Increasingly, location-aware sensors in urban transportation networks are generating a wide variety of data which has spatio-temporal network semantics. Examples include temporally detailed roadmaps, GPS tracks, traffic signal timings, and vehicle measurements. These datasets, which we collectively call Big Spatio-Temporal Network (BSTN) Data, have value addition potential for several smart-city use-cases including navigation services which recommend eco-friendly routes. However, BSTN data pose significant computational challenges regarding the assumptions of the current state-of-the-art analytic-techniques used in these services. This article attempts to put forth some potential research directions towards addressing the challenges of scalable analytics on BSTN data. Two kinds of BSTN data are considered here, viz., the vehicle measurement big data and the travel-time big data.
|Original language||English (US)|
|Title of host publication||Springer Geography|
|Number of pages||14|
|State||Published - 2017|
Bibliographical noteFunding Information:
Acknowledgment This work was supported by: NSF IIS-1320580 and 0940818; USDOD HM1582-08-1-0017 and HM0210-13-1-0005; IDF from UMN. We would also like to thank Prof William Northrop and Andrew Kotz of University of Minnesota for providing visualizations of the vehicle measurement big data and an initial insight into interpreting it. The content does not necessarily reflect the position or the policy of the government and no official endorsement should be inferred.
- Road networks
- Spatial data mining
- Spatial databases
- Spatio-temporal networks