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The VLDB Journal

, Volume 27, Issue 2, pp 179–200 | Cite as

Risk-aware path selection with time-varying, uncertain travel costs: a time series approach

  • Jilin Hu
  • Bin Yang
  • Chenjuan Guo
  • Christian S. Jensen
Regular Paper

Abstract

We address the problem of choosing the best paths among a set of candidate paths between the same origin–destination pair. This functionality is used extensively when constructing origin–destination matrices in logistics and flex transportation. Because the cost of a path, e.g., travel time, varies over time and is uncertain, there is generally no single best path. We partition time into intervals and represent the cost of a path during an interval as a random variable, resulting in an uncertain time series for each path. When facing uncertainties, users generally have different risk preferences, e.g., risk-loving or risk-averse, and thus prefer different paths. We develop techniques that, for each time interval, are able to find paths with non-dominated lowest costs while taking the users’ risk preferences into account. We represent risk by means of utility function categories and show how the use of first-order and two kinds of second-order stochastic dominance relationships among random variables makes it possible to find all paths with non-dominated lowest costs. We report on empirical studies with large uncertain time series collections derived from a 2-year GPS data set. The study offers insight into the performance of the proposed techniques, and it indicates that the best techniques combine to offer an efficient and robust solution.

Keywords

Risk preferences Stochastic dominance Uncertain time series Utility functions 

Notes

Acknowledgements

This research was supported in part by a grant from the Obel Family Foundation and by the DiCyPS center, funded by Innovation Fund Denmark.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceAalborg UniversityAalborgDenmark

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