Highlights Use of GPS data in estimating truck activities, commodities carried and freight analytics. Maximum likelihood estimation approach is adopted for calibration. To determine the activity patterns, commodity trip chains and load/unload status. Outperforms the cut-off and SVM methods. Empirical example from Thailand with multiple commodities.
Abstract To optimally plan/design freight-related infrastructures, it is crucial to understand the activities of freight-related traffic. This paper proposes a statistical approach to estimate truck activities and freight analytics from Global Positioning System (GPS) data of trucks. Commodities carried are also determined by the locations and types of truck stops. With the estimated activities and commodities carried, the characteristics of trip chains for different commodities are then determined and analysed. An empirical example from Thailand is adopted to illustrate the proposed approaches in estimating activities, activity patterns, commodity trip chains and status of trips legs from the collected truck GPS data.
Statistical estimation of freight activity analytics from Global Positioning System data of trucks
2020-05-23
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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