Road risk assessment using fuzzy Context-free Grammar based Association Rule Miner

Abstract

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 unsafe roads, respectively.

SVM-Lattice: A Recognition and Evaluation Frame for Double-Peaked Profiles

“…The association rule is a machine learning method based on rules, and it is used to find the implicit relationship (A ⇒ B) between transactions from a sample. The method can find the relationship between basket data and other data types, including medical diagnosis [54], web data [55], risk assessment [56], and so on.…”

An operative constellation rate for smart safety units using Internet of Things

“…But the signs provided will be very difficult to follow where precision and accuracy can never be obtained. 6 Since road accidents occur frequently the truck proportion needs to be reduced for providing freeway traffic. 7 For monitoring the freeway traffic field data will be provided and the data received will be calibrated as per requirements.…”

“…Incorporating the projected model by considering total cost and error by covering long distance with less number of units is a crucial task and for overcoming the encounters such as over speed, improper lane diversion etc., a lot of techniques have been offered. [2][3][4][5][6][7][8][9][10][11][12][13] Also, there are few measures that are able to decipher these challenging errands. But all the procedures enumerated [5][6][7][8][9][10][11][12][13][14][15][16] grieves from any one drawback.…”

“…Even though the proposed system is implemented in real time the more number of cameras installation is required and it is not a cost effective solution. A risk‐free assessment with fuzzy logic controller has been incorporated for national highways with a rule‐based technique 6 where, the separate rules will be incorporated with natural signs. But the signs provided will be very difficult to follow where precision and accuracy can never be obtained 6 .…”

“…A risk‐free assessment with fuzzy logic controller has been incorporated for national highways with a rule‐based technique 6 where, the separate rules will be incorporated with natural signs. But the signs provided will be very difficult to follow where precision and accuracy can never be obtained 6 . Since road accidents occur frequently the truck proportion needs to be reduced for providing freeway traffic 7 .…”