Date of Award
Civil and Environmental Engineering
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This work is licensed under a Creative Commons Attribution 4.0 International License.
The main aim of this research is to develop a new car-following model that realistically predicts the trajectories of speed, acceleration, jerk and spacing in congested and uncongested freeway conditions. The research has three objectives. First, the start time of driver reaction was investigated in various car-following conditions. Specifically, the assumption of a constant reaction time of the existing car-following models was investigated using the observed driver behaviour data collected from a driving simulator. Moreover, the perception limits and the process that drivers use to start reaction were also studied. Second, a new car-following model was developed to reproduce the observed driver’s intermittent start time of acceleration/deceleration and realistic ranges of magnitudes of speed, spacing, acceleration and jerk. For this task, the model adapted the Markkula’s Framework of Sensorimotor Control in Sustained Motion Tasks. Third, the effect of lead vehicle type (car and truck) and the effect of the lead vehicle brake lights on the start time of driver reaction in car-following conditions were studied.
For this purpose, a total of 50 drivers’ car-following behaviour was observed in 4 different scenarios using a driving simulator – reaction to a decelerating lead vehicle, reaction to a stopped lead vehicle, perception of a lead vehicle’s speed change, and perception of a slow-moving lead vehicle. It was found that the drivers neither reacted after a specific reaction time from the start of perception nor reacted at a specific value of a perceptual variable. Rather, the drivers generally reacted when the accumulation of evidence (e.g., perceptual variable) over time reached a threshold. This demonstrates that the evidence accumulation framework was a promising method of predicting the start time of driver reaction in car-following conditions.
Therefore, a new car-following model called “Intermittent Intelligent Driver Model (IIDM)” was developed based on evidence accumulation to start driver reaction unlike the existing car-following models that use a constant reaction time parameter. Moreover, the IIDM uses the shape and duration of acceleration adjustments that accurately represents the actual shape and duration of acceleration maneuvers in the data. The prediction of accuracy of the new car-following model was evaluated using both the driving simulator data and real-world trajectory data.
Compared to the three existing car-following models – the Gipps’ Model, the Wiedemann Model and the Intelligent Driver Model (IDM), the IIDM realistically reproduced trajectories of speed, acceleration, jerk and spacing for both types of data. Moreover, the estimated surrogate measures of safety from trajectories predicted using the IIDM were similar to the surrogate measures of safety estimated from the observed data. Furthermore, the IIDM can incorporate the effects of lead vehicle brake lights and lead vehicle type (car and truck) for more accurate estimation of the driver reaction time. This demonstrates that the IIDM can generate more realistic vehicle trajectories (start time of reaction and magnitude of reaction) in various car-following conditions, which can be used to predict vehicle speeds and assess safety.
Durrani, Umair, "A New Car-Following Model with Incorporation of Markkula’s Framework of Sensorimotor Control in Sustained Motion Tasks" (2022). Electronic Theses and Dissertations. 8896.