A Conjoint Analysis of Road Accident Data using K-modes Clustering and Bayesian Networks (Road Accident Analysis using clustering and classification)

Kumar, S. Suresh; Semwal, Vijay Bhasker; Solanki, Vijender Kumar; Tiwari, Prayag; Kalitin, Denis

doi:10.15439/2017r44

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…For data sets with only categorical variables, the k-modes clustering algorithm was introduced. This algorithm was jointly applied with Bayesian networks for road accident analysis [41]. Taamneh et al [35] combined hierarchical clustering and ANNs for classification of traffic crashes.…”

Section: Clustering Algorithm In Road Crash-related Studiesmentioning

confidence: 99%

Comparison of Cluster-Based Sampling Approaches for Imbalanced Data of Crashes Involving Large Trucks

Tahfim,

Chen

2024

Information

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Severe and fatal crashes involving large trucks result in significant social and economic losses for human society. Unfortunately, the notably low proportion of severe and fatal injury crashes involving large trucks creates an imbalance in crash data. Models trained on imbalanced crash data are likely to produce erroneous results. Therefore, there is a need to explore novel sampling approaches for imbalanced crash data, and it is crucial to determine the appropriate combination of a machine learning model, sampling approach, and ratio. This study introduces a novel cluster-based under-sampling technique, utilizing the k-prototypes clustering algorithm. After initial cluster-based under-sampling, the consolidated cluster-based under-sampled data set was further resampled using three different sampling approaches (i.e., adaptive synthetic sampling (ADASYN), NearMiss-2, and the synthetic minority oversampling technique + Tomek links (SMOTETomek)). Later, four machine learning models (logistic regression (LR), random forest (RF), gradient-boosted decision trees (GBDT), and the multi-layer perceptron (MLP) neural network) were trained and evaluated using the geometric mean (G-Mean) and area under the receiver operating characteristic curve (AUC) scores. The findings suggest that cluster-based under-sampling coupled with the investigated sampling approaches improve the performance of the machine learning models developed on crash data significantly. In addition, the GBDT model combined with ADASYN or SMOTETomek is likely to yield better predictions than any model combined with NearMiss-2. Regarding changes in sampling ratios, increasing the sampling ratio with ADASYN and SMOTETomek is likely to improve the performance of models up to a certain level, whereas with NearMiss-2, performance is likely to drop significantly beyond a specific point. These findings provide valuable insights for selecting optimal strategies for treating the class imbalance issue in crash data.

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Section: Clustering Algorithm In Road Crash-related Studiesmentioning

confidence: 99%

Comparison of Cluster-Based Sampling Approaches for Imbalanced Data of Crashes Involving Large Trucks

Tahfim,

Chen

2024

Information

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“…The k-modes clustering method is more appropriate for datasets that include only categorical features and uses the mismatches between the observations as a dissimilarity measure. Kumar et al [39] have used the k-modes clustering method to improve classification performances. The hierarchical clustering method segments the data by building a tree of clusters [34].…”

Section: Unobserved Heterogeneity In Crash Datamentioning

confidence: 99%

Analysis of Severe Injuries in Crashes Involving Large Trucks Using K-Prototypes Clustering-Based GBDT Model

Tahfim

Chen

2021

Safety

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The unobserved heterogeneity in traffic crash data hides certain relationships between the contributory factors and injury severity. The literature has been limited in exploring different types of clustering methods for the analysis of the injury severity in crashes involving large trucks. Additionally, the variability of data type in traffic crash data has rarely been addressed. This study explored the application of the k-prototypes clustering method to countermeasure the unobserved heterogeneity in large truck-involved crashes that had occurred in the United States between the period of 2016 to 2019. The study segmented the entire dataset (EDS) into three homogeneous clusters. Four gradient boosted decision trees (GBDT) models were developed on the EDS and individual clusters to predict the injury severity in crashes involving large trucks. The list of input features included crash characteristics, truck characteristics, roadway attributes, time and location of the crash, and environmental factors. Each cluster-based GBDT model was compared with the EDS-based model. Two of the three cluster-based models showed significant improvement in their predicting performances. Additionally, feature analysis using the SHAP (Shapley additive explanations) method identified few new important features in each cluster and showed that some features have a different degree of effects on severe injuries in the individual clusters. The current study concluded that the k-prototypes clustering-based GBDT model is a promising approach to reveal hidden insights, which can be used to improve safety measures, roadway conditions and policies for the prevention of severe injuries in crashes involving large trucks.

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“…The cost of these road accidents can inflict a substantial problem for the victims, the government, and insurance companies for it can cost millions (Luhach, 2017). These road accidents can take place at any time (Tiwari & Kalitin, 2017;Xi, Zhao, Li, & Wang, 2016). It has become a big concern of the government, private groups, and even individuals, considering the prevention and for classification of various reasons for road accidents (Solanke & Gotmare, 2018).…”

Section: Introductionmentioning

confidence: 99%

Data Mining technique: Application of Apriori algorithm for road accident analysis

Perez

2023

HCMCOUJS - ENGINEERING AND TECHNOLOGY

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Road accidents can happen due to various factors. These factors that contribute to road accidents have cost damage to properties, injuries or deaths and most road accidents are attributable to the lack of knowledge on road safety. To provide safe driving and road safety plans, critical analysis of road accident data is needed, to identify the causes of road accidents. Annually, 1,250,000 people die and 50,000,000 are injured in road accidents worldwide, and fatal road accidents are caused by human error. Improving road conditions is not sufficient, but significantly understanding human errors that cause road accidents, and negligence of corrective and safety driving protocols provided by the concerned government agencies or private organizations. The study aimed to help get insights about the causes of road accidents, and to provide knowledge of road accidents for road safety using Association Rule Mining with the application of the Apriori Algorithm. Association rule mining using the Apriori Algorithm produces significant patterns and insights that help identify the causes of road accidents.

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A Conjoint Analysis of Road Accident Data using K-modes Clustering and Bayesian Networks (Road Accident Analysis using clustering and classification)

Cited by 6 publications

References 19 publications

Comparison of Cluster-Based Sampling Approaches for Imbalanced Data of Crashes Involving Large Trucks

Comparison of Cluster-Based Sampling Approaches for Imbalanced Data of Crashes Involving Large Trucks

Analysis of Severe Injuries in Crashes Involving Large Trucks Using K-Prototypes Clustering-Based GBDT Model

Data Mining technique: Application of Apriori algorithm for road accident analysis

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