Identifying tour structures in freight transport by mining of large trip databases

Abstract

Scheduling and Routing in freight transport are usually the end products of an optimization process. However, the results may differ due to the heterogeneity of rules in different transport markets. Since the understanding of these decision rules is important for disaggregate freight modeling, this paper investigates the development of an effective decision tree method for extracting them from an extensive freight transport data. We applied the method to model departure time and type of tours in freight transport of agricultural products. Having these two models together help us understand the whole anatomy of the freight activities for the selected transport segment. The models highlight the characteristics of time-of-day freight activities for this sector and indicate the importance of spatial and temporal characteristics in capturing the distinctions of the type of tours.