Traveler information delivered through variable message signs and other mobile devices has been proved beneficial for traffic network performance. However, travelers' en-route responses to real-time traffic conditions without such information are not well-understood and essential for integrated corridor management and strategic investment in Intelligent Transportation Systems (ITS) targeting congestion mitigation. In order to evaluate traveler's diversion and its impact, many studies have tried various approaches, such as survey study and traffic simulations. This paper describes a threshold-based method to replicate different scenarios using loop detector data on freeways and arterials. Unlike the traffic simulation approaches, the threshold-based method can quantitatively capture en-route travelers' real-time decision on diversion and the diversion's impact on the alternative route without the need for theoretical assumptions. By monitoring the freeway congestion level, the proposed method extracts three types of scenarios, normal congestion, worse-than-expect, and abrupt-traffic-change condition to investigate travelers' decisions on diversion. This method was applied to the City of Bellevue in WA, as an example and can be applicable to other cities.