Modeling lateral movement decisions of powered two wheelers in disordered heterogeneous traffic conditions

Abstract

ABSTRACT This article reports a systematic investigation carried out to model the lateral movement decisions of Powered-Two-Wheelers (PTWs) in disordered heterogeneous traffic conditions. The lateral maneuvering decisions of PTWs were framed as a typical multiclass classification problem, and significant factors governing such decisions were identified. Four machine learning models, along with the statistical model, were built to achieve this objective. The comparative analysis regarding the predictive performance of these models shown that the random forest model outperforms the rest of the considered models in terms of its classification power. To this end, the results from this investigation revealed that the lateral movement pattern of PTWs could be predicted using speed and spatial information of its surrounding vehicles. Interestingly, this information can be seamlessly collected with some sensors presently deployed in the advanced vehicles. Thus, the developed models would help in the design of active safety and driving assistance systems for such vehicles.