Abstract
Increasing the on-time rate of bus service can prompt the people’s willingness to travel by bus, which is an effective measure to mitigate the city traffic congestion. Performing queries on the bus arrival can be used to identify and analyze various kinds of non-on-time events that happened during the bus journey, which is helpful for detecting the factors of delaying events, and providing decision support for optimizing the bus schedules. We propose a data management model, called Bus-OLAP, for querying bus monitoring data, considering the characteristics of bus monitoring data and the scenarios of on-time analysis. While fulfilling typical requirements of bus monitoring data analysis, Bus-OLAP not only provides a flexible way to manage the data and to implement multiple granularity data query and update, but also supports distributed query and computation. The experiments on real-world bus monitoring data verify that Bus-OLAP is effective and efficient.
This work was supported in part by NSFC 61572332, the Fundamental Research Funds for the Central Universities 2016SCU04A22, the China Postdoctoral Science Foundation 2016T90850, and the Academy of Finland Foundation 295694.
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Chen, M., Liu, Y., Yu, X.: Predicting next locations with object clustering and trajectory clustering. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS, vol. 9078, pp. 344–356. Springer, Cham (2015). doi:10.1007/978-3-319-18032-8_27
Eldawy, A., Mokbel, M.F.: A demonstration of spatialhadoop: an efficient mapreduce framework for spatial data. Proc. VLDB Endow. 6(12), 1230–1233 (2013)
Ghosh, B., Basu, B., O’Mahony, M.: Multivariate short-term traffic flow forecasting using time-series analysis. IEEE Trans. Intell. Transp. Syst. 10(2), 246–254 (2009)
Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: Proceedings of Annual Meeting on SIGMOD 1984, pp. 47–57 (1984)
Han, Y., Moutarde, F.: Analysis of large-scale traffic dynamics in an urban transportation network using non-negative tensor factorization. Int. J. Intell. Transp. Syst. Res. 14(1), 36–49 (2016)
Jagadish, H.V., Ooi, B.C., Tan, K.L., Yu, C., Zhang, R.: iDistance: an adaptive B\({}^{\text{+ }}\)-tree based indexing method for nearest neighbor search. ACM Trans. Database Syst. 30(2), 364–397 (2005)
Kong, X., Xu, Z., Shen, G., Wang, J., Yang, Q., Zhang, B.: Urban traffic congestion estimation and prediction based on floating car trajectory data. Future Gener. Comput. Syst. 61, 97–107 (2016)
Liu, D., Chen, H., Qi, H., Yang, B.: Advances in spatiotemporal data mining. J. Comput. Res. Dev. 50(2), 225–239 (2013)
Pang, L.X., Chawla, S., Liu, W., Zheng, Y.: On mining anomalous patterns in road traffic streams. In: Tang, J., King, I., Chen, L., Wang, J. (eds.) ADMA 2011. LNCS, vol. 7121, pp. 237–251. Springer, Heidelberg (2011). doi:10.1007/978-3-642-25856-5_18
Sistla, A.P., Wolfson, O., Chamberlain, S., Dao, S.: Modeling and querying moving objects. In: Proceedings of the 13th International Conference on Data Engineering, pp. 422–432 (1997)
Stathopoulos, A., Karlaftis, M.G.: A multivariate state space approach for urban traffic flow modeling and prediction. Transp. Res. Part C Emerg. Technol. 11(2), 121–135 (2003)
Syrjärinne, P., Nummenmaa, J.: Improving usability of open public transportation data. In: 22nd ITS World Congress, pp. 5–9 (2015)
Ting, R.H., De Almeida, T., Ding, Z.: Modeling and querying moving objects in networks. Int. J. VLDB 15(2), 165–190 (2006)
Wang, Y., Papageorgiou, M., Messmer, A.: Real-time freeway traffic state estimation based on extended kalman filter: a case study. Transp. Sci. 41(2), 167–181 (2007)
Wu, X., Duan, L., Pang, T., Nummenmaa, J.: Detection of statistically significant bus delay aggregation by spatial-temporal scanning. APWeb 2016. LNCS, vol. 9865, pp. 277–288. Springer, Cham (2016). doi:10.1007/978-3-319-45835-9_24
Xia, D., Li, H., Wang, B., Li, Y.: A map reduce-based nearest neighbor approach for big-data-driven traffic flow prediction. IEEE Access 4, 2920–2934 (2016)
Xie, X., Xiong, Z., Hu, X., Zhou, G., Ni, J.: On massive spatial data retrieval based on spark. In: Chen, Y., et al. (eds.) WAIM 2014. LNCS, vol. 8597, pp. 200–208. Springer, Cham (2014). doi:10.1007/978-3-319-11538-2_19
Yu, X., Pu, K.Q., Koudas, N.: Monitoring k-nearest neighbor queries over moving objects. In: Proceedings of the 21st International Conference on Data Engineering, pp. 631–642 (2005)
Yuan, J., Zheng, Y., Zhang, L., Xie, X., Sun, G.: Where to find my next passenger. In: Proceedings of the 13th International Conference on Ubiquitous Computing, pp. 109–118 (2011)
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Pang, T., Duan, L., Nummenmaa, J., Zuo, J., Zhang, P. (2017). Bus-OLAP: A Bus Journey Data Management Model for Non-on-time Events Query. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10367. Springer, Cham. https://doi.org/10.1007/978-3-319-63564-4_15
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