In existing highway systems the interpretation and analysis of traffic data is done usually by threshold tables. In these tables different criteria are considered, as for example number of cars, number of trucks, average speed, and so on. Each value for these criteria belongs to certain, pre-defined intervals based on which the current traffic state is assigned to a special standard situation. This procedure has the disadvantage that stict thresholds often lead to inadequate state identifications. Also, the aggregation of the different criteria is not solved sufficiently. Fuzzy logic provides methods to overcome these disadvantages. Strict thresholds are relaxed and therefore continuous changes between states can be described properly. Besides this basic approach more sophisticated fuzzy methods deliver additional knowledge by compensatory aggregation of the criteria. In this paper a concept for fuzzy data analysis is suggested which can improve the existing vehicle-highway systems.
Improvment of intelligent vehicle-highway system by fuzzy data analysis
Verbesserung intelligenter Fahrzeug/Autobahn-Systeme durch Fuzzy-Datenanalyse
1993
8 Seiten, 9 Bilder, 1 Tabelle, 6 Quellen
Aufsatz (Konferenz)
Englisch
Improvement of intelligent vehicle-highway systems by fuzzy data analysis
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