Optimizing evacuation instructions while anticipating traveler compliance behavior

Instructing evacuees on their departure time, destination and route can lead to more efficient traffic operations. Empirical findings on evacuation behavior support the view that in practice a share of travelers decides not to comply, while current evacuation plan optimization techniques are limited to assessing mandatory evacuation under the assumption of full compliance. In this contribution we show I) how traveler compliance behavior affects evacuation efficiency, and II) how evacuation efficiency can be improved in case of partial compliance when this traveler compliance is anticipated on. The optimization method and case study application presented here underline the relevance and importance of capturing traveler compliance behavior, as this has a large impact upon the evacuation efficiency

Modelling lane changing behaviour in approaches to roadworks: Contrasting and combining driving simulator data with stated choice data

Drivers approaching lane closures due to roadworks tend to choose a target lane (plan) and seek suitable gaps to execute the plan (action). The plan is however latent or unobserved as the driver may or may not be able to move to the target lane due to the constraints imposed by the surrounding traffic. Hence, only the actions of the driver (as manifested by their final lane occupancies) are observed in the trajectory data. This paper analyses such mandatory lane changing behaviour in a roadworks environment in detail with data from a controlled driving simulator experiment and a simple stated preference survey with the same group of participants. While in the former drivers face similar constraints in implementing the plans as in the real world, in the simple stated choice survey the same drivers elicit their preferred target lanes without a need to put the plan into action. We contrast the findings from the two sources and also show correlations between the latent plan and stated target components in a latent class model. The results provide new insights into lane changing behaviour that may be useful for example for traffic management purposes. Furthermore, using stated choice data potentially reduces the cost of data collection for model development

Stated choices and simulated experiences: Differences in the value of travel time and reliability

Surveys with stated choice experiments (SCE) are widely used to derive values of time and reliability for transport project appraisal purposes. However, such methods ask respondents to make hypothetical choices, which in turn could create a bias between choices made in the experiment compared to those in an environment where the choices have consequence. In this paper, borrowing principles of experimental economics, we introduce an incentive compatible driving simulator experiment, where participants are required to experience the travel time of their chosen route and actually pay any toll costs associated with the choice of a tolled road. In a first for the literature, we use a within respondent design to compare both the value of travel time savings (VTT) and value of travel time reliability (VOR) across a typical SCE and an environment with simulated consequence. Given the importance of VTT and VOR to transport decision making and the difficulty in estimating VOR using revealed preference data, our results are noteworthy and emphasise that more research on this topic is imperative. We provide suggestions on how the results herein may be used in future studies, to potentially reduce hypothetical bias that may be exhibited in SCE

Are Healthcare Choices Predictable? The Impact of Discrete Choice Experiment Designs and Models

© 2019 ISPOR–The Professional Society for Health Economics and Outcomes Research Background: Lack of evidence about the external validity of discrete choice experiments (DCEs) is one of the barriers that inhibit greater use of DCEs in healthcare decision making. Objectives: To determine whether the number of alternatives in a DCE choice task should reflect the actual decision context, and how complex the choice model needs to be to be able to predict real-world healthcare choices. Methods: Six DCEs were used, which varied in (1) medical condition (involving choices for influenza vaccination or colorectal cancer screening) and (2) the number of alternatives per choice task. For each medical condition, 1200 respondents were randomized to one of the DCE formats. The data were analyzed in a systematic way using random-utility-maximization choice processes. Results: Irrespective of the number of alternatives per choice task, the choice for influenza vaccination and colorectal cancer screening was correctly predicted by DCE at an aggregate level, if scale and preference heterogeneity were taken into account. At an individual level, 3 alternatives per choice task and the use of a heteroskedastic error component model plus observed preference heterogeneity seemed to be most promising (correctly predicting >93% of choices). Conclusions: Our study shows that DCEs are able to predict choices—mimicking real-world decisions—if at least scale and preference heterogeneity are taken into account. Patient characteristics (eg, numeracy, decision-making style, and general attitude for and experience with the health intervention) seem to play a crucial role. Further research is needed to determine whether this result remains in other contexts

Experimental Design

AbstractThis chapter covers various issues related to the experimental design, a statistical technique at the core of a discrete choice experiment. Specifically, it focuses on the dimensionality of a choice experiment and the statistical techniques used to allocate attribute levels to choice tasks. Among others, the pros and cons of orthogonal designs, optimal orthogonal in the differences designs as well as efficient designs are addressed. The last section shows how a simulation exercise can help to test the appropriateness of the experimental design

Protective behaviour of citizens to transport accidents involving hazardous materials: A discrete choice experiment applied to populated areas nearby waterways

Background To improve the information for and preparation of citizens at risk to hazardous material transport accidents, a first important step is to determine how different characteristics of hazardous material transport accidents will influence citizens' protective behaviour. However, quantitative studies investigating citizens' protective behaviour in case of hazardous material transport accidents are scarce. Methods A discrete choice experiment was conducted among subjects (19-64 years) living in the direct vicinity of a large waterway. Scenarios were described by three transport accident characteristics: odour perception, smoke/vapour perception, and the proportion of people in the environment that were leaving at their own discretion. Subjects were asked to consider each scenario as realistic and to choose the alternative that was most appealing to them: staying, seekin

Latent variables and route choice behavior

In the last decade, a broad array of disciplines has shown a general interest in enhancing discrete choice models by considering the incorporation of psychological factors affecting decision making. This paper provides insight into the comprehension of the determinants of route choice behavior by proposing and estimating a hybrid model that integrates latent variable and route choice models. Data contain information about latent variable indicators and chosen routes of travelers driving regularly from home to work in an urban network. Choice sets include alternative routes generated with a branch and bound algorithm. A hybrid model consists of measurement equations, which relate latent variables to measurement indicators and utilities to choice indicators, and structural equations, which link travelers' observable characteristics to latent variables and explanatory variables to utilities. Estimation results illustrate that considering latent variables (i.e., memory, habit, familiarity, spatial ability, time saving skills) alongside traditional variables (e.g., travel time, distance, congestion level) enriches the comprehension of route choice behavior