TY - JOUR
AU - Setiyawan, B.
AU - Narendra, A.
PY - 2022/08/31
Y2 - 2024/09/19
TI - Pemodelan Jumlah Kematian di Jalan Raya Per Satu Juta Penduduk
JF - REKONSTRUKSI TADULAKO: Civil Engineering Journal on Research and Development
JA - RTCEJRD
VL - 3
IS - 2
SE -
DO - 10.22487/renstra.v3i2.426
UR - https://new.jurnal.untad.ac.id/index.php/renstra/article/view/426
SP - 15-20
AB - <p>This study aims to decide the factors that influence Road fatalities per one million inhabitants in member countries of the Organization for Economic Co-operation and Development (OECD). This research data uses a dataset covering 19 member countries of the Organization for Economic Co-operation and Development in 2010 – 2018. This research method uses multivariate analysis with multiple linear regression techniques using several indicators. Nine independent variables are used in this model, namely (X<sub>1</sub>) road passenger transport in passenger-km per one thousand units of current Gross Domestic Product (GDP) United States Dollar (USD), (X<sub>2</sub>) road freight transport in tonne-km per one thousand units of current Domestic Product (GDP) United States Dollar (USD), (X<sub>3</sub>) density of road (km per 100 sq. km), (X<sub>4</sub>) road transport infrastructure investment in constant United States Dollar (USD) per inhabitant, (X<sub>5</sub>) goods road motor vehicles per one thousand inhabitants, (X<sub>6</sub>) motorcycles per one thousand inhabitants, (X<sub>7</sub>) passenger cars per one thousand inhabitants, (X<sub>8</sub>) road motor vehicles per one thousand inhabitants, (X<sub>9</sub>) road traffic in thousand vehicle -km per road motor vehicle. One variable (Y) road fatalities per one million inhabitants, as the dependent variable. The results of the analysis showed that the independent variables had a partial significant effect on road fatalities. From the validation results obtained the value of R² the model is 60%, the F-statistic is 16.03 with a significance value of 0.0000 and the correlation value of the model is 0.66. The resulting multiple regression analysis model is: Y = 69.3071 - 0.0936.X<sub>1</sub> + 0.1225X<sub>2</sub> + 0.0020X<sub>3</sub> - 0.0484X<sub>4</sub> + 0.2070X<sub>5</sub> + 0.3829X<sub>6</sub> - 0.0364X<sub>7</sub> -0.0354X<sub>8</sub> + 0.0010X<sub>9</sub></p>
ER -