Traffic Flow Forecasting in Leisure Farm Areas Using Artificial Neural Networks

• Autorzy: Hou C.-I.

Warianty tytułu

PL

Prognozowanie płynności ruchu ulicznego w rejonach ośrodków wypoczynkowych, z zastosowaniem sztucznych sieci neuronowych

• Języki publikacji: EN

Abstrakty

EN

Leisure agriculture experiences continuous development. However, because most leisure farm areas are located in isolated or remote regions and the planning and construction of traffic networks is greatly restricted by terrain and geographical features, the roads in these areas are narrower than those in other regions. This study focuses on traffic flow forecasting using the advanced artificial intelligence technology of artificial neural networks (ANNs) and makes a positive contribution to the forecasting methods for traffic flow regarding leisure farm areas.

PL

W artykule przedstawiono zaawansowany sposób prognozowania ruchu ulicznego w rejonach ośrodków wypoczynkowych, oparty na budowie sieci neuronowych (ANN). Opracowana metoda, zwiększa stan wiedzy na temat przewidywania płynności ruchu ulicznego w tych rejonach.

Słowa kluczowe

EN

traffic flow; artificial neural network (ANN); leisure farm areas; artificial intelligence

PL

płynność ruchu ulicznego; sztuczne sieci neuronowe; rejony wypoczynkowe; sztuczna inteligencja

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 1b
• Strony: 205--207
• Opis fizyczny: Bibliogr. 10 poz., rys.

Twórcy

autor

Hou C.-I.

• Yu Da University

Bibliografia

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• [2] Dia H., “An Object-Oriented Neural Network Approach to shorttern Traffic Forecasting”, European Journal of Operational Research, 131 (2001), No. 2, 253-261.
• [3] NooruA. Haq l, Ramanan T., Shashikant K. S., Sridharan R., "A hybrid neural network-genetic algorithm approach for permutation flow shop scheduling", International Journal of Production Research, 48 (2010), Issue 14, 4217-4231.
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• [6] Lo C.Y., Hou C.I. and Pai Y.Y., “An Intelligent Demand Forecasting Model With Back Propagation Neural Network for Fish Product”, Research Journal of Applied Sciences, Engineering and Technology, 3 (2011), No. 5, 447-455.
• [7] Issanchou S., and Gauchi J.P., “Computer-aided optimal designs for improving neural network generalization”, Neural Networks, 21 (2008), No. 7, 945-950.
• [8] Wang J.D., and Xiao D.Q., (2002). “Introduction of Neural Network and Fuzzy Control Theory”, Quanhua Science & Technology Books Co. Ltd.
• [9] Han F., Gu T.Y., Ju S.G., “An Improved Hybrid Algorithm Based on PSO and BP for Feedforward Neural Networks”, JDCTA: International Journal of Digital Content Technology and its Applications, 5 (2011), No. 2, 106-115.
• [10] Wang T.Y., Huang C.Y., ”Optimizing back-propagation networks via a calibrated heuristic algorithm with an orthogonal array”, Expert Systems with Applications, 34 (2008), No. 3, 1630-1641.