Modern vehicles are increasingly being equipped with rich instrumentation that enables them to collect location aware data on a wide variety of travel related phenomena such as the real-world performance of engines and powertrain, driver preferences, context of the vehicle with respect to others nearby, and-indirectly-traffic on the transportation network itself. Combined with their increased access to the Internet, these connected vehicles are opening up vast opportunities to improve the safety, environmental friendliness, and the overall experience of urban travel. However, significant spatial computing challenges need to be addressed before we can realize the full potential of connected vehicles. This paper presents some of the open research questions under this theme from the perspectives of query processing, data science and data engineering.
|Original language||English (US)|
|Title of host publication||23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015|
|Editors||Yan Huang, Mohamed Ali, Jagan Sankaranarayanan, Matthias Renz, Michael Gertz|
|Publisher||Association for Computing Machinery|
|State||Published - Nov 3 2015|
|Event||23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015 - Seattle, United States|
Duration: Nov 3 2015 → Nov 6 2015
|Name||GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems|
|Other||23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015|
|Period||11/3/15 → 11/6/15|
Bibliographical noteFunding Information:
This material is based upon work supported by the National Science Foundation under Grant No. 1029711, IIS-1320580, 0940818, and IIS-1218168, the USDOD under Grant No. HM0210-13-1-0005, and the University of Minnesota under the OVPR U-Spatial. We are particularly grateful to Kim Koffolt for her help in editing this paper.
© 2015 ACM.
- Connected vehicles
- Spatial and spatio-temporal data mining
- Spatial and spatio-temporal graphs
- Spatial big data
- Spatial statistics