Identifying Predictable Patterns in the Unconventional Overtaking Decisions of PTW for Cooperative ITS

Abstract

In the modern challenging urban environment, powered two wheelers (PTW) are selected for their everyday commuting. The way motorcycles and scooters travel through traffic is systematically emphasized in the recent relevant literature as being complex, especially in relation to overtaking. In this paper, meta-optimized Decision Trees, a special case of machine learning (ML) models, are developed in order to model the unconventional overtaking patterns of PTW drivers. Based on detailed naturalistic trajectory data collected using video footage from unmanned aerial vehicles (UAV) in a three-lane arterial in Athens, Greece, two different models of PTW driving behavior are developed. The first model addresses the decision of the PTW driver to overtake or not the preceding vehicle. The second model focuses on PTW driver's intention to overtake or undertake (pass from the right) it. The developed decision tree models are further analyzed in relation to the revealed significant factors during overtaking. Following, the applicability of the developed algorithms in the context of intelligent transportation systems and connected vehicles is discussed, which reveals the importance of acquiring quality data using advanced equipment combined with advanced ML approaches for advanced modeling methods.