Multi-day Activity-Travel Pattern Sampling Based on Single-Day Data: Application of Battery Electric Vehicles Feasibility Assessment

Although it is important to consider multi-day activities in transportation planning, multi-day activity-travel data are expensive to acquire. In this study, we propose to generate multi-day activity-travel data through sampling from readily available single-day household travel survey data with considerations of day-to-day intrapersonal variability. One of the key observations we make is that the distribution of interpersonal variability in single-day travel activity datasets is similar to the distribution of intrapersonal variability in multi-day datasets. Thus, interpersonal variability observed in cross-sectional single-day data of a large population can be used to generate the day-to-day intrapersonal variability. The proposed sampling method is based on activity-travel pattern type clustering, travel distance and variability distribution to extract such information from single-day data. Validation and stability tests of the proposed sampling methods are presented. A case study on the battery electric vehicle (BEV) feasibility considering the state-of-charge level is also conducted based on the proposed methods, and results show that our sampling method combined with single-day based trivial method provides better estimation of the population wide BEV feasibility than using cross-sectional data only.