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Toward personalized, context-aware routing

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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.

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Notes

  1. A recent study [27] from Microsoft Research Asia suggests that although some services (e.g., Google Maps, Bing Maps, and Yahoo! Maps) can display time-dependent traffic conditions, such information is not used in their routing services.

  2. FlexDanmark is a large fleet manager in Denmark. See https://www.flexdanmark.dk/.

  3. http://www.openstreetmap.org.

  4. Given a vector \({\mathbf {c}}\), the \(i\)th element of the vector is denoted as \({\mathbf {c}}[i]\).

  5. We use “personalized skyline route” instead of “skyline route” because when deriving the cost vector for a route, we consider driver \(d_m\)’s driving behavior which is quantified by \(d_m\)’s personal ratio.

  6. http://www.lix.polytechnique.fr/~nielsen/MEF/.

  7. http://commons.apache.org/proper/commons-math/.

  8. Here, the weights refer to the lengths of edges. Alternative weights may refer to the travel times or the fuel consumption of edges. We do not consider them in the experiments because both travel time and fuel consumption of edges are uncertain, rendering them unattractive for use as weights.

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Acknowledgments

This work was supported by the REDUCTION project, funded as EU FP7 STREP Project No. 288254.

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Authors and Affiliations

  1. Department of Computer Science, Aalborg University, Ålborg, Denmark

    Bin Yang, Chenjuan Guo & Christian S. Jensen

  2. Department of Computer Science, Aarhus University, Århus, Denmark

    Yu Ma

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  1. Bin Yang
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  2. Chenjuan Guo
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  3. Yu Ma
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  4. Christian S. Jensen
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Correspondence to Chenjuan Guo.

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Yang, B., Guo, C., Ma, Y. et al. Toward personalized, context-aware routing. The VLDB Journal 24, 297–318 (2015). https://doi.org/10.1007/s00778-015-0378-1

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