  In this paper, we present a novel approach to identify the Spatio-temporal
and environmental features that influence the safety of a road and predict its
accident proneness based on these features. A total of 14 features were
compiled based on Time, Weather, and Location (TWL) specificity along a road.
To determine the influence each of the 14 features carries, a sensitivity study
was performed using Principal Component Analysis. Using the locations of
accident warnings, a Safety Index was developed to quantify how accident-prone
a particular road is. We implement a novel approach to predict the Safety Index
of a road-based on its TWL specificity by using a Graph Neural Network (GNN)
architecture. The proposed architecture is uniquely suited for this application
due to its ability to capture the complexities of the inherent nonlinear
interlinking in a vast feature space. We employed a GNN to emulate the TWL
feature vectors as individual nodes which were interlinked vis-\`a-vis edges of
a 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 alert
locations along the routes of inter-state buses. The results achieved through
this GNN architecture, using a TWL input feature space proved to be more
feasible than the other predictive models, having reached a peak accuracy of
65%.
