Development of a novel car-following model using fractional differential equations (Spring 2022)

In the coming years, vehicles will be controlled autonomously and will be connected via vehicle-to-everything (V2X) wireless communication networks. Ridesharing companies are already undertaking test runs of autonomous taxi services. Car-following models (also known as driver longitudinal behavior models), which can mimic human driving behavior, can be utilized in an automated vehicle controller to have it driven through a mixed traffic environment. It has potential positive impacts such as reduced fuel consumption, reduced greenhouse gas emissions, reduced travel time, and improved traffic safety. With the advent of self-driving vehicles (Tesla et al.), the study of car-following models for connected and automated vehicles constitutes a rapidly growing research area. While there exist many car-following models, only few have been created using the Fractional Differential Equations (FDE) modeling technique, which has the potential to improve predicting traffic dynamics.

Over the course of the semester, interested students will work in a team and study existing car- following models (for example, the Optimal Velocity Model and the Intelligent Driver Model) learn FDE modeling techniques. Together, we intend to develop an improved FDE-based car- following model for connected and automated vehicles. Upon completion of the project, students will be familiar with several existing car-following models, have learned how to create a new model, and to analyze and evaluate the performance of these models.

For more information contact Md Rafiul Islam (rafiul@iastate.edu)

People:

  • Md Rafiul Islam (Postdoc)

  • Claus Kadelka (Faculty)

Pre-requisites:

  • Some experience with differential equations

  • Programming (Matlab/R/Python) is desirable but not required)

Project website:

https://www.mdrafiulislam.com/ismart-2022

Initial reading materials: