Uncertainty Aware Routing in Stochastic Transportation Networks with Correlated Link Travel-Times

Cornell University, Ithaca NY

Investigators

Abstract

The goal of this project is to develop mathematical models and algorithms for enabling large-scale route planning tools that account for the uncertainty of travel-times on road networks. The project addresses two major challenges. The first is to develop methods for converting very large amounts of historical vehicle trajectory data (e.g. where vehicles are at what times) into compact data representations. The approaches that are to be developed will account for real-time information (such as weather status), propagation in traffic congestion (accidents slow down nearby road segments) and user preferences (some drivers are less aggressive than others). The second stage involves developing route planning algorithms that will efficiently integrate these rich data models, and provide users with advanced route planning assistance. The project is expected to have strong educational impacts by providing undergraduate research opportunities, internships with industry, and research projects for graduate courses and projects. The outreach activities will target women and underrepresented minorities (including programs targeted at high school students) to expose them to cutting edge transportation research. Enabling large-scale uncertainty aware route planning applications requires advances in the areas of probabilistic travel-time predictions using large-scale GPS-based vehicle trajectory data and stochastic routing algorithms for networks with correlated link travel-times. This project aims to solve a problem at the intersection of large-scale data modeling and routing algorithms in the context of transportation networks. From a technical perspective, this requires developing new models, work-flows and algorithms that span the areas of traffic modeling and prediction, machine learning, and algorithm engineering. The project will have a strong focus on developing computational tractable solutions, in the context of real-world applications, with the goal of enabling the deployment of these tools in practice through industry collaborations. It is expected that successful completion of the project will lead to further research on how the availability of such route planning tools can feed back into the system and influence network performance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.