Drivers’ behaviour is modelled based on video recording of traffic in traffic-calmed links. Survey area is filmed vertically down with an unmanned quadrocopter flying approximately 70 meters above ground level, covering the area of 100x60 meters. Later, during the video processing, with the use of neural networks classifiers and computer vision tracking algoritms, street users are recognised with its type (pedestrian, bike, car, bus, heavy vehicle), and given the specific ID and coordinates in every frame. With this data, with resolution of approx. 40 frames per second, following data can be acquired: traffic flow, vehicle composition, trajectory, speed, acceleration and deceleration. This data will are used to build a precise microscopic simulation with estimated parameters of Wiedemann car following model, which is widely used in PTV Vissim microsimulation traffic program. Results of microscopic link model, done in PTV Vissim will lead to modelling traffic assignment in macroscopic, four-step model, by estimating network link parameters. That kind of model consists of two main parts: demand and supply. Demand is represented by origin-destination matrix between the traffic areas, with homogenous urban and transport function. Supply is the transport network, set for various transport modes, both individual and public. The aim of the model is to assign the trips, from origin to destination to the road network. Traffic assignment is calculated by setting a path with the lowest cost (for example travel time) for every network users. In this project, demand remains unchanged, although link parameters regarding traffic will be modified. Traffic on the link is described with three basic variables: traffic flow q – amount of vehicles passing one point in the unit of time, traffic density – amount of vehicles on the link, velocity – is a length of the link divided by time to pass it or traffic flow divided by traffic density. In PTV Visum model, relation between the amount of traffic flow and link travel time are described by a volume-delay function. Its shape can be chosen from default function formulas, as well as its parameter can be changed freely. Measuring and modelling drivers’ behaviour and creating a microsimulation model will help to create a volume-delay function for traffic calmed road. Vissim microsimulation will be tested on various traffic volumes, with various traffic compositions, with the estimated speed profiles and other Wiedemann’s model parameters. Finally, macrosimulation results in areas, where traffic calming devices are planned or built are likely to deliver more authoritative forecasts than it could be reached by traditional methods.