of rest areas, the topic has remained rather underresearched in Europe [11].…”

“…The scientific interest increased in light of the large amounts of digital traffic data generated on itineraries of heavy vehicles. With the spreading of GNSS (Global Navigation Satellite Systems) technology researchers started to incorporate navigation and fleet tracking data into their studies, in combination with the results of the overnight studies [8] and WIM (weight-in-motion) sensors [16]. These studies established an expansion factor to their respective study area, which estimates and attempts to mitigate the deviation coming from the different data collection methods.…”

“…al. (2019) [8], it was expected to observe a higher number of trucks parked during the night with longer average rest periods. Instead, the highest number of trucks parked is associated with the hours around noon (10 a.m. to 3 p.m.); the elongation of rest periods starts already at 4 p.m., and after midnight these periods get shorter again (Figure 4).…”

Impact of Truck Parking Facilities on Commercial and Industrial Land Values: A Spatial Hedonic Model

“…Higher truck volume yields a larger demand for truck parking ( 2 ). However, as suggested in the literature, truck parking usage patterns are not well estimated based on nearby roadway truck volumes alone ( 35 ). Both the state and the city have a long list of overcrowded parking sites, which makes it important to invest in strategic capacity improvement projects ( 36 ).…”