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The objective of this study is to develop the Freight Trip Generation (FTG) models concerning establishment classification process for estimating freight trip activity. Establishment Based Freight Survey (EBFS) was performed to collect various supply chain variables involved in the urban freight system. Extensive data of 647 samples were collected from Tiruchirappalli city, India. Economic activity analysis shows that pure receiver and intermediate establishments do not follow the uniform freight trip activity among the industrial segments. Hence, a suitable methodology for the establishment classification process is developed using traditional and machine learning (ML) techniques to classify the establishment into an intermediate and pure receiver before estimating the freight trip activity. Supply chain variables like employment, FTA, industrial segment, mode of the commercial vehicle, type of delivery, number of suppliers, and gross floor area play a crucial role in the establishment classification process. Then, employment-based FTG models are developed with the classified data using linear and non-linear functional forms for each industrial segment. Finally, it is observed that FTG models result enhanced, when the establishment classification process is performed as an initial stage, with lower error and accurate estimation of urban freight trip activity. |
