A new model for black spots identification using Weighted Severity Index

Mbarek, Abdelilah; Jiber, Mouna; Yahyaouy, Ali; Sabri, My Abdelouahed

doi:10.1109/icds53782.2021.9626758

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…The results showed that the identification was reliable thus contributing to the safety of the identified segments. Another previous study combined WSI and XGBoost to improve black spot identification [20]. The objective of their study is to evaluate the deadliest sections of the road to get an idea of the level of safety.…”

Section: Introductionmentioning

confidence: 99%

Black spots identification on rural roads based on extreme learning machine

Mbarek

Jiber

Yahyaouy

et al. 2023

IJECE

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<span lang="EN-US">Accident black spots are usually defined as road locations with a high risk of fatal accidents. A thorough analysis of these areas is essential to determine the real causes of mortality due to these accidents and can thus help anticipate the necessary decisions to be made to mitigate their effects. In this context, this study aims to develop a model for the identification, classification and analysis of black spots on roads in Morocco. These areas are first identified using extreme learning machine (ELM) algorithm, and then the infrastructure factors are analyzed by ordinal regression. The XGBoost model is adopted for weighted severity index (WSI) generation, which in turn generates the severity scores to be assigned to individual road segments. The latter are then classified into four classes by using a categorization approach (high, medium, low and safe). Finally, the bagging extreme learning machine is used to classify the severity of road segments according to infrastructures and environmental factors. Simulation results show that the proposed framework accurately and efficiently identified the black spots and outperformed the reputable competing models, especially in terms of accuracy 98.6%. In conclusion, the ordinal analysis revealed that pavement width, road curve type, shoulder width and position were the significant factors contributing to accidents on rural roads.</span>

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Section: Introductionmentioning

confidence: 99%

Black spots identification on rural roads based on extreme learning machine

Mbarek

Jiber

Yahyaouy

et al. 2023

IJECE

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“…To assess the black spot risk of accidents, the weighted severity index (WSI) risk score was used, as revised in 2021 [22]. The WSI was intended to be a straightforward representation of the risk of black spots.…”

Section: Risk Of Black Spotsmentioning

confidence: 99%

Accident black spots identification based on association rule mining

Mbarek,

Jiber,

Yahyaouy

et al. 2024

Bulletin EEI

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This paper presents an analytical approach to identifying the important characteristics of accident black spots on Moroccan rural roads. An association rule mining method is applied to extract road spatial characteristics associated with fatal accidents. The weighted severity index was calculated for each section, which was then used to determine the severity levels of black spots. The apriori algorithm is applied to find the correlation between road characteristics and the severity levels of black spots. Then, a general rule selection method is proposed to identify the rules strongly associated with each severity level. The results show that the proposed approach is effective in identifying the most important factors contributing to accidents. Furthermore, it shows that the combination of several road characteristics, such as road width, road surface, and bridge presence, may contribute to fatal accidents. The general rule selection found that wet, bad surfaces, and narrow shoulders were significantly associated with accidents on rural roads. The findings of the present study can help develop effective strategies to reduce road accidents and thus improve road safety in the country.

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A new model for black spots identification using Weighted Severity Index

Cited by 2 publications

References 7 publications

Black spots identification on rural roads based on extreme learning machine

Black spots identification on rural roads based on extreme learning machine

Accident black spots identification based on association rule mining

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