A Hybrid Model for Forecasting Local Traffic Parameters

Autor:	Klaus Bogenberger, Ronald Kates, Heidrun Belzner
Rok vydání:	2003
Předmět:	Engineering Artificial neural network business.industry Machine learning computer.software_genre Traffic prediction Traffic engineering Data mining Autoregressive integrated moving average Artificial intelligence business computer Traffic generation model Hybrid model Test data
Zdroj:	IFAC Proceedings Volumes. 36:269-273
ISSN:	1474-6670
DOI:	10.1016/s1474-6670(17)32431-x
Popis:	Predicting traffic is certainly one of the greatest challenges facing current traffic engineering research. The work presented here focuses on the prediction of local traffic parameters such as the local mean speed aggregated over short periods such as one minute. For this purpose a new hybrid model comprising an ARIMA 1 model and a neural network has been developed and validated with real traffic data. This model has been evaluated and compared with conventional methods of traffic prediction using independent real test data. It was found that the new hybrid model produces more accurate and more reliable results than conventional methods.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::2033111c21cc4c4aa0c5d056e0fd80cd https://doi.org/10.1016/s1474-6670(17)32431-x Zobrazit plný text záznamu