Towards an integrated longitudinal and lateral movement data-driven model for mixed traffic

In recent years, there has been an increasing interest in modeling driving behavior in developing countries, where conditions, such as non-lane discipline and heterogeneity in vehicle types, prevail. Traffic flow in such conditions is very complex in nature and safety issues arise. Traffic simulation models have been formulated for lane-based conditions. The existing models do not consider the wider range of situations that drivers in mixed traffic may face compared to drivers in homogeneous lane-based traffic, such as multiple-leader following, and passing and lateral shifts. In this research, we define the concept of virtual lanes for modeling mixed traffic conditions. A methodology based on temporary virtual lanes and data-driven approaches is developed to simulate driving behavior in developing countries. The role of vehicle type in model efficiency is also explored. The proposed methodology is validated on mixed traffic trajectory data from India.