Revisiting the Task–Capability Interface model for incorporating human factors into car-following models

Saifuzzaman, Mohammad; Zheng, Zuduo; Boyle, Linda Ng; Washington, Simon

doi:10.1016/j.trb.2015.09.011

Cited by 95 publications

(45 citation statements)

References 37 publications

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“…Both Hoogendoorn et al [11] and Saifuzzaman et al [12] assume a relationship between driver capability and preferred time headway. We assume that a driver’s preferred time headway T ′( t ) can be reasonably estimated as a geometric mean of the time headway time series: .…”

Section: Methodsmentioning

confidence: 99%

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Task-Difficulty Homeostasis in Car Following Models: Experimental Validation Using Self-Paced Visual Occlusion

et al. 2017

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Car following (CF) models used in traffic engineering are often criticized for not incorporating “human factors” well known to affect driving. Some recent work has addressed this by augmenting the CF models with the Task-Capability Interface (TCI) model, by dynamically changing driving parameters as function of driver capability. We examined assumptions of these models experimentally using a self-paced visual occlusion paradigm in a simulated car following task. The results show strong, approximately one-to-one, correspondence between occlusion duration and increase in time headway. The correspondence was found between subjects and within subjects, on aggregate and individual sample level. The long time scale aggregate results support TCI-CF models that assume a linear increase in time headway in response to increased distraction. The short time scale individual sample level results suggest that drivers also adapt their visual sampling in response to transient changes in time headway, a mechanism which isn’t incorporated in the current models.

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Section: Methodsmentioning

confidence: 99%

“…As a response to this criticism, the TCI has recently been incorporated in car following models by Hoogendoorn et al [11] and Saifuzzaman et al [12]. Both propose that parameters of car following vary as functions of driver capability, so as to maintain the task difficulty at a peferred level.…”

Section: Introductionmentioning

confidence: 99%

Task-Difficulty Homeostasis in Car Following Models: Experimental Validation Using Self-Paced Visual Occlusion

et al. 2017

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“…Besides, the model takes into consideration different types of drivers in terms of their overtaking behaviors. Compared with incorporating human factors into classic car-following models [17], it is much easier for the proposed model to achieve, which is understandable and has no trouble in solving more complicated equations. Finally, the safety coefficient in our model can conveniently make an evaluation of safety condition.…”

Section: Introductionmentioning

confidence: 99%

Micro-simulation model of two-lane freeway vehicles for obtaining traffic flow characteristics including safety condition

Yang

Luo

2016

J. Mod. Transport.

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Unidirectional two-lane freeway is a typical and the simplest form of freeway. The traffic flow characteristics including safety condition on two-lane freeway is of great significance in planning, design, and management of a freeway. Many previous traffic flow models are able to figure out flow characteristics such as speed, density, delay, and so forth. These models, however, have great difficulty in reflecting safety condition of vehicles. Besides, for the cellular automation, one of the most widely used microscopic traffic simulation models, its discreteness in both time and space can possibly cause inaccuracy or big errors in simulation results. In this paper, a micro-simulation model of two-lane freeway vehicles is proposed to evaluate characteristics of traffic flow, including safety condition. The model is also discrete in time but continuous in space, and it divides drivers into several groups on the basis of their preferences for overtaking, which makes the simulation more aligned with real situations. Partial test is conducted in this study and results of delay, speed, volume, and density indicate the preliminary validity of our model, based on which the proposed safety coefficient evaluates safety condition under different flow levels. It is found that the results of this evaluation coincide with daily experience of drivers, providing ground for effectiveness of the safety coefficient.

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“…This observation implies the necessity to adequately accommodate human factors in the microscopic modeling of driving behaviors (primarily, their lane-changing maneuvers and car-following behavior), as advocated in the recent literature (e.g., [5,42,43]). However, the explicit incorporation of risk perceptions in the modeling of the merging process was beyond the scope of this study and is a topic for future research.…”

Section: Conclusion and Further Workmentioning

confidence: 99%

Modeling and Predicting Stochastic Merging Behaviors at Freeway On-Ramp Bottlenecks

Sun

Zuo

Jiang

et al. 2018

Journal of Advanced Transportation

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Merging behavior is inevitable at on-ramp bottlenecks and is a significant factor in triggering traffic breakdown. In modeling merging behaviors, the gap acceptance theory is generally used. Gap acceptance theory holds that when a gap is larger than the critical gap, the vehicle will merge into the mainline. In this study, however, analyses not only focus on the accepted gaps, but also take the rejected gaps into account, and the impact on merging behavior with multi-rejected (more than once rejecting behavior) gaps was investigated; it shows that the multi-rejected gaps have a great influence on the estimation of critical gap and merging prediction. Two empirical trajectory data sets were collected and analyzed: one at Yan'an Expressway in Shanghai, China, and the other at Highway 101 in Los Angeles, USA. The study made three main contributions. First, it gives the quantitative measurement of the rejected gap which is also a detailed description of non-merging event and investigated the characteristics of the multi-rejected gaps; second, taking the multi-rejected gaps into consideration, it further expanded the concept of the "critical gap" which can be a statistic one and the distribution function of merging probability with respect to such gaps was analyzed by means of survival analysis. This way could make the full use of multi-rejected gaps and accepted gaps and reduce the sample bias, thus estimating the critical gap accurately; finally, considering multi-rejected gaps, it created logistic regression models to predict merging behavior. These models were tested using field data, and satisfactory performances were obtained.

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Revisiting the Task–Capability Interface model for incorporating human factors into car-following models

Cited by 95 publications

References 37 publications

Task-Difficulty Homeostasis in Car Following Models: Experimental Validation Using Self-Paced Visual Occlusion

Task-Difficulty Homeostasis in Car Following Models: Experimental Validation Using Self-Paced Visual Occlusion

Micro-simulation model of two-lane freeway vehicles for obtaining traffic flow characteristics including safety condition

Modeling and Predicting Stochastic Merging Behaviors at Freeway On-Ramp Bottlenecks

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