Road risk assessment using fuzzy Context-free Grammar based Association Rule Miner
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Road traffic accidents are a major social concern as well as a crucial issue for the public in recent days due to the risk factors involved. Analysing and identifying the major risk factors of road accident is still a challenging task. In this paper, a fuzzy Context-free Grammar (FCFG)-based association rule mining (ARM) technique is proposed to categorize a heterogeneous road accident dataset into two categories based on the critical factors such as total number of accidents (TA), persons killed (PK) and persons injured (PI). The role of the fuzzy grammar in this paper is to govern the entire algorithm using the prescribed grammar rules to proceed further. The considered road accident dataset does not have class labels; hence there is a need to assign class labels for the available data instance. The accident data with assigned class labels are given as input to K-nearest neighbour (KNN) machine learning algorithm in order to train the classifier for testing purpose. Further, the collected test data from the user are utilized by the KNN classifier for carrying out the performance analysis of the proposed algorithm. The case study is conducted on the National Highway roads, India, to examine the proposed approach. The experimentations are executed for road accident records using MATLAB software and the analysis is made using the following performance measures: accuracy, recall or sensitivity, precision or specificity and F1 score. A comparative study is accomplished with existing algorithms in order to show that the proposed algorithm works with improved accuracy of more than 83%. The results suggested that the road users are responsible for the acceptance or rejection of safe or un-safe roads, respectively.
KeywordsRoad accidents data mining association rule mining formal grammars K-nearest neighbour fuzzy logic
The first author would like to thank the management of Kalasalingam Academy of Research and Education (KARE) for providing fellowship to carry the research work.
- 1.Traffic accidents in India. https://en.wikipedia.org/wiki/Traffic_collisions_in_India
- 14.Hahn K S 1989 Investigation of a fuzzy grammar for automated visual inspection. Doctoral dissertation, Texas Tech UniversityGoogle Scholar
- 18.Kumar S and Toshniwal D 2015 Analysing road accident data using association rule mining. In: Proceedings of the 2015 International Conference on Computing, Communication and Security (ICCCS), IEEE, pp. 1–6Google Scholar
- 20.Hopcroft J E 2008 Introduction to automata theory, languages, and computation. Pearson Education IndiaGoogle Scholar
- 22.Selvi O 2009 Traffic accident predictions based on fuzzy logic approach for safer urban environments, case study: İzmir Metropolitan Area. PhD thesisGoogle Scholar
- 23.Mandal S N, Choudhury J P and Chaudhuri S B 2012 In search of suitable fuzzy membership function in prediction of time series data. Int. J. Comput. Sci. Issues 9(3): 293–302Google Scholar
- 24.Road Accidents Statistics in India 2015 http://pibphoto.nic.in/documents/rlink/2016/jun/p20166905.pdf
- 25.Moghaddam H K and Wang X 2014 Vehicle accident severity rules mining using fuzzy granular decision tree. In: Proceedings of the International Conference on Rough Sets and Current Trends in Computing. Cham: Springer, pp. 280–287Google Scholar