Freight Trip Generation Modeling: A Narrative Review

The challenge this study addresses involves understanding and modeling the complex issues related to freight transportation, which is crucial because of its significant economic effect. Traditionally, freight demand modeling starts with analyzing freight production (FP) and freight attraction (FA) values. However, traditional transportation modeling methods often struggle to accurately capture and predict freight demand because of complex user behaviors and the involvement of multiple stakeholders throughout the supply chain. Previous research has predominantly focused on developing quantitative demand models, leading to varied perspectives in this field. To address these issues, a comprehensive literature review was conducted to compare and synthesize findings from various studies into a coherent narrative. The FP and FA models were critically examined, including their techniques, key variables, data requirements, and evaluation methods. This literature review highlighted current challenges and proposed future research directions. The key findings from the literature reveal significant insights, such as the weak correlation between freight demand and traffic because of the diverse goods and variations in shipment sizes. This underscores the need for separate freight and trip generation models. Therefore, future research should integrate traditional surveys with emerging data sources and combine conventional statistical models with advanced deep learning techniques, leveraging the strengths of both approaches. This narrative review offers valuable context and insights that can complement systematic reviews or meta-analyses on freight modeling, providing a broader understanding that quantitative analyses alone might overlook.