Capturing the Uncertainty of Moving-Object Representations
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
On map-matching vehicle tracking data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Dynamic Travel Time Maps - Enabling Efficient Navigation
SSDBM '06 Proceedings of the 18th International Conference on Scientific and Statistical Database Management
A Service Oriented Approach to Traffic Dependent Navigation Systems
SERVICES '08 Proceedings of the 2008 IEEE Congress on Services - Part I
A new perspective on efficient and dependable vehicle routing
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
T-drive: driving directions based on taxi trajectories
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Clustering user trajectories to find patterns for social interaction applications
W2GIS'12 Proceedings of the 11th international conference on Web and Wireless Geographical Information Systems
EcoMark: evaluating models of vehicular environmental impact
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
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The application domain of intelligent transportation is plagued by a shortage of data sources that adequately assess traffic situations. Typically, to provide routing and navigation solutions map attributes in the form of static weights as derived from road categories and speed limits used for road networks. With the advent of Floating Car Data (FCD) and specifically the GPS-based tracking data component, a means was found to derive accurate and up-to-date travel times, i.e., qualitative traffic information. FCD is a by-product in fleet management applications and given a minimum number and uniform distribution of vehicles, this data can be used for accurate traffic assessment and also prediction. This work showcases a system that facilitates the collection of FCD, produces dynamic travel time information, and provides value-added services based on the dynamic travel times. The essential components that will be discussed are a Web-services-based data collection approach, sophisticated map-matching algorithms, a data management architecture and an online visualization platform.