Pattern Mining Predictor System for Road Accidents

Joshi, Sisir; Alsadoon, Abeer; Senanayake, S. M. N. Arosha; Prasad, P. W. C.; Yong, Shiaw Yin; Elchouemi, A.; Vo, Trung Hung

doi:10.1007/978-3-030-63119-2_49

Cited by 4 publications

(5 citation statements)

References 8 publications

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“…The SEA is implemented using new transaction mapping which avoids the repetitive scan of RAP miner, improving the joining efficiency by pruning two RAP sets (frequent and candidate) and achieving higher efficiency by reusing existing RAP from RAP CL. Hence, the results illustrate that a novel SEA algorithm is the optimal solution with a significant decrease of processing time compared to the rest of algorithms based on the similar comparisons reported in Joshi et al (2020).…”

Section: Mathematical Modelling For the Rap Minersupporting

confidence: 65%

“…RAP pre-processing starts with balancing the RAP set in the RAP database (RAP DB) as illustrated in Figure 1. RAP set in RAP DB goes through three fundamental operations: duplication, missing attributes of a RAP (more than 50% zeros in a RAP) and incomplete pattern due to less than 50% zeros in a RAP which were similarly carried out in Joshi et al (2020), but in this work, it is done differently. According to the first operation for a RAP set, as illustrated in Figure 1, if a duplication RAP exists, it is removed from the RAP DB, otherwise subsequently checked for a RAP with more than 50% zeros in its attributes.…”

Section: Rap Pre-processingmentioning

confidence: 99%

“…These fuzzy values are processed using the rule base established in the FIE. The output is obtained by transforming processed fuzzy values into crisp domain using defuzzification, as reported in Joshi et al (2020). But, in this work case repository is considered such a way that the evolving cases of accident occurrences are populated progressively to establish a knowledge base which is called RAP CL, as illustrated in Figure 1.…”

Section: Fuzzy Inference Engine For the Classified Rapmentioning

confidence: 99%

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A road accident pattern miner (RAP miner)

Senanayake

Joshi

2021

Journal of Information and Telecommunication

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Domain-specific data service models can retrieve critical features from frequently occurring road accident patterns (RAPs). The aim of this research is to propose scan efficient association rules' mining-based pattern analysis which provides more accurate RAP prediction in frequent accident locations with the fastest matching pattern search from a RAP database (RAP DB). Association rules' mining technique derives a correlation between frequent RAP and association among various attributes of a road accident. While the clustering technique discriminates different RAPs, Naïve Bayes Classification classifies and then predicts the severity of accident using Fuzzy Inference Engine (FIE) interfaced with RAP Case Library (RAP CL) using hybrid intelligence. The results of the proposed road accident data service model prove a significant increase in the accuracy of accident prediction compared to the reported results. A novel hybrid learning algorithm, interfaced with Scan Efficient Apriori (SEA) algorithm implemented, leads the fast RAP search from the first scan through RAP CL and retain new RAP in the RAP CL using casebased reasoning (CBR) during subsequent scanning. Thus, the RAP miner built proves road accident prediction using SEA, FIE and CBR with the highest accuracy and fast RAP set processing.

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Section: Mathematical Modelling For the Rap Minersupporting

confidence: 65%

Section: Rap Pre-processingmentioning

confidence: 99%

Section: Fuzzy Inference Engine For the Classified Rapmentioning

confidence: 99%

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A road accident pattern miner (RAP miner)

Senanayake

Joshi

2021

Journal of Information and Telecommunication

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“…A predictive model based on fuzzy logic was developed to predict the probability of an accident occurrence. This study was further improved by [22], which proposed a patternmining predictor system. The system continued the previous study by implementing an improved Apriori algorithm, which was more efficient in terms of processing time.…”

Section: Related Workmentioning

confidence: 99%

Comparative analysis of regional variations in road traffic accident patterns with association rule mining

Chai,

Lau,

Tee

et al. 2023

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Road Traffic Accidents (RTAs) patterns discovery is vital to formulate mitigation strategies based on the characteristics of RTAs.OBJECTIVES: Various studies have utilised Apriori algorithm for RTA pattern discovery. Hence, this work aimed to explore the applicability of FP-Growth algorithm to discover and compare the RTA patterns in several regions.METHODS: Orange data mining toolkit is used to discover RTA patterns from the open-access RTA datasets from Addis Ababa city (12,317 samples), Finland (371,213 samples), Berlin city-state (50,119 samples), New Zealand (776,878 samples), the UK (1,048,575 samples), and the US (173,829 samples).RESULTS: There are similarities and differences in RTA patterns among the six regions. The five common factors contributing to RTAs are road characteristics, type of road users or objects involved, environment, driver’s profile, and characteristics of RTA location. These findings could be beneficial for the authorities to formulate strategies to reduce the risk of RTAs.CONCLUSION: Discovery of RTA patterns in different regions is beneficial and future work is essential to discover the RTA patterns from different perspectives such as seasonal or periodical variations of RTA patterns.

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“…Traditionally, the enormous mass of research [2] relevant to accident evaluations is based on different forms of regression modeling with the mainly concerned with accident occurrence rather than on the estimation of accident intensity. Moreover, prior research employing computational modeling approaches [3][4][5], shows the unpredictable effects due to the socio-economic circumstances of a specific location using their site-specific accident data. Many prevailing studies [6,7] are somewhat constrained by the problem of limited dataset availability and model over or underfitting due to the usage of single-level classification modeling.…”

Section: Introductionmentioning

confidence: 99%

Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

Awan¹,

Mehmood²,

Chaudhry³

et al. 2022

Computers, Materials &Amp; Continua

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To determine the individual circumstances that account for a road traffic accident, it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels. Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model. In this article, we proposed a multi-model hybrid framework of the weighted majority voting (WMV) scheme with parallel structure, which is designed by integrating individually implemented multinomial logistic regression (MLR) and multilayer perceptron (MLP) classifiers using three different accident datasets i.e., IRTAD, NCDB, and FARS. The proposed WMV hybrid scheme overtook individual classifiers in terms of modern evaluation measures like ROC, RMSE, Kappa rate, classification accuracy, and performs better than state-of-theart approaches for the prediction of casualty severity level. Moreover, the proposed WMV hybrid scheme adds up to accident severity analysis through knowledge representation by revealing the role of different accident-related factors which expand the risk of casualty in a road crash. Critical aspects related to casualty severity recognized by the proposed WMV hybrid approach can surely support the traffic enforcement agencies to develop better road safety plans and ultimately save lives.

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Pattern Mining Predictor System for Road Accidents

Cited by 4 publications

References 8 publications

A road accident pattern miner (RAP miner)

A road accident pattern miner (RAP miner)

Comparative analysis of regional variations in road traffic accident patterns with association rule mining

Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

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