ABSTRACT

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.

摘要

将人为因素（HF）纳入数学跟车（CF）模型一直是研究的热点。无视这种包容性将不可避免地妨碍我们对交通流现象进行全面的了解。为了弥补现有的三个研究空白，本文提出了一种新颖的CF模型：将HF进一步纳入纵向控制模型（LCM）的需求；对任务能力接口（TCI）模型需要更理想的基础CF模型的要求；建模任务难度（TD）时人脑模糊的无知。具体地，为了模仿驾驶员的TD的自然或主观不确定性和模糊性，引入了模糊逻辑方法，然后将TD合并到LCM中。之后，数值模拟和现场数据验证均已执行。结果表明，我们提出的模型比以前的模型更能容纳HF，并且表现出更好的性能。