The identification of freight pick-ups and deliveries, referred to as “freight activity” in this paper, is crucial to characterizing freight operations and assessing the performance of freight transportation systems. However, identifying freight activity stops from global positioning system (GPS) data is challenging, particularly in urban freight where congested traffic is common. This paper presents a mechanistic—because it is based on the physics of driving patterns—procedure to identify freight activity stops from raw GPS data. The procedure was implemented to identify stops in three distinct case studies that present a wide range of traffic conditions: Barranquilla, Colombia; Dhaka, Bangladesh; and New York City, United States. The results show that the procedure achieves an average accuracy of above 98.6% when identifying freight activity stops. The results of the proposed procedure were compared with results from support vector machines, random forest, and k nearest neighbors. The mechanistic procedure outperformed these methods in correctly classifying freight activity using second-by-second GPS data.
This paper summarizes the research conducted by the authors concerning the development of an analytical freight demand model that explicitly considers freight pickup and delivery flows by industry sector, the multiclass tour flow model (MC-TFM). The mathematical properties of the resultant model are investigated, including the convexity of the optimization model, the interconnection between the MC-TFM and the family of gravity models, and the MC-TFM's elasticities. The MC-TFM is then integrated into a multiclass freight tour synthesis model (MC-FTS) that is capable of inferring freight tour demand on the basis of secondary data such as traffic counts and estimates of freight trip generation by industry sector. The ability of the resulting MC-FTS is tested by means of numerical experiments involving test cases.
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