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Mathematical Modeling of Average Driver Speed Control with the Integration of Queuing Network-Model Human Processor and Rule-Based Decision Field Theory

Quantitative prediction and understanding of driver speed control is important to prevent speeding behavior and design vehicle systems. Speed control is a complex behavior of driver longitudinal vehicle control, involving speed perception, decision making (setting a target speed), motor control (foot movement for pedal control), and vehicle mechanics. However, few of existing models is able to cover all of these important aspects together. To address this problem, the current work built a new mathematical driver speed control model with analytical solutions based on rigorous understanding of human cognitive mechanisms in driving, integrated Queuing Network-Model Human Processor (QN-MHP, which already modeled driver lateral control) structure and Rule-Based Decision Field Theory (RDFT), and offered a relatively complete picture of driver speed control in free-flow driving settings. This new model can provide predictions with regard to driving speed, pedal angle and acceleration for average driver.