Spatial and Temporal Characteristics of Freight Tours: A Data-Driven Exploratory Analysis
This work addresses freight transport management for practitioners by providing insights into congestion sensitivity, though it is incremental as it builds on existing data-driven methods.
The paper tackled the problem of understanding freight tour scheduling and routing patterns using digital activity data, developing a discrete-continuous decision tree approach and applying it to Netherlands data to reveal that carriers adjust tours, departure times, and stops in response to congestion.
This paper presents a modeling approach to infer scheduling and routing patterns from digital freight transport activity data for different freight markets. We provide a complete modeling framework including a new discrete-continuous decision tree approach for extracting rules from the freight transport data. We apply these models to collected tour data for the Netherlands to understand departure time patterns and tour strategies, also allowing us to evaluate the effectiveness of the proposed algorithm. We find that spatial and temporal characteristics are important to capture the types of tours and time-of-day patterns of freight activities. Also, the empirical evidence indicates that carriers in most of the transport markets are sensitive to the level of congestion. Many of them adjust the type of tour, departure time, and the number of stops per tour when facing a congested zone. The results can be used by practitioners to get more grip on transport markets and develop freight and traffic management measures.