Application of Fuzzy Systems in the Car-Following Behaviour Analysis

Abstract

Realistic understanding and description of car following behaviour is fundamental in many applications of Intelligent Transportation Systems. Historical car following studies had been focused on car following behaviour measured under experiment settings, either at test track or on open road, mainly using statistical analysis. This might introduce errors when they were used to represent everyday driving behaviour because differences might exist between everyday and experiment behaviours, and intelligent data analysis might be necessary in order to identify subtle differences. This paper presents the results of an observation and analysis of driver’s car following behaviour on motorway. Car following behaviours were measured under normal driving conditions where drivers were free to follow any vehicles. A time-series database was then established. The data was analysed using neuro-fuzzy systems and driver car following behaviour was quantified using several dynamic behavioural indices, which were combinations of parameters of trained neuro-fuzzy systems. The results indicated that in normal driving conditions, car following was conducted in a ‘loose’ way in terms of close-loop coupling, and car following performance was slightly ‘worse’ in terms of tracking error, than in experiment settings.