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Due to modern pervasive wireless technologies and high-performance monitoring systems, spatio-temporal information plays an important role in areas such as intelligent transportation systems (ITS), surveillance, scheduling, planning, or industrial automation. Security or criminal/terrorist threat prevention in modern ITS is one of today's most relevant concerns. This paper presents an algorithm for online spatio-temporal risk assessment in urban environments. In its first phase, the algorithm uses the online nearest neighbor clustering (NNC) algorithm to identify a set of significant places. In the second phase, a fuzzy inference engine is employed to quantify the level of risk that each significant place poses to the place of interest (e.g., vehicle, person, building, or an object of high assets). The contributions of the presented algorithm are given as follows: 1) recognition and extraction of the set of the most significant places; 2) dynamic adaptation of the solution to time-dependent traffic distributions; 3) parametric control by adjusting geographical proximity threshold, significance threshold, and discount factor; and 4) online risk assessment. The performance of the algorithm was demonstrated on a problem of traffic density estimation and risk assessment in a virtual urban environment.
Linda et al. (Tue,) studied this question.