Mining groups of factors influencing bus/minibus crash severities on poor pavement condition roads considering different lighting status

Tamakloe, Reuben; Sam, Enoch F.; Bencekri, Madiha; Das, Subasish; Park, Dongjoo

doi:10.1080/15389588.2022.2066658

Cited by 9 publications

(2 citation statements)

References 35 publications

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“…Several studies recently explored the influence of risk factors on traffic crashes using machine learning methods [1,[33][34][35][36][37]. Rahman et al [1] established a Bayesian belief network (BBN) model by incorporating an expectation-maximization algorithm to examine the relationships between crash factors with driving behavior in Saudi Arabia in a northern city (Al-Ahsa).…”

Section: Machine Learning (Ml) Models In Rtc Analysesmentioning

confidence: 99%

“…And reckless driving was related to single-vehicle crashes at intersections. Tamakloe et al [36] employed the Association Rules Mining (ARM) algorithm to discover hidden groups of crash-risk factors leading to different crash severity levels in poor road conditions. They analyzed the crashes under different lighting conditions and determined the effect of factors on the severity of bus/minibus crashes in Ghana.…”

Section: Machine Learning (Ml) Models In Rtc Analysesmentioning

confidence: 99%

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Identifying Causes of Traffic Crashes Associated with Driver Behavior Using Supervised Machine Learning Methods: Case of Highway 15 in Saudi Arabia

et al. 2022

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Identifying the causes of road traffic crashes (RTCs) and contributing factors is of utmost importance for developing sustainable road network plans and urban transport management. Driver-related factors are the leading causes of RTCs, and speed is claimed to be a major contributor to crash occurrences. The results reported in the literature are mixed regarding speed-crash occurrence causality on rural and urban roads. Even though recent studies shed some light on factors and the direction of effects, knowledge is still insufficient to allow for specific quantifications. Thus, this paper aimed to contribute to the analysis of speed-crash occurrence causality by identifying the road features and traffic flow parameters leading to RTCs associated with driver errors along an access-controlled major highway (761.6 km of Highway 15 between Taif and Medina) in Saudi Arabia. Binomial logistic regression (BNLOGREG) was employed to predict the probability of RTCs associated with driver errors (p < 0.001), and its results were compared with other supervised machine learning (ML) models, such as random forest (RF) and k-nearest neighbor (kNN) to search for more accurate predictions. The highest classification accuracy (CA) yielded by RF and BNLOGREG was 0.787, compared to kNN’s 0.750. Moreover, RF resulted in the largest area under the ROC (a receiver operating characteristic) curve (AUC for RF = 0.712, BLOGREG = 0.608, and kNN = 0.643). As a result, increases in the number of lanes (NL) and daily average speed of traffic flow (ASF) decreased the probability of driver error-related crashes. Conversely, an increase in annual average daily traffic (AADT) and the availability of straight and horizontal curve sections increased the probability of driver-related RTCs. The findings support previous studies in similar study contexts that looked at speed dispersion in crash occurrence and severity but disagreed with others that looked at absolute speed at individual vehicle or road segment levels. Thus, the paper contributes to insufficient knowledge of the factors in crash occurrences associated with driver errors on major roads within the context of this case study. Finally, crash prevention and mitigation strategies were recommended regarding the factors involved in RTCs and should be implemented when and where they are needed.

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