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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 is currently ranked by the American Transportation Research Institute (ATRI) as the fifth most critical issue for the trucking industry and, more importantly, as the second most important issue for truck drivers. Part of the problem can be attributed to inadequate supply of parking and federal hours of service (HOS) regulations. Recent truck driver stated-preference surveys reveal that amenities including restrooms, fuel, and showers are important considerations while seeking available parking. A link between parking usage patterns and facility amenity bundles can guide transportation agency investments in relation to the design and type of parking facilities with high potential to mitigate overcrowding issues, and can be used for predictive modeling in real-time parking availability algorithms and information systems. This paper used historical, anonymous truck global positioning system (GPS) data to determine the extent to which hourly parking usage patterns, that is, average parking duration, percentage of parked trucks, and parking usage ratio, vary by amenity availability. A K-means clustering model grouped parking facilities by time of day parking usage patterns, season, and geographic region. Each cluster, represented by parking usage patterns, was then tied to unique amenity bundles. Three usage pattern clusters were identified: overnight usage with long parking durations ( Cluster 1), off-peak usage with long parking durations, ( Cluster 2), and off-peak usage with short parking durations ( Cluster 3). In general, overnight and longer duration parking was associated with facilities that had fewer amenities, notably without showers, while peak and off-peak hours and shorter duration parking was associated with full-service facilities.

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