Modeling of Household Evacuation Decision, Departure Timing, and Number of Evacuating Vehicles from Hurricane Matthew

This dissertation investigates households’ evacuation decision, number of household vehicles used in evacuation, and departure timing from Hurricane Matthew. Regarding the evacuation decision, this dissertation takes a step further by presenting three level evacuation decision models that include full, partial, and no evacuation alternatives rather than the binary evacuate/stay decision. Multinomial (MNL) regression and random parameter MNL techniques were utilized to develop the prediction models. Results showed that some of the variables which affect the evacuate/stay decision have different influences on the three alternatives. The preferred MNL model was tested for random parameters and one random parameter (age of the respondent) was identified for the utility expression pertaining to the no evacuation alternative. For the vehicle choice study, zero truncated Poisson regression was utilized with the survey data. This modeling approach has rarely been applied to the evacuation context and the prediction of the number of household vehicles used is relatively understudied, compared to other evacuation-related decisions. The final preferred model contains three significant variables (marital status, gender, and evacuation timing from 6 am to noon). The final part of this dissertation investigates the factors affecting departure timing choice. Having an accurate estimate of the departure time will allow the prediction of dynamic evacuation demand and developing effective evacuation strategies which will enhance the overall evacuation planning and management. A Cox proportional-hazards model was utilized to model the evacuation departure timing. Four significant variables were identified in the final model, two of them are related to uncertainty. This part of the dissertation also studies evacuees’ stated preference about whether or not they would change their evacuation timing if they relived the hurricane event. In our study, almost 34% of respondents reported that they would change their departure timing if they relived the hurricane event. A binary logit model was utilized in this part and the preferred model contains five significant variables related to past experience, the type of evacuation order received, and the evacuation destination

Exploring Uncertainties in Households’ Hurricane Evacuations

Hurricane Matthew was the most powerful hurricane during the Atlantic Hurricane Season in 2016. It caused tremendous damages to infrastructure and coastal areas of the United States. This thesis uses survey data collected in 2017 from residents in the Jacksonville Metropolitan Area after Hurricane Matthew. Survey questions were designed to capture evacuation-related decisions, information sources usage, socio-economic factors, perceived certainty and intra-familial interactions. The first part of the thesis modeled households’ perceived certainty to identify factors that affect different perceived certainty topics. Certainty topics included were: whether one lives in an evacuation zone, time of hurricane impact, evacuation preparation time needed, when to evacuate, evacuation travel mode, evacuation route, and evacuation destination. The modeling results showed similarities and disparities among perceived certainty topics. Household archetypes were created to offer insights for both decision makers and stakeholders for hurricane emergency management. The second part of this thesis explored the connection between the evacuation decision and perceived certainty using a two-stage modeling concept. Adding contextual factors usually leads to endogeneity bias which means parameters of variables will be overestimated or underestimated. A control function approach was used to account for potential endogeneity bias when linking perceived certainty with the evacuate/stay decision caused by unobserved attributes. The uncorrected base model was found to have a downward bias of the perceived certainty of evacuation destination, and with endogeneity bias corrected the parameter for this variable increased by 91.6%

Coordinated Transit Response Planning and Operations Support Tools for Mitigating Impacts of All-Hazard Emergency Events

This report summarizes current computer simulation capabilities and the availability of near-real-time data sources allowing for a novel approach of analyzing and determining optimized responses during disruptions of complex multi-agency transit system. The authors integrated a number of technologies and data sources to detect disruptive transit system performance issues, analyze the impact on overall system-wide performance, and statistically apply the likely traveler choices and responses. The analysis of unaffected transit resources and the provision of temporary resources are then analyzed and optimized to minimize overall impact of the initiating event

Performance Measures to Assess Resiliency and Efficiency of Transit Systems

Transit agencies are interested in assessing the short-, mid-, and long-term performance of infrastructure with the objective of enhancing resiliency and efficiency. This report addresses three distinct aspects of New Jersey’s Transit System: 1) resiliency of bridge infrastructure, 2) resiliency of public transit systems, and 3) efficiency of transit systems with an emphasis on paratransit service. This project proposed a conceptual framework to assess the performance and resiliency for bridge structures in a transit network before and after disasters utilizing structural health monitoring (SHM), finite element (FE) modeling and remote sensing using Interferometric Synthetic Aperture Radar (InSAR). The public transit systems in NY/NJ were analyzed based on their vulnerability, resiliency, and efficiency in recovery following a major natural disaster

SOCIAL NETWORK INFLUENCE ON RIDESHARING, DISASTER COMMUNICATIONS, AND COMMUNITY INTERACTIONS

The complex topology of real networks allows network agents to change their functional behavior. Conceptual and methodological developments in network analysis have furthered our understanding of the effects of interpersonal environment on normative social influence and social engagement. Social influence occurs when network agents change behavior being influenced by others in the social network and this takes place in a multitude of varying disciplines. The overarching goal of this thesis is to provide a holistic understanding and develop novel techniques to explore how individuals are socially influenced, both on-line and off-line, while making shared-trips, communicating risk during extreme weather, and interacting in respective communities. The notion of influence is captured by quantifying the network effects on such decision-making and characterizing how information is exchanged between network agents. The methodologies and findings presented in this thesis will benefit different stakeholders and practitioners to determine and implement targeted policies for various user groups in regular, special, and extreme events based on their social network characteristics, properties, activities, and interactions

Development of dynamic travel demand models for hurricane evacuation

Little attention has been given to estimating dynamic travel demand in transportation planning in the past. However, when factors influencing travel are changing significantly over time – such as with an approaching hurricane - dynamic demand and the resulting variation in traffic flow on the network become important. In this study, dynamic travel demand models for hurricane evacuation were developed with two methodologies: survival analysis and sequential choice model. Using survival analysis, the time before evacuation from a pending hurricane is modeled with those that do not evacuate considered as censored observations. A Cox proportional hazards regression model with time-dependent variables and a Piecewise Exponential model were estimated. In the sequential choice model the decision to evacuate in the face of an oncoming hurricane is considered as a series of binary choices over time. A sequential logit model and a sequential complementary log-log model were developed. Each model is capable of predicting the probability of a household evacuating at each time period before hurricane landfall as a function of the household’s socio-economic characteristics, the characteristics of the hurricane (such as distance to the storm), and policy decisions (such as the issuing of evacuation orders). Three datasets were used in this study. They were data from Southwest Louisiana collected following Hurricane Andrew, data from South Carolina collected following Hurricane Floyd, and stated preference survey data collected from New Orleans area. Based on the analysis, the sequential logit model was found to be the best alternative for modeling dynamic travel demand for hurricane evacuation. The sequential logit model produces predictions which are superior to those of current evacuation participation rate models with response curves. Transfer of the sequential logit model estimated on the Floyd data to the Andrew data demonstrated that the sequential logit model is capable of estimating dynamic travel demand in a different environment than the one in which it was estimated, with reasonable accuracy. However, more study is required on the transferability of models of this type, as well as the development of procedures that would allow the updating of transferred model parameters to better reflect local evacuation behavior