Backpropagation – Artificial Neural Network (BP-ANN): Understanding gender characteristics of older driver accidents in West Midlands of United Kingdom

Amin, Shohel

doi:10.1016/j.ssci.2019.104539

Cited by 39 publications

(18 citation statements)

References 48 publications

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“…e programming feature of Scilab software has been used in this analysis to verify the highest sensitivity on the expected neural network [53]. Amin used the backpropagation ANN approach to investigate gender characteristics of older driver accidents and model the variables of their accidents and finally illustrated that journey purpose was the highest contributor factor of accident risk for older drivers and lighting condition was the second most important factor [54]. Ghasedi et al used the logit model, factor analysis, and machine learning approaches to recognize the most effective variables on suburban accidents and generate the most accurate model for predicting future accidents in the busiest suburban highway of Guilan Province located in the north of Iran.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Urban Traffic Accident Analysis and Prediction Using Logit and Machine Learning-Based Pattern Recognition Models

Gilani

Hosseinian

Ghasedi

et al. 2021

Mathematical Problems in Engineering

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Modeling the severity of accidents based on the most effective variables accounts for developing a high-precision model presenting the possibility of occurrence of each category of future accidents, and it could be utilized to prioritize the corrective measures for authorities. The purpose of this study is to identify the variables affecting the severity of the injury, fatal, and property damage only (PDO) accidents in Rasht city by collecting information on urban accidents from March 2019 to March 2020. In this regard, the multiple logistic regression and the pattern recognition type of artificial neural network (ANN) as a machine learning solution are used to recognize the most influential variables on the severity of accidents and the superior approach for accident prediction. Results show that the multiple logistic regression in the forward stepwise method has R2 of 0.854 and an accuracy prediction power of 89.17%. It turns out that the accidents occurred between 18 and 24 and KIA Pride vehicle has the highest effect on increasing the severity of accidents, respectively. The most important result of the logit model accentuates the role of environmental variables, including poor lighting conditions alongside unfavorable weather and the dominant role of unsafe and poor quality of vehicles on increasing the severity of accidents. In addition, the machine learning model performs significantly better and has higher prediction accuracy (98.9%) than the logit model. In addition, the ANN model’s greater power to predict and estimate future accidents is confirmed through performance and sensitivity analysis.

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Section: Introductionmentioning

confidence: 99%

Data-Driven Urban Traffic Accident Analysis and Prediction Using Logit and Machine Learning-Based Pattern Recognition Models

Gilani

Hosseinian

Ghasedi

et al. 2021

Mathematical Problems in Engineering

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show abstract

“…Examples are speeding or overtaking on the inside. As proved by various research [14][15][16] violations appeared to be most strongly related to crashes. There may also found studies devoted to the physical or mental factors, which have an impact on driver style causing unusual driving behavior.…”

Section: Rescue Facilities Congestionmentioning

confidence: 83%

Analysis of the Causes of Vehicle Accidents in Poland in 2009-2019

Szumska

Frej

Grabski

2020

LOGI – Scientific Journal on Transport and Logistics

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The article presents the general characteristics of road transport safety in Poland over the years 2009-2019. The key objective of this study was to investigate the main factors of road accidents in Poland. Up till now, the number of road accidents has been analysed in detail on the basis of data on collisions from rear, side and frontal perspective. Moreover, in the article, statistics regarding the number of perpetrators of accidents by gender and age are summarized, as well as dynamics aspects of changes related to new passenger vehicles and trucks in Poland in 2009-2019 are indicated. As a result, the intensity of rear collisions rather than frontal collisions is apparent. Hence, an inconspicuous rear collision by not braking a speeding vehicle carries a risk of the upper cervical spine.

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“…is is a commonly employed method for training NNs, which assists in calculating the gradient of the loss function relative to all weights in the network and enhancing the connectivity between layers to obtain the optimal solution [27]. A BPNN usually has three or more layers.…”

Section: Neural Networkmentioning

confidence: 99%

Analysis and Prediction of Overloaded Extra-Heavy Vehicles for Highway Safety Using Machine Learning

Lin

et al. 2020

Mobile Information Systems

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Along with the prosperity and rapid development of the national economy, the transportation industry has rapidly developed in China. However, overloaded vehicles have been causing frequent traffic accidents. Thus, to alleviate or resolve the corresponding problems associated with highway engineering safety and the market economy, an improved technique for overload management is urgently required. In this study, to analyze the overload data on expressways and highways in China, we developed a machine learning model by comparing the performances of cluster analysis, backpropagation neural network (BPNN), generalized regression neural network (GRNN), and wavelet neural network (WNN) in analyzing global and local time series overload data. In a case study, our results revealed the trends of overloading on highways in Jiangsu Province. Given sufficient data, BPNN performed better than GRNN and WNN. As the amount of training data increased, GRNN performed better, but the runtime increased. WNN had the shortest runtime among the three methods and could reflect the future trends of the overload rate in the monthly data prediction of overload. Our model provides information with potential value for expressway network management departments through data mining. This information could help management departments allocate resources reasonably and optimize the information utilization rate.

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Backpropagation – Artificial Neural Network (BP-ANN): Understanding gender characteristics of older driver accidents in West Midlands of United Kingdom

Cited by 39 publications

References 48 publications

Data-Driven Urban Traffic Accident Analysis and Prediction Using Logit and Machine Learning-Based Pattern Recognition Models

Data-Driven Urban Traffic Accident Analysis and Prediction Using Logit and Machine Learning-Based Pattern Recognition Models

Analysis of the Causes of Vehicle Accidents in Poland in 2009-2019

Analysis and Prediction of Overloaded Extra-Heavy Vehicles for Highway Safety Using Machine Learning

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