Toward personalized, context-aware routing

Toward personalized, context-aware routing A driver’s choice of a route to a destination may depend on the route’s length and travel time, but a multitude of other, possibly hard-to-formalize aspects, may also factor into the driver’s decision. There is evidence that a driver’s choice of route is context dependent, e.g., varies across time, and that route choice also varies from driver to driver. In contrast, conventional routing services support little in the way of context dependence, and they deliver the same routes to all drivers. We study how to identify context-aware driving preferences for individual drivers from historical trajectories, and thus how to provide foundations for personalized navigation, but also professional driver education and traffic planning. We provide techniques that are able to capture time-dependent and uncertain properties of dynamic travel costs, such as travel time and fuel consumption, from trajectories, and we provide techniques capable of capturing the driving behaviors of different drivers in terms of multiple dynamic travel costs. Further, we propose techniques that are able to identify a driver’s contexts and then to identify driving preferences for each context using historical trajectories from the driver. Empirical studies with a large trajectory data set offer insight into the design properties of the proposed techniques and suggest that they are effective. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Toward personalized, context-aware routing

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2015 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-015-0378-1
Publisher site
See Article on Publisher Site

Abstract

A driver’s choice of a route to a destination may depend on the route’s length and travel time, but a multitude of other, possibly hard-to-formalize aspects, may also factor into the driver’s decision. There is evidence that a driver’s choice of route is context dependent, e.g., varies across time, and that route choice also varies from driver to driver. In contrast, conventional routing services support little in the way of context dependence, and they deliver the same routes to all drivers. We study how to identify context-aware driving preferences for individual drivers from historical trajectories, and thus how to provide foundations for personalized navigation, but also professional driver education and traffic planning. We provide techniques that are able to capture time-dependent and uncertain properties of dynamic travel costs, such as travel time and fuel consumption, from trajectories, and we provide techniques capable of capturing the driving behaviors of different drivers in terms of multiple dynamic travel costs. Further, we propose techniques that are able to identify a driver’s contexts and then to identify driving preferences for each context using historical trajectories from the driver. Empirical studies with a large trajectory data set offer insight into the design properties of the proposed techniques and suggest that they are effective.

Journal

The VLDB JournalSpringer Journals

Published: Apr 1, 2015

References

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