Incorporating activity-travel time uncertainty and stochastic space-time prisms in multistate supernetworks for activity-travel scheduling

Multistate supernetwork approach has been advanced recently to study multimodal, multi-activity travel behavior. The approach allows simultaneously modeling multiple choice facets pertaining to activity-travel scheduling behavior, subject to space-time constraints, in the context of full daily activity-travel patterns. In that sense, multistate supernetworks offer an alternative to constraints-based time-geographic activity-based models. To date, most research on time-geographic models and supernetworks alike has represented time and space in a deterministic fashion. To enhance the validity and realism of the scheduling process and the underlying space-time decisions, this paper pioneers incorporating time uncertainty in multistate supernetworks for activity-travel scheduling. Solutions based on the concept of the alpha-shortest path are proposed to find the reliable activity-travel pattern with alpha confidence level. An algorithm combining label correcting and Monte-Carlo integration is proposed to finding the alpha-shortest paths in the presence of time window constraints. An example of a typical daily activity program is executed to demonstrate the applicability of the proposed extension.