III: Small: Investigating Spatial Big Data for Next Generation Routing Services

Project Summary Increasingly, location-aware datasets are of a size, variety, and update rate that exceed the capability of spatial computing technologies. This project addresses the emerging challenges posed by such datasets, which we call Spatial Big Data (SBD). SBD examples include trajectories of cell-phones and gps devices, temporally detailed (TD) road maps, vehicle engine measurements, etc. SBD has the potential to transform society. A recent McKinsey Global Institute report estimates that personal location data could save consumers hundreds of billions of dollars annually by 2020 by helping vehicles avoid congestion via next-generation routing services such as eco-routing. Eco-routing may leverage various forms of SBD to compare routes by fuel consumption or greenhouse gas (GHG) emissions rather than total distance or travel-time. However, the envisaged SBD-based next-generation routing services pose several challenges for current techniques. First, SBD requires a change in frame of reference, moving from a global snapshot perspective to the perspective of an individual traveling through a transportation network. Second, SBD magnifies the impact of partial information and ambiguity of traditional routing queries specified by a start location and an end location. For example, traditional routing identifies a unique (or a small set of) route(s), given historical and current travel-times. In contrast, SBD may identify a much larger set of solutions, e.g., one route each for thousands of possible start-times in a week, significantly increasing computational costs. Third, SBD challenges the assumption that a single algorithm utilizing a specific dataset is appropriate for all situations. The tremendous diversity of SBD sources substantially increases the diversity of solution methods. For example , methods for determining fuel efficient routes leveraging engine measurement and gps track datasets may be quite different from algorithms used to identify minimal travel-time routes exploiting temporally detailed roadmaps. Newer algorithms may emerge as new SBD becomes available, creating the need for a flexible architecture to rapidly integrate new datasets and associated algorithms. Intellectual Merit: This project is expected to result in III innovations in three areas. First, La-grangian Xgraphs, a novel concept in computer science, will be explored at conceptual, logical and physical database levels to model travelers' frame of reference, a major departure from traditional binary relationship (e.g., adjacency) graphs. Second, to address increased computational cost from partial query specification, we will explore the concept of route-collections, and scalable algorithms for finding route-collections. For example, to identify a route-collection over all possible start-times of a given …