Statistical modeling of emergency medical services’ response and rescue times to road traffic crashes in the Kingdom of Saudi Arabia

Al-Zabidi, Ayoub; Almannaa, Mohammed; Elhenawy, Mohammed; Gharbi, Anis

doi:10.1016/j.cstp.2022.11.009

Cited by 11 publications

(5 citation statements)

References 64 publications

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“…Our study also found that accident characteristics such as accident type, place, and region are strongly associated with RTA response time. An earlier study from the KSA reported that geographical region, type of injury, and weather conditions are significantly associated with response and rescue time [13]. Like our findings, Al Kabbi et al [12] examined predictors of accident response time in Abu Dhabi and concluded that accident response time is significantly related to temporal characteristics, accident type, and location of the accident.…”

Section: Discussionsupporting

confidence: 78%

“…Existing studies indicate that the accident management system and response time are substandard in the KSA. The average RTA response time to road traffic crashes is 14 min, which is almost double compared to the international standard (i.e., 8 min) [13]. Mansuri et al [14] reported that post-crash care is often delayed, which contributes to the rise of deaths and disabilities in the KSA.…”

Section: Introductionmentioning

confidence: 98%

“…Mansuri et al [14] reported that post-crash care is often delayed, which contributes to the rise of deaths and disabilities in the KSA. Al-Zabidi et al [13] also reported that the long response time is associated with the geographical region, crash time, number of injured people, type of injury, and weather conditions in the KSA. To date, no studies have reported the impact of socio-demographic characteristics on RTA response time.…”

Section: Introductionmentioning

confidence: 99%

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Emergency Medical Service Response Time for Road Traffic Accidents in the Kingdom of Saudi Arabia: Analysis of National Data (2016–2020)

Alslamah

Alsofayan

Imam

et al. 2023

IJERPH

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Despite preventive measures and initiatives, road traffic accidents are on the rise in the Kingdom of Saudi Arabia. This study aimed to investigate the emergency medical service unit’s response to RTA by socio-demographic and accident-related variables in the Kingdom of Saudi Arabia. This retrospective survey included Saudi Red Crescent Authority data on road traffic accidents between 2016 and 2020. As part of the study, information on sociodemographic characteristics (e.g., age, sex, and nationality), accident-related data (type and place of the accident), and response time to road traffic accidents were extracted. Our study included 95,372 cases of road traffic accidents recorded by the Saudi Red Crescent Authority in the Kingdom of Saudi Arabia between 2016 and 2020. Descriptive analyses were performed to explore the emergency medical service unit’s response time to road traffic accidents, and linear regression analyses were performed to investigate the predictors of response time. Most of the road traffic accident cases were among males (59.1%), and the age group of 25–34 years accounted for about a quarter (24.3%), while the mean age of the road traffic accident cases was 30.13 (±12.86) years. Among the regions, the capital city of Riyadh experienced the highest proportion of road traffic accidents (25.3%). In most road traffic accidents, the mission acceptance time was excellent (0–60 s; 93.7%), movement duration was excellent (<120 s; 91.1%), reaching site duration was excellent (<12 min; 57.9%), treatment start time was excellent (<120 s; 76.4%), duration at the scene was poor (>15 min; 40.8%), reaching hospital duration was good (30–60 min; 52.7%), and in-hospital duration was poor (>15 min; 44.1%). Regions, places and types of accidents, age, gender, and nationality of victims were significantly associated with different parameters of response time. Excellent response time was observed in most of the parameters except the duration at the scene, reaching hospital duration, and in-hospital duration. Apart from the initiatives to prevent road traffic accidents, policymakers should focus on strategies to improve accident response time to save lives.

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Section: Discussionsupporting

confidence: 78%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Emergency Medical Service Response Time for Road Traffic Accidents in the Kingdom of Saudi Arabia: Analysis of National Data (2016–2020)

Alslamah

Alsofayan

Imam

et al. 2023

IJERPH

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“…Hierarchical clustering is used to divide the individuals in the current population equally according to their differences 24 , and the fuzzy weight value of the optimal dispatching of emergency dispatching vehicles is obtained: where p is the prior probability. Initialize the path distribution of cross regional emergency material dispatching vehicles, give the estimated prior probability of cross regional emergency material dispatching vehicles 25 , and record the individuals with the best quality in each group as . Using seed swarm algorithm for CREMS vehicle optimization scheduling and path planning 26 , initialize the adaptive weight to 1/N, and normalize the weight of the path space planning for CREMS vehicle optimization scheduling: …”

Section: A Grid Distribution Model and Feature Analysis For Materials...mentioning

confidence: 99%

Research on cross regional emergency material scheduling algorithm based on seed optimization algorithm

Li,

Cui,

Chu

et al. 2023

Sci Rep

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In order to improve the response capability of cross regional emergency material scheduling (CREMS), a CREMS algorithm based on seed optimization algorithm is proposed. Construct a segmented regional grid distribution model structure for CREMS, use a grid matching algorithm based on block link distribution to construct the optimization objective function during the emergency material scheduling process, use variable neighborhood search technology to solve the diversity problem of cluster optimization in CREMS, and combine seed optimization algorithms for combination control and recursive analysis in the emergency material scheduling process. Based on the combination of deep learning and reinforcement learning, the optimal route and configuration scheme design for CREMS process is achieved. The simulation results show that this method has better active configuration capability, better path optimization capability and stronger spatial regional planning capability for CREMS.

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“…Road rescue plays an important role in reducing casualties and property losses in trafc accidents [1][2][3][4], which can provide rescue services for faulty vehicles, such as fuel delivery, tire replacement, battery connection, on-site repair, clearing, and towing. Te reasonable and adequate distribution of rescue resources can improve the efciency of rescue and enhance the road resilience [5][6][7][8]. Te lack of road rescue demand prediction may lead to insufcient rescue forces and untimely rescue in emergency.…”

Section: Introductionmentioning

confidence: 99%

Road Rescue Demand Prediction for the Improvement of Traffic System Resilience

Yang

2023

Journal of Advanced Transportation

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Road rescue can provide rescue services for faulty vehicles, such as fuel delivery, tire replacement, battery connection, on-site repair, clearing, and towing, which plays an important role in reducing casualties and property losses in traffic accidents. Based on the historical data of road rescue, this paper analyzes the influencing factors of the road rescue demand and establishes a prediction model of the road rescue demand without data grouping. In order to further improve the prediction accuracy, the data are divided into nine groups according to the importance of the influencing factors, and nine submodels are established for the nine groups of data. When the influencing factors are known, the submodel corresponding to the most important influencing factor is selected to predict the road rescue demand. A case study in Beijing is used to verify the effectiveness and superiority of the proposed models, which can effectively predict the road rescue demand under various conditions, including the normal condition, the Spring Festival, National Day, the three-day holiday (e.g., Qingming, May Day, the Dragon Boat Festival, the Mid-Autumn Festival, and New Year’s Day,), and extreme weather (e.g., low temperature, high temperature, heavy snow, heavy rain, and rainstorm). The research findings can provide scientific basis for the rescue department to deploy rescue equipment and rescue personnel in advance, raise the efficiency and quality of rescue, and improve the resilience of the transportation system.

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Statistical modeling of emergency medical services’ response and rescue times to road traffic crashes in the Kingdom of Saudi Arabia

Cited by 11 publications

References 64 publications

Emergency Medical Service Response Time for Road Traffic Accidents in the Kingdom of Saudi Arabia: Analysis of National Data (2016–2020)

Emergency Medical Service Response Time for Road Traffic Accidents in the Kingdom of Saudi Arabia: Analysis of National Data (2016–2020)

Research on cross regional emergency material scheduling algorithm based on seed optimization algorithm

Road Rescue Demand Prediction for the Improvement of Traffic System Resilience

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