摘要：AbstractHeterogeneous mixture of vehicle types and lack of lane discipline are common characteristics of cities in the developing countries. These conditions lead to driving manoeuvres that combine both longitudinal and lateral movements. Modeling this driving behavior tends to be complex and cumbersome, as various phenomena, such as multiple–leader following, should be addressed. This research attempts to simplify mixed traffic modeling by developing a methodology, which is based on data–driven models. The methodology is applied on mixed traffic, weak lane–discipline trajectory data, which have been collected in India. A well–known car–following model, Gipps’ model, is also applied on the same data and is used as a reference benchmark. Regarding the lateral manoeuvres, the focus is given on identification of significant lateral changes, which could indicate a lane–changing situation. Methods that allow monitoring structural changes in regression models could be used for this purpose. The ability of capturing lane changes is explored. A typical example is illustrated and further discussion is motivated.
关键词：Mixed traffic;Weak lane discipline;Data-driven models;Machine-learning;Virtual lanes
