Performance of Traffic Accidents’ Prediction Models
نویسندگان
چکیده
Modeling traffic-accident frequency is a critical issue to better understand the accident trends and effectiveness of current traffic policies practices in different countries. The main objectives this study are model road accidents, fatalities injuries Jordan, using modeling techniques, including regression, artificial neural network (ANN) autoregressive integrated moving average (ARIMA) models evaluate safety impact travel-restriction strategies during Covid-19 pandemic on trafficaccident statistics for year 2020. To accomplish these objectives, data registered vehicles (REGV), population (POP) economic gross domestic product (GDP) from 1995 through 2020 were obtained related sources Jordan. analysis revealed that have an increasing trend Root mean square error (RMSE), absolute (MAE) coefficient multiple determination (R2) sued performance developed prediction models. Based performance, ANN best, followed by ARIMA then regression Finally, it was concluded undertaken government Jordan combat Covid-19, complete partial banning travel, resulted considerable reduction about 35%, 37% 50%, respectively. KEYWORDS: Traffic Artificial network, pandemic, Regression, Timeseries analysis, Prediction
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ژورنال
عنوان ژورنال: Jordan Journal of Civil Engineering
سال: 2023
ISSN: ['1993-0461', '2225-157X']
DOI: https://doi.org/10.14525/jjce.v17i1.04