Modeling the impact of traffic management strategies on households' stated evacuation decisions. (October 2022)

• Record Type: Journal Article
• Authors: Bian, Ruijie; Murray-Tuite, Pamela; Edara, Praveen; Triantis, Konstantinos
• Is Part Of: Progress in disaster science. Volume 15(2022)
• Publication Date: 2022-10
• Subjects: Hurricane evacuation -- Traffic management -- Stated evacuation decisions -- Contraflow; Disasters -- Periodicals; Disaster relief -- Planning -- Periodicals; Emergency management -- Periodicals; 363.3405
• DOI: 10.1016/j.pdisas.2022.100246
• Languages: English
• ISSNs: 2590-0617

Abstract

Abstract: Evacuation traffic management has been implemented in large-scale disaster evacuations (such as hurricanes) to facilitate traffic flow and reduce travel delay. The outcomes of these strategies were quantified via traffic simulation but were assumed to have no/limited impacts on households' evacuation-related decisions. This study analyzed and modeled the impact of these strategies on five evacuation related household choices (evacuate/stay, departure time, route, vehicle, and destination) separately based on 415 responses collected from a stated preference survey. The survey was conducted in 2017 in coastal areas near Hampton Roads. Traffic management is likely to motivate some (32%) households to evacuate instead of sheltering in place. In addition, not all households take the interstates with traffic management even though route choice is the most likely to be affected by traffic management. Households need more information for their departure time and destination choices in response to traffic management since they are more likely to feel uncertain of the impact of the strategies on these decisions. Such uncertainty in departure time and destination choice is especially true for those who evacuate late and for those who choose accommodations other than the home of friends/relatives. Emergency management agencies should also be aware that some households may intentionally depart before traffic management starts. Among the five choices, vehicle use is the choice that is least likely to be affected. All the above-mentioned findings potentially affect parameter specifications in evacuation traffic simulation studies. This study then used multinomial logit models to estimate the impacts of traffic management on each of the five evacuation related choices. The model estimation results can help improve evacuation demand predictions and guide evacuation information dissemination. Highlights: Evacuation traffic management is likely to motivate some households to evacuate instead of sheltering in place. Evacuation route choice is the most likely to be affected by traffic management, while vehicle use is the least likely one. Households need more information in assisting their departure time and destination choices in response to traffic management. Some households may intentionally depart before traffic management starts. This study quantified the impacts of traffic management on multiple evacuation decisions, which was overlooked in the past.