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 Faculty Publications Conference Paper Faculty Publications- Conference Papers 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/. Creator An entity primarily responsible for making the resource Sheetal; Alli, A.; Deepa, S.; Janardhanan, Jitha; Vasantha Kumari, N.; Rosita Kamala, F. Title A name given to the resource Adaptive Risk-Aware Ride Assignment (ARARA) Algorithm to Improve Efficiency to Lower Cancellation Rates in Bengaluru Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of the 2025 International Conference on Computational Innovations and Sustainable Technologies, ICCIST 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCIST67338.2025.11438386" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCIST67338.2025.11438386</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037441627?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037441627?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sheetal, Presidency College, Department of Computer Applications, Karnataka, Bengaluru, India; Alli A., Presidency College, Department of Computer Applications, Karnataka, Bengaluru, India; Deepa S., Christ University, Department of Computer Science, Bengaluru, India; Janardhanan J., Presidency College, Department of Computer Applications, Karnataka, Bengaluru, India; Vasantha Kumari N., Presidency College, Department of Computer Applications, Karnataka, Bengaluru, India; Rosita Kamala F., Presidency College, Department of Computer Applications, Karnataka, Bengaluru, India Description An account of the resource Ride cancellations on urban mobility platforms like Rapido, OLA, Uber and other service provide platforms are negatively impacting user experience, driver earnings, and platform efficiency due to high cancellation of rides. This study addresses the challenge by developing a machine learning based adaptive userride matching algorithm that is trained on real world ride dataset from Bengaluru. The dataset includes features such as ride time, source, destination, distance, fare, payment method, and ride status. Through data preprocessing and feature engineering, key patterns influencing ride cancellations are identified. A classification model is developed to predict the likelihood of cancellation before ride assignment by using few Machine learning models among various model XGBoost and Logistic Regression outperformed with nearly 9 0% accuracy. Later to enhance the performance in allocation based on cancellation prediction the ARARA algorithm suggests that reallocates rides dynamically based on cancellation risk using inference and assignment Algorithm. Experimental results shows that how to reduce cancellation rates and improved accuracy by choosing best allocation based on top three best captains for allocation to optimize chances of cancellation. This framework can be integrated by ride platforms to enhance service reliability and optimize fleet efficiency. 2025 IEEE. Subject The topic of the resource KNN; Logistic Regression; Machine learning Algorithms; Random Forest; Ride; XGBoost Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159676-7; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online