Speaker
Description
Two-stage trucking systems in New Zealand forest harvesting operations have become increasingly common, with their adoption rising from 8% to 24% since 2016. Despite this growth, productivity modelling research remains largely reliant on elemental time studies, which typically generate small datasets covering only several days of work. This study presents an automated, data-driven framework for constructing a digital twin of forest road networks and applying it to two stage trucking operations to model truck productivity over extended periods.
Controller Area Network bus and GNSS position data were collected from a two-stage truck operating in the Hawke’s Bay region of New Zealand over a 230-day period across two harvesting sites. Latitude, longitude, altitude, and truck pitch data were filtered, resampled, and projected to a metric coordinate system. A raster-based skeletonisation approach was applied to buffered GNSS traces to reconstruct road centreline geometry. The extracted network was segmented into road sections and attributed with geometric properties including gradient, curvature, and length.
The segmented road network enabled each GNSS point to be mapped to a corresponding segment, allowing precise identification of truck position within the network. A segment-based state logic integrating road geometry and CAN data was developed to classify operational states (loading, driving loaded, unloading, driving unloaded, and idle). This allowed the capture of individual cycles without having to manual delineate the data depending on which landings the truck loaded or unloaded from. Segment-level metrics—including speed profiles, tonnage carted, and cycle times—were derived and linked to network topology to model productivity.
Results demonstrate that the digital twin road network and segment-based state logic enables productivity modelling without reliance on pre-existing GIS datasets or manual elemental time studies. The proposed method supports automated cycle detection, road topology performance benchmarking, and scenario testing for two-stage transport planning optimisation. The method could also be integrated into a live dashboard for real-time analytics and tracking of trucks during their operations.
| Keywords | Two-staging; Roading; Productivity; Digital-Twin |
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