Efficient Graph Partitioning Approaches for Enhanced Accident Prediction
Divya Teja Reddy Tadi, Kartikaaditya Sirigeri, Sahukar Reshmi Panda, Geetha M · Kalpa publications in computing · 2024
The increasing amount of data generated by increasing road networks poses a significant challenge in the area of accident prediction. This investigation delves into the use of graph partitioning techniques to address the complexities associated with handling large graph datasets in the context of accident prediction. Graph partitioning techniques are critical in breaking down complex graph structures into manageable components, promoting parallelism, and enabling scalable computations. The computational burdenis distributed by strategically dividing the road network into sub-networks, resulting in faster analysis. This study investigates various graph partitioning algorithms and evalu- ates their effectiveness in maintaining the overall integrity of the road network during the partitioning process. Furthermore, using robust evaluation metrics, this study compre- hensively compares various graph partition methods, providing valuable information to choose the most effective strategy for a specific traffic network, thereby advancing robust and optimized solutions for accident prediction.