Incorporating human factors into LCM using fuzzy TCI model.

Incorporation of Human Factors (HF) into the mathematical car-following (CF) models has always been the research hotspot. Ignorance of such inclusion would inevitably hinder us from acquiring a comprehensive understanding of traffic flow phenomena. This paper proposed a novel CF model in order to bridge three existing research gaps: the demand for more inclusion of HF into the Longitudinal Control Model (LCM); the requirement for a more desirable underlying CF model for the Task Capability Interface (TCI) model; the ignorance of the fuzziness of human brains when modeling Task Difficulty (TD). Specifically, in order to imitate driver's natural or subjective uncertainty and ambiguity of his TD, the fuzzy logic approach is introduced, and the TD is then incorporated into the LCM. Thereafter, both numerical simulation and field-data validation have been performed. Results indicate that our proposed model is more capable of accommodating HF and exhibits better performance than its predecessor. [ABSTRACT FROM AUTHOR]