Towards Sustainable Road Safety in Saudi Arabia: Exploring Traffic Accident Causes Associated with Driving Behavior Using a Bayesian Belief Network

Rahman, Muhammad Muhitur; Islam, Md. Kamrul; Al-Shayeb, Ammar; Arifuzzaman, Md

doi:10.3390/su14106315

Cited by 20 publications

(30 citation statements)

References 70 publications

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“…This clearly reveals that among the cities in the Eastern Province, Al-Ahsa is the most prone to traffic crashes. Al-Ahsa has recently been recognized as one of Saudi Arabia's UNESCO-listed heritage sites and has also been given a Guinness World Record, which means that the city has tremendous potential as an international tourist attraction [51]. However, the city's high crash rate may jeopardize that possibility, necessitating immediate action.…”

Section: Study Area and Data Sourcesmentioning

confidence: 99%

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Involvement of Road Users from the Productive Age Group in Traffic Crashes in Saudi Arabia: An Investigative Study Using Statistical and Machine Learning Techniques

et al. 2022

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Road traffic crashes (RTCs) are a major problem for authorities and governments worldwide. They incur losses of property, human lives, and productivity. The involvement of teenage drivers and road users is alarmingly prevalent in RTCs since traffic injuries unduly impact the working-age group (15–44 years). Therefore, research on young people’s engagement in RTCs is vital due to its relevance and widespread frequency. Thus, this study focused on evaluating the factors that influence the frequency and severity of RTCs involving adolescent road users aged 15 to 44 in fatal and significant injury RTCs in Al-Ahsa, Saudi Arabia. In this study, firstly, descriptive analyses were performed to justify the target age group analysis. Then, prediction models employing logistic regression and CART were created to study the RTC characteristics impacting the target age group participation in RTCs. The most commonly observed types of crashes are vehicle collisions, followed by multiple-vehicle and pedestrian crashes. Despite its low frequency, the study area has a high severity index for RTCs, where 73% of severe RTCs include individuals aged 15 to 44. Crash events with a large number of injured victims and fatalities are more likely to involve people in the target age range, according to logistic regression and CART models. The CART model also suggests that vehicle overturn RTCs involving victims in the target age range are more likely to occur as a result of driver distraction, speeding, not giving way, or rapid turning. As compared with the logistic regression model, the CART model was more convenient and accurate for understanding the trends and predicting the involvement probability of the target age group in RTCs; however, this model requires a higher processing time for its development.

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Section: Study Area and Data Sourcesmentioning

confidence: 99%

“…Figure1. Study area and locations of traffic crashes in the Al-Ahsa region of Saudi Arabia (adopted from[51]). …”

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Involvement of Road Users from the Productive Age Group in Traffic Crashes in Saudi Arabia: An Investigative Study Using Statistical and Machine Learning Techniques

et al. 2022

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“…This illustrates how R.T.C.s are greater in severity and involve more victims in the research area, although having lower occurrence rates than in other regions of the world. Further details about the crash dataset can be found in [63,65].…”

Section: Descriptive Statisticsmentioning

confidence: 99%

Predicting Road Crash Severity Using Classifier Models and Crash Hotspots

et al. 2022

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The rapid increase in traffic volume on urban roads, over time, has altered the global traffic scenario. Additionally, it has increased the number of road crashes, some of which are severe and fatal in nature. The identification of hazardous roadway sections using the spatial pattern analysis of crashes and recognition of the primary and contributing factors may assist in reducing the severity of road traffic crashes (R.T.C.s). For crash severity prediction, along with spatial patterns, various machine learning models are used, and the spatial relations of R.T.C.s with neighboring areas are evaluated. In this study, tree-based ensemble models (gradient boosting and random forest) and a logistic regression model are compared for the prediction of R.T.C. severity. Sample data of road crashes in Al-Ahsa, the eastern province of Saudi Arabia, were obtained from 2016 to 2018. Random forest (R.F.) identifies significant features strongly correlated with the severity of the R.T.C.s. The analysis findings showed that the cause of the crash and the type of collision are the most crucial elements affecting the severity of injuries in traffic crashes. Furthermore, the target-specific model interpretation results showed that distracted driving, speeding, and sudden lane changes significantly contributed to severe crashes. The random forest (R.F.) method surpassed other models in terms of injury severity, individual class accuracies, and collective prediction accuracy when using k-fold (k = 10) based on various performance metrics. In addition to taking into account the machine learning approach, this study also included spatial autocorrelation analysis based on G.I.S. for identifying crash hotspots, and Getis Ord Gi* statistics were devised to locate cluster zones with high- and low-severity crashes. The results demonstrated that the research area’s spatial dependence was very strong, and the spatial patterns were clustered with a distance threshold of 500 m. The analysis’s approaches, which included Getis Ord Gi*, the crash severity index, and the spatial autocorrelation of accident incidents according to Moran’s I, were found to be a successful way of locating and rating crash hotspots and crash severity. The techniques used in this study could be applied to large-scale crash data analysis while providing a useful tool for policymakers looking to improve roadway safety.

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“…Emergency vehicle driving is important for sustainable and safe road user behaviour. Emergency vehicles are responsible for responding to emergencies and providing critical medical and other services to the community [6]. Therefore, drivers need to be conscious of their safety and the safety of others when driving an emergency vehicle.…”

Section: Introductionmentioning

confidence: 99%

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

Anjum

Shahab

2023

Sustainability

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Emergency vehicle transportation is important for responding to and transporting individuals during emergencies. This type of transportation faces several issues, such as road safety, navigation and communication, time-critical operations, resource utilisation, traffic congestion, data processing and analysis, and individual safety. Vehicle navigation and coordination is a critical aspect of emergency response that involves guiding emergency vehicles, such as ambulances, to the location of an emergency or medical centre as quickly and safely as possible. Therefore, it requires additional effort to reduce driving risks. The roadside units support emergency vehicles and infrastructure to decrease collisions and enhance optimal navigation routes. However, during the emergency vehicle’s data communication and navigation process, communication is interrupted due to vehicle outages. Therefore, this study proposes the Navigation Data Processing for Assisted Driving (NDP-AD) method to address the problem. The proposed approach assimilates infrastructure and neighbouring location information during driving. The integrated information is processed for distance and traffic during the previous displacement interval. The NDP-AD method employs a recurrent neural network learning approach to analyse opposing vehicle distance and traffic to provide accurate, independent guidance. This effective learning-based guidance process minimises false navigations and deviation in displacement. System efficiency is evaluated based on processing latency, displacement error, data utilisation, false rate, and accuracy metrics.

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Towards Sustainable Road Safety in Saudi Arabia: Exploring Traffic Accident Causes Associated with Driving Behavior Using a Bayesian Belief Network

Cited by 20 publications

References 70 publications

Involvement of Road Users from the Productive Age Group in Traffic Crashes in Saudi Arabia: An Investigative Study Using Statistical and Machine Learning Techniques

Involvement of Road Users from the Productive Age Group in Traffic Crashes in Saudi Arabia: An Investigative Study Using Statistical and Machine Learning Techniques

Predicting Road Crash Severity Using Classifier Models and Crash Hotspots

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

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