Abstract

Freight truck movements exhibit extensive trip interaction between shippers, receivers, and carriers of goods, logistics constraints, and use of advanced information technology. Such characteristics cannot be accurately captured by the traditional four-step approach which has been widely used in state and regional government agencies under the assumption that trips are independent. In this dissertation, it is possible to develop tour-based models using with two main approaches, in order to properly capture the trip-chaining behavior of clean drayage truck movements at the San Pedro Bay Ports (SPBP): 1) disaggregate level tour-based model using the Sequential Selective Vehicle Routing Problem (SSVRP) providing a utility-maximizing decision-making optimization framework and 2) aggregate level tour-based model using Entropy Maximization Algorithm. Before discussing the two different tour-based models, the first step is to analyze GPS data for interpreting the drayage trucks’ characteristics and providing model inputs. The brief background of GPS data is as follows: In recent years the Clean Trucks Program (CTP) has been implemented 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” (meeting 2007 model year emission standards) that utilized public funds to replace older polluting drayage trucks were required to be fitted with GPS units for compliance monitoring. In late 2010, 94% of cargo moves at the SPBPs were reportedly made by clean trucks. The study reported in this dissertation is based on a year of such GPS data for a sample that in 2010 comprised 545 clean drayage trucks. With the background, an analytical framework is introduced for processing GPS data to both interpret the trip chaining (or tour behavior) of the clean drayage trucks, and to prepare sufficient tour data for clean truck modeling at the SPBPs. After analyzing the data using the toolkit, one of the significant findings on the clean drayage truck operations is that the tours could be classified under four types, three of which contain repetitive trip patterns in a tour while the fourth tends to show travel in circulatory patterns. This analysis amply demonstrated why current models cannot address drayage truck behavior and why tour-based modeling of the drayage trucks is needs to be developed with sufficient care towards the type of routes the trucks operation. 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 (SVRP). 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 general commercial vehicles. After introducing new constraints regarding sequential stops to Traffic Analysis Cells (TACs), the clean drayage truck tour behavior can be addressed with complex tour patterns. The revised tour-based entropy maximization model with a Primal Dual Convex Optimization (PDCO) algorithm is seen to converge very quickly. At the disaggregate level, the SSVRP model 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 ability of Inverse Sequential Selective Vehicle Routing Problem (InvSSVRP) to calibrate the objective coefficients and arrival time constraints such that observed patterns are optimal values. The nodes (or TACs) are sequence-expanded to allow multiple stops at each node and divided into two arrival states (from depot or not from depot) in SSVRP, which provides for 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.