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A Survey on Spatiotemporal Data Processing Techniques in Smart Urban Rail

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Database Systems for Advanced Applications. DASFAA 2022 International Workshops (DASFAA 2022)

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Abstract

The smart urban rail has been developed rapidly and widely in recent years. The spatiotemporal data plays an important role in the field of smart urban rail, and is widely used in various application scenarios such as traffic flow prediction. However, there is a lack of systematic reviews of related technologies about spatiotemporal data. Therefore, this article has reviewed the spatiotemporal data and applications in smart urban rail. Firstly, the technologies of spatiotemporal in urban rail data are comprehensively studied. Secondly, the application of AI in smart urban rail is investigated. And the existing intelligent urban rail-related technologies about spatiotemporal data is summarized from four typical applications: intelligent scheduling, intelligent operation platform, intelligent perception, and intelligent train control. Finally, some interesting topics in smart urban rail applications have been listed. And we make a summary for the smart urban rail.

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References

  1. Andrienko, N., Andrienko, G.: A visual analytics framework for spatio-temporal analysis and modelling. Data Min. Knowl. Discov. 27(1), 55–83 (2013)

    Article  MathSciNet  Google Scholar 

  2. Bai, L., Yao, L., Li, C., Wang, X., Wang, C.: Adaptive graph convolutional recurrent network for traffic forecasting. arXiv preprint arXiv:2007.02842 (2020)

  3. Banerjee, S., Carlin, B.P., Gelfand, A.E.: Hierarchical Modeling and Analysis for Spatial Data. CRC Press, Boca Raton (2014)

    Google Scholar 

  4. Berger, A., Gebhardt, A., Müller-Hannemann, M., Ostrowski, M.: Stochastic delay prediction in large train networks. In: 11th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (2011)

    Google Scholar 

  5. Bo, W.: Smart urban rail: digital transformation and high-quality development china urban rail transit owners leaders summit 2021 In: Shenzhen Annual Meeting Held. China Metros, vol. 11, p. 4 (2021)

    Google Scholar 

  6. Borthakur, D.: The Hadoop distributed file system: architecture and design. Hadoop Pro. Website 11(2007), 21 (2007)

    Google Scholar 

  7. Burbey, I., Martin, T.L.: A survey on predicting personal mobility. Int. J. Perv. Comput. Commun. 8 (2012)

    Google Scholar 

  8. Barbour, W., et al.: Prediction of arrival times of freight traffic on us railroads using support vector regression. Transp. Res. Part C Emerg. Technol. 93, 211–227 (2018)

    Google Scholar 

  9. Calabrese, F., Di Lorenzo, G., Ratti, C.: Human mobility prediction based on individual and collective geographical preferences. In: 13th International IEEE Conference on Intelligent Transportation Systems, pp. 312–317 (2010)

    Google Scholar 

  10. Castro, P.S., Zhang, D., Chen, C., Li, S., Pan, G.: From taxi GPS traces to social and community dynamics. ACM Comput. Surv. 46, 1–34 (2013)

    Article  Google Scholar 

  11. Chao, W., Zhongcan, L., Ping, H., Rui, T., Weiwei, M., Li, L.: Progress and perspective of data driven train delay propagation. China Saf. Sci. J. 29(S2), 1 (2019)

    Google Scholar 

  12. Chen, D., Lu, C.T., Kou, Y., Chen, F.: On detecting spatial outliers. Geoinformatica 12(4), 455–475 (2008)

    Article  Google Scholar 

  13. Chen, D., Gao, C.: Soft computing methods applied to train station parking in urban rail transit. Appl. Soft Comput. 12(2), 759–767 (2012)

    Article  Google Scholar 

  14. Chen, X.C., Faghmous, J.H., Khandelwal, A., Kumar, V.: Clustering dynamic spatio-temporal patterns in the presence of noise and missing data. In: IJCAI (2015)

    Google Scholar 

  15. Chen, Z., Li, W.: Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network. IEEE Trans. Instrum. Measur. 66(7), 1693–1702 (2017)

    Article  Google Scholar 

  16. Cheng, R., Chen, D., Cheng, B., Zheng, S.: Intelligent driving methods based on expert knowledge and online optimization for high-speed trains. Exp. Syst. Appl. 87, 228–239 (2017)

    Article  Google Scholar 

  17. Cressie, N.: Statistics for spatial data. John Wiley & Sons, New York (2015)

    Google Scholar 

  18. Cui, Z., Henrickson, K., Ke, R., Wang, Y.: Traffic graph convolutional recurrent neural network: a deep learning framework for network-scale traffic learning and forecasting. IEEE Trans. Intell. Transp. Syst. 21(11), 4883–4894 (2019)

    Article  Google Scholar 

  19. De Brébisson, A., Simon, É., Auvolat, A., Vincent, P., Bengio, Y.: Artificial neural networks applied to taxi destination prediction. arXiv preprint arXiv:1508.00021 (2015)

  20. Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  21. Deng, A., Hooi, B.: Graph neural network-based anomaly detection in multivariate time series. Proc. AAAI Conf. Artif. Intell. 35(5), 4027–4035 (2021)

    Google Scholar 

  22. Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning. arXiv (2017)

    Google Scholar 

  23. Dunwei, G., Yong, Z., Jianhua, Z., Yong, Z.: Novel particle swarm optimization algorithm. Control Theory Appl. 25(1), 5 (2008)

    Google Scholar 

  24. Feng, J., et al.: DeepMove: predicting human mobility with attentional recurrent networks. In: Proceedings of the 2018 World Wide Web Conference, pp. 1459–1468 (2018)

    Google Scholar 

  25. Gambs, S., Killijian, M.O., del Prado Cortez, M.N.: Show me how you move and I will tell you who you are. In: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS, pp. 34–41 (2010)

    Google Scholar 

  26. Gelfand, A.E., Diggle, P., Guttorp, P., Fuentes, M.: Handbook of Spatial Statistics. CRC Press, Boca Raton (2010)

    Google Scholar 

  27. Goverde, R.M.: A delay propagation algorithm for large-scale railway traffic networks. Transpo. Res. Part C Emerg. Technol. 18(3), 269–287 (2010)

    Article  Google Scholar 

  28. Han, Z., et al.: Calibrating trajectory data for spatio-temporal similarity analysis. VLDB J. Int. J. Very Large Data Bases 24(1), 93–116 (2015)

    Article  Google Scholar 

  29. Heglund, J.S., Taleongpong, P., Hu, S., Tran, H.T.: Railway delay prediction with spatial-temporal graph convolutional networks. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), pp. 1–6 (2020)

    Google Scholar 

  30. Henderson, P., et al.: Ethical challenges in data-driven dialogue systems. In: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pp. 123–129 (2018)

    Google Scholar 

  31. Heo, J.: Development and implementation of a spatio-temporal data model for parcel-based land information systems. Ph.D. thesis, The University of Wisconsin - Madison (2001)

    Google Scholar 

  32. Hongjiang, C., Kui, F.: Research on clustering search method in collaborative filtering recommendation system. Comput. Eng. Appl. 50(5), 16–20 (2014)

    Google Scholar 

  33. Hua, G., Zhu, L., Wu, J., Shen, C., Zhou, L., Lin, Q.: Blockchain-based federated learning for intelligent control in heavy haul railway. IEEE Access 8, 176830–176839 (2020)

    Article  Google Scholar 

  34. Huang, J., Liu, Y., Xia, Y., Zhong, Z., Sun, J.: Train driving data learning with s-CNN model for gear prediction and optimal driving. In: 2019 Chinese Automation Congress (CAC), pp. 2227–2232 (2019)

    Google Scholar 

  35. Huang, J., Zhang, E., Zhang, J., Huang, S., Zhong, Z.: Deep reinforcement learning based train driving optimization. In: 2019 Chinese Automation Congress (CAC), pp. 2375–2381 (2019)

    Google Scholar 

  36. Huang, P., Chao, W., Fu, L., Peng, Q., Tang, Y.: A deep learning approach for multi-attribute data: A study of train delay prediction in railway systems. Inf. Sci. 516, 234–253 (2019)

    Article  Google Scholar 

  37. Jie, F., Hong, H.: Prediction of railway passenger traffic volume based on verhulst-RBF. Railway Comput. Appl. 28(11), 5 (2019)

    Google Scholar 

  38. Jin, W., Ma, Y., Liu, X., Tang, X., Wang, S., Tang, J.: Graph structure learning for robust graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 66–74 (2020)

    Google Scholar 

  39. Kharade, S.S., Khiani, S.: Fault prediction and relay node placement in wireless sensor network-a survey. Int. J. Sci. Res 3(10), 702–704 (2014)

    Google Scholar 

  40. Kizito, R., Scruggs, P., Li, X., Devinney, M., Jansen, J., Kress, R.: Long short-term memory networks for facility infrastructure failure and remaining useful life prediction. IEEE Access 9, 67585–67594 (2021)

    Article  Google Scholar 

  41. Kyriakidis, P.C., Journel, A.G.: Geostatistical space-time models: a review. Math. Geol. 31(6), 651–684 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  42. Lee, W.H., Yen, L.H., Chou, C.M.: A delay root cause discovery and timetable adjustment model for enhancing the punctuality of railway services. Transp. Res. Part C Emerg. Technol. 73(Dec.), 49–64 (2016)

    Google Scholar 

  43. Lessan, J., Fu, L., Wen, C.: A hybrid Bayesian network model for predicting delays in train operations. Comput. Ind. Eng. 127, 1214–1222 (2019)

    Article  Google Scholar 

  44. Levinson, J., et al.:Towards fully autonomous driving: Systems and algorithms. In: IEEE (2011)

    Google Scholar 

  45. Li, Y., Yin, M., Zhu, K.: Short term passenger flow forecast of metro based on inbound passenger plow and deep learning. In: 2021 International Conference on Communications, Information System and Computer Engineering (CISCE), pp. 777–780 (2021). https://doi.org/10.1109/CISCE52179.2021.9446016

  46. Linardatos, P., Papastefanopoulos, V., Kotsiantis, S.: Explainable AI: a review of machine learning interpretability methods. Entropy (Basel, Switzerland) 23(1), 18 (2020)

    Google Scholar 

  47. Liu, B., Adeli: Spatiotemporal relationship reasoning for pedestrian intent prediction. In :IEEE Robotics and Automation Letters, pp. 3485–3492 (2020)

    Google Scholar 

  48. Liu, Q., Wu, S., Wang, L., Tan, T.: Predicting the next location: a recurrent model with spatial and temporal contexts. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)

    Google Scholar 

  49. Liu, W., Zheng, Y., Chawla, S., Yuan, J., Xie, X.: Discovering spatio-temporal causal interactions in traffic data streams. In: KDD (2011)

    Google Scholar 

  50. Liu, W., Tang, T., Su, S., Cao, Y., Bao, F., Gao, J.: An intelligent train control approach based on the Monte Carlo reinforcement learning algorithm. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 1944–1949 (2018)

    Google Scholar 

  51. Lynch, H.J., Moorcroft, P.R.: A spatiotemporal Ripley’s k-function to analyze interactions between spruce budworm and fire in British Columbia, Canada. Cana. J. Forest Res. 38, 3112–3119 (2008)

    Article  Google Scholar 

  52. Miotto, R., Fei, W., Shuang, W., Jiang, X., Dudley, J.T.: Deep learning for healthcare: review, opportunities and challenges. Brief. Bioinform. 19(6) (2017)

    Google Scholar 

  53. Monreale, A., Pinelli, F., Trasarti, R., Giannotti, F.: WhereNext: a location predictor on trajectory pattern mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 637–646 (2009)

    Google Scholar 

  54. Peng, D., Liu, Z., Wang, H., Qin, Y., Jia, L.: A novel deeper one-dimensional CNN with residual learning for fault diagnosis of wheelset bearings in high-speed trains. IEEE Access 7, 10278–10293 (2018)

    Article  Google Scholar 

  55. Peng Hui, Z.Y., Zhanghao, H.: Railway passenger volume forecast based on multiple linear regression model. J. Chong. Insti. Technol. 32(09), 190–193 (2018)

    Google Scholar 

  56. Ping, H., Chao, W., Zhongcan, L., Yuxiang, Y., Qiyuan, P.: A neural network model for real-time prediction of high-speed railway delays. China Saf. Sci. J. 29(S1), 20 (2019)

    Google Scholar 

  57. Qi, L., Wu, L., Chen, P.Y., Dimakis, A.G., Witbrock, M.: Discrete attacks and submodular optimization with applications to text classification. arXiv preprint arXiv:1812.00151 (2018)

  58. Qiusheng, T., Peng, C., Na, L.: Short time forecasting of passenger flow in urban railway using GSO-BPNN method. Technol. Econ. Areas Commun. 19(1), 5 (2017)

    Google Scholar 

  59. Qiyuan, P., Jia, N., Gongyuan, L.: Model and algorithm for train platform scheme rescheduling at large high-speed railway station. J. China Railway Soc. 41(1), 10 (2019)

    Google Scholar 

  60. Rößler, D., Reisch, J., Hauck, F., Kliewer, N.: Discerning primary and secondary delays in railway networks using explainable AI. Transpo. Res. Procedia 52, 171–178 (2021)

    Article  Google Scholar 

  61. Salzmann, T., Ivanovic, B., Chakravarty, P., Pavone, M.: Trajectron++: multi-agent generative trajectory forecasting with heterogeneous data for control (2020)

    Google Scholar 

  62. Shekhar, S., Lu, C., Zhang, P.: Graph-based outlier detection: algorithms and applications (a summary of results). In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2001)

    Google Scholar 

  63. Shi, L., et al.: SGCN: sparse graph convolution network for pedestrian trajectory prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8994–9003 (2021)

    Google Scholar 

  64. Takenaka, H., Fujii, Y.: A compact representation of spatio-temporal slip distribution on a rupturing fault. J. Seismol. 12(2), 281–293 (2008)

    Article  Google Scholar 

  65. Tan, C.F., Wahidin, L., Khalil, S., Tamaldin, N., Hu, J., Rauterberg, G.: The application of expert system: A review of research and applications. ARPN J. Eng. Appl. Sci. 11(4), 2448–2453 (2016)

    Google Scholar 

  66. Tedjopurnomo, D.A., Bao, Z., Zheng, B., Choudhury, F., Qin, A.: A survey on modern deep neural network for traffic prediction: Trends, methods and challenges. IEEE Trans. Knowl. Data Eng. 34 (2020)

    Google Scholar 

  67. Toma, R.N., Prosvirin, A.E., Kim, J.M.: Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers. Sensors 20(7), 1884 (2020)

    Article  Google Scholar 

  68. Tovar, E., Vasques, F.: Using worldFIP networks to support periodic and sporadic real-time traffic. In: IECON 1999. Conference Proceedings. 25th Annual Conference of the IEEE Industrial Electronics Society (Cat. No. 99CH37029), vol. 3, pp. 1216–1221 (1999)

    Google Scholar 

  69. Tzeng, C.B., Wey, T.S., Ma, S.H.: Building a flexible energy management system with LonWorks control network. In: 2008 Eighth International Conference on Intelligent Systems Design and Applications, vol. 3, pp. 587–593 (2008)

    Google Scholar 

  70. Vlahogianni, E.I., Karlaftis, M.G., Golias, J.C.: Short-term traffic forecasting: where we are and where we’re going. Transpo. Res. Part C Emerg. Technol. 43, 3–19 (2014)

    Article  Google Scholar 

  71. Wang, X., Zhou, X., Lu, S.: Spatiotemporal data modelling and management: a survey. In: Proceedings 36th International Conference on Technology of Object-Oriented Languages and Systems. TOOLS-Asia 2000, pp. 202–211 (2000)

    Google Scholar 

  72. Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., Zhang, C.: Connecting the dots: multivariate time series forecasting with graph neural networks. In: KDD 2020: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2020)

    Google Scholar 

  73. Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph waveNet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)

  74. Yao, D., Zhang, C., Huang, J., Bi, J.: SERM: a recurrent model for next location prediction in semantic trajectories. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 2411–2414 (2017)

    Google Scholar 

  75. Yin, J., Chen, D.: An intelligent train operation algorithm via gradient descent method and driver’s experience. In: 2013 IEEE International Conference on Intelligent Rail Transportation Proceedings, pp. 54–59 (2013)

    Google Scholar 

  76. Yin, J., Chen, D., Li, L.: Intelligent train operation algorithms for subway by expert system and reinforcement learning. IEEE Trans. Intell. Transpo. Syst. 15(6), 2561–2571 (2014)

    Article  Google Scholar 

  77. Yin, X., Wu, G., Wei, J., Shen, Y., Qi, H., Yin, B.: Deep learning on traffic prediction: methods, analysis and future directions. IEEE Trans. Intell. Transp. Syst. (2021)

    Google Scholar 

  78. Yuan, J., Goverde, R., Hansen, I.: Propagation of train delays in stations. WIT Trans. Built Environ. 61 (2002)

    Google Scholar 

  79. Yuan, J., Hansen, I.A.: Optimizing capacity utilization of stations by estimating knock-on train delays. Transpo. Res. Part B Methodol. 41(2), 202–217 (2007)

    Article  Google Scholar 

  80. Zhang, D., Peng, Y., Zhang, Y., Wu, D., Wang, H., Zhang, H.: Train time delay prediction for high-speed train dispatching based on spatio-temporal graph convolutional network. IEEE Trans. Intell. Transp. Syst. 23, 2434–2444 (2021)

    Google Scholar 

  81. Zhang, M., Zhang, Q., Boyuan, Z.: A policy-based reinforcement learning algorithm for inteligent train control. J. China Railway Soc. (2020)

    Google Scholar 

  82. Zhang Qi, Chen Feng, Z.T.Y.Z.M.: ntelligent prediction and characteristic recognition for joint delay of high speed railway trains. Acta Automatica Sinica, 45(12) (2019)

    Google Scholar 

  83. Zhao, H., et al.: Multivariate time-series anomaly detection via graph attention network. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 841–850 (2020)

    Google Scholar 

  84. Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., Li, H.: T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transpo. Syst. 21(9), 3848–3858 (2019)

    Article  Google Scholar 

  85. Zhou, K., Song, S., Xue, A., You, K., Wu, H.: Smart train operation algorithms based on expert knowledge and reinforcement learning. In: IEEE Transactions on Systems, Man, and Cybernetics: Systems (2020)

    Google Scholar 

  86. Zhou, M., Dong, H., Liu, X., Zhang, H., Wang, F.Y.: Integrated timetable rescheduling for multidispatching sections of high-speed railways during large-scale disruptions. In: IEEE Transactions on Computational Social Systems (2021)

    Google Scholar 

  87. Zhu, H.Y.: N days average volume based ARIMA forecasting model for shanghai metro passenger flow. In: 2010 International Conference on Artificial Intelligence and Education (ICAIE) (2010)

    Google Scholar 

  88. Zhu, R., Zhou, H.: Railway passenger flow forecast based on hybridPVAR-NN model. In: 2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE) (2020)

    Google Scholar 

  89. Zügner, D., Günnemann, S.: Adversarial attacks on graph neural networks via meta learning. arXiv preprint arXiv:1902.08412 (2019)

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Authors and Affiliations

  1. East China Normal University, Shanghai, China

    Li Jian, Huanran Zheng & Bofeng Chen

  2. CASCO Signal Ltd., Shanghai, China

    Tingliang Zhou

  3. Panzhihua University, Panzhihua, Sichuan, China

    Hui Chen

  4. Shanghai Normal University, Shanghai, China

    Yanjun Li

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  1. Li Jian
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  6. Yanjun Li
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Correspondence to Bofeng Chen .

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  1. University of Aizu, Aizu, Japan

    Uday Kiran Rage

  2. Indraprastha Institute of Information Technology, Delhi, India

    Vikram Goyal

  3. Data Sciences and Analytics Center, International Institute of Information Technology, Hyderabad, Telangana, India

    P. Krishna Reddy

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Jian, L., Zheng, H., Chen, B., Zhou, T., Chen, H., Li, Y. (2022). A Survey on Spatiotemporal Data Processing Techniques in Smart Urban Rail. In: Rage, U.K., Goyal, V., Reddy, P.K. (eds) Database Systems for Advanced Applications. DASFAA 2022 International Workshops. DASFAA 2022. Lecture Notes in Computer Science, vol 13248. Springer, Cham. https://doi.org/10.1007/978-3-031-11217-1_17

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