  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.
