Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource PHD PhD PhD Thesis Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Relation A related resource 61000364 Title A name given to the resource Large Scale Transportation Data Analysis and Distributed Computational Pipeline for Optimal Metro Passenger Flow Prediction Subject The topic of the resource Computer Science and Engineering Description An account of the resource Transportation has a signifcant impact on controlling traffc around a busy city. Among the transport system, metro rails became the backbone by operating above the traffc. For this reason, we have to take special consideration of the passenger and#64258;ow in the transport system and, by understanding the needs, take timely actions for smooth running. Every metro system stores information about the and#64258;ow of passengers in the form of transactions known as Automatic Fare Collection (AFC) data. For this research, AFC data is taken as the primary newlinesource of information to identify the passenger and#64258;ow within the metro rail platform. Each metro system generates massive data throughout its running period and stores data within the system and considering the size of data generated, the analytic platform has to process them in a distributed paradigm to handle quotBig Dataquot. Artifcial Intelligence (AI) algorithms can derives information, insights, and patterns from this data. The patterns in time series can be identifed from the passenger and#64258;ow data using exploratory analysis. The step is an essential step in data science for understanding the underlying properties of the raw data. The research uses a data platform with a distributed computing and storage mechanism called the JP-DAP. The research leverages the above mentioned platform to extract passenger and#64258;ow data from AFC Ticketing data. After the data engineering, the results of passenger and#64258;ow information underwent further visualization and trend analysis. Based on the facts or patterns identifed from the passenger and#64258;ow information, a decision is taken for forecasting. The initial study will reveal the characteristics of metro usage and practices within the system and fnally derive a solution with machine learning-based forecasting method. The passenger and#64258;ow newlineforecasts based on the above patterns depend on factors like seasonality, trends, cyclicity, location, events, and random effects. Creator An entity primarily responsible for making the resource Sajanraj, T D Source A related resource from which the described resource is derived Author's Submission Publisher An entity responsible for making the resource available Christ(Deemed to be University) Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Contributor An entity responsible for making contributions to the resource S, Raghavendra and Mulerikkal, Jaison Paul Rights Information about rights held in and over the resource Open Access Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource English Type The nature or genre of the resource PhD Identifier An unambiguous reference to the resource within a given context <a href="http://hdl.handle.net/10603/585429" target="_blank" rel="noreferrer noopener">http://hdl.handle.net/10603/585429</a>