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Highway accidents significantly impact normal traffic flow. Consequently, automatic detection of abnormal traffic events has gradually attracted the attention of researchers interested in intelligent transportation system. This work presents a vision-based approach for automatic traffic congestion and incident detection. The proposed approach involves extracting entropy-based features to create a grid model that simulates dynamic traffic flow behavior. When an unusual event occurs in the lane of the vehicle employing the system, the system can immediately detect it and issue signals to approaching vehicles to prevent accidents. Experiments conducted using various simulation results clearly demonstrate the validity and effectiveness of the proposed approach for managing traffic congestion and detecting incidents.

The main theme of this paper is that almost fixed point properties of discrete structures and fixed point properties of (topological) spaces are interdeducible via a suitable category which contains both graphs and spaces as objects. To carry out the program, we have to consider (almost) fixed points of multifunctions, and for this we need a preliminary discussion of power structures for graphs and simplicial complexes. Specific applications developed are: a “digital convexity” (discrete) version of Kakutani's fixed point theorem for convex-valued multifunctions; and fixed point properties of dendrites in terms of those of finite discrete trees.