Deep Learning–Driven Crime-Aware Multi-Factor Route Safety ‎Prediction Using Real-Time Environmental and Contextual Data

  • Authors

    • Thilagavathi T Research Scholar, Department of Computer and Information Science, Annamalai University, Annamalai ‎Nagar, Tamil Nadu, India
    • Dr. Subashini A Assistant Professor, Department of Computer Application, Government Arts College, Chidambaram, Tamil ‎Nadu, India
    https://doi.org/10.14419/xtbvpb87

    Received date: November 17, 2025

    Accepted date: December 20, 2025

    Published date: December 26, 2025

  • Crime-Aware Routing; Deep Neural Network; Gradient Boosting; Multi-Factor Risk Modeling; Real-Time Data ‎Fusion; Intelligent Transportation Systems

  • Abstract

    Traveler safety is a critical concern in modern Intelligent Transportation Systems (ITS), where conventional ‎navigation algorithms primarily optimize for distance or travel time while overlooking contextual safety factors. ‎This study introduces a Deep Learning–Driven Crime-Aware Route Safety Prediction Framework that fuses ‎static, dynamic, and crime-based contextual attributes for segment-level risk assessment. The model extends ‎earlier machine-learning frameworks by incorporating a Deep Neural Network (DNN) capable of learning ‎complex nonlinear relationships among environmental, demographic, and crime-contextual variables. Real-‎time data from Google Maps, OpenStreetMap, OpenWeatherMap, and TomTom APIs are combined with ‎synthetically generated crime-risk indicators derived from spatial density, place-type exposure, and temporal ‎crime propensity. Comparative evaluation of Decision Tree, Gradient Boosting, Random Forest, and DNN ‎models demonstrates that the DNN achieves the highest predictive accuracy (R² = 0.9987, ROC–AUC = 0.999) ‎while maintaining robust classification reliability. All models consistently identified Route 3 as the safest and ‎Route 1 as the most risk-prone corridor. By combining the interpretability of Gradient Boosting with the deep ‎learning capacity of DNNs, the proposed framework enhances route-level safety prediction and scalability for ‎real-time, human-centric ITS applications‎.

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  • How to Cite

    T, T., & A, D. S. (2025). Deep Learning–Driven Crime-Aware Multi-Factor Route Safety ‎Prediction Using Real-Time Environmental and Contextual Data. International Journal of Basic and Applied Sciences, 14(8), 570-582. https://doi.org/10.14419/xtbvpb87