 A generic multi-scale framework for microscopic traffic simulation part II – Anticipation Reliance as compensation mechanism for potential task overloadSimeon C. Calvert, Wouter J. Schakel and J.W.C. van Lint Transportation Research Part B: Methodological, 2020, vol. 140, issue C, 42-63 Abstract: The inclusion of human factors (HF) in mathematical models is proving crucial to allow complex driving behaviour and interactions to be explicitly considered to capture driving phenomena. An important area where such integration is required is for the role of anticipation by drivers to compensate for critical traffic situations. In this paper, we introduce the concept of Anticipation Reliance (AR), which acts as a demand lowering compensative effect for the driving task by relying more on anticipation. We implement AR in a generic multi-scale microscopic traffic modelling and simulation framework to explore and explain the effects of HF on traffic operations and safety in critical traffic situations. This concept addresses a disparity in the description of driver workload in relation to the execution of driving tasks in regard to the confidence that drivers place on tasks that are sub-consciously catered for. The crossover from HF to a mathematical description of this role of AR introduces a ground-breaking concept that explains and models the mechanisms that allow drivers to compensate and avoid accidents in many circumstances, even when driving errors or sub-optimal driving performance occurs. By and large, the HF effects can be subdivided in effects on perception and anticipation; effects on sensitivity and response to stimuli; and effects on personal attributes and characteristics. A key aspect of the framework are two intertwined driver-specific mental state variables—total workload and awareness—that bridge between classic collision-free idealized models for lane changing and car following, and HF models that explain under which conditions the performance of drivers deteriorates in terms of reaction time, sensitivity to stimuli or other parameters. In this paper, we focus on the awareness construct, as described by AR, and explore the effects. We prove the effectiveness of the approach with a case example that demonstrates the ability of the model to dissect a complex traffic situation with both longitudinal and lateral driving tasks, while endogenously considering human factors and that produces accident avoidance and occurrence within the same order of magnitude compared to real traffic accident statistics. Keywords: Driving behaviour; Traffic modelling; Awareness; Anticipation strategies; Traffic safety (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:transb:v:140:y:2020:i:c:p:42-63 Ordering information: This journal article can be ordered from DOI: 10.1016/j.trb.2020.07.011 Access Statistics for this article Transportation Research Part B: Methodological is currently edited by Fred Mannering More articles in Transportation Research Part B: Methodological from Elsevier |
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