Adaptive Routing for Road Traffic
IEEE Computer Graphics and Applications
TURAS: a personalised route planning system
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Adaptive learning of semantic locations and routes
Proceedings of the 1st international conference on Autonomic computing and communication systems
Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Review: Ambient intelligence: Technologies, applications, and opportunities
Pervasive and Mobile Computing
Realistic Driving Trips For Location Privacy
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
Maximum entropy inverse reinforcement learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Inference attacks on location tracks
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
Driving with knowledge from the physical world
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A Bayesian nonparametric approach to modeling motion patterns
Autonomous Robots
Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Mining driving preferences in multi-cost networks
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
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Popular route planning systems (Windows Live Local, Yahoo! Maps, Google Maps, etc.) generate driving directions using a static library of roads and road attributes. They ignore both the time at which a route is to be traveled and, more generally, the preferences of the drivers they serve. We present a set of methods for including driver preferences and time-variant traffic condition estimates in route planning. These methods have been incorporated into a working prototype named TRIP. Using a large database of GPS traces logged by drivers, TRIP learns time-variant traffic speeds for every road in a widespread metropolitan area. It also leverages a driver's past GPS logs when responding to future route queries to produce routes that are more suited to the driver's individual driving preferences. Using experiments with real driving data, we demonstrate that the routes produced by TRIP are measurably closer to those actually chosen by drivers than are the routes produced by routers that use static heuristics.