Predicting hurricane evacuation behavior synthesizing data from travel surveys and social media

Evacuation behavior models estimated using post-disaster surveys are not adequate to predict real-time dynamic population response as a hurricane unfolds. With the emergence of ubiquitous technology and devices in recent times, social media data with its higher spatio-temporal coverage has become a potential alternative for understanding evacuation behaviour during hurricanes. However, these data are often associated with selection bias and population representativeness issues. To that extent, the current study proposes a novel data fusion algorithm to combine heterogeneous data sources from transportation systems and social media, in a unified framework to understand and predict real-time population response during hurricanes. Specifically, Twitter data of 2300 users are collected for evacuation response during Hurricane Irma and augmented behaviourally (probabilistically) with a representative National Household Travel Survey (NHTS) data, thus creating a hybrid dataset to improve the representativeness as well as provide a rich set of explanatory variables for understanding the evacuation behavior. The fusion process is conducted using a probabilistic matching method based on a set of common attributes across NHTS and Twitter. The fused dataset is employed to estimate the evacuation model and a comparison exercise is conducted to evaluate the performance of the model via fusion. The model fitness measures clearly demonstrate the improvement in data fit for the evacuation model through the proposed fusion algorithm. Further, we conduct a prediction assessment to illustrate the applicability of the proposed fusion technique and the results clearly highlight the improvement in the evacuation prediction rate achieved through the fused models. The proposed data-driven methods will enhance our ability to predict time-dependent evacuation demand for better hurricane response operations such as targeted warning dissemination and improved evacuation traffic management, allowing emergency plans to be more adaptive.