The optimal time to evacuate: A behavioral dynamic model on Louisiana resident data

• Published in: Transportation research. Part B: methodological 2017-12, Vol.106, p.447-463
• Main Authors: Urena Serulle, Nayel, Cirillo, Cinzia
• Format: Article
• Language: English
• Subjects: Dynamic model; Emergency; Evacuation; Expected utility; Optimal time
• ISSN: 0191-2615; 1879-2367
• DOI: 10.1016/j.trb.2017.06.004

•This research develops a dynamic discrete choice approach to model the optimal time to evacuate under an emergency situation.•The proposed framework accounts for dynamic variables and for the expected utilities over future time periods.•These models have been (scarcely) used in the past within the field of transportation, and this research study is the first to do so from an evacuation perspective.•Additionally, this research proposes two methods for incorporating respondent's expectation into the modeling framework: a) Perfect Knowledge, where expectation and future conditions are a perfect match; and b) Stochastic Growth, where expectation is modeled through a mathematical approach that uses past observations to predict future conditions.•Results indicate that the proposed “dynamic+expectations” approach yield more accurate results than simpler models and it is able to incorporate socioeconomic data, allowing for more robust policy evaluations. Understanding what affects the decision process leading to evacuation of a population at risk from the threat of a disaster is of upmost importance to successfully implement emergency planning policies. Literature on this is broad; however, the vast majority of behavioral models is limited to conventional structures, such as aggregate participation rate models or disaggregate multinomial logit models. This research introduces a dynamic discrete choice model that takes into account the threat's characteristics and the population's expectation of them. The proposed framework is estimated using Stated Preference (SP) evacuation data collected from Louisiana residents. The results indicate that the proposed dynamic discrete choice model outperforms sequential logit, excels in incorporating demographic information of respondents, a key input in policy evaluation, and yields significantly more accurate predictions of the decision and timing to evacuate.