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.
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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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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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DOI: https://doi.org/10.1007/s11042-023-15637-x