Abstract
In current public transportation of modern cities, the passenger volume analysis counts the bus passengers in multiple perspectives, and it is significant to optimize the bus scheduling and evaluate transportation capacity. On the smart card data of passengers taking buses, traditional solutions have inherent limitations about long processing delay, inaccuracy result and poor scalability. In this paper, the spatio-temporal correlation with business restrictions is considered, and an effective platform service for passenger volumes analyses are proposed on massive smart card. Our service has been applied in practical usage for three types of passenger volume, and holds minute-level latencies on weekly data with nearly linear scalability in extensive conditions.
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References
Zhang, J., Zheng, Y., Qi, D., Li, R., Yi, X., Li, T.: Predicting citywide crowd flows using deep spatio-temporal residual networks. Artif. Intell. 259, 147–166 (2018)
Chen, M., Mao, S., Liu, Y.: Big data: a survey. Mob. Netw. Appl. 19, 171–209 (2014)
Xiong, G., et al.: A kind of novel ITS based on space-air-ground big-data. IEEE Intell. Transp. Syst. Mag. 8, 10–22 (2016)
Ma, X., Wu, Y.-J., Wang, Y., Chen, F., Liu, J.: Mining smart card data for transit riders’ travel patterns. Transp. Res. Part C: Emerg. Technol. 36, 1–12 (2013)
Tang, N.: Big data cleaning. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds.) APWeb 2014. LNCS, vol. 8709, pp. 13–24. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11116-2_2
Long, Y., Zhang, Y., Cui, C.: Identifying commuting pattern of Beijing using bus smart card data (in Chinese). Acta Geogr. Sin. 67, 1339–1352 (2012)
Zhang, C., Song, R., Sun, Y.: Kalman filter-based short-term passenger flow forecasting on bus stop (in Chinese). J. Transp. Syst. Eng. Inf. Technol. 11, 154–159 (2011)
Zhou, J., Sun, Y., He, L.: Multi-model hybrid traffic flow forecast algorithm based on multivariate data. In: Sun, Y., Lu, T., Xie, X., Gao, L., Fan, H. (eds.) ChineseCSCW 2018. CCIS, vol. 917, pp. 188–200. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-3044-5_14
Dugane, R.A., Raut, A.: A survey on big data in real time. Int. J. Recent Innov. Trends Comput. Commun. 2, 794–797 (2014)
Tao, S., Rohde, D., Corcoran, J.: Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap. J. Transp. Geogr. 41, 21–36 (2014)
Zhou, X., Yang, X., Wu, X.: Origin-destination matrix estimation method of public transportation flow based on data from bus integrated-circuit cards (in Chinese). J. Tongji Univ. (Nat. Sci.) 40, 1027–1030 (2012)
Zhang, J., Yu, X., Tian, C., Zhang, F., Tu, L., Xu, C.: Analyzing passenger density for public bus: inference of crowdedness and evaluation of scheduling choices. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC 2014), pp. 2015–2022. IEEE, (Year)
Carey, M.J., Jacobs, S., Tsotras, V.J.: Breaking BAD: a data serving vision for big active data. In: Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems, pp. 181–186. ACM, Irvine (2016)
Ding, W., Cao, Y.: A data cleaning method on massive spatio-temporal data. In: Wang, G., Han, Y., Martínez Pérez, G. (eds.) Advances in Services Computing: 10th Asia-Pacific Services Computing Conference, APSCC 2016, Proceedings, pp. 173–182. Springer International Publishing, Cham (2016). https://doi.org/10.1007/978-3-319-49178-3_13
Pelletier, M.-P., Trépanier, M., Morency, C.: Smart card data use in public transit: a literature review. Transp. Res. Part C: Emerg. Technol. 19, 557–568 (2011)
Zhang, D., Zhao, J., Zhang, F., He, T.: UrbanCPS: a cyber-physical system based on multi-source big infrastructure data for heterogeneous model integration. In: Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems, pp. 238–247. ACM, Seattle (2015)
Zhang, D., Zhao, J., Zhang, F., He, T.: coMobile: real-time human mobility modeling at urban scale using multi-view learning. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 1–10. ACM, Bellevue (2015)
Wang, Y., Ram, S., Currim, F., Dantas, E., Saboia, L.A.: A big data approach for smart transportation management on bus network. In: 2016 IEEE International Smart Cities Conference (ISC2), pp. 1–6. IEEE (2016)
Ram, S., Wang, Y., Currim, F., Dong, F., Dantas, E., Saboia, L.A.: SMARTBUS: a web application for smart urban mobility and transportation. In: Proceedings of the 25th International Conference Companion on World Wide Web, pp. 363–368. International World Wide Web Conferences Steering Committee, Montreal (2016)
Acknowledgements
This work was supported by the Youth Program of National Natural Science Foundation of China under Grant 61702014, the General Program of Beijing Natural Science Foundation under Grant 4192020, and Top Young Innovative Talents of North China University of Technology under Grant XN018022.
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Ding, W., Wang, Z., Zhao, Z. (2019). A Platform Service for Passenger Volume Analysis on Massive Smart Card Data in Public Transportation Domain. In: Wang, X., Gao, H., Iqbal, M., Min, G. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 292. Springer, Cham. https://doi.org/10.1007/978-3-030-30146-0_46
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