A Microscopic Model for Lane-Less Traffic

TLDR: It is verified that the proposed model, in addition to macroscopic patterns, can also accurately predict complex maneuvers, such as overtaking, sideways movements and avoiding collisions with slower moving vehicles.

Abstract: In this paper, a new model is introduced for traffic on broad roads, where the drivers do not follow lane-discipline. For both longitudinal and lateral motions, the driver reactions are assumed to be influenced by possibly a number of vehicles, obstacles, and unmodeled entities in visibility cones to the front and to the sides of each vehicle. The network of influences and the resultant interaction is modeled by “influence graphs.” In congested traffic situations, it is assumed that the influence structure is time invariant and all drivers are forced to behave homogeneously. Then, the collection converges to a layered formation with fixed intervehicle distances. In sparse and heterogeneous traffic, the velocity and intervehicle separations in the set of modeled vehicles, though can oscillate continuously, are uniformly bounded. These model-based predictions are verified experimentally. Videos of typical traffic on a sample road in Mumbai city, India, are recorded. Detailed motion information of groups of cars is extracted through image processing techniques. The proposed model is initialized with the extracted data and the computed trajectories are compared with the actual ones calculated from the images. It is verified that the proposed model, in addition to macroscopic patterns, can also accurately predict complex maneuvers, such as overtaking, sideways movements and avoiding collisions with slower moving vehicles.