The prediction of traffic situations is a vital issue in modern Intelligent Transport Systems (ITS). Particularly the real time detection and proper assessment of incidents may save live and may contribute to keep the transport network available. However, the influence factors of traffic are subject to multi changes; the influence factors are varying traffic demand, weather, seasons, etc. This allows only a very poor performance in real time assessment of traffic situations. This article focuses on the fact that the possible traffic patterns - depicted as time series - vary only very little on each site, representing specific traffic situations or "normal time series". That the use of intelligent dedicated digital signal processing systems and communication media is in a position to improve the requirement of Real-Time traffic situational analysis in an efficient and effective way. Digital Finite Impulse Response (FIR) filters are used to analyse sensor measurement data in a way that allows an instantaneous assessment about the actual traffic situation and the detection of abnormal behaviour. A FIR filter cascade is used, each filter represents a specific normal time series like working day, weekend day, rain, winter, etc. The representation of each normal time series is achieved by discrete transformation of the normal time series in order to generate its frequency spectrum and by designing a filter structure with a corresponding frequency response. The deployment of the FIR filters can be in Traffic Control Centres with a vast amount of computational power as well as in the controller cabinets of local sensors on the basis of Digital Signal Processors. © 2011 Published by Elsevier Ltd.
Leihs, D., & Adamski, A. (2011). Situational analysis in real-time traffic systems. In Procedia - Social and Behavioral Sciences (Vol. 20, pp. 506–513). https://doi.org/10.1016/j.sbspro.2011.08.057