Optimizing copious activity type classes based on classification accuracy and entropy retention

Abstract

Despite the advantages, big transport data are characterized by a considerable disadvantage as well. Personal and activity-travel information are often lacking, making it necessary to deduce this information with data mining techniques.However, some studies predict many unique activity type classes (ATCs), optimization parameter U (based on classification accuracy and information retention) which is maximized in an iterative local search algorithm. The optimal set of ATCs for the NHTS 2009 data set was determined. A comparison finds that this optimum is considerably better than many expert opinion activity type classification systems. Convergence was confirmed and large performance gains were found.