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Finding Risk-Averse Shortest Path with Time-Dependent Stochastic Costs

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Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10053))

Abstract

In this paper, we tackle the problem of risk-averse route planning in a transportation network with time-dependent and stochastic costs. To solve this problem, we propose an adaptation of the A* algorithm that accommodates any risk measure or decision criterion that is monotonic with first-order stochastic dominance. We also present a case study of our algorithm on the Manhattan, NYC, transportation network.

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Notes

  1. 1.

    The SFIFO property states that for any confidence level \(\alpha \), leaving later cannot lead to an earlier arrival time: \(t \le t' \implies t + F^-1_{C_t}(\alpha ) \le t' + F^-1_{C_{t'}}(\alpha )\) where \(t, t'\) are departure times, \(C_t, C_{t'}\) random costs of an edge and \(\alpha \in [0, 1]\).

  2. 2.

    For space reasons, we do not include the proofs.

  3. 3.

    http://www.opentripplanner.org.

  4. 4.

    http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml.

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Li, D., Weng, P., Karabasoglu, O. (2016). Finding Risk-Averse Shortest Path with Time-Dependent Stochastic Costs. In: Sombattheera, C., Stolzenburg, F., Lin, F., Nayak, A. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2016. Lecture Notes in Computer Science(), vol 10053. Springer, Cham. https://doi.org/10.1007/978-3-319-49397-8_9

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  • DOI: https://doi.org/10.1007/978-3-319-49397-8_9

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