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In this paper, we present a novel approach to identify the Spatio-temporaland environmental features that influence the safety of a road and predict itsaccident proneness based on these features. A total of 14 features werecompiled based on Time, Weather, and Location (TWL) specificity along a road.To determine the influence each of the 14 features carries, a sensitivity studywas performed using Principal Component Analysis. Using the locations ofaccident warnings, a Safety Index was developed to quantify how accident-pronea particular road is. We implement a novel approach to predict the Safety Indexof a road-based on its TWL specificity by using a Graph Neural Network (GNN)architecture. The proposed architecture is uniquely suited for this applicationdue to its ability to capture the complexities of the inherent nonlinearinterlinking in a vast feature space. We employed a GNN to emulate the TWLfeature vectors as individual nodes which were interlinked vis-\`a-vis edges ofa graph. This model was verified to perform better than Logistic Regression,simple Feed-Forward Neural Networks, and even Long Short Term Memory (LSTM)Neural Networks. We validated our approach on a data set containing the alertlocations along the routes of inter-state buses. The results achieved throughthis GNN architecture, using a TWL input feature space proved to be morefeasible than the other predictive models, having reached a peak accuracy of65%.

Road Accident Proneness Indicator Based On Time, Weather And Location Specificity Using Graph Neural Networks