, McKnight Distinguished Professor, Computer Science and Engineering
The goal of this project is to build next-generation spatio-temporal informatics (STI) tools to analyze emerging vehicle big data such as on-board diagnostics data to further the understanding of real-world emissions and energy consumption. The specific aims are to explore a set of concepts and develop a set of spatio-temporal informatics tools to: (a) provide a mapping between the concepts in transportation science and current informatics methods, (b) conveniently represent common patterns of interest to transportation scientists and practitioners, (c) efficiently mine novel, useful, and interesting spatio-temporal patterns from
vehicle big data, (d) use mined patterns to improve the physical science models of real-world vehicle emissions and energy use, and (e) integrate research results in education via eco-driving activities. The project will advance STI knowledge and understanding in multiple ways. For example, it will probe new algorithms to detect statistically significant linear hotspots of high emissions or energy inefficiency--even if these are not along shortest paths--by considering simple paths in a transportation network. Furthermore, it will design new strategies to efficiently mine spatio-temporal co-occurrence patterns even when those are not globally prominent over the entire road network. The project will broaden STI's focus from simple
GPS-trajectory data to multi-attributed trajectory data such as vehicle on-board diagnostics data with hundreds of physical variables and constraints. It will also enrich current laboratory and test-track-focused transportation science by improving understanding of real-world energy use, emissions, and physical science models used to predict these factors.
- Project number: 2020024
- Start date: 08/2019
- Project status: Completed
- Research area: Planning and Economy