Rule-based Information Extraction from Long Distance Travel Data

The purpose of this study is to extract important information from long distance travel data based on the Artificial Intelligence technique-rough set theory. Rough set theory can learn and refine decision rules or hidden facts from the incomplete observed data without the constraints of statistical assumptions. In the study, we conducted a survey to collect the peoples’ most preferred travel mode choices for the given destination and people’s demographic information. We analyzed the observed data using rough set theory, calculated and discussed the approximation, core, reduct and rules of the data. The induced decision rules are expressed in natural language representing the relationships between personal demographic attributes and long distance travel mode choices, which can help policymakers in the decision making process. The results of validation test were very promising, which showed that the induced decision rules could represent the relationships between data with the accuracy of 74.59%.