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• A latent class-based framework to model motorized two-wheeler driver behaviour incorporating drivers' strategic intents, multi-vehicle anticipation, and two-dimensional (2D) movements in heterogeneous, disorderly (HD) traffic conditions. • An empirical application using vehicular trajectory data of motorized two-wheelers from an arterial road in Chennai, India. This study formulates a latent class-based driving behaviour framework for modelling vehicles' two-dimensional (2D) movements while considering drivers' strategic intents and multi-vehicle anticipation (MVA) in heterogeneous, disorderly (HD) traffic conditions. Specifically, five extensions are proposed to a typical stimulus–response based driving behaviour framework. First, the subject vehicle's 2D movements are represented as a combination of the angular direction of movement with respect to the longitudinal axis and the magnitude of acceleration or deceleration along the angle. Second, a latent class framework is used to recognise drivers' strategic intents (latent to the analyst) in two aspects: (a) the intent to accelerate, decelerate, or maintain a constant speed, and (b) the intent to steer to the left of, right of, or straight along the longitudinal axis. It is hypothesised that these strategic intents precede tactical decisions, such as how much to accelerate or decelerate and which specific angular direction to move along. Third, the MVA effect is accommodated to recognise that drivers consider stimuli from multiple vehicles in their vicinity. Fourth, a multi-stimuli model of acceleration (deceleration) is formulated, assuming that drivers choose an angle of movement that allows them to move with the highest (lowest) possible longitudinal acceleration (deceleration). Fifth, drivers' execution errors are recognised as the difference between their planned acceleration and executed acceleration. The proposed framework is applied for an analysis of motorised two-wheeler driver behaviour using a vehicular trajectory dataset from India. The empirical results highlight the importance of incorporating MVA and considering driver's intents while modelling 2D movements of vehicles in HD traffic conditions. Further, the microscopic traffic environment variables are found to have a stronger influence on drivers' higher-level, strategic intents than on their lower-level, tactical decisions. [ABSTRACT FROM AUTHOR]