Network Models for Connected and Autonomous Vehicles

CE 56601

Credit Hours:

3

Instructor:

Professor Satish V. Ukkusuri

Learning Objectives:

  1. Analyze transportation systems using game theory and optimization by drawing on analogies between the interactions of supply and demand in economic markets.
    a. Define selfish routing (user equilibrium) and system optimal problems
    b. Develop solution algorithms for large scale networks
  2. Apply specialized network structures to solve selfish routing games efficiently.
  3. Rigorously formulate and apply various extensions of the selfish routing game – traffic assignment with elasticity, stochastic user equilibrium, network design and OD estimation.
  4. Understand the basic concepts of Connected and Autonomous vehicles (CAVs).
  5. Apply the static network models to CAV planning and operations problems.
  6. Use software to analyze various large-scale transportation networks and understand issues from real world practitioners.

Description:

This course provides an introduction to mathematical foundations of the analysis of transportation networks. The course will be divided into two main sections. Section 1 will introduce the basic foundations of network routing problems including user equilibrium (selfish routing) and system optimal games on networks. Various optimization based formulations, algorithms and extensions will be discussed. A particular emphasis will be on devising efficient algorithms and computation on city networks. Students will be expected to know how to design efficient algorithms for network analysis and implement them on various datasets. The second half of the course will tailor the network models learned in the first half to understanding the impacts of connected and autonomous vehicles (CAVs). This will be done by taking specific example problems such as autonomous intersection control, parking design, network design for CAVs etc. Recent research papers will form the basis for developing these models. Extensive use of intuitive arguments, counterintuitive phenomenon (paradoxes) and network structures will be utilized to illustrate many situations graphically. In addition, computing the solutions efficiently using various network algorithms will be discussed. The course is research based and students in addition to learning the concepts will extend the concepts to a research project to be finished within the semester.

Topics Covered:

Conceptual Foundation, User Equilibrium, Extensions of User Equilibrium, Network Models for Connected Vehicles

Prerequisites:

Undergraduate calculus, knowledge of probability, statistics, and linear algebra at the undergraduate level. Basic programming skills in languages such as Matlab or C++.

Applied / Theory:

Minimization problem, Graph theory User equilibrium problem, Solving algorithm, Stochastic user equilibrium, Network modeling for connected and autonomous vehicles

Web Address:

https://purdue.brightspace.com

Web Content:

Syllabus, grades, lecture notes, homework assignments, solutions, quizzes, exam, and project

Homework:

  • Problem sets will be given, and the analysis of these assignments will be the basis for some class discussion
  • Problem sets are due at the beginning of class on designated days; late problem sets will not be accepted.

Projects:

The paper must be scholarly and, if possible, should have some original input from the student.  The paper should be prepared according to Transportation Research Part B format.  The instructor will discuss with the student extensively and will have the final approval of the topic.

Exams:

The exam will be open textbook and open class notes.

Textbooks:

Texts:

  • Sheffi, Y. Urban Transportation Networks: Equilibrium Analysis with Mathematical programming methods. Prentice-Hall Inc., Englewood Cliffs, NJ, 1985. [Out of print].  Required handout from this text will be distributed in class. You can download a free copy of the book here: http://web.mit.edu/sheffi/www/urbanTransportation.html
  • Various readings from the instructor given in class.

Main References:

  • Ahuja, R.K., Magnanti, T.L. and Orlin, J.B. Network Flows: Theory, Algorithms and Applications. Prentice-Hall Inc., 1993.
  • Cascetta, E. Transportation systems engineering: theory and methods. Dordrecht ; Boston, MA : Kluwer Academic, 2001.
  • Bell, M.G.H., and Iida, Y. Transportation Network Analysis. John Wiley & Sons, 1997. ISBN 0471 96493 X

Journal References:

  • Transportation Research – Part B
  • IEEE Transactions of ITS
  • Transportation Research – Part C

Tentative Textbook Listing:

N/A

Computer Requirements:

Basic programming skills in languages such as Matlab or C++.