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Eco- Routing Using Spatial Big Data

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  1. Eco- Routing Using Spatial Big Data http://www.spatial.cs.umn.edu/eco-routing/files/iii_2012.pdf

  2. Outline of the Talk • What is Spatial Big Data ? • Examples of Spatial Big Data. • GPS traces • Temporally Detailed roadmaps • Engine measurement data • Transformative Potential of Spatial Big Data • Broad Challenges raised by Spatial Big Data • Model traveler's frame of reference • Partial nature of the query. • Scalable architecture for spatial big data. • Other resources.

  3. What is Spatial Big Data Spatial datasets exceeding capacity of current computing systems • To manage, process, or analyze the data with reasonable effort • Due to Volume, Velocity, Variety Examples of Spatial Big Data (SBD): • GPS traces • Temporally detailed roadmaps • Engine measurement data e.g. GHG emissions and fuel consumption

  4. Traditional Spatial Big Data: Digital Roadmaps

  5. Examples of Spatial Big Data: Temporally Detailed Roadmaps Temporally Detailed Roadmaps • List speed/travel time for several start-times in a typical week • Shortest route for a specific start-time. • Can compare a route across start-times: best start-time

  6. Spatial Big Data: GPS traces And Engine Measurement Data Sources: Mobile devices Smart phones, in car/truck GPS devices, GPS collars etc Use Cases: • Tracking, Tracing, • Improve service, deter theft • Model traveler’s frame of ref. • Patterns of Life • Eco-routing

  7. Outline of the Talk • What is Spatial Big Data ? • Examples of Spatial Big Data. • GPS traces • Temporally Detailed roadmaps • Engine measurement data • Transformative Potential of Spatial Big Data • Broad Challenges raised by Spatial Big Data • Model traveler's frame of reference • Partial nature of the query • Growing diversity of sources • Other resources.

  8. Transformative Potential of Spatial Big Data:Businesses

  9. Transformative Potential of Spatial Big Data:Society and Environment • Significantly reduce US consumption of petroleum, the dominant source of energy for transportation. • Reduce the gap between domestic petroleum consumption and production. • Reduce greenhouse gas (GHG) emissions

  10. U.P.S. Embraces High-Tech Delivery Methods (July 12, 2007) By “The research at U.P.S. is paying off. ……..— savingroughlythree million gallons of fuel in good part by mapping routes thatminimize left turns.” Transformative Potential of SBD: Eco-Routing Do you idle at green light during traffic congestion? • Minimize fuel consumption and GPG emission • rather than proxies, e.g. distance, travel-time • avoid congestion, idling at red-lights, turns and elevation changes, etc.

  11. Outline of the Talk • What is Spatial Big Data ? • Examples of Spatial Big Data. • GPS traces • Temporally Detailed roadmaps • Engine measurement data • Transformative Potential of Spatial Big Data • Broad Challenges raised by Spatial Big Data • Model traveler's frame of reference • Partial nature of the query • Growing diversity of sources • Other resources.

  12. SBD Challenge: Modeling Traveler’s Frame of Reference • GOAL: Candidate routes should be evaluated from the perspective of a person moving through the transportation network. Lagrangian Frame of Reference

  13. SBD Challenge: Modeling Traveler’s Frame of Reference • Other experiences: • Synchronized traffic signals ? • Turn delays ? • Waiting at traffic signals? • Need new models for these experiences. • GPS traces obtained from in-car • navigation devices may have these already Waiting at signals

  14. Modeling Traveler’s Frame of Reference: task 1 Task: Exploring Data Representations for Modeling Traveler's Frame of Reference in Routing Queries • Goal: Explore the challenges raised while designing a data model for routing queries on TD roadmaps. Ref section 3.1 in proposal References: • Erik G. Hoel, Wee-Liang Heng, and Dale Honeycutt. High performance multimodal networks. In Advances in Spatial and Temporal Databases, pages 308--327, 2005. Springer. LNCS 3633. • G. Gallo, G. Longo, S. Pallottino, and S. Nguyen. Directed hypergraphs and applications. Elsevier, Discrete applied mathematics, 42(2):177--201, 1993.

  15. Modeling Traveler’s Frame of Reference: Task 2a Task: Scalable query processing techniques for route recommendation using GPS traces • Goal:explore scalable query processing to make route recommendations from GPS tracks without graph traversal algorithms.. Ref section 3.1 in proposal • Constraint: assume there exits a GPS trace between the given source and destination References: • Y. Zheng and X. Zhou. Computing with spatial trajectories. Springer, 2011. • Long-Van Nguyen-Dinh, Walid G. Aref, and Mohamed F. Mokbel. Spatio-temporal access methods: Part 2 (2003 - 2010). IEEE Data Eng. Bull., 33(2):46--55, 2010. • Mohamed F. Mokbel, Thanaa M. Ghanem, and Walid G. Aref. Spatio-temporal access methods. IEEE Data Eng. Bull., 26(2):40--49, 2003.

  16. Modeling Traveler’s Frame of Reference: Task 2b Task: Scalable query processing techniques for route recommendation using GPS traces • Goal:explore scalable query processing to make route recommendations from GPS tracks without graph traversal algorithms.. Ref section 3.1 in proposal • Constraint: Assumption in Task 2a is dropped. References: • Y. Zheng and X. Zhou. Computing with spatial trajectories. Springer, 2011. • Long-Van Nguyen-Dinh, Walid G. Aref, and Mohamed F. Mokbel. Spatio-temporal access methods: Part 2 (2003 - 2010). IEEE Data Eng. Bull., 33(2):46--55, 2010. • Mohamed F. Mokbel, Thanaa M. Ghanem, and Walid G. Aref. Spatio-temporal access methods. IEEE Data Eng. Bull., 26(2):40--49, 2003.

  17. SBD Challenge: Partial Nature of Traditional Routing Query SBD magnifies the partial nature of the traditional routing query • Traditional routing query: “Find shortest path between source and destination” ref section 3.2 in the proposal Additional questions raised by SBD • At what start time? Different routes may be optimal at different start-times • Preference metric? Route minimizing fuel and GHG may not shortest!

  18. SBD Challenge: Scalable Architecture for Spatial Big Data Task: Exploring the a `Distributed Architecture' for Routing Queries on TD roadmaps • Goal:The goal of this project is to explore efficient storage systems for TD roadmaps which support a big workload of common queries involving SP-TAG, BEST and CTAS algorithms [1,2] • You would need to evaluate the performance of G*[3] for SBD work loads. References: • [1] Betsy George, Sangho Kim, ShashiShekhar: Spatio-temporal Network Databases and Routing Algorithms: A Summary of Results. SSTD 2007: 460-477 • [2] Venkata M. V. Gunturi, Ernesto Nunes, KwangSoo Yang, ShashiShekhar: A Critical-Time-Point Approach to All-Start-Time Lagrangian Shortest Paths: A Summary of Results. SSTD 2011: 74-91 • [3] G* Dynamic Graph Database: http://www.cs.albany.edu/~gstar/

  19. Some Resources • GeoLife project from MSR: • http://research.microsoft.com/en-us/downloads/b16d359d-d164-469e-9fd4-daa38f2b2e13/ • T-Drive project from MSR • http://research.microsoft.com/en-us/projects/tdrive/