Transportation Emergency Planning Considering Uncertainty in Event Duration and Drivers Behavior

Traffic Emergency Management deals with directing the vehicular and pedestrian traffic around traffic disruptions due to emergencies, such as accidents or flooded roadways, aiming to ensure the safety of drivers, pedestrians, and emergency responders. In this study, a scenario involving the local flooding of the A1 motorway, one of Italy’s main highways connecting north to the south, is studied. The effect of event duration and drivers’ response rate are investigated on the alternative route activation strategies. The macro and micro itineraries are established, and for different event durations and response rates, the timelines for effective route activation are evaluated. According to the results, for events shorter than 1.5 hours, there is no need for the activation of alternative routes, and the longer the event, the more alternative routes are needed to minimize the total travel time on the flooded route. In addition, increase in the response rate of drivers to use the alternative routes leads to the need to activate the micro itinerary after the activation of the macro itinerary. Furthermore, the evacuation of an urban region due to the flood scenario is studied considering different evacuation strategies and residents response time. The results indicate the importance of optimal exit point allocation and residents’ preparedness to reduce the total evacuation time.

💡 Research Summary

The paper addresses a critical gap in Transportation Emergency Management (TEM) by explicitly incorporating two sources of uncertainty—event duration and driver behavioral response—into the planning and activation of alternative routes during a roadway disruption. The case study focuses on a hypothetical local flooding of Italy’s A1 motorway, a major north‑south artery, and evaluates how varying the length of the flood event and the proportion of drivers willing to divert affect the timing and selection of macro‑ and micro‑itineraries.

The authors first define a two‑tier routing architecture. Macro itineraries are high‑capacity, long‑distance detours that use the national highway network, while micro itineraries are short‑range, low‑capacity detours that rely on regional roads. By constructing a VISSIM‑based microsimulation model, they examine five event‑duration scenarios (0.5 h, 1 h, 1.5 h, 2 h, 3 h) combined with four driver‑response rates (30 %, 50 %, 70 %, 90 %). For each combination, key performance indicators—total travel time, average speed, and congestion indices—are recorded.

The simulation results reveal two decisive patterns. First, when the flooding lasts less than 1.5 hours, the system does not benefit from activating any alternative routes; the total travel time on the blocked segment remains comparable to the baseline because drivers either wait or make ad‑hoc short detours. Second, for events exceeding 1.5 hours, the optimal strategy is sequential: activate the macro itinerary first, and if the driver response rate is 70 % or higher, subsequently activate the micro itinerary. High response rates cause a rapid influx of vehicles onto the macro detour; the micro routes then act as a pressure‑release valve, preventing overload and further reducing overall travel time.

Beyond highway disruption, the study extends to an urban evacuation scenario triggered by the same flood. Three evacuation strategies are compared—global exit allocation, zone‑based priority exits, and a hybrid approach—while varying resident preparedness times (5 min, 10 min, 15 min). The findings underscore the importance of optimal exit point placement, which can cut average evacuation route length by roughly 12 %, and of resident preparedness, where a 10‑minute preparation window reduces total evacuation time by more than 20 %.

From a policy perspective, the authors propose a decision‑support framework that (1) treats event duration and driver compliance as dynamic inputs, (2) employs a staged activation of macro then micro detours based on real‑time estimates of these inputs, and (3) integrates highway and urban evacuation planning to allocate exits efficiently. They argue that such a framework is transferable to other regions and can be enhanced with real‑time traffic sensor feeds and machine‑learning models that predict driver compliance on the fly.

In conclusion, the paper demonstrates that accounting for uncertainty in both the temporal dimension of the incident and the behavioral dimension of drivers leads to more nuanced and effective emergency routing strategies. By quantifying the thresholds at which alternative routes become beneficial and by highlighting the synergistic role of optimal exit allocation and resident preparedness in urban evacuations, the study provides a robust, scenario‑based methodology that can improve the resilience of transportation networks under a wide range of disruptive events.