Mathematical modeling of driver speed control with its applications in intelligent transportation system design
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The quantitative prediction and understanding of a driver's speed control is an essential component to preventing speeding and the design of vehicle systems. Speed control is a complex behavior of longitudinal vehicle control consisting of speed perception, decision making (i.e. setting a target speed), motor control (i.e. foot movement required for pedal control), vehicle mechanics, and individual driver differences. However, there are few existing models that can integrate all of these aspects in cohesive manner. To address this problem, this work introduces a mathematical model for a driver's speed control with analytical solutions based on a rigorous understanding of the human cognitive mechanisms involved in driving. This model includes an integrated Queuing Network-Model Human Processor (QN-MHP) structure, and the Rule-based Decision Field Theory (RDFT). In combining these two computational models, the new model offers a relatively complete picture of driver speed control in free-flow driving settings. This new model consequently provides new predictions with regard to several components involved in driving: driving speed, throttle/brake pedal angle, acceleration, speeding (the time at which a drivers exceeds the speed limit and the magnitude of speeding), and the frequency of speedometer inspection. This model can be applied not only to the average driver, but also to individual drivers considering their decision making references and impulsiveness. Based on the new model, this work also develops a new intelligent speeding prediction system (ISPS) to provide a proactive warning message before a speeding occurs.