Investigation of Favorable Neural Network Methods to Estimate Traffic Components

Year 2023, , 377 - 383, 20.06.2023

Sedat Ozcanan

https://doi.org/10.24012/dumf.1219818

Abstract

Neural networks provide the opportunity to estimate specific components of engineering problems. They are decomposed complex problems into different parts. Thus, it can be easy to compete with each of them through neural networks. In this paper, it was purposed to estimate the average speed of a 6-line road’s cross-section by observed traffic variables, such as numbers of vehicles and occupancy values, using radial basis function neural network (RBFNN), generalized regression neural network (GRNN) and the feed-forward back propagation neural network (FFBPNN) models. A comparison was fulfilled between different neural networks and checked against multivariate linear regression (MVLR), a conventional statistical model. After each simulation of neural networks, results show that different forecasts were obtained under the same conditions. The best forecasting is made by FFBPNN, GRNN, and RBFNN, respectively. When compared with multivariate linear regression (MVLR), FFBPNN performs better than MVLR, but GRNN and RBFNN perform lower than it.

Keywords

Estimate traffic components, Artificial neural network, FFBPNN, RBFNN, GRNN, MVLR, Trafik bileşenlerini tahmin etme, Yapay sinir ağı, FFBPNN, RBFNN, GRNN, MVLR

Trafik Bileşenlerini Tahmin Etmek İçin Uygun Sinir Ağı Yöntemlerinin Araştırılması

Abstract

Sinir ağları, mühendislik problemlerinin belirli bileşenlerini tahmin etme fırsatı sağlar. Karmaşık problemleri farklı parçalara ayırırlar. Böylece, sinir ağları aracılığıyla her biri ile rekabet etmek kolay olabilmektedir. Bu çalışmada, radyal tabanlı fonksiyon sinir ağı (RBFNN), genelleştirilmiş regresyon sinir ağı (GRNN) ve ileri beslemeli geri yayılımlı sinir ağı (FFBPNN) modelleri kullanılarak araç sayısı ve yoğunluk/işgal değerleri gibi gözlenen trafik değişkenleri ile 6 hatlı bir yolun enkesitindeki ortalama hızın tahmin edilmesi amaçlanmıştır. Bunun için sinir ağları arasında bir karşılaştırma yapılarak, ayrıca sonuçlar geleneksel bir istatistiksel model olan çok değişkenli doğrusal regresyona (MVLR) ile kontrol edilmiştir. Yapay sinir ağlarının her simülasyonundan sonra, sonuçlar aynı koşullar altında farklı tahminlerin elde edildiğini göstermektedir. En iyi tahmin sırasıyla FFBPNN, GRNN ve RBFNN tarafından yapılmıştır. Çok değişkenli doğrusal regresyon (MVLR) ile karşılaştırıldığında, FFBPNN, MVLR'den daha iyi performans gösterirken GRNN ve RBFNN, ondan daha düşük performans göstermiştir.

Keywords

Trafik bileşenlerini tahmin etme, Yapay sinir ağı, FFBPNN, RBFNN, GRNN, MVLR

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