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

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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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Notes

  1. http://www.postnord.dk/.

  2. https://www.flexdanmark.dk/.

  3. http://academic.udayton.edu/kissock/http/Weather/.

  4. https://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/.

References

  1. Guo, C., Jensen, C.S., Yang, B.: Towards total traffic awareness. SIGMOD Record 43(3), 18–23 (2014)

    Article  Google Scholar 

  2. Walpen, J., Mancinelli, E.M., Lotito, P.A.: A heuristic for the od matrix adjustment problem in a congested transport network. Eur. J. Oper. Res. 242(3), 807–819 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  3. Youngblom, E.: Travel time in macroscopic traffic models for origin-destination estimation. Master’s Thesis, University of Wisconsin-Milwaukee (2013)

  4. Multimodal accessibility measures in the Helsinki metropolitan region: metropaccess-travel time matrix. The Department of Geosciences and Geography, University of Helsinki. http://blogs.helsinki.fi/accessibility/data/ (2015)

  5. Wang, F., Yanqing, X.: Estimating o-d travel time matrix by google maps api: implementation, advantages, and implications. Ann. GIS 17(4), 199–209 (2011)

    Google Scholar 

  6. Yang, B., Kaul, M., Jensen, C.S.: Using incomplete information for complete weight annotation of road networks. IEEE Trans. Knowl. Data Eng. 26(5), 1267–1279 (2014)

    Article  Google Scholar 

  7. Yang, B., Guo, C., Jensen, C.S., Kaul, M., Shang, S.: Stochastic skyline route planning under time-varying uncertainty. In: ICDE, pp. 136–147 (2014)

  8. Yang, B., Guo, C., Jensen, C.S.: Travel cost inference from sparse, spatio-temporally correlated time series using markov models. PVLDB 6(9), 769–780 (2013)

    Google Scholar 

  9. Erkut, E., Ingolfsson, A., Erdoğan, G.: Ambulance location for maximum survival. Naval Res. Log. (NRL) 55(1), 42–58 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  10. Woodard, D., Nogin, G., Koch, P., Racz, D., Goldszmidt, M., Horvitz, E.: Predicting travel time reliability using mobile phone GPS data. Transp. Res. Part C Emerg. Technol. 75, 30–44 (2017)

    Article  Google Scholar 

  11. Jenelius, E.: The value of travel time variability with trip chains, flexible scheduling and correlated travel times. Transp. Res. Part B Methodol. 46(6), 762–780 (2012)

    Article  Google Scholar 

  12. Newson, P., Krumm, J.: Hidden Markov map matching through noise and sparseness. In: SIGSPATIAL, pp. 336–343 (2009)

  13. Ding, Z., Yang, B., Güting, R.H., Li, Y.: Network-matched trajectory-based moving-object database: models and applications. IEEE Trans Intell Transp Syst 16(4), 1918–1928 (2015)

    Article  Google Scholar 

  14. Yang, B., Guo, C., Ma, Y., Jensen, C.S.: Toward personalized, context-aware routing. VLDB J. 24(2), 297–318 (2015)

    Article  Google Scholar 

  15. Ding, Z., Yang, B., Chi, Y., Guo, L.: Enabling smart transportation systems: A parallel spatio-temporal database approach. IEEE Trans Comput 65(5), 1377–1391 (2016)

    Article  MathSciNet  Google Scholar 

  16. Dai, J., Yang, B., Guo, C., Jensen, C.S., Jilin, H.: Path cost distribution estimation using trajectory data. PVLDB 10(3), 85–96 (2016)

    Google Scholar 

  17. Jilin, H., Yang, B., Jensen, C.S., Ma, Y.: Enabling time-dependent uncertain eco-weights for road networks. GeoInformatica 21(1), 57–88 (2017)

    Article  Google Scholar 

  18. Yang, B., Dai, J., Guo, C., Jensen, CS.: PACE: A PAth-CEntric paradigm for stochastic path finding. VLDB J (2017)

  19. Kijima, M., Ohnishi, M.: Stochastic orders and their applications in financial optimization. Math. Methods Oper. Res. 50(2), 351–372 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  20. Levy, H.: Stochastic dominance and expected utility: survey and analysis. Manag. Sci. 38(4), 555–593 (1992)

    Article  MATH  Google Scholar 

  21. Weinstein, A., Marsden, J.: Calculus II, 2nd edn. Springer, New York (1985)

    MATH  Google Scholar 

  22. Yang, B., Ma, Q., Qian, W., Zhou, A.: TRUSTER: TRajectory data processing on ClUSTERs. DASFAA 768–771 (2009)

  23. Dallachiesa, M., Nushi, B., Mirylenka, K., Palpanas, T.: Uncertain time-series similarity: return to the basics. PVLDB 5(11), 1662–1673 (2012)

    Google Scholar 

  24. Aßfalg, J., Kriegel, H.P., Kröger, P., Renz, M.: Probabilistic similarity search for uncertain time series. In: SSDBM, pp. 435–443 (2009)

  25. Dallachiesa, M., Palpanas, T., Ilyas, I.F.: Top-k nearest neighbor search in uncertain data series. PVLDB 8(1), 13–24 (2014)

    Google Scholar 

  26. Lin, X., Zhang, Y., Zhang, W., Cheema, M.A.: Stochastic skyline operator. In: ICDE, pp. 721–732 (2011)

  27. Zhang, W., Lin, X., Zhang, Y., Cheema, M.A., Zhang, Q.: Stochastic skylines. TODS 37(2), 14 (2012)

    Article  Google Scholar 

  28. Li, Q., Nie, Y.M., Vallamsundar, S., Lin, J., Homem-de Mello, T.: Finding efficient and environmentally friendly paths for risk-averse freight carriers. Netw. Spat. Econ. 16(1), 255–275 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  29. Börzsönyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: ICDE, pp. 421–430 (2001)

  30. Pei, J., Jiang, B., Lin, X., Yuan, Y.: Probabilistic skylines on uncertain data. In: PVLDB, pp. 15–26 (2007)

  31. Zhang, W., Lin, X., Zhang, Y., Wang, W., Yu, J.X.: Probabilistic skyline operator over sliding windows. In: ICDE, pp. 1060–1071 (2009)

  32. Guo, C., Yang, B., Andersen, O., Jensen, C.S., Torp, K.: Ecosky: Reducing vehicular environmental impact through eco-routing. In: ICDE, pp. 1412–1415 (2015)

  33. Sarma, A.D., Benjelloun, O., Halevy, A., Widom, J.: Working models for uncertain data. In: ICDE, p. 7 (2006)

  34. Yeh, M.-Y., Wu, K.-L., Yu, P.S., Chen, M.-S.: PROUD: a probabilistic approach to processing similarity queries over uncertain data streams. In: EDBT, pp. 684–695 (2009)

  35. Sarangi, S. R., Murthy, K.: DUST: a generalized notion of similarity between uncertain time series. In: SIGKDD, pp. 383–392 (2010)

  36. Guoliang He, L., Chen, C.Z., Zheng, Q., Zhou, G.: Probabilistic skyline queries on uncertain time series. Neurocomputing 191, 224–237 (2016)

    Article  Google Scholar 

  37. Dai, J., Yang, B., Guo, C., Ding, Z.: Personalized route recommendation using big trajectory data. In: ICDE, pp. 543–554 (2015)

  38. Delling, D., Goldberg, A. V., Goldszmidt, M., Krumm, J., Talwar, K., Werneck, R.F.: Navigation made personal: inferring driving preferences from GPS traces. In: SIGSPATIAL, pp. 31:1–31:9 (2015)

  39. Balteanu, A., Jossé, G., Schubert, M.: Mining driving preferences in multi-cost networks. In: SSTD, pp. 74–91 (2013)

  40. Liu, H., Jin, C., Yang, B., Zhou, A.: Finding top-k shortest paths with diversity. IEEE Trans Knowl Data Eng (2017)

  41. Shang, S., Zheng, K., Jensen, C.S., Yang, B., Kalnis, P., Li, G., Wen, J.-R.: Discovery of path nearby clusters in spatial networks. IEEE Trans Knowl Data Eng 27(6), 1505–1518 (2015)

    Article  Google Scholar 

  42. Aljubayrin, S., Yang, B., Jensen, C.S., Zhang, R: Finding non-dominated paths in uncertain road networks. In: SIGSPATIAL, pp. 15:1–15:10 (2016)

  43. Guo, C., Yang, B., Andersen, O., Jensen, C.S., Torp, K.: Ecomark 2.0: empowering eco-routing with vehicular environmental models and actual vehicle fuel consumption data. GeoInformatica 19(3), 567–599 (2015)

    Article  Google Scholar 

  44. Guo, C., Ma, Y., Yang, B., Jensen, C.S., Kaul, M.: EcoMark: evaluating models of vehicular environmental impact. In: SIGSPATIAL, pp. 269–278 (2012)

  45. Andersen, O., Jensen, C.S., Torp, K., Yang, B.: Ecotour: Reducing the environmental footprint of vehicles using eco-routes. In: MDM, pp. 338–340 (2013)

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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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Authors and Affiliations

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

    Jilin Hu, Bin Yang, Chenjuan Guo & Christian S. Jensen

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  1. Jilin Hu
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  2. Bin Yang
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  3. Chenjuan Guo
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  4. Christian S. Jensen
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Correspondence to Bin Yang.

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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

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