Traveller Safety Web Application using License Plate Recognition, GPS Tracking, Route Deviation Detection, and Emergency Alert System

This paper has presented a comprehensive, integrated Traveller Safety Web Application that addresses longstanding gaps in intelligent transportation safety systems through the combination of automated License Plate Recognition, real-time GPS tracking, A*-based route optimisation, HMM-based route deviation detection, and autonomous emergency SMS alerting. The system's multi-modal architecture eliminates the dependency on manual passenger action that constrains the effectiveness of existing safety solutions. Experimental evaluation on domain-specific datasets confirmed that the proposed system achieves a license plate recognition mAP of 94.7% with EasyOCR character accuracy of 96.2%, route deviation detection F1-score of 0.923 with a false positive rate of only 1.45%, and end-to-end alert latency below 2.7 seconds at the 95th percentile. Comparative analysis demonstrates measurable improvements over both commercial platforms and academic systems across all evaluated safety metrics. The mathematical formulations presented for HMM route deviation modelling, the Haversine-based geodesic distance computation, and the weighted alert priority scoring provide a rigorous theoretical foundation that enables systematic optimisation of system parameters. The modular Docker-based deployment architecture ensures that the system is practically deployable in real-world transportation monitoring scenarios without requiring specialised hardware. The convergence of computer vision, probabilistic sequence modelling, and cloud-native web architecture demonstrated in this work establishes a blueprint for next-generation intelligent traveller safety systems that are simultaneously powerful, accessible, and deployable at scale. The reported results support the broader thesis that AI-augmented public safety systems can meaningfully improve traveller protection outcomes through autonomous, low-latency threat detection and response.

[1] National Crime Records Bureau (NCRB), "Crime in India — Statistics 2023," Ministry of Home Affairs, Government of India, New Delhi, 2024. [2] S. R. Prabhu and K. Venkatesh, "Analysis of passenger safety features in ride-hailing applications: A comparative study of Ola and Uber in Indian urban markets," Journal of Transport Policy, vol. 118, pp. 45-58, Mar. 2022. [3] Y. Wu, A. Kirillov, F. Massa, W.-Y. Lo, and R. Girshick, "Detectron2," Facebook AI Research, 2019. [Online]. Available: https://github.com/facebookresearch/detectron2 [4] J. Baek, G. Kim, J. Lee, S. Park, D. Han, S. Yun, S. J. Oh, and H. Lee, "What is wrong with scene text recognition model comparisons? Dataset and model analysis," in Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), Seoul, South Korea, 2019, pp. 4715-4723. [5] P. E. Hart, N. J. Nilsson, and B. Raphael, "A formal basis for the heuristic determination of minimum cost paths," IEEE Trans. Syst. Sci. Cybern., vol. 4, no. 2, pp. 100-107, Jul. 1968. [6] L. R. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition," Proc. IEEE, vol. 77, no. 2, pp. 257-286, Feb. 1989. [7] A. Sharma, R. Gupta, and P. Singh, "Deep learning-based automatic number plate recognition for Indian vehicles under adverse conditions," Expert Syst. Appl., vol. 193, pp. 116-430, May 2022. [8] L. Zhang, H. Wang, and Q. Liu, "TransLPR: Transformer-based license plate recognition in complex scenarios," IEEE Trans. Intell. Transp. Syst., vol. 24, no. 3, pp. 2891-2903, Mar. 2023. [9] R. Laroca, E. Severo, L. A. Zanlorensi, L. S. Oliveira, G. R. Gonçalves, W. R. Schwartz, and D. Menotti, "A robust real-time automatic license plate recognition based on the YOLO detector," in Proc. Int. Joint Conf. Neural Netw. (IJCNN), Budapest, Hungary, 2019, pp. 1-10. [10] Z. Li, X. Zheng, and C. Li, "Spatial attention-enhanced license plate character recognition using convolutional neural networks," Pattern Recognit., vol. 129, pp. 108-741, Sep. 2022.