Operational pattern forecast improvement with outlier detection in metro rail transport system
Forecast; Metro rail; Outliers detection; Passenger flow data; Station clustering
Transportation is an unavoidable part of every humans 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 researchers 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. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Thandassery S.; Mulerikkal J.; Raghavendra S.
Multimedia Tools and Applications, Vol-83, No. 4, pp. 11229-11245.
Thandassery S., Department of Computer Science and Engineering, Christ (Deemed to be University), Karnataka, Bengaluru, India, Department of Computer Science and Engineering, Rajagiri School of Engineering and Technology, Kochi, India; Mulerikkal J., Department of Information Technology, Rajagiri School of Engineering and Technology, Kochi, India; Raghavendra S., Department of Computer Science and Engineering, Christ (Deemed to be University), Karnataka, Bengaluru, India