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Operational pattern forecast improvement with outlier detection in metro rail transport system

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Abstract

Transportation is an unavoidable part of every human’s life. The mobility system handles the transport of humans from different places using various transport modes. According to a station in a populated area, the main problem is the presence of traffic in peak hours and wasting their valuable time on the road. The only medium which runs above the traffic is metro rails/subways. For these reasons, metro rails become a point of interest for each researcher’s prophecy and provide valuable recommendations for the smooth functioning of services. Even though, in many cases, the metro systems are affected by abnormal passenger flow. So, this study handles abnormal passenger flow detection and station clustering for the behavior study of a passenger flow system. The research compares outlier detection and anomaly identification for the behavioral analysis of the metro rail passenger flow. The study use data from Kochi Metro Rail Limited for the period 2017 to 2019. Outlier removal has used in passenger flow data before building a forecasting system. In pattern recognition algorithm those components which lie outside the patterns can be considered abnormal (anomaly).The outliers are the component falling apart from the region of interest. The effect of removing the outlier from the time-series pattern is studied against the outlier included pattern to show the improvement.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Alghushairy O, Alsini R, Soule T, Ma X (2020) A review of local outlier factor algorithms for outlier detection in big data streams. Big Data and Cognitive Computing 5(1):1

    Article  Google Scholar 

  2. Alghushairy O, Alsini R, Soule T, Ma X (2021) A review of local outlier factor algorithms for outlier detection in big data streams. Big Data and Cognitive Computing 5(1):1–24. https://doi.org/10.3390/bdcc5010001

    Article  Google Scholar 

  3. Altman EI, Iwanicz-Drozdowska M, Laitinen EK, Suvas A (2017) Financial distress prediction in an international context: A review and empirical analysis of altman’s z-score model. Journal of International Financial Management & Accounting 28(2):131–171

    Article  Google Scholar 

  4. Antrim A, Barbeau SJ et al (2013) The many uses of gtfs data–opening the door to transit and multimodal applications. Location-Aware Information Systems Laboratory at the University of South Florida 4

  5. Asuncion A, Newman D (2007) UCI Machine Learning Repository. https://archive-beta.ics.uci.edu/

  6. Cheng W, Jl Li, Xiao HC, Ln Ji (2022) Combination predicting model of traffic congestion index in weekdays based on lightgbm-gru. Scientific Reports 12(1):1–13

    Google Scholar 

  7. Cheng Z, Zou C, Dong J (2019) Outlier detection using isolation forest and local outlier. Proceedings of the 2019 Research in Adaptive and Convergent Systems, RACS 2019 pp. 161–168. https://doi.org/10.1145/3338840.3355641

  8. Domingues R, Filippone M, Michiardi P, Zouaoui J (2018) A comparative evaluation of outlier detection algorithms: Experiments and analyses. Pattern Recognition 74:406–421

    Article  Google Scholar 

  9. Erfani SM, Rajasegarar S, Karunasekera S, Leckie C (2016) High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning. Pattern Recognition 58:121–134

    Article  Google Scholar 

  10. Ghofrani F, He Q, Goverde RM, Liu X (2018) Recent applications of big data analytics in railway transportation systems: A survey. Transportation Research Part C: Emerging Technologies 90:226–246

    Article  Google Scholar 

  11. Gordon JB, Koutsopoulos HN, Wilson NH, Attanucci JP (2013) Automated inference of linked transit journeys in london using fare-transaction and vehicle location data. Transportation research record 2343(1):17–24

    Article  Google Scholar 

  12. Gu J, Jiang Z, Fan WD, Wu J, Chen J (2020) Real-Time Passenger Flow Anomaly Detection Considering Typical Time Series Clustered Characteristics at Metro Stations. Journal of Transportation Engineering, Part A: Systems 146(4):04020015. https://doi.org/10.1061/jtepbs.0000333

    Article  Google Scholar 

  13. Hubert M, Vandervieren E (2008) An adjusted boxplot for skewed distributions. Computational Statistics & Data Analysis 52(12):5186–5201

    Article  MathSciNet  Google Scholar 

  14. Jian S, Li D, Yu Y (2021) Research on taxi operation characteristics by improved dbscan density clustering algorithm and k-means clustering algorithm. In: Journal of Physics: Conference Series, vol. 1952. IOP Publishing, pp 042103

  15. Kochi Metro Rail Ltd.: Open Data. https://kochimetro.org/open-data/

  16. Li J, Izakian H, Pedrycz W, Jamal I (2021) Clustering-based anomaly detection in multivariate time series data. Applied Soft Computing 100:106919. https://doi.org/10.1016/j.asoc.2020.106919

    Article  Google Scholar 

  17. Li W, Luo Y, Zhu Q, Liu J, Le J.: Applications of ar\(^\ast \)-grnn model for financial time series forecasting. Neural Computing and Applications 17(5):441–448

  18. Mulerikkal J, Thandassery S, K DMD, Rejathalal V, Ayyappan B (2021) Jp-dap: An intelligent data analytics platform for metro rail transport systems. IEEE Transactions on Intelligent Transportation Systems pp 1–11. https://doi.org/10.1109/TITS.2021.3091542

  19. Mulerikkal J, Thandassery S, Rejathalal V, Kunnamkody DMD (2021) Performance improvement for metro passenger flow forecast using spatio-temporal deep neural network. Neural Computing and Applications. https://doi.org/10.1007/s00521-021-06522-5

    Article  Google Scholar 

  20. Pasupathi S, Shanmuganathan V, Madasamy K, Yesudhas HR, Kim, M (2021) Trend analysis using agglomerative hierarchical clustering approach for time series big data. The Journal of Supercomputing pp 1–20

  21. Sajanraj TD, Mulerikkal J, Raghavendra S, Vinith R, Fabera V (2021) Passenger flow prediction from AFC data using station memorizing LSTM for metro rail systems. Neural Network World 31(3):173–189. https://doi.org/10.14311/NNW.2021.31.009

  22. Schölkopf B, Smola AJ, Bach F et al (2002) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press

  23. Sridhar V (2012) Automated system design for metro train. International Journal of Computer Science Engineering (IJCSE)

  24. Torres JF, Hadjout D, Sebaa A, Martínez-Álvarez F, Troncoso A (2021) Deep learning for time series forecasting: a survey. Big Data 9(1):3–21

    Article  Google Scholar 

  25. Vinutha H, Poornima B, Sagar B (2018) Detection of outliers using interquartile range technique from intrusion dataset. In: Information and decision sciences, Springer, pp 511–518

  26. Wang K, Tsung F (2021) Sparse and Robust Multivariate Functional Principal Component Analysis for Passenger Flow Pattern Discovery in Metro Systems. IEEE Transactions on Intelligent Transportation Systems pp 1–13. https://doi.org/10.1109/TITS.2021.3078816

  27. Wang Q, Wang C, Zy Feng, Jf Ye (2012) Review of k-means clustering algorithm. Electronic design engineering 20(7):21–24

    Google Scholar 

  28. Wang S, Li C, Lim A (2021) A Model for Non-Stationary Time Series and its Applications in Filtering and Anomaly Detection. IEEE Transactions on Instrumentation and Measurement 70. https://doi.org/10.1109/TIM.2021.3059321

  29. Wang X, Zhang Y, Liu H, Wang Y, Wang L, Yin B (2018) An improved robust principal component analysis model for anomalies detection of subway passenger flow. Journal of Advanced Transportation. https://doi.org/10.1155/2018/7191549

    Article  Google Scholar 

  30. World Resources Institute: Research. https://www.wri.org/research

  31. Xu D, Wang Y, Meng Y, Zhang Z (2017) An improved data anomaly detection method based on isolation forest. In: 2017 10th international symposium on computational intelligence and design (ISCID), vol. 2, pp 287–291. IEEE

  32. Xu R, Wunsch D (2008) Clustering, vol. 10. John Wiley & Sons

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Acknowledgements

This research is supported by Interdisciplinary Division of Department of Science and Technology (DST), Government of India (Project ID: DST/ICPS/CPS Individual/2018/1091) under the Principal Investigator, Fr. Dr. Jaison Paul Mulerikkal CMI, Vice Principal & Professor, Department of Information Technology, Rajagiri School of Engineering & Technology, Kochi, Kerala, India. The authors also wish to thank Kochi Metro Rail Limited for sharing their data with us for this project under a mutually agreed MoU.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, Karnataka, India

    Sajanraj Thandassery & Raghavendra S

  2. Department of Computer Science and Engineering, Rajagiri School of Engineering and Technology, Kochi, India

    Sajanraj Thandassery

  3. Department of Information Technology, Rajagiri School of Engineering and Technology, Kochi, India

    Jaison Mulerikkal

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  1. Sajanraj Thandassery
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  2. Jaison Mulerikkal
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  3. Raghavendra S
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Correspondence to Sajanraj Thandassery.

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Thandassery, S., Mulerikkal, J. & S, R. Operational pattern forecast improvement with outlier detection in metro rail transport system. Multimed Tools Appl 83, 11229–11245 (2024). https://doi.org/10.1007/s11042-023-15637-x

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