Efficient Graph Partitioning Approaches for Enhanced Accident Prediction

13 pages•Published: August 6, 2024

Divya Teja Reddy Tadi, Kartikaaditya Sirigeri, Sahukar Reshmi Panda and Geetha M

Abstract

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.

Keyphrases: data parallelism, fennel partitioning, graph partitioning, metis, road networks

In: Rajakumar G (editor). Proceedings of 6th International Conference on Smart Systems and Inventive Technology, vol 19, pages 432-444.

Links:	https://easychair.org/publications/paper/wqdB
	https://doi.org/10.29007/nkq7

BibTeX entry

@inproceedings{ICSSIT2024:Efficient_Graph_Partitioning_Approaches,
  author    = {Divya Teja Reddy Tadi and Kartikaaditya Sirigeri and Sahukar Reshmi Panda and Geetha M},
  title     = {Efficient Graph Partitioning Approaches for Enhanced Accident Prediction},
  booktitle = {Proceedings of 6th International Conference on Smart Systems and Inventive Technology},
  editor    = {Rajakumar G},
  series    = {Kalpa Publications in Computing},
  volume    = {19},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/wqdB},
  doi       = {10.29007/nkq7},
  pages     = {432-444},
  year      = {2024}}

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