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Spatial Model of Traffic Congestion by the Changes on City Transportation Route

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@article{IJASEIT6752,
   author = {S. Supriatna and M. Dimyati and Dede Amrillah},
   title = {Spatial Model of Traffic Congestion by the Changes on City Transportation Route},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {5},
   year = {2020},
   pages = {2044--2047},
   keywords = {spatial model; traffic congestion; multiple regression; routes of public transportation.},
   abstract = {Traffic congestion is a problem for every city in Java Island, Indonesia, including Bogor City. Factors causing congestion in Bogor City are thought to come from land use, the geometry and performance of road, and public transport routes (urban transport). The change of urban transportation (angkot) route carried out by Bogor City Government aims to reduce traffic density and congestion, but it is not a guarantee that the main problem will be solved. This study aims to determine the spatial patterns of traffic density and congestion with current angkot routes and to construct a model to predict traffic density and congestion when new angkot routes are used. Variables in this research are land use (number of schools and markets/malls), geometry and performance of road (vehicle volume, road capacity, average velocity, road type, number of lanes, number of signalled and non-signal intersection), and an angkot route passing a road. The method used in modelling is multiple regression using one dummy variable and a stepwise regression method. The result of modelling shows that the variables affecting traffic density are velocity, some signalled and non-signal intersection, and angkot route with R2 value 66.9%. At the same time, the influential variables in the traffic congestion model are vehicle volume, road capacity, number of the signalled intersection, and angkot route with R2 value 81.4%. To see accuracy in predicting model of traffic density and congestion, Mean Absolute Percentage Error (MAPE) validation is used. The results show a value of 12.46% for traffic density, which means that the model has good prediction accuracy and 5.62% for traffic congestion, which means the model has high prediction accuracy. Thus, in this study, the land use of school and market is not a factor causing traffic density and congestion, while the geometry and performance of roads and public transportation routes as a factor causing congestion.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6752},
   doi = {10.18517/ijaseit.10.5.6752}
}

EndNote

%A Supriatna, S.
%A Dimyati, M.
%A Amrillah, Dede
%D 2020
%T Spatial Model of Traffic Congestion by the Changes on City Transportation Route
%B 2020
%9 spatial model; traffic congestion; multiple regression; routes of public transportation.
%! Spatial Model of Traffic Congestion by the Changes on City Transportation Route
%K spatial model; traffic congestion; multiple regression; routes of public transportation.
%X Traffic congestion is a problem for every city in Java Island, Indonesia, including Bogor City. Factors causing congestion in Bogor City are thought to come from land use, the geometry and performance of road, and public transport routes (urban transport). The change of urban transportation (angkot) route carried out by Bogor City Government aims to reduce traffic density and congestion, but it is not a guarantee that the main problem will be solved. This study aims to determine the spatial patterns of traffic density and congestion with current angkot routes and to construct a model to predict traffic density and congestion when new angkot routes are used. Variables in this research are land use (number of schools and markets/malls), geometry and performance of road (vehicle volume, road capacity, average velocity, road type, number of lanes, number of signalled and non-signal intersection), and an angkot route passing a road. The method used in modelling is multiple regression using one dummy variable and a stepwise regression method. The result of modelling shows that the variables affecting traffic density are velocity, some signalled and non-signal intersection, and angkot route with R2 value 66.9%. At the same time, the influential variables in the traffic congestion model are vehicle volume, road capacity, number of the signalled intersection, and angkot route with R2 value 81.4%. To see accuracy in predicting model of traffic density and congestion, Mean Absolute Percentage Error (MAPE) validation is used. The results show a value of 12.46% for traffic density, which means that the model has good prediction accuracy and 5.62% for traffic congestion, which means the model has high prediction accuracy. Thus, in this study, the land use of school and market is not a factor causing traffic density and congestion, while the geometry and performance of roads and public transportation routes as a factor causing congestion.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6752
%R doi:10.18517/ijaseit.10.5.6752
%J International Journal on Advanced Science, Engineering and Information Technology
%V 10
%N 5
%@ 2088-5334

IEEE

S. Supriatna,M. Dimyati and Dede Amrillah,"Spatial Model of Traffic Congestion by the Changes on City Transportation Route," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 5, pp. 2044-2047, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.5.6752.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Supriatna, S.
AU  - Dimyati, M.
AU  - Amrillah, Dede
PY  - 2020
TI  - Spatial Model of Traffic Congestion by the Changes on City Transportation Route
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 5
Y2  - 2020
SP  - 2044
EP  - 2047
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - spatial model; traffic congestion; multiple regression; routes of public transportation.
N2  - Traffic congestion is a problem for every city in Java Island, Indonesia, including Bogor City. Factors causing congestion in Bogor City are thought to come from land use, the geometry and performance of road, and public transport routes (urban transport). The change of urban transportation (angkot) route carried out by Bogor City Government aims to reduce traffic density and congestion, but it is not a guarantee that the main problem will be solved. This study aims to determine the spatial patterns of traffic density and congestion with current angkot routes and to construct a model to predict traffic density and congestion when new angkot routes are used. Variables in this research are land use (number of schools and markets/malls), geometry and performance of road (vehicle volume, road capacity, average velocity, road type, number of lanes, number of signalled and non-signal intersection), and an angkot route passing a road. The method used in modelling is multiple regression using one dummy variable and a stepwise regression method. The result of modelling shows that the variables affecting traffic density are velocity, some signalled and non-signal intersection, and angkot route with R2 value 66.9%. At the same time, the influential variables in the traffic congestion model are vehicle volume, road capacity, number of the signalled intersection, and angkot route with R2 value 81.4%. To see accuracy in predicting model of traffic density and congestion, Mean Absolute Percentage Error (MAPE) validation is used. The results show a value of 12.46% for traffic density, which means that the model has good prediction accuracy and 5.62% for traffic congestion, which means the model has high prediction accuracy. Thus, in this study, the land use of school and market is not a factor causing traffic density and congestion, while the geometry and performance of roads and public transportation routes as a factor causing congestion.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6752
DO  - 10.18517/ijaseit.10.5.6752

RefWorks

RT Journal Article
ID 6752
A1 Supriatna, S.
A1 Dimyati, M.
A1 Amrillah, Dede
T1 Spatial Model of Traffic Congestion by the Changes on City Transportation Route
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 5
YR 2020
SP 2044
OP 2047
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 spatial model; traffic congestion; multiple regression; routes of public transportation.
AB Traffic congestion is a problem for every city in Java Island, Indonesia, including Bogor City. Factors causing congestion in Bogor City are thought to come from land use, the geometry and performance of road, and public transport routes (urban transport). The change of urban transportation (angkot) route carried out by Bogor City Government aims to reduce traffic density and congestion, but it is not a guarantee that the main problem will be solved. This study aims to determine the spatial patterns of traffic density and congestion with current angkot routes and to construct a model to predict traffic density and congestion when new angkot routes are used. Variables in this research are land use (number of schools and markets/malls), geometry and performance of road (vehicle volume, road capacity, average velocity, road type, number of lanes, number of signalled and non-signal intersection), and an angkot route passing a road. The method used in modelling is multiple regression using one dummy variable and a stepwise regression method. The result of modelling shows that the variables affecting traffic density are velocity, some signalled and non-signal intersection, and angkot route with R2 value 66.9%. At the same time, the influential variables in the traffic congestion model are vehicle volume, road capacity, number of the signalled intersection, and angkot route with R2 value 81.4%. To see accuracy in predicting model of traffic density and congestion, Mean Absolute Percentage Error (MAPE) validation is used. The results show a value of 12.46% for traffic density, which means that the model has good prediction accuracy and 5.62% for traffic congestion, which means the model has high prediction accuracy. Thus, in this study, the land use of school and market is not a factor causing traffic density and congestion, while the geometry and performance of roads and public transportation routes as a factor causing congestion.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=6752
DO  - 10.18517/ijaseit.10.5.6752