TY - GEN
T1 - Generalized Regression Neural Network for predicting traffic flow
AU - Buliali, Joko Lianto
AU - Hariadi, Victor
AU - Saikhu, Ahmad
AU - Mamase, Saprina
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/4/24
Y1 - 2017/4/24
N2 - Forecasting traffic flow is a popular research topic in Intelligent Transportation System. There have been several methods used for this forecasting, such as statistical methods, Bayesian Network, Neural Network Model, Hybrid ARIMA and ANN. Generalized Regression Neural Network (GRNN) is an interesting model to be used in forecasting traffic flow, as it can predict data with dynamic change and non-linear in nature, which is generally found in traffic flow data. In this research, a GRNN model is set up to process traffic flow data, and comparing its results and the results from other predicting methods (ARIMA, Single Exponential Smoothing, and Moving Average). Leave One Out Cross Validation (LOOCV) is used in testing traffic flow data and Mean Absolute Percentage Error (MAPE) is used as the evaluation criterion in the testing. The results show that using GRNN method on the testing data can improve the accuracy of predictions by reducing the value of MAPE when three other predicting methods: ARIMA, Single Exponential Smoothing, and Moving Average.
AB - Forecasting traffic flow is a popular research topic in Intelligent Transportation System. There have been several methods used for this forecasting, such as statistical methods, Bayesian Network, Neural Network Model, Hybrid ARIMA and ANN. Generalized Regression Neural Network (GRNN) is an interesting model to be used in forecasting traffic flow, as it can predict data with dynamic change and non-linear in nature, which is generally found in traffic flow data. In this research, a GRNN model is set up to process traffic flow data, and comparing its results and the results from other predicting methods (ARIMA, Single Exponential Smoothing, and Moving Average). Leave One Out Cross Validation (LOOCV) is used in testing traffic flow data and Mean Absolute Percentage Error (MAPE) is used as the evaluation criterion in the testing. The results show that using GRNN method on the testing data can improve the accuracy of predictions by reducing the value of MAPE when three other predicting methods: ARIMA, Single Exponential Smoothing, and Moving Average.
KW - Generalized Regression Neural Network
KW - Leave One Out Cross Validation
KW - forecasting
UR - http://www.scopus.com/inward/record.url?scp=85019472964&partnerID=8YFLogxK
U2 - 10.1109/ICTS.2016.7910298
DO - 10.1109/ICTS.2016.7910298
M3 - Conference contribution
AN - SCOPUS:85019472964
T3 - Proceedings of 2016 International Conference on Information and Communication Technology and Systems, ICTS 2016
SP - 199
EP - 202
BT - Proceedings of 2016 International Conference on Information and Communication Technology and Systems, ICTS 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 International Conference on Information and Communication Technology and Systems, ICTS 2016
Y2 - 12 October 2016
ER -