Motorcyclist characteristics and traffic behaviour in urban Northern Ghana: Implications for road traffic accidents

Dapilah, Frederick; Guba, Bismarck Yelfogle; Owusu-Sekyere, Ebenezer

doi:10.1016/j.jth.2016.03.001

Cited by 38 publications

(26 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, Dapilah et al 14 . studied the traits of the bike riders in the north of Ghana and reported the following results that age, occupation and ownership of motorcycle significantly associated with wearing-helmet.…”

Section: Discussionmentioning

confidence: 99%

Analysis and identification of the hidden relationships between effective factors in the mortality rate caused by road accidents: A case study of Fars Province, Iran

Lotfi

Honarvar

Gholamzadeh

2019

Chinese Journal of Traumatology

View full text Add to dashboard Cite

Purpose An analysis and identification of the hidden relationships between effective factors in the mortality rate caused by road accidents in Fars Province of Iran to prevent and reduce traffic accidents in the future. Methods This cross-sectional study was conducted to integrate all the pervious researches performed on mortality rate of road traffic accidents in Fars Province from March 21, 2013 to March 20, 2017. In order to reveal the relationships between the factors affecting mortality rates of road traffic accidents, the data regarding road traffic accidents extracted from resources such as Legal Medicine Organization, Traffic Police, Accident & Emergency Department, as well as Department of Roads and Urban Development of Fars Province, then cleaned and the applicable attributes embedded in the data all aggregated for further analysis. It should be noted that the data not related to Fars Province were deleted, the data analyzed, converted and the aggregation between various attributes identified. The aggregation between these different attributes as well as the FP-growth algorithm and two indexes of support and confidence calculated and interesting and effective rules extracted. In the end, several accident-provoking factors, the degree of consecutive and interdependence of each one in road accidents identified and introduced. The statistical analysis was conducted by using Rapid Miner software. Results Of the 6216 people dead due to road traffic accidents, 4865 (79.02%) were male and 1292 (20.98%) were female, 59 of them have no clear gender. The largest portion of people died of road traffic accidents belonged to married and self-employed men who collided with motorcycles in autumn. Moreover, young individuals (aged 19–40 years) with secondary educational level who died of accidents in summer at 12:00 a.m. and then 5:00 p.m. in outer city main roads of Kazerun-Shiraz, then Darab-Shiraz, Fasa-Darab and in within-city main streets had the highest mortality rates. Among women, the middle-aged group (aged 41–65 years) followed by young-aged group (aged 19–40 years) with elementary educational level and then illiterate accounted for the highest mortality rate of road traffic accidents. The automobiles involved in accidents included Pride, Peugeot 405, Peykan pickup, Samand, Peugeot Pars, other vehicles and motorcycles. Conclusion The high mortality rate of illiterate and low-literate in various age groups indicates that educational level plays a crucial role as a factor in road accidents, requiring related organizations such as Traffic Police and Ministry of Education to take necessary measures and policies.

show abstract

Section: Discussionmentioning

confidence: 99%

Analysis and identification of the hidden relationships between effective factors in the mortality rate caused by road accidents: A case study of Fars Province, Iran

Lotfi

Honarvar

Gholamzadeh

2019

Chinese Journal of Traumatology

View full text Add to dashboard Cite

show abstract

“…It can be seen from Table 4 that when under the influence of alcohol, the fatality rate is 4.645%, while the number drops to 0.714% for those without alcohol. Previous studies have demonstrated that driving under the influence of alcohol is considered an elevated risk of traffic accidents [18], [43]. The reason behind is that the response time and the awareness of the drivers will be impaired under the influence of alcohol.…”

Section: A Drink-drivingmentioning

confidence: 99%

“…For example, Russo and Savolainen [17] applied three regression models namely negative binomial, order logit and multinomial logit to ascertain the relationship between different median barrier types and freeway median crash frequency and severity. Dapilah et al [18] examined how motorcyclist characteristics influence the road traffic accidents using chi-square analysis. Karimi and Kashi [19] investigated the effect of geometric parameters on accident reduction using sensitivity analysis.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Leading Causes of Traffic Fatalities Using XGBoost and Grid-Based Analysis: A City Management Perspective

Ding²,

Cheng

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Traffic accidents have been one of the most important global public problems. It has caused a severe loss of human lives and property every year. Studying the influential factors of accidents can help find the reasons behind. This can facilitate the design of effective measures and policies to reduce the traffic fatality rate and improve road safety. However, most of the existing research either adopted methods based on linear assumption or neglected to further evaluate the spatial relationships. In this paper, we proposed a methodology framework based on XGBoost and grid analysis to spatially analyze the leading factors on traffic fatality in Los Angeles County. Characteristics of the collision, time and location, and environmental factors are considered. Results show that the proposed method has the best modeling performance compared with other commonly seen machine learning algorithms. Eight factors are found to have the leading impact on traffic fatality. Spatial relationships between the eight factors and the fatality rates within the Los Angeles County are further studied using the grid-based analysis in GIS. Specific suggestions on how to reduce the fatality rate and improve road safety are provided accordingly. INDEX TERMS XGBoost, factors analysis, GIS, grid-based analysis, non-linear machine learning, traffic fatality.

show abstract

“…Reference [10] estimated the characteristics of road traffic accidents in Bhutan and found the main causes were careless driving and drunk driving. Reference [11] studied how the characteristics of motorcyclists (age, occupation, motorcycle ownership) influenced their behavior on the road and the implications for traffic accidents. Reference [12] analyzed the changes in the driving, speed and traffic safety characteristics before and after automated enforcement using the methods of traffic investigation and data analysis.…”

Section: Million In 2015mentioning

confidence: 99%

Feature Recognition of Urban Road Traffic Accidents Based on GA-XGBoost in the Context of Big Data

Lin

et al. 2019

IEEE Access

View full text Add to dashboard Cite

The identification of the characteristics of urban road traffic accidents is of great significance for reducing traffic accidents and the corresponding losses. In the context of big data, to accurately understand the characteristics of traffic accidents, the feature set of urban road traffic accidents is proposed, the XGBoost model is used to classify traffic accidents into minor accidents, general accidents, major accidents and serious accidents, and a GA-XGBoost feature recognition model is built. The GA-XGBoost feature recognition model is based on the genetic algorithm (GA) as a factor search algorithm and is verified by applying the big data of traffic accidents in a Chinese city from 2006 to 2016; in addition, the model is compared with the GA-RF, GA-GBDT and GA-LightGBM models. The results show that the GA-XGBoost model can accurately identify the features of the traffic accidents in 7 cities, including driving experience, illegal driving behavior, vehicle age, road intersection type, weather conditions, traffic flow and time interval. Compared with the GA-RF, GA-GBDT and GA-LightGBM models, the recognition features are more accurate, and the performance is better. INDEX TERMS Urban traffic, feature recognition, traffic accidents, XGBoost algorithm, genetic algorithm (GA).

show abstract

Motorcyclist characteristics and traffic behaviour in urban Northern Ghana: Implications for road traffic accidents

Cited by 38 publications

References 13 publications

Analysis and identification of the hidden relationships between effective factors in the mortality rate caused by road accidents: A case study of Fars Province, Iran

Analysis and identification of the hidden relationships between effective factors in the mortality rate caused by road accidents: A case study of Fars Province, Iran

Analyzing the Leading Causes of Traffic Fatalities Using XGBoost and Grid-Based Analysis: A City Management Perspective

Feature Recognition of Urban Road Traffic Accidents Based on GA-XGBoost in the Context of Big Data

Contact Info

Product

Resources

About