Adaptive Risk-Aware Ride Assignment (ARARA) Algorithm to Improve Efficiency to Lower Cancellation Rates in Bengaluru
KNN; Logistic Regression; Machine learning Algorithms; Random Forest; Ride; XGBoost
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.
Sheetal; Alli, A.; Deepa, S.; Janardhanan, Jitha; Vasantha Kumari, N.; Rosita Kamala, F.
Proceedings of the 2025 International Conference on Computational Innovations and Sustainable Technologies, ICCIST 2025;
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICCIST67338.2025.11438386 https://www.scopus.com/pages/publications/105037441627?origin=resultslist
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