An Interactive Computer-Based Interface to Support the Discovery of Individuals’ Mental Representations and Preferences in Decisions Problems: An Application to Travel Behavior

Growing emphasis is currently given in decision modeling on process data to capture behavioral mechanisms that ground decision-making processes. Nevertheless, advanced applications to elicit such data are still lacking. The Causal Network Elicitation Technique interview and card-game, both face-to-face interviews, are examples of a behavioral process method to obtain individuals’ decision-making by eliciting temporary mental representations of particular problems. However, to portray and model these representations into formal modeling approaches, such as Bayesian decision networks, an extensive set of parameters has to be gathered for each individual. Thus, data collection procedures for large sample groups can be costly and time consuming. This paper reports on the methodological conversion and enhancement of the existing elicitation methods into a computer-based interface that allows to not only uncover individuals’ mental representations but also to automate the generation of preference parameter elicitation questions. Results of such studies can be used to understand individuals’ constructs and beliefs with respect to decision alternatives, predict individuals’ decision behavior at a disaggregate level, and to assess behavioral changes due to differences in contexts and constraints

Collaborative Evidential Clustering

International audienc

Mining traffic data for the development of an accident warning application for tourists

Tourist drivers belong to a category of drivers that are more vulnerable to road accidents due to their unfamiliarity of the road network at a destination. This paper presents a method followed to develop a tool that alert tourist drivers of their accident risks based on situational factors obtained from mobile phone sensors and knowledge distilled from historical records of traffic accidents. The knowledge necessary for the development of a context aware mobile accident warning application was extracted from a spatiotemporal analysis of historical accidents data, to identify patterns of conditions that lead to accidents. Results from this analysis were used to develop heuristics rules that were programmed in a mobile application. The developed system warns travelers of possible threats on the road network of Nicosia, given driver’s location and situational factors. The system aims to improve tourists’ safety