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© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Paper ID:10.55041/IJSREM.RACCES001

24 Hour Driver Tracking System

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM.RACCES001

Volume:

10 |

4 |

April 2026

Pages:

1-6

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem.RACCES001

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

S

S.Kaviyarasu

Department of Master of Computer Application

AVS Engineering college , Salem, Tamil Nadu, India

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P

P.Vallarasu

Department of Master of Computer Application

AVS Engineering college , Salem, Tamil Nadu, India

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Abstract

The rapid growth of urban transportation demands intelligent, automated systems to manage cab services efficiently. This paper presents the design, development, and evaluation of a 24 Hr driver Tracking System, a web-based platform engineered to automate taxi service operations, provide GPS-based real-time ride tracking, enable dynamic driver allocation, and facilitate seamless passenger management. The proposed system addresses critical challenges inherent in manual booking workflows, including absence of ride visibility, inefficient driver dispatch, and delayed notifications. Built using a Java-PHP hybrid backend, MySQL relational database, and Bootstrap- JQuery frontend, the platform integrates AJAX and JSON technologies to deliver asynchronous, real-time updates. Eight primary functional modules have been implemented: User Registration & Login, Ride Booking, Driver Allocation, Real-Time Tracking, Payment Processing, Rating & Feedback, Ride History, and an Admin Dashboard. Experimental evaluation demonstrates significant improvements in booking response time, driver allocation efficiency, and overall user satisfaction when compared to conventional manual dispatch systems. The system provides a scalable, extensible architecture that can accommodate increased user loads and additional service features, establishing a robust foundation for next-generation urban mobility platforms.

KEYWORDS

Index Terms—cab management system real-time tracking GPS driver allocation web-based application Java MySQL AJAX ride booking urban mobility.

Cite This Article

S.Kaviyarasu, P.Vallarasu, "24 Hour Driver Tracking System," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1-6, April 2026. https://doi.org/10.55041/ijsrem.RACCES001

Full Article/Conclusion

This paper presented the comprehensive design and implementation of a Cab Driver & Ride Tracking System, a fully integrated web-based platform addressing the operational limitations of traditional taxi service management. The system's eight modular components collectively deliver automated ride booking, proximity-based driver allocation, GPS-enabled real-time tracking, secure payment processing, bidirectional rating mechanisms, and centralized administrative monitoring. Performance evaluation confirmed substantial efficiency gains over manual dispatch workflows, including a 93% reduction in booking time and an 89% reduction in driver allocation latency. User acceptance testing validated high levels of satisfaction across usability, tracking accuracy, and booking convenience dimensions. The Java-PHP hybrid backend architecture, supported by a normalized MySQL schema and AJAX-driven frontend, provides a scalable and maintainable foundation for ongoing platform evolution. The proposed system contributes a practical, deployable reference architecture for small and medium taxi enterprises seeking to modernize operations without the capital investment required by proprietary ride-hailing platforms. Future work will focus on machine learning-driven demand forecasting, mobile application development, and dynamic pricing mechanisms.  

References

[1] S. Shaheen and A. Cohen, "Shared Mobility: Current Practices and Guiding Principles," Federal Highway Administration, Washington, DC, USA, Tech. Rep. FHWA-HOP-16-022, 2016. [2] N. Agatz, A. Erera, M. Savelsbergh, and X. Wang, "Optimization for dynamic ride-sharing: A review," European Journal of Operational Research, vol. 223, no. 2, pp. 295-303, 2012. [3] X. Qian, S. Ukkusuri, C. Yang, and F. Yan, "Short-term demand forecasting for on-demand mobility service," IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 12, pp. 4490-4499, 2019. [4] A. Mourad, J. Puchinger, and C. Chu, "A survey of models and algorithms for optimizing shared mobility," Transportation Research Part B: Methodological, vol. 123, pp. 323-346, 2019. [5] H. Kaur and R. Singh, "Secure payment processing framework for mobile ride-hailing applications," in Proc. IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Bangalore, India, 2020, pp. 1-6. [6] M. Chen, T. Sheldon, and Y. Xu, "Reputation systems for ride-hailing: Theory and evidence," Management Science, vol. 67, no. 11, pp. 6777-6799, 2021. [7] A. Furuhata, M. Dessouky, F. Ordonez, M. E. Brunet, X. Wang, and S. Koenig, "Ridesharing: The state-of-the-art and future directions," Transportation Research Part B: Methodological, vol. 57, pp. 28-46, 2013. [8] N. Masoud and R. Jayakrishnan, "A real-time algorithm to solve the peer-to-peer ride-matching problem in a flexible ridesharing system," Transportation Research Part B: Methodological, vol. 106, pp. 218-236, 2017

Editorial & Review Transparency

Publication Date

5/1/2026

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Double-Blind Peer Review

Review Rounds: 2

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