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present study aims to develop vehicle type-wise operating-speed prediction (OSP) models for heterogeneous traffic on twolane rural highways in Indian hilly terrains. For the present study, 27 curves with varying geometric characteristics located along the National Highway (NH-953) connecting Netrang and Rajpipla in the western state of Gujarat, India, were selected. Speed data were collected using radar guns at three curve locations (entry point, midpoint, and exit point) in each travel direction for three dominant types of vehicles: motorized two-wheelers (2W), cars, and heavy commercial vehicles (HCVs). OSP models were developed for different vehicle types at three curve points using the backward elimination stepwise regression (BSR) technique. The results revealed that the preceding curve point's operating speed, curve length, and tangent length positively affected operating speed. In contrast, deflection angle, curve sharpness, and grade had adverse effects. The curve geometric characteristics had the most negligible impact on the operating speed of 2Wand a significant effect on HCV. Among all the curve-related aspects, curve length was the most significant variable and affected the speed of all three vehicle types, followed by curve sharpness. Further, the developed OSP models were applied to the other hilly terrain to check the transferability of the model. As an important outcome, the developed OSP models were used to evaluate geometric design consistency. This highlights the need for geometric and traffic-calming measures to improve highway operating-speed consistency and driver safety. [ABSTRACT FROM AUTHOR]

• 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]