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 the 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 descriptions of activity characteristics, in terms of probabilities and ranges, are collected using an interactive computer assisted personal interview. Study vehicles were equipped with digital cameras and GPS and data acquisition equipment to validate the survey responses.
Pilot study survey responses are used as inputs for the mPHASE model to illustrate the feasibility of generating complex, realistic household schedules. The generated schedules exhibit many of the characteristics of complex household travel, including distinct patterns of weekday and weekend travel, occasional non-travel and heavy-travel days, and schedule coordination among household members.
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.