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.
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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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Hu, J., Yang, B., Guo, C. et al. Risk-aware path selection with time-varying, uncertain travel costs: a time series approach. The VLDB Journal 27, 179–200 (2018). https://doi.org/10.1007/s00778-018-0494-9
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DOI: https://doi.org/10.1007/s00778-018-0494-9