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@article{199831,
        author = {Dr.K.Umadevi},
        title = {Robustness-Oriented Geometric Assessment of Vehicle Turning Complexity for Rural Agricultural Transport and Emergency Response Planning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {16082-16086},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199831},
        abstract = {Reliable vehicle maneuverability is critical for rural transportation systems supporting agricultural freight and emergency response services. Rural road networks frequently contain sharp bends and irregular geometric alignments that may impede heavy vehicles and delay ambulances under time-sensitive conditions. This paper presents a computationally efficient geometric framework for assessing turning complexity using polyline-based road representations derived from Open Street Map. Turning angles are computed at each road vertex using vector-based geometry and transformed into a normalized GREEN robustness score. Turns are classified into GREEN (low complexity), AMBER (moderate complexity), and RED (high complexity) categories. The framework is implemented in Python, deployed via a Fast API web service, and integrated into open source QGIS for spatial visualization. The proposed system enables scalable screening of rural transport corridors and supports infrastructure prioritization for agricultural logistics and emergency route planning.},
        keywords = {Turning trajectory estimation, Vehicle trajectory analysis, Transportation network analysis, Uncertainty modeling, Geographic Information Systems (GIS), Road geometry analysis, Turning complexity classification,},
        month = {April},
        }