Published on: April 2026
LET’S GO – DRIVER BOOKING APP
Yashashwini K C Vyshnavi S Tejas R C
Prof. Raghavendra B
Article Status
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Abstract
All traditional ride-booking platforms have been encountering some challenges with regards to inefficiency associated with ride booking and tracking. The Driver Booking App presents an efficient ride-sharing solution with a focus on emphasizing salient aspects of ride-sharing services, including user authentication, location tracking, ride booking, and tracking.
It is created with Kotlin as its frontend and Node.js/Express.js with MongoDB on the backend side for efficient data handling.
Its architectural designs provide it with an excellent user interface that enhances ease of convenience for ride booking. It presents a technological remedy for the current transport problem.
How to Cite this Paper
C, Y. K., S, V. & C, T. R. (2026). Let’s Go – Driver Booking App. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.576
C, Yashashwini, et al.. "Let’s Go – Driver Booking App." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.576.
C, Yashashwini,Vyshnavi S, and Tejas C. "Let’s Go – Driver Booking App." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.576.
References
- Vidushi Lukhoo, Raj Kishen Moloo, “A Carpooling System To Ease Traffic In Mauritius”, 2024.
- “Xuan Wang, Yaojie Li, Scott Smith, Helmut Schneider, “Data-driven decision-making: the case of ridesharing with implications for engineering managers”, 2024.
- Abhishek Shinde, Prajyot Bhoir, Sahil Shinde, Ms. Bushra Shaikh, “An Efficient Ridesharing Model using Machine Learning Based on Riders Reviews”, 2023.
- Haining Yu et al., “A Machine-Learning-Based Ride-Sharing Model Using Rider Reviews for Efficiency and Reliability,” 2023.
- Wang Peng and Lili Du, “A Novel Real-Time Coordinated Ridesharing Route Choice Mechanism”, 2023.
- Callista Ivana Mogie, Ricky, Gunawan Wang, Sfenrianto, Azlee bin Zabidi, Anderes Gui, “Improving Driver Loyalty through using Gamification Approach”, 2023.
- Haining Yu, Xiaohua Jia, Fellow, IEEE, Hongli Zhang, and Jiangang Shu, “Efficient and Privacy-Preserving Ride Matching Using Exact Road Distance in Online Ride Hailing Services”, 2022.
- Saidur Rahman, Apostolos Kalatzis, Mike P. Wittie, David L. Millman, Laura Stanley, “Dynamic Checkpoint Initiation in Serverless MEC”, 2022.
- Haining Yu, Xiangzhan Yu, Xiaojiang Du, Hongli Zhang and Mohsen Guizani, “PGRide: Privacy-Preserving Group Ridesharing Matching in Online Ride Hailing Services”, 2021.
- Tingting Kang, Lei Zhang, Yan Huang, “Development of App-based Ride-hailing Calculating Mileage and Time Detection Device”, 2021.
Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Apr 30 2026
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