Uncovering heterogeneous decision-making in truck tour organization: A shipment-based behavioral modeling approach

The efficient movement of freight trucks is vital for modern logistics, where the organization of truck tours is central to optimizing supply chain operations and minimizing transportation costs. The truck tour patterns embody a complex interplay of diverse decision-making factors, reflecting the effectiveness of transport and logistics planning initiatives. Despite previous research efforts, an in-depth analysis of understanding how various decision-making factors, such as time of the day and product type, interact and their heterogeneous effects on shaping truck tour patterns is still lacking. This limitation hinders the development of more effective and sustainable strategies for freight transportation. In this paper, by leveraging large-scale carrier and shipment dataset in the Netherlands, we capture and analyze the heterogeneity of over 1000 diverse tour patterns of freight trucks in terms of temporal, spatial, and shipment requirements characteristics. We further introduce a novel modeling approach using random parameters logit models with heterogeneity in means and variances to uncover the heterogeneous effects of various factors on the choice of truck tour patterns. The model results highlight the multifaceted nature of truck travel behaviors, offering practical implications for logistics operations, policy development and freight planning.