Collaborative Research: Transportation Network Identification: Information Fusion via Stochastic Optimization

Rensselaer Polytechnic Institute, Troy NY

Investigators

Abstract

Knowledge of the traffic network state, in terms of, for example, network-level traffic flow and travel time, is critically needed for network monitoring and effective transportation system management and control under both normal and extreme conditions. Recent technological advances, such as mobile sensing and connected vehicles, can generate big data. In general, this research aims to address challenges on how to best use these inherently large-scale, dynamic, and heterogeneous (multi-source) data streams. More specifically, the project seeks an innovative systems approach for estimating the statistical properties of traffic flow and travel time via integrating various traffic data pieces over a complex network structure (such as traffic counts collected by fixed sensors and individual "urban digital footprints" collected by Bluetooth tag reader logs, cellular phone records, and global positioning system traces). A successful outcome of this project will directly benefit society through more effective utilization of information, and thus more sustainable and efficient transportation system planning and operations. This project includes curricula development and student mentoring activities that help better prepare next-generation transportation professionals for challenges brought by the big data era. Mathematically, the question addressed in this project is: Given directly measurable network parameters x, which tend to be localized and incomplete, how can one infer global network parameters y that are often difficult to be measured directly, with the mapping between y and x built on a complex network structure? This problem category has broad applications in transportation, communication, and energy networks; although, the focus in this project is on transportation networks. Built on knowledge in transportation network science, stochastic optimization, variational analysis, and non-parametric estimation, the research team will pursue the following main tasks: (1) Creation of an optimization framework for network identification based on stochastic optimization and non-parametric estimation; (2) Integration of multi-source traffic data (hard information) with domain-knowledge-based soft information via constraints and functional mapping; (3) Linking historical-data-based offline estimation with real-time information for online estimation and decision support through Bayesian methods; (4) Testing and validating the project's methods using both real-world data and computer simulations. This research establishes a unifying theoretical framework for traffic network identification that integrates knowledge in transportation network science and data analytics. By providing greater modeling flexibility than existing methods in handling various types of hard and soft information and in capturing transportation network physics, this new method ushers a paradigm-shift in traffic network system identification. The project also creates a real-world engineering platform for strengthening connections between optimization and statistics.