A Short Review of Behavioral Intention and Factors Using the Theory of Planned Behavior and Data Mining Techniques

Abstract

The growing need for urban mobility alongside environmental issues has undertaken several investigations into the factors that lead people to abandon private cars in favour of public transportation. The Theory of Planned Behavior (TPB) is one of the most developed theories in regard to behavioral intentions that explain decision-making processes considering attitude towards the behavior, social pressure, and perceived control over the action impact choices regarding transportation. There has been some progress in extending TPB by adding constructs such as concern for the environment, moral standards, and previous actions taken, which strengthens the explanation. At the same time, the combination of data mining techniques and machine learning, such as structural equation modeling, decision tree, and support vector machine, has led to better prediction of travel behavior as well as providing greater understanding into the behavior. This work reviews empirical research applying TPB alongside data-driven methods to plan shifts towards public transport and analyses the methodology used. It reveals the dominant influence of socio-psychological factors and other elements related to information technologies on transport behavior and shows new possibilities offered by modern analytical methods aimed at policy decisions. In conclusion, the paper offers new approaches and focus of studies, arguing that models need to be tailored to certain conditions, and that advanced technologies should be adapted to encourage sustainable transport