access icon free Data-driven car-following model based on rough set theory

The car-following model is an important micro-traffic model for simulating car-following behaviour in traffic engineering and research studies. Conventional car-following models are always presented using mathematical equations reflecting ideal traffic conditions. In the big data era, data-driven models become a popular trend. In this study, a data-driven car-following model based on the rough set theory is proposed to consider information hidden in a field data set. On the basis of field data obtained from measurement devices such as the next generation simulation (NGSIM) trajectory data set, and using the methods of the rough set theory, an optimal decision rule set is established. Redundant attributes and redundant attribute values are removed for simplifying the car-following behaviour decision problem. Attribute significance and weights are computed for selecting matching rules. A car-following behaviour decision algorithm is designed to choose appropriate rules to determine the follower's velocity according to current observations. Simulations illustrate that the proposed data-driven car-following model can simulate the micro-traffic behaviour of followers well.

Inspec keywords: data handling; pattern matching; intelligent transportation systems; knowledge based systems; decision theory; rough set theory

Other keywords: data-driven car-following model; microtraffic behaviour; car-following behaviour decision problem; measurement devices; matching rules selection; redundant attribute values; NGSIM trajectory data set; optimal decision rule set; rough set theory

Subjects: Combinatorial mathematics; Data handling techniques; Expert systems and other AI software and techniques; Traffic engineering computing

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