Transportation sector has been a major contributing factor to the overall emissions of most pollutants and thus their impacts on the environment. Among all transportation activities, on-road travel accounts for most part of the Greenhouse gas (GHG) emissions and fuel use. It also has a very un-desirable impact on the transportation network conditions increasing the traffic congestion levels. The main aim of transportation planning agencies is to implement the policy changes that will reduce automobile dependency and increase transit and non-motorized modes usage. However, planning agencies can come up with proactive economic, land-use and transportation policies provided they have a model which is sensitive to all the above mentioned factors to predict the vehicle fleet composition and usage of households. Moreover, the type of vehicle that a household gets (vehicle type choice) and the annual mileage (usage) associated with that vehicle is very closely related to the person in the household who uses that vehicle the most (allocation to primary driver). So, it is no longer possible to view all these decisions separately. Instead, we need to model all these decisions- vehicle type choice, usage, and allocation to primary driver simultaneously at a household level. In this study, we estimate and apply a joint household-level model of the number of vehicles owned by the household, the vehicle type choice of each vehicle, the annual mileage on each vehicle, as well as the individual assigned as the primary driver for each vehicle. A version of the proposed model system currently serves as the engine for a household vehicle composition and evolution simulator, which itself has been embedded within the larger SimAGENT (for Simulator of Activities, Greenhouse emissions, Networks, and Travel) activity-based travel and emissions forecasting system for the Southern California Association of Governments (SCAG) planning region. / text

The trucking industry is vitally important to the economy, providing an essential service by transporting goods between businesses and consumers. The less-than-truckload (LTL) industry is an important segment, serving businesses that ship quantities between 150 lbs and 10,000 lbs. Large LTL carriers use thousands of drivers to move loads between terminals in their network, and each driver may be used for multiple dispatches between rest periods. Driver wages are a major component of transportation costs. Consequently, cost-effective driver management is of crucial importance for the profitability of LTL carriers. This thesis investigates a variety of issues related to driver management. In this thesis, we describe a dynamic driver scheduling scheme developed for a large U.S. LTL carrier. Dynamic driver scheduling is challenging because drivers must abide by a complex set of rules, including government and union regulations, and trucking moves are not pre-scheduled. The technology developed combines greedy search with enumeration of time-feasible driver duties, and is capable of generating cost-effective schedules covering 15,000 20,000 loads in minutes. One of the key tactical questions faced by an LTL carrier is how many drivers to locate at each terminal. Unionized carriers have bid drivers that can only move loads between their domicile and a designated region. The developed allocation technology determines the number of drivers to allocate to each terminal as well as the designated region for bid drivers. Computational experiments based on real-life dispatch data demonstrate the effectiveness of our domiciling methodology, and show that union rules may result in substantially larger driver fleets, in some cases up to 50% larger. Finally, we investigate a fundamental question related to driver management in order to obtain some fundamental insights: determining the minimum number of drivers required to cover a set of loaded moves. The problem is shown to be polynomially solvable without any restrictions on driver schedules. For variants with restrictions, several easily computable lower bounds are derived, integer programming formulations are presented, and fast heuristics are designed and analyzed. A computational study provides insights into the quality of the lower bounds and heuristic solutions.