Abstract

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.

Original languageEnglish
Title of host publicationProceedings of 2016 International Conference on Information and Communication Technology and Systems, ICTS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages199-202
Number of pages4
ISBN (Electronic)9781509013791
DOIs
Publication statusPublished - 24 Apr 2017
Event2016 International Conference on Information and Communication Technology and Systems, ICTS 2016 - Surabaya, Indonesia
Duration: 12 Oct 2016 → …

Publication series

NameProceedings of 2016 International Conference on Information and Communication Technology and Systems, ICTS 2016

Conference

Conference2016 International Conference on Information and Communication Technology and Systems, ICTS 2016
Country/TerritoryIndonesia
CitySurabaya
Period12/10/16 → …

Keywords

  • Generalized Regression Neural Network
  • Leave One Out Cross Validation
  • forecasting

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