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Under transportation network supply and demand uncertainty, travel time reliability emerges from nonlinear interactions among numerous travelers who are driven by self-interest, learn and adapt in changing situations, and therefore can be modeled as an emergent network property. This paper proposes a novel theoretical framework for the study of complexity regarding travel time reliability on the basis of empirically-derived individual decision rules, and develops an agent-based evolutionary model for travel time reliability analysis. Findings show that actual route choice behaviors related to network reliability are often non-optimal, and that these behaviors themselves are important determinants of travel time reliability under network supply and demand uncertainty. While many travelers search for alternative routes under uncertainty, few travelers actually change routes. Route choice rules employed by travelers can successfully improve travel time reliability in most tested uncertainty scenarios, but they do not necessarily reduce absolute travel time. While reducing demand uncertainty improves travel time reliability, it is not always the case when network supply uncertainty is reduced. The proposed model can be applied to improve transportation operations and planning under uncertainty and to analyze the impact of traveler information systems on individual travel behavior and network performance. It can also serve as an empirically-estimated router for microscopic traffic simulators or act as a behavioral assignment algorithm for activity-based travel demand models.
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