What is the best design for a transportation network? This question is especially important today because of the pervasive nature of transportation congestion. However, the design of efficient transportation networks is very challenging because of their extraordinary complex nature. That complexity arises from both the immense size of many transportation networks and their intrinsic coupling to social and economic networks that influence transportation patterns. This project will employ a type of mathematical model known as a leader-foll0wer dynamic game to mimic the experience of a metropolitan planning organization (MPO) charged with deciding how to expand and operate a complex urban road network, wherein individual drivers compete for available network capacity and retain considerable autonomy in destination, start time, and route choice for the trips they undertake. The proposed MPO-driver game will be the first mathematical model to explicitly consider the coupled nature of dynamic social, economic, and transportation networks. Through both numerical simulations and mathematical analyses of the dynamic MPO-driver game, it will determine which network architectures and operating rules make a traffic network resilient to incidents such as accidents, signal failures, and inclement weather.
This project will also explore the role of social networking and ubiquitous personal computing in achieving and maintaining efficient traffic flows. Likewise, it will investigate the role of taxes, tolls, and maintenance in the financial sustainability of road infrastructure. Finally, it will explore what contributions network design can make to the important issue of environmental sustainability. Results of such individual explorations will be combined to create general principles for designing complex road networks. Additionally, via a project website, this project will make available the methodology, including details of game-theoretic mathematical model and solution software, for complex road network design.
|Effective start/end date||9/1/10 → 8/31/15|
- National Science Foundation: $310,000.00