Mass evacuation microsimulation modeling considering traffic disruptions

This study presents a framework of traffic evacuation microsimulation modeling that accounts for uncertain network disruptions endogenous to traffic operations. While evacuation modeling considers external stresses such as flooding-related network disruptions, the risks inherent to the transport operations, particularly vehicle collisions may also cause disruptions to evacuation traffic flows. This study adopts a combined Bayes theory and Monte Carlo simulation approach to identify collision hotspots and their occurrence over different times of an evacuation day. A traffic evacuation microsimulation model is developed which explicitly incorporates vehicle collision-related disruptions at the hotspots identified by this probabilistic model. The proposed probabilistic approach identifies 128 candidate collision locations within the study area. The probabilities of candidate locations to anticipate a vehicle collision range between 0.21 and 7.0%. Based on the probabilities, the Monte Carlo simulation approach identifies five hotspots for traffic microsimulation modeling of vehicle collisions during the evacuation. The results from the traffic simulation reveal that due to concurrent collision occurrence, evacuation times vary within 23–31 h depending on the time required to remove traffic disruptions from the network. On the other hand, the concurrent collision occurrence at the hotspots increases the complete evacuation time by almost 11 h if the disruption is not removed from the network, an increase of 50%, compared to an evacuation scenario without disruptions. The analysis of simulated queue length reveals that the hotspots’ traffic queues range from 0.28 to 2.06 km depending on their locations in the study area. The study asserts that an evacuation model without the consideration of the network disruptions due to endogenous risks may underestimate the traffic impacts and network clearance time for an evacuation. These results will provide emergency professionals with insights into managing emergency traffic operation subjected to uncertainties.