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PACE: a PAth-CEntric paradigm for stochastic path finding

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Abstract

With the growing volumes of vehicle trajectory data, it becomes increasingly possible to capture time-varying and uncertain travel costs, e.g., travel time, in a road network. The current paradigm for doing so is edge-centric: it represents a road network as a weighted graph and splits trajectories into small fragments that fit the underlying edges to assign time-varying and uncertain weights to edges. It then applies path finding algorithms to the resulting, weighted graph. We propose a new PAth-CEntric paradigm, PACE, that targets more accurate and more efficient path cost estimation and path finding. By assigning weights to paths, PACE avoids splitting trajectories into small fragments. We solve two fundamental problems to establish the PACE paradigm: (i) how to compute accurately the travel cost distribution of a path and (ii) how to conduct path finding for a source–destination pair. To solve the first problem, given a departure time and a query path, we show how to select an optimal set of paths that cover the query path and such that the weights of the paths enable the most accurate joint cost distribution estimation for the query path. The joint cost distribution models well the travel cost dependencies among the edges in the query path, which in turn enables accurate estimation of the cost distribution of the query path. We solve the second problem by showing that the resulting path cost distribution estimation method satisfies an incremental property that enables the method to be integrated seamlessly into existing stochastic path finding algorithms. Further, we propose a new stochastic path finding algorithm that fully explores the improved accuracy and efficiency provided by PACE. Empirical studies with trajectory data from two different cities offer insight into the design properties of the PACE paradigm and offer evidence that PACE is accurate, efficient, and effective in real-world settings.

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References

  1. Abraham, I., Delling, D., Goldberg, A.V., Werneck, R., Fonseca, F.: Hierarchical hub labelings for shortest paths. In: ESA, pp. 24–35 (2012)

  2. 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)

  3. 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)

  4. Asghari, M., Emrich, T., Demiryurek, U., Shahabi, C.: Probabilistic estimation of link travel times in dynamic road networks. In: SIGSPATIAL, pp. 47:1–47:10 (2015)

  5. Chang, T.-S., Nozick, L.K., Turnquist, M.A.: Multiobjective path finding in stochastic dynamic networks, with application to routing hazardous materials shipments. Transp. Sci. 39(3), 383–399 (2005)

    Article  Google Scholar 

  6. Chen, A., Ji, Z.: Path finding under uncertainty. J. Adv. Transp. 39(1), 19–37 (2005)

    Article  Google Scholar 

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

  8. 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 

  9. Darroch, J.N., Speed, T.P.: Additive and multiplicative models and interactions. Ann. Stat. 11(3), 724–738 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  10. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  12. 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 

  13. Geisberger, R., Sanders, P., Schultes, D., Delling, D.: Contraction hierarchies: faster and simpler hierarchical routing in road networks. In: WEA, pp. 319–333 (2008)

  14. Geisberger, R., Vetter, C.: Efficient routing in road networks with turn costs. In: SEA, pp. 100–111 (2011)

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

    Article  Google Scholar 

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

  17. 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 

  18. 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)

  19. 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 

  20. Hua, M., Pei, J.: Probabilistic path queries in road networks: traffic uncertainty aware path selection. In: EDBT, pp. 347–358 (2010)

  21. Idé, T., Sugiyama, M.: Trajectory regression on road networks. In: AAAI, pp. 203–208 (2011)

  22. Jagadish, H.V., Koudas, N., Muthukrishnan, S., Poosala, V., Sevcik, K.C., Suel, T.: Optimal histograms with quality guarantees. In: VLDB, pp. 275–286 (1998)

  23. Kaul, M., Yang, B., Jensen, C.S.: Building accurate 3D spatial networks to enable next generation intelligent transportation systems. In: MDM, pp. 137–146 (2013)

  24. Lim, S., Sommer, C., Nikolova, E., Rus, D.: Practical route planning under delay uncertainty: stochastic shortest path queries. In: Proceedings of “Robotics: Science and Systems VIII”, paper number 32 (2012)

  25. Liu, H., Jin, C., Yang, B., Zhou, A.: Finding top-k shortest paths with diversity. TKDE, 1–15 (2017). (online first)

  26. Malvestuto, F.M.: Approximating discrete probability distributions with decomposable models. IEEE Trans. Syst. Man Cybern. 21(5), 1287–1294 (1991)

    Article  Google Scholar 

  27. Miller-Hooks, E., Mahmassani, H.: Optimal routing of hazardous materials in stochastic, time-varying transportation networks. Transp. Res. Rec. J. Transp. Res. Board 1645, 143–151 (1998)

    Article  Google Scholar 

  28. Miller-Hooks, E., Mahmassani, H.S.: Path comparisons for a priori and time-adaptive decisions in stochastic, time-varying networks. Eur. J. Oper. Res. 146(1), 67–82 (2003)

    Article  MathSciNet  MATH  Google Scholar 

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

  30. Niknami, M., Samaranayake, S.: Tractable path finding for the stochastic on-time arrival problem. In: SEA, pp. 231–245 (2016)

  31. Nikolova, E., Brand, M., Karger, D.R: Optimal route planning under uncertainty. In: ICAPS, pp. 131–141 (2006)

  32. Sabran, G., Samaranayake, S., Bayen, A.: Precomputation techniques for the stochastic on-time arrival problem. In: ALENEX, pp. 138–146. SIAM (2014)

  33. Smyth, P.: Model selection for probabilistic clustering using cross-validated likelihood. Stat. Comput. 10(1), 63–72 (2000)

    Article  Google Scholar 

  34. Wang, Y., Zheng, Y., Xue, Y.: Travel time estimation of a path using sparse trajectories. In: SIGKDD, pp. 25–34 (2014)

  35. Wellman, M.P., Ford, M., Larson, K.: Path planning under time-dependent uncertainty. In: UAI, pp. 532–539 (1995)

  36. Wijeratne, A.B., Turnquist, M.A., Mirchandani, P.B.: Multiobjective routing of hazardous materials in stochastic networks. Eur. J. Oper. Res. 65(1), 33–43 (1993)

    Article  MATH  Google Scholar 

  37. 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 

  38. 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)

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

    Article  Google Scholar 

  40. 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 

  41. Yuan, J., Zheng, Y., Xie, X., Sun, G.: T-drive: enhancing driving directions with taxi drivers’ intelligence. IEEE Trans. Knowl. Data Eng. 25(1), 220–232 (2013)

    Article  Google Scholar 

  42. Zheng, J., Ni, L.M: Time-dependent trajectory regression on road networks via multi-task learning. In: AAAI, pp. 1048–1055 (2013)

Download references

Acknowledgements

This research was supported in part by the National Research Foundation, Prime Minister’s Office, Singapore, under its Competitive Research Programme (CRP Award No. NRF CRP8-2011-08), by a Grant from the Obel Family Foundation, and by the DiCyPS project.

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Authors and Affiliations

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

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

  2. School of Computer Science, National University of Singapore, Singapore, Singapore

    Jian Dai

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

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Yang, B., Dai, J., Guo, C. et al. PACE: a PAth-CEntric paradigm for stochastic path finding. The VLDB Journal 27, 153–178 (2018). https://doi.org/10.1007/s00778-017-0491-4

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  • DOI: https://doi.org/10.1007/s00778-017-0491-4

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