Modeling Emergency Managers’ Hurricane Evacuation Decisions

2017 ◽  
Vol 2604 (1) ◽  
pp. 82-87 ◽  
Author(s):  
Ravindra Gudishala ◽  
Chester Wilmot
Keyword(s):  
Discrete Choice ◽  
Research Study ◽  
Choice Model ◽  
Independent Predictor ◽  
Time Of Day ◽  
Clearance Time ◽  
Demand Model ◽  
Hurricane Evacuation ◽  
Emergency Managers ◽  
Emergency Manager

Emergency management and decision support system (EMDSS) tools play an important role in assisting emergency managers with making important decisions about the movement of people to safety when a jurisdiction is threatened by a storm. One of the important components of an EMDSS is an evacuation demand model that predicts whether and when households will evacuate when they are threatened by a storm. A critical input to that model is an emergency manager's decision to issue an evacuation notice. No existing mathematical models predict whether and when an emergency manager will issue an evacuation notice on the basis of a hurricane forecast and other contextual factors. To fill this gap, this research study sought to develop a model that would predict if and when an emergency manager would issue an evacuation notice when a jurisdiction was threatened by a storm. Data from poststorm assessment surveys and newspaper archives were used to retrieve past decisions made by evacuation managers for five storms in 45 coastal counties or parishes. The data were then used to develop a discrete choice model by use of the time-dependent sequential logit paradigm. Five independent predictor variables—storm surge, clearance time, time to landfall, hurricane category, and time of day—were found to be good predictors of the decisions made by emergency managers. This model could be useful to emergency managers to estimate how other emergency managers decide to evacuate an area when they are faced with an evacuation decision. The model could also benefit researchers and practitioners engaged in modeling and understanding hurricane evacuation behavior.

scholarly journals Heating Costs and Household Wood Stove Acquisition: A Discrete Choice Demand Model

1985 ◽  
Vol 14 (1) ◽  
pp. 65-70 ◽  
Author(s):  
Paul Francis Scodari ◽  
Ian W. Hardie
Keyword(s):  
New Hampshire ◽  
Discrete Choice ◽  
Choice Model ◽  
Operating Costs ◽  
Demand Model ◽  
Wood Stove ◽  
Home Heating ◽  
Fuel Burning ◽  
Discrete Choice Demand ◽  
Wood Stoves

This paper examines the acquisition of wood stoves by New Hampshire households through use of a utility-maximizing discrete choice model. The analysis is based on the hypothesis that wood stoves are acquired to decrease the monetary costs of home-heating. Operating costs associated with heating with conventional fuel burning capital and with a combination of conventional and wood stove heating capital are estimated. These operating costs are used to estimate probabilities of 1979 wood stove acquisition for particular types of New Hampshire households.

scholarly journals What Matters Most in Transportation Demand Model Specifications: A Comparison of Outputs in a Mid-size Network

2015 ◽  
Author(s):  
T. Donna Chen ◽  
Kara Kockelman ◽  
Yong Zhao
Keyword(s):  
Travel Demand ◽  
Choice Model ◽  
Mode Choice ◽  
Demographic Data ◽  
Time Of Day ◽  
Demand Model ◽  
Land Use Patterns ◽  
Transportation Demand ◽  
Step Model ◽  
The Impact

This paper examines the impact of travel demand modeling (TDM) disaggregation techniques in the context of medium-sized communities. Specific TDM improvement strategies are evaluated for predictive power and flexibility with case studies based on the Tyler, Texas, network. Results suggest that adding time-of-day disaggregation, particularly in conjunction with multi-class assignment, to a basic TDM framework has the most significant impacts on outputs. Other strategies shown to impact outputs include adding a logit mode choice model and incorporating a congestion feedback loop. For resource-constrained communities, these results show how model output and flexibility vary for different settings and scenarios.BACKGROUND Transportation directly provides for the mobility of people and goods, while influencing land use patterns and economic activity, which in turn affect air quality, social equity, and investment decisions. Driven by the need to forecast future transportation demand and system performance, Manheim (1979) and Florian et al. (1988) introduced a transportation analysis framework for traffic forecasting using aggregated data that provide the basis for what is known as the four-step model: a process involving trip generation, then trip distribution and mode choice, followed by route choice. Aggregating demographic data at the zone level, the four-step model generates trip productions based on socioeconomic data (e.g., household counts by income and size) and trip attractions primarily based on jobs counts. The model then proportionally distributes trips between each origin and destination (OD) zone pair based on competing travel attractions and impedances, under the assumption that OD pairings with higher travel costs draw fewer trips. Trips between each OD pair are split among a variety of transportation modes, allocating trips to private vehicle, transit, or other

Dynamics of Activity Time-of-Day Choice

2017 ◽  
Vol 2665 (1) ◽  
pp. 51-59 ◽  
Author(s):  
Ramin Shabanpour ◽  
Nima Golshani ◽  
Joshua Auld ◽  
Abolfazl (Kouros) Mohammadian
Keyword(s):  
Travel Time ◽  
Decision Process ◽  
Travel Demand ◽  
Choice Model ◽  
Time Of Day ◽  
Demand Model ◽  
Start Time ◽  
Time Duration ◽  
Time Horizons ◽  
Activity Attributes

This study explored travelers’ decision behavior in selecting activity start times. The study examined the problem in the context of the Agent-based Dynamic Activity Planning and Travel Simulation (ADAPTS) activity-based travel demand model for the Chicago, Illinois, metropolitan area. A unique feature of the ADAPTS framework is its consideration of planning horizons for various activity attributes. Naturally, the various attributes of an activity—such as start time, duration, location, party involvement, and mode of travel—can be planned in different time horizons. An attribute that is planned affects the choice of other activity attributes. Therefore, developing a true behavioral time-of-day choice model would not be possible unless the planning order of activity attributes and the dynamics of travelers’ decision-making processes are taken into account. Similarly, it can be argued that there should be fundamental differences in the time-of-day decision process when other attributes of the activity are not yet planned but are to be decided at a later time. The presented time-of-day model aims to capture the dynamics of this decision process by considering the planning time horizons of other attributes of the activity, as well as the outcomes of the decisions. The study adopted the discrete choice approach to model activity timing decisions and a hybrid utility maximization and developed a regret minimization model to account for the heterogeneity of decision rules across choice variables. Analysis of the estimation results and parameter elasticities indicates that higher expected travel time, variations in travel time, and schedule occupancy rates for different time choices can significantly increase the regret value of the corresponding choice and therefore affect the time-of-day choice.

The Impact of Zoning and Built Environment Characteristics on Transit, Biking, and Walking

2019 ◽  
Vol 11 (1) ◽  
pp. 108-129
Author(s):  
Andrew G. Mueller ◽  
Daniel J. Trujillo
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Discrete Choice ◽  
Travel Behavior ◽  
Choice Model ◽  
Mode Choice ◽  
Land Use Regulation ◽  
Policy Action ◽  
Development Goals ◽  
The Impact

This study furthers existing research on the link between the built environment and travel behavior, particularly mode choice (auto, transit, biking, walking). While researchers have studied built environment characteristics and their impact on mode choice, none have attempted to measure the impact of zoning on travel behavior. By testing the impact of land use regulation in the form of zoning restrictions on travel behavior, this study expands the literature by incorporating an additional variable that can be changed through public policy action and may help cities promote sustainable real estate development goals. Using a unique, high-resolution travel survey dataset from Denver, Colorado, we develop a multinomial discrete choice model that addresses unobserved travel preferences by incorporating sociodemographic, built environment, and land use restriction variables. The results suggest that zoning can be tailored by cities to encourage reductions in auto usage, furthering sustainability goals in transportation.

Air Passengers’ Preferences for Aviation Voluntary Carbon Offsetting: A Co-benefits Perspective

2021 ◽  
pp. 004728752110303
Author(s):  
Beile Zhang ◽  
Brent W. Ritchie ◽  
Judith Mair ◽  
Sally Driml
Keyword(s):  
Discrete Choice ◽  
Latent Class ◽  
Choice Model ◽  
Choice Modeling ◽  
Positive Outcomes ◽  
Market Segments ◽  
Discrete Choice Modeling ◽  
Educational Benefits ◽  
Carbon Offsetting ◽  
Simple Reduction

Co-benefits are positive outcomes from voluntary carbon offsetting (VCO) programs beyond simple reduction in carbon emissions, which include biodiversity, air quality, economic, health, and educational benefits. Given the rates of aviation VCOs remain at less than 10%, this study investigated air passengers’ preferences for co-benefits as well as certification, location, and cost of VCO programs. Using discrete choice modeling, this study shows that aviation VCO programs with higher levels of co-benefits, particularly biodiversity and health benefits, are preferred by air passengers and confirms a preference for domestically based and certified VCO programs. The latent class choice model identified three classes with different preferences for VCO program attributes and demographic characteristics. The results of this study contribute to the knowledge of VCO co-benefits and imply that airlines should take note of this preference for biodiversity and health co-benefits when designing VCO programs and differentiate between market segments to increase the uptake of VCOs.

scholarly journals Emergency Manager Perceptions of the Effectiveness and Limitations of Mass Notification Systems: A Mixed Method Study

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
William V. Pelfrey
Keyword(s):  
Text Messaging ◽  
Policy Implications ◽  
Notification System ◽  
Emergency Managers ◽  
Emergency Manager ◽  
Wide Variability ◽  
Manager Perceptions ◽  
Critical Resource ◽  
Multi Method Approach ◽  
Method Approach

AbstractDisasters can move quickly. Effective communication is a critical resource that can significantly enhance public safety. A mass notification system (MNS) uses text messaging to inform constituents of crisis, provide recommendations, connect to resources, and has the advantage of speed. Limited research has been conducted on the variables that influence the effectiveness, utilization, and perceptions of MNS. The extant study employs a multi-method approach to advance the scholarly knowledge on MNS. All emergency managers in a state were surveyed on issues of MNS enrollment, utilization, and brand. A subgroup of emergency managers were then interviewed to provide depth to the survey findings. Key findings indicate wide variability in MNS usage, little relationship between population size and enrollment, and a high perceived importance of MNS as a communication modality. Policy implications and recommendations are offered.

scholarly journals Patient preferences for use of virtual consultations in an orthopaedic rehabilitation setting: Results from a discrete choice experiment

2021 ◽  
pp. 135581962110354
Author(s):  
Anthony W Gilbert ◽  
Emmanouil Mentzakis ◽  
Carl R May ◽  
Maria Stokes ◽  
Jeremy Jones
Keyword(s):  
Conceptual Model ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Patient Preferences ◽  
Choice Experiment ◽  
Best Practice ◽  
Time Of Day ◽  
Problem Area ◽  
Fractional Factorial ◽  
The Impact

Objective Virtual Consultations may reduce the need for face-to-face outpatient appointments, thereby potentially reducing the cost and time involved in delivering health care. This study reports a discrete choice experiment (DCE) that identifies factors that influence patient preferences for virtual consultations in an orthopaedic rehabilitation setting. Methods Previous research from the CONNECT (Care in Orthopaedics, burdeN of treatmeNt and the Effect of Communication Technology) Project and best practice guidance informed the development of our DCE. An efficient fractional factorial design with 16 choice scenarios was created that identified all main effects and partial two-way interactions. The design was divided into two blocks of eight scenarios each, to reduce the impact of cognitive fatigue. Data analysis were conducted using binary logit regression models. Results Sixty-one paired response sets (122 subjects) were available for analysis. DCE factors (whether the therapist is known to the patient, duration of appointment, time of day) and demographic factors (patient qualifications, access to equipment, difficulty with activities, multiple health issues, travel costs) were significant predictors of preference. We estimate that a patient is less than 1% likely to prefer a virtual consultation if the patient has a degree, is without access to the equipment and software to undertake a virtual consultation, does not have difficulties with day-to-day activities, is undergoing rehabilitation for one problem area, has to pay less than £5 to travel, is having a consultation with a therapist not known to them, in 1 weeks’ time, lasting 60 minutes, at 2 pm. We have developed a simple conceptual model to explain how these factors interact to inform preference, including patients’ access to resources, context for the consultation and the requirements of the consultation. Conclusions This conceptual model provides the framework to focus attention towards factors that might influence patient preference for virtual consultations. Our model can inform the development of future technologies, trials, and qualitative work to further explore the mechanisms that influence preference.

scholarly journals Pre-calibration of a Discrete Choice Model and Evaluation of Cycling Mobility for Île-de-France

2021 ◽  
Vol 184 ◽  
pp. 172-177
Author(s):  
Guoxi Feng ◽  
Maxime Jean ◽  
Alexandre Chasse ◽  
Sebastian Hörl
Keyword(s):  
Discrete Choice ◽  
Choice Model ◽  
Discrete Choice Model
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