A Hybrid System With K-Nearest Neighbor (KNN) and MLP Method: Traffic Flow Estimation

Yavuz Selim Balcıoğlu; Bülent Sezen

Makale detayları

A Hybrid System With K-Nearest Neighbor (KNN) and MLP Method: Traffic Flow Estimation

Yazarlar:

Yavuz Selim Balcıoğlu | Bülent Sezen

Yayıncı:

Afyon Kocatepe University

Dergi:

Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi Vol.26 Issue 4, pp.1801-1816

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001331838

pISSN:

1302-1265

Anahtar kelimeler:

Özet:

 In this research, we offer a hybrid traffic flow prediction approach that combines k-nearest neighbor KNN and multilayer perceptron MLP. This model is referred to as the KNN-MLP model. The goal of this method is to increase the accuracy of the predictions. KNN is used to pick surrounding stations that are mostly connected to the test station and to capture the spatial characteristics of traffic flow. The Multi-Layer Perceptron MLP algorithm is used to mine the temporal variability of traffic flow, and a four-layer MLP network is used to forecast traffic flow correspondingly at chosen stations. The result-level fusion combined with the rank-exponent weighting approach is used to get the final prediction results. The accuracy of the forecast is determined using data on the current flow of traffic that is collected in real time by the Department of Transportation Data Center of the Istanbul Metropolitan Municipality. According to the findings of the experiments, the proposed model has the potential to achieve a higher level of performance when compared to well-known prediction models such as support vector regression SVR, LSTM, and MLP models. Furthermore, the proposed model has the potential to achieve an improvement in accuracy of approximately 2 on average.

 In this research, we offer a hybrid traffic flow prediction approach that combines k-nearest neighbor KNN and multilayer perceptron MLP. This model is referred to as the KNN-MLP model. The goal of this method is to increase the accuracy of the predictions. KNN is used to pick surrounding stations that are mostly connected to the test station and to capture the spatial characteristics of traffic flow. The Multi-Layer Perceptron MLP algorithm is used to mine the temporal variability of traffic flow, and a four-layer MLP network is used to forecast traffic flow correspondingly at chosen stations. The result-level fusion combined with the rank-exponent weighting approach is used to get the final prediction results. The accuracy of the forecast is determined using data on the current flow of traffic that is collected in real time by the Department of Transportation Data Center of the Istanbul Metropolitan Municipality. According to the findings of the experiments, the proposed model has the potential to achieve a higher level of performance when compared to well-known prediction models such as support vector regression SVR, LSTM, and MLP models. Furthermore, the proposed model has the potential to achieve an improvement in accuracy of approximately 2 on average.

 In this research, we offer a hybrid traffic flow prediction approach that combines k-nearest neighbor KNN and multilayer perceptron MLP. This model is referred to as the KNN-MLP model. The goal of this method is to increase the accuracy of the predictions. KNN is used to pick surrounding stations that are mostly connected to the test station and to capture the spatial characteristics of traffic flow. The Multi-Layer Perceptron MLP algorithm is used to mine the temporal variability of traffic flow, and a four-layer MLP network is used to forecast traffic flow correspondingly at chosen stations. The result-level fusion combined with the rank-exponent weighting approach is used to get the final prediction results. The accuracy of the forecast is determined using data on the current flow of traffic that is collected in real time by the Department of Transportation Data Center of the Istanbul Metropolitan Municipality. According to the findings of the experiments, the proposed model has the potential to achieve a higher level of performance when compared to well-known prediction models such as support vector regression SVR, LSTM, and MLP models. Furthermore, the proposed model has the potential to achieve an improvement in accuracy of approximately 2 on average.

 Bu araştırmada, k-en yakın komşu KNN ve çok katmanlı algılayıcıyı MLP birleştiren hibrit bir trafik akışı tahmin yaklaşımı sunuyoruz. Bu model KNN-MLP modeli olarak adlandırmaktadır. Bu yöntemin amacı tahminlerin doğruluğunu arttırmaktır. KNN, çoğunlukla test istasyonuna bağlı olan çevredeki istasyonları seçmek ve trafik akışının mekansal özelliklerini yakalamak için kullanılır. Trafik akışının zamansal değişkenliğini araştırmak için Çok Katmanlı Algılayıcı MLP algoritması kullanılmış ve seçilen istasyonlarda buna uygun olarak trafik akışını tahmin etmek için dört katmanlı bir MLP ağı kullanılmıştır. Nihai tahmin sonuçlarını elde etmek için sıra-üs ağırlıklandırma yaklaşımıyla birleştirilmiş sonuç düzeyinde füzyon kullanılmıştır. Tahminin doğruluğu, İstanbul Büyükşehir Belediyesi Ulaşım Daire Başkanlığı Veri Merkezi tarafından gerçek zamanlı olarak toplanan mevcut trafik akışı verileri kullanılarak belirlenmiştir. Deneylerden elde edilen bulgulara göre, önerilen model destek vektör regresyon SVR, LSTM ve MLP modelleri gibi bilinen tahmin modellerine göre daha yüksek performans düzeyine ulaşma potansiyeline sahiptir. Ayrıca, önerilen modelin doğruluğunda ortalama olarak yaklaşık 2lik bir iyileşme sağlanmıştır.

Makale detayları

A Hybrid System With K-Nearest Neighbor (KNN) and MLP Method: Traffic Flow Estimation

Yazarlar:

Yavuz Selim Balcıoğlu | Bülent Sezen

Yayıncı:

Afyon Kocatepe University

Dergi:

Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi Vol.26 Issue 4, pp.1801-1816

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001331838

pISSN:

1302-1265

prediction

traffic

knn

mlp

machine learningtrafik

tahmin

knn

mlp

makine öğrenmesi

Sayı

Makale detayları

Yazarlar: Yavuz Selim Balcıoğlu | Bülent Sezen

Yayıncı: Afyon Kocatepe University

Dergi: Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi Vol.26 Issue 4, pp.1801-1816

Belge Tipi: Makale

Yayın Tarihi: 12/2024

Belge Dili: Türkçe

Document ID (Org No.): 2001331838

pISSN: 1302-1265

Sayı

Yazarlar:

Yavuz Selim Balcıoğlu | Bülent Sezen

Yayıncı:

Afyon Kocatepe University

Dergi:

Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi Vol.26 Issue 4, pp.1801-1816

Belge Tipi:

Makale

Yayın Tarihi:

12/2024

Belge Dili:

Türkçe

Document ID (Org No.):

2001331838

pISSN:

1302-1265