Research Publications

The lack of multi-day data for household travel and vehicle capability requirements is an impediment to evaluations of energy savings strategies, since 1) travel requirements vary from day-to-day, and 2) energy-saving transportation options often have reduced capability. This work demonstrates a survey methodology and modeling system for evaluating the energy-savings potential of household travel, considering multi-day travel requirements and capability constraints imposed by the available transportation resources. A stochastic scheduling model is introduced – the multi-day Household Activity Schedule Estimator (mPHASE) – which generates synthetic daily schedules based on “fuzzy” descriptions of activity characteristics using a finite-element representation of activity flexibility, coordination among household members, and scheduling conflict resolution. Results of a thirty-household pilot study are presented in which responses to an interactive computer assisted personal interview were used as inputs to the mPHASE model in order to illustrate the feasibility of generating complex, realistic multi-day household schedules. Study vehicles were equipped with digital cameras and GPS data acquisition equipment to validate the model results. The synthetically generated schedules captured an average of 60 percent of household travel distance, and exhibited many of the characteristics of complex household travel, including day-to-day travel variation, and schedule coordination among household members. Future advances in the methodology may improve the model results, such as encouraging more detailed and accurate responses by providing a selection of generated schedules during the interview. Finally, the Constraints-based Transportation Resource Assignment Model (CTRAM) is introduced. Using an enumerative optimization approach, CTRAM determines the energy-minimizing vehicle-to-trip assignment decisions, considering trip schedules, occupancy, and vehicle capability. Designed to accept either actual or synthetic schedules, results of an application of the optimization model to the 2001 and 2009 National Household Travel Survey data show that U.S. households can reduce energy use by 10 percent, on average, by modifying the assignment of existing vehicles to trips. Households in 2009 show a higher tendency to assign vehicles optimally than in 2001, and multi-vehicle households with diverse fleets have greater savings potential, indicating that fleet modification strategies may be effective, particularly under higher energy price conditions.