In recent years the Clean Trucks Program (CTP) has been enacted at
California’s San Pedro Bay Ports (SPBPs) of Long Beach and Los Angeles
to help address major environmental issues associated with port
operations. “Clean trucks” that utilized public funds to replace older
polluting drayage trucks were required to be fitted with GPS units for
compliance monitoring. Such GPS data collected by the clean drayage
trucks provide a significant opportunity to investigate drayage truck
tour behaviors distinct from general commercial vehicles.
With the background, this dissertation consists of three topics: 1) Tour
Behavior of Clean Drayage Trucks; 2) Tour-Based Entropy Maximization
Model of Drayage Trucks; and 3) Drayage Truck Tour Modeling Using the
Inverse Selective Vehicle Routing Problem (InvSSVRP) in Southern
California. As expected, the first step is to analyze GPS data for
interpreting the drayage trucks’ characteristics. In the second and
third steps, tour-based models are developed using aggregate and
disaggregate level approaches.
An analytical framework is introduce for processing GPS data to both
interpret the trip chaining of the clean drayage trucks, and to prepare
sufficient tour data for clean truck modeling at the SPBPs. After
analyzing data using the toolkit, one of the significant findings from
the clean drayage truck behaviors is that the tours could be classified
under four types, three of which contain repetitive trip patterns in a
tour while the remainder tends to travel in circulative patterns to
avoid visiting the same location multiple times. This provides both the
answer that the current tour-based model cannot address drayage truck
behavior and why tour-based modeling of the drayage trucks is developed
separately.
Two other theoretical advances in the research are the development of
tour-based models using an Entropy Maximization Algorithm and a
Selective Vehicle Routing Problem.
For the aggregate level, the revised tour-based entropy maximization
model upgrades the tour-based entropy maximization model by Wang and
Holguín-Veras (2009) which mostly focuses on other commercial vehicles.
After introducing new constraints regarding sequential visits to nodes,
the clean drayage truck tour behavior can be well addressed.
At the disaggregate level, the SSVRP provides a utility-maximizing
decision-making optimization framework under spatial-temporal
constraints to explain observed truck patterns as activity participation
analogous to household activity patterns. This would be impossible
without the capability of the InvSSVRP to calibrate the objective
coefficients and arrival time constraints such that observed patterns
are optimal values. The nodes are sequence-expanded to allowing multiple
visits at each node and divided into two arrival states (from depot or
not from depot) in the SSVRP provide much more realism in capturing the
drayage truck behavior.
To make better use of the two proposed models, the framework of each
tour-based model estimation and forecasting process is illustrated.
Lastly, several future topics of relevance to improving the tour-based
models are discussed.
