Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

Autor:	Seyed Hadi Hosseini, Behzad Moshiri, Ashkan Rahimi-Kian, Babak Nadjar Araabi
Jazyk:	angličtina
Rok vydání:	2014
Předmět:	ITS mutual information traffic flow forecasting noisy data data loss Transportation engineering TA1001-1280
Zdroj:	Promet (Zagreb), Vol 26, Iss 5, Pp 393-403 (2014)
Druh dokumentu:	article
ISSN:	0353-5320 1848-4069
DOI:	10.7307/ptt.v26i5.1429
Popis:	Traffic flow forecasting is useful for controlling traffic flow, traffic lights, and travel times. This study uses a multi-layer perceptron neural network and the mutual information (MI) technique to forecast traffic flow and compares the prediction results with conventional traffic flow forecasting methods. The MI method is used to calculate the interdependency of historical traffic data and future traffic flow. In numerical case studies, the proposed traffic flow forecasting method was tested against data loss, changes in weather conditions, traffic congestion, and accidents. The outcomes were highly acceptable for all cases and showed the robustness of the proposed flow forecasting method.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/7e0fd66ba411412f9046d3ce4f8b3b87 Zobrazit plný text záznamu View record in DOAJ