Expanding the Uses of Truck GPS Data in Freight Modeling and Planning Activities

Camargo, Pedro H. C.; Hong, Shuyao; Livshits, Vladimir

doi:10.3141/2646-08

Cited by 26 publications

(45 citation statements)

References 10 publications

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“…Like AIS data, it is necessary to employ geospatial fusion methods to map GPS traces to a defined transportation network. Methods for map matching and route identification for truck GPS data have been carried out in several prior studies (Camargo et al 2017;Hashemi and Karimi 2014;Ciscal-Terry et al 2016).…”

Section: Truck Gps Datamentioning

confidence: 99%

“…The reader is directed to Akter et al (2018) and Asborno and Hernandez (2020) for further details on the development of the map-matching algorithm for GPS and AIS data, respectively. From the several map-matching algorithms available (Camargo et al 2017; Hashemi and Karimi 2014), Camargo et al is used because it has the advantage of wide applicability to multimodal data sets. 1 Briefly, the algorithm first identifies stops made by each vehicle.…”

Section: Data Quality Controlmentioning

confidence: 99%

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GIS-based identification and visualization of multimodal freight transportation catchment areas

2021

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To estimate impacts, support cost–benefit analyses, and enable project prioritization, it is necessary to identify the area of influence of a transportation infrastructure project. For freight related projects, like ports, state-of-the-practice methods to estimate such areas ignore complex interactions among multimodal supply chains and can be improved by examining the multimodal trips made to and from the facility. While travel demand models estimate multimodal trips, they may not contain robust depictions of water and rail, and do not provide direct observation. Project-specific data including local traffic counts and surveys can be expensive and subjective. This work develops a systematic, objective methodology to identify multimodal “freight-shed” (or “catchment” areas) for a facility from vehicle tracking data and demonstrates application with a case study involving diverse freight port terminals. Observed truck Global Positioning System and maritime Automatic Identification System data are subjected to robust pre-processing algorithms to handle noise, cluster stops, assign data points to the network (map-matching), and address spatial and temporal conflation. The method is applied to 43 port terminals on the Arkansas River to estimate vehicle miles and hours travelled, origin, destination, and pass-through zones, and areas of modal overlap within the catchment areas. Case studies show that the state-of-the-practice 100-mile diameter influence areas include between 15 and 34% of the multimodal freight-shed areas mined from vehicle tracking data, demonstrating that adoption of an arbitrary radial area for different ports would lead to inaccurate estimates of project benefits.

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Section: Truck Gps Datamentioning

confidence: 99%

Section: Data Quality Controlmentioning

confidence: 99%

GIS-based identification and visualization of multimodal freight transportation catchment areas

2021

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“…The GPS data used in the study represented a sample of around 10% of the truck population for the state of Arkansas. The data was processed according to the heuristics developed by Camargo et al and Akter et al to produce time of day patterns of Parked Trucks, Facility Usage Ratio , and Average Parking Duration ( 16 , 17 ).…”

Section: Resultsmentioning

confidence: 99%

“…Pre-processing is usually required to determine stop locations, stop arrival and departure times, and stop duration. Algorithms to identify stop locations samples of GPS truck records have been developed by Camargo et al and adapted for statewide applications by Akter et al ( 16 , 17 ). These algorithms are summarized here, and the reader is directed to the mentioned studies for further details.…”

Section: Methodsmentioning

confidence: 99%

Truck Parking Usage Patterns by Facility Amenity Availability

Mahmud

Akter²,

Hernandez

2020

Transportation Research Record

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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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“…The GPS data used in this study require pre-processing to determine stop locations corresponding to parking activity. Using methods for pre-processing GPS data developed in Camargo et al ( 19 ) and validated for Arkansas in Akter and Hernandez ( 20 ), stop locations, stop arrival and departure times, and stop duration were determined. A brief overview of the approach to determine stops is described here and the reader is directed other studies ( 19 , 20 ) for further details.…”

Section: Methodsmentioning

confidence: 99%

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

Corro

Akter

Hernandez

2019

Transportation Research Record: Journal of the Transportation R

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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.

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Expanding the Uses of Truck GPS Data in Freight Modeling and Planning Activities

Cited by 26 publications

References 10 publications

GIS-based identification and visualization of multimodal freight transportation catchment areas

GIS-based identification and visualization of multimodal freight transportation catchment areas

Truck Parking Usage Patterns by Facility Amenity Availability

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

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