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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: May 2026
Volume 11 | Issue 5
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Impact Factor : 8.14
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| Paper Title: | XGBoost-Based Secure Dynamic Fare Prediction for Ride-Hailing Services Using Fernet Encryption and Demand-Supply Ratio Feature Engineering with Tiered Surge Pricing |
| Authors Name: | S. Mushfira Mehek , S. Mahamad Shakeer , P. Rajasekhar , K. Manoj Kumar |
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IJRTI_211300
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| Published Paper Id: | IJRTI2604255 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | Dynamic pricing in ride-on-demand (RoD) services is a crucial tool for real-time optimization of supply-demand equilibrium while maximizing revenue for drivers and service providers. However, many existing approaches fail to provide proper feature engineering, transparent surge pricing mechanisms, and robust security for predictions. This paper proposes a robust and interpretable dynamic fare prediction system for RoD services using XGBoost regression combined with a demand-supply surge multiplier mechanism. The proposed sys-tem ingests multi-attribute ride request data such as number of riders and drivers, ride duration, vehicle type, booking time, loca-tion type, customer loyalty program membership, and competitor prices, while employing symmetric Fernet-based encryption to secure sensitive inputs during inference. A demand-supply ratio (DSR) feature is engineered from rider and driver counts, and a three-stage surge pricing mechanism is applied post-prediction. Experiments conducted on 1,000 samples of a ride pricing dataset yield R2 = 0.9247, RMSE = $43.82, and MAE = $30.16. Compared to Linear Regression, Random Forest, and Gradient Boosting baselines, XGBoost demonstrates superior accuracy and stability. Business metric estimation yields a gross margin of approximately 25%. Index Terms—Dynamic Pricing, Ride-on-Demand, XGBoost, Surge Pricing, Demand-Supply Ratio, Secure Inference, Fare Prediction, Gradient Boosting |
| Keywords: | Dynamic Pricing, Ride-on-Demand, XGBoost, Surge Pricing, Demand-Supply Ratio, Secure Inference, Fare Prediction, Gradient Boosting |
| Cite Article: | "XGBoost-Based Secure Dynamic Fare Prediction for Ride-Hailing Services Using Fernet Encryption and Demand-Supply Ratio Feature Engineering with Tiered Surge Pricing", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b865-b871, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604255.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604255
Registration ID:211300
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: b865-b871 Country: Nandyal, Andhra Pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604255 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604255 |
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ISSN: 2456-3315
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