Your browser doesn't support javascript.
Performance Of Traffic Accidents Prediction Models
Jordan Journal of Civil Engineering ; 17(1), 2023.
Article in English | ProQuest Central | ID: covidwho-2167615
ABSTRACT
Modeling traffic accident frequency is an important issue to better understand the accident trends and the effectiveness of current traffic policies and practices in different countries. The main objective of this study is to model traffic road accidents, fatalities, and injuries in Jordan, using different modeling techniques including regression, Artificial Neural Network (ANN), and Autoregressive Integrated Moving Average (ARIMA) models, and to evaluate the impact of Covid-19 pandemic on traffic accident statistics for the year of 2020. To accomplish these objectives, traffic accidents, registered vehicles (REGV), population (POP), and economic gross domestic product (GDP) data from 1995 through 2020 were obtained from related sources in Jordan. Results of the analysis revealed that accidents, fatalities, and injuries have an increasing trend in Jordan. Also, it was found that the developed ANN models were more accurate for accidents, injuries, and fatalities prediction than ARIMA, which was also better than regression which comes in the last place in terms of its prediction power. Finally, it was concluded that strategies are undertaken by the government of Jordan to combat Covid-19;including complete and partial banning on travel, had resulted in a considerable reduction of accidents, injuries, and fatalities by about 35, 37, and 50%, respectively.
Keywords
Search on Google
Collection: Databases of international organizations Database: ProQuest Central Type of study: Prognostic study Language: English Journal: Jordan Journal of Civil Engineering Year: 2023 Document Type: Article

Similar

MEDLINE

...
LILACS

LIS

Search on Google
Collection: Databases of international organizations Database: ProQuest Central Type of study: Prognostic study Language: English Journal: Jordan Journal of Civil Engineering Year: 2023 Document Type: Article