Freight vehicle origin-destination (O-D) matrices play a key role within the assessment procedures of city logistics scenarios, and simulation of delivery tours allows such O-D flows to be obtained. Therefore, the paper revisits a modeling framework for simulating delivery tours using an aggregate approach proposed by the authors and presents the preliminary results of its implementation to a real test case using global position system (GPS) traces for extracting vehicle tours/trip chains which are available on a large scale for such uses as fleet management or vehicle insurance. The modeling framework was calibrated using a dataset of light goods vehicles operating in the Veneto region (Italy) for which automated vehicle monitoring (AVM) data were available. The results show that such an approach enables satisfactory results to be obtained in terms of freight distribution O-D matrices.

Aggregate delivery tour modeling through AVM data: experimental evidence for light goods vehicles

Polimeni A.
2021-01-01

Abstract

Freight vehicle origin-destination (O-D) matrices play a key role within the assessment procedures of city logistics scenarios, and simulation of delivery tours allows such O-D flows to be obtained. Therefore, the paper revisits a modeling framework for simulating delivery tours using an aggregate approach proposed by the authors and presents the preliminary results of its implementation to a real test case using global position system (GPS) traces for extracting vehicle tours/trip chains which are available on a large scale for such uses as fleet management or vehicle insurance. The modeling framework was calibrated using a dataset of light goods vehicles operating in the Veneto region (Italy) for which automated vehicle monitoring (AVM) data were available. The results show that such an approach enables satisfactory results to be obtained in terms of freight distribution O-D matrices.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3244293
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