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Finding Vacant Taxis Using Large Scale GPS Traces

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Web-Age Information Management (WAIM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8485))

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Abstract

In modern cities, more and more vehicles, such as taxis, have been equipped with GPS devices for localization and navigation. The GPS-equipped taxis can be viewed as pervasive sensors and the large scale traces allow us to reveal many hidden “facts” about the city dynamics. In this paper, we aim to estimate the wait time and probability of taking a vacant taxi according to time and position. Further more, we provide recommendations for passengers who want to take a vacant taxi. To achieve these objectives, firstly we preprocess the large scale taxi GPS traces data set to generate the Map Grid Based(MGB) index. Secondly, with the MGB index, we apply the nonhomogeneous Poisson process corrected by the conditions of road and weather(NPPCRW) method to perform estimation and recommendation. We build our system based on a large scale real-world GPS traces data set generated from more than 12000 taxis in Beijing over a 110 days period. Then we validate the system with extensive evaluations including in-the-field user studies.

This work was supported by Natural Science Foundation of China (No.60973002 and No.61170003), the National High Technology Research and Development Program of China (Grant No. 2012AA011002), and MOE-CMCC Research Fund.

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References

  1. Lu, J., Wang, W.: Confirming method of urban taxi quantity. Journal of Traffic and Transportation Engineering 4(1), 92–95 (2004)

    Google Scholar 

  2. Zheng, Y., Liu, Y., Yuan, J., Xie, X.: Urban computing with taxicabs. In: Proceedings of the 13th International Conference on Ubiquitous Computing, pp. 89–98. ACM (2011)

    Google Scholar 

  3. Castro, P.S., Zhang, D., Li, S.: Urban traffic modelling and prediction using large scale taxi GPS traces. In: Kay, J., Lukowicz, P., Tokuda, H., Olivier, P., Krüger, A. (eds.) Pervasive 2012. LNCS, vol. 7319, pp. 57–72. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  4. Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)

    Article  Google Scholar 

  5. Li, X., Pan, G., Wu, Z., Qi, G., Li, S., Zhang, D., Zhang, W., Wang, Z.: Prediction of urban human mobility using large-scale taxi traces and its applications. Frontiers of Computer Science 6(1), 111–121 (2012)

    MathSciNet  Google Scholar 

  6. Chang, H.W., Tai, Y.C., Hsu, J.Y.J.: Context-aware taxi demand hotspots prediction. International Journal of Business Intelligence and Data Mining 5(1), 3–18 (2010)

    Article  Google Scholar 

  7. Reades, J., Calabrese, F., Sevtsuk, A., Ratti, C.: Cellular census: Explorations in urban data collection. IEEE Pervasive Computing 6(3), 30–38 (2007)

    Article  Google Scholar 

  8. Wong, K., Wong, S., Bell, M., Yang, H.: Modeling the bilateral micro-searching behavior for urban taxi services using the absorbing markov chain approach. Journal of Advanced Transportation 39(1), 81–104 (2005)

    Article  Google Scholar 

  9. Liu, L., Andris, C., Ratti, C.: Uncovering cabdrivers behavior patterns from their digital traces. Computers Environment and Urban Systems 34(6), 541–548 (2010)

    Article  Google Scholar 

  10. Yang, H., Fung, C., Wong, K., Wong, S.: Nonlinear pricing of taxi services. Transportation Research Part A: Policy and Practice 44(5), 337–348 (2010)

    Google Scholar 

  11. Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Communications of the ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  12. Lee, D.H., Wang, H., Cheu, R.L., Teo, S.H.: Taxi dispatch system based on current demands and real-time traffic conditions. Transportation Research Record: Journal of the Transportation Research Board 1882(1), 193–200 (2004)

    Article  Google Scholar 

  13. Yang, H., Yang, T.: Equilibrium properties of taxi markets with search frictions. Transportation Research Part B: Methodological 45(4), 696–713 (2011)

    Article  Google Scholar 

  14. Yamamoto, K., Uesugi, K., Watanabe, T.: Adaptive routing of cruising taxis by mutual exchange of pathways. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part II. LNCS (LNAI), vol. 5178, pp. 559–566. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  15. Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., Huang, Y.: T-drive: driving directions based on taxi trajectories. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 99–108. ACM (2010)

    Google Scholar 

  16. Ge, Y., Xiong, H., Tuzhilin, A., Xiao, K., Gruteser, M., Pazzani, M.: An energy-efficient mobile recommender system. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 899–908. ACM (2010)

    Google Scholar 

  17. Li, B., Zhang, D., Sun, L., Chen, C., Li, S., Qi, G., Yang, Q.: Hunting or waiting? Discovering passenger-finding strategies from a large-scale real-world taxi dataset. In: 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 63–68. IEEE (2011)

    Google Scholar 

  18. Phithakkitnukoon, S., Veloso, M., Bento, C., Biderman, A., Ratti, C.: Taxi-aware map: Identifying and predicting vacant taxis in the city. In: de Ruyter, B., Wichert, R., Keyson, D.V., Markopoulos, P., Streitz, N., Divitini, M., Georgantas, N., Mana Gomez, A. (eds.) AmI 2010. LNCS, vol. 6439, pp. 86–95. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  19. Yuan, N., Zheng, Y., Zhang, L., Xie, X.: T-finder: A recommender system for finding passengers and vacant taxis (2012)

    Google Scholar 

  20. Zheng, X., Liang, X., Xu, K.: Where to wait for a taxi? In: Proceedings of the ACM SIGKDD International Workshop on Urban Computing, pp. 149–156. ACM (2012)

    Google Scholar 

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Qiu, Z. et al. (2014). Finding Vacant Taxis Using Large Scale GPS Traces. In: Li, F., Li, G., Hwang, Sw., Yao, B., Zhang, Z. (eds) Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science, vol 8485. Springer, Cham. https://doi.org/10.1007/978-3-319-08010-9_85

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  • DOI: https://doi.org/10.1007/978-3-319-08010-9_85

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08009-3

  • Online ISBN: 978-3-319-08010-9

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