Speeding – exceeding posted speed limits, driving too fast for conditions or racing – is the leading contributing factor in fatal motor vehicle accidents in NY State. More than 34 percent of all fatal accidents were due to unsafe speed in 2009 (Summary of Motor Vehicle Accidents, NY State Department of Motor Vehicles, 2009). Understanding and modeling speeding and speed control is one of major challenges in human performance modeling which involves: a) the modeling of several aspects of human cognitive system: perception, decision making and motor control as well as their interaction with the vehicle model; b) individual differences in speed control and prediction of speeding in real time. However, few of existing computational models is able to cover all of these important aspects together. To address this problem, the main objective of this project is to build a new mathematical driver speeding behavior model and apply it to develop an intelligent speeding control system.

Multi-disciplinary approaches will be used to build the mathematical model of driver speeding behavior, integrating methods in operations research (Queuing Network-Model Human Processor, QN-MHP) and theories in psychology (Rule-Based Decision Field Theory, RDFT) to predict driving speed, pedal angle, acceleration, the time when drivers exceed the speed limit, and the magnitude of speeding. The model not only quantifies an average driver’s speed control behavior, but also models individual drivers’ decision making references and impulsiveness. A human driver experimental study will be conducted to validate the prediction of the model. The model will also be implemented in a real-time intelligent speeding control system, which will provide warnings to drivers to prevent speeding proactively. The intelligent system will online monitor the pedal behavior of a driver, calculate the probability of speeding for that driver in the next few seconds, and proactively provide necessary warnings to that driver to prevent his or her speeding behavior in real-time.

This project will produce a mathematical model of driver speeding, consisting of four major components: speed perception, decision making (setting a target speed), motor control (foot movement for pedal control) and a vehicle mechanical model. It will not model all of speed control behavior of human driver, but focus on the prediction of speeding behavior and the design of human-machine interface (HMI) to present warning message to driver before the speeding behavior actually occurs. It is potentially helpful for Region II to reduce number of traffic accidents due to speeding, and both undergraduate and minority students will be involved in this project.