Investigating the Significant Individual Historical Factors of Driving Risk Using Hierarchical Clustering Analysis and Quasi-Poisson Regression Model

Naji, Hasan A. H.; Xue, Qingji; Zheng, Ke; Lyu, Nengchao

doi:10.3390/s20082331

Cited by 10 publications

(4 citation statements)

References 36 publications

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“…Seacrist et al [27] utilized naturalistic driving data to compare and analyze the frequency and characteristics of a high-risk driver's near-crashes. Naji et al [4,28] adopted two logit regression models to explore the affecting factors of driving risk on near-crashes and individual drivers. Perez adopted a method for identifying and validating near-crash events using different kinematic thresholds [29].…”

Section: Related Workmentioning

confidence: 99%

Risk Levels Classification of Near-Crashes in Naturalistic Driving Data

et al. 2022

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Identifying dangerous events from driving behavior data has become a vital challenge in intelligent transportation systems. In this study, we compared machine and deep learning-based methods for classifying the risk levels of near-crashes. A dataset was built for the study by considering variables related to naturalistic driving, temporal data, participants, and road geometry, among others. Hierarchical clustering was applied to categorize the near-crashes into several risk levels based on high-risk driving variables. The adaptive lasso variable model was adopted to reduce factors and select significant driving risk factors. In addition, several machine and deep learning models were used to compare near-crash classification performance by training the models and examining the model with testing data. The results showed that the deep learning models outperformed the machine learning and statistical models in terms of classification performance. The LSTM model achieved the highest performance in terms of all evaluation metrics compared with the state-of-the-art models (accuracy = 96%, recall = 0.93, precision = 0.88, and F1-measure = 0.91). The LSTM model can improve the classification accuracy and prediction of most near-crash events and reduce false near-crash classification. The finding of this study can benefit transportation safety in predicting and classifying driving risk. It can provide useful suggestions for reducing the incidence of critical events and forward road crashes.

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Section: Related Workmentioning

confidence: 99%

Risk Levels Classification of Near-Crashes in Naturalistic Driving Data

et al. 2022

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“…In other related studies [48][49][50], Poisson regression models were fitted for RTF data with the basic assumption that the data produced same mean and variance. However, Shaik and Hossain [51] faulted the use of the Poisson regression model, as the underlying assumptions are difficult to satisfy.…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive Probability Models of Road Traffic Human Deaths with Demographic Factors in Ghana

2022

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Road traffic carnages are global concerns and seemingly on the rise in Ghana. Several risk factors have been studied as associated with road traffic fatalities. However, inadequate road traffic fatality (RTF) data and inconsistent probability outcomes for RTF remain major challenges. The objective of this study was to illustrate and estimate probability models that can predict road traffic fatalities. We relied on 66,159 recorded casualties who were involved in road traffic accidents (RTAs) in Ghana from 2015 to 2019. Three generalized linear models, namely, logistic regression, probit regression, and linear probability model, were used for the analysis. We found that gender and age groups have significant effects in predicting the probability of road traffic fatality for all three models. Through a likelihood ratio test, however, it was determined that the logit regression model produced consistent probabilities of traffic fatalities which are very close to the actual probability values across the age groups and gender, compared to the other two models. Thus, we recommend intensified campaign for the use of seat belts in vehicles, targeted at the aged and male users of road transport, to reduce the possibility of death in any RTA.

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“…Jiho Lee et al proposed a hierarchical clustering analysis algorithm to construct a learner emotion analysis model for learning experience text and classi ed learners through hierarchies. us, the emotion analysis of each learner is realized [7,8]. Agnivesh et al applied clustering algorithm to data classi cation, which showed good performance and e ect [9].…”

Section: Related Workmentioning

confidence: 99%

Construction of Alumni Information Analysis Model Based on Big Data

Wang

2022

Mathematical Problems in Engineering

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In order to integrate and utilize alumni resources in a better way, big data is utilized to construct alumni information analysis model based on improved hierarchical clustering algorithm, so as to realize mining and retrieval of alumni information. First,the basic principle of hierarchical clustering algorithm is analyzed concretely. Moreover, the improvement is performed on this basis, and a method of calculating the distance between class clusters based on the ant colony optimization is proposed, which uses the shortest distance of the ant colony algorithm to optimally solve the distance between hierarchical class clusters, so as to improve the clustering accuracy. Then, the alumni information analysis model based on improved hierarchical clustering algorithm is constructed, and the model is divided into text preprocessing, keyword extraction, text feature vector generation, name disambiguation, and alumni recognition modules. Finally, the improved hierarchical clustering algorithm and construction of model are verified by experiments. The results show that the accuracy of the improved agglomerative hierarchical clustering algorithm is as high as 86.4% on average and 3.8% and 4.8% more than the two traditional algorithms. Thus, the clustering effect of the algorithm is better, and the proposed alumni analysis model can effectively process text disambiguation of web pages and identification of alumni information, which has certain effectiveness.

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Investigating the Significant Individual Historical Factors of Driving Risk Using Hierarchical Clustering Analysis and Quasi-Poisson Regression Model

Cited by 10 publications

References 36 publications

Risk Levels Classification of Near-Crashes in Naturalistic Driving Data

Risk Levels Classification of Near-Crashes in Naturalistic Driving Data

Predictive Probability Models of Road Traffic Human Deaths with Demographic Factors in Ghana

Construction of Alumni Information Analysis Model Based on Big Data

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