YAPAY SİNİR AĞLARI ve DESTEK VEKTÖR MAKİNELERİ YÖNTEMLERİ ile BÖLGESEL TRAFİK YOĞUNLUK TAHMİNİ

Year 2021, , 378 - 390, 31.12.2021

İclal Çetin Taş , Ahmet Anıl Müngen

https://doi.org/10.54365/adyumbd.971461

Abstract

Büyük şehirlerde kilometre başına düşen insan yoğunluğu arttıkça trafik sıkışıklığı artmakta ve yolcuların daha fazla sürelerini trafikte harcamaktadırlar. Trafik sıkışıklığı nedeni ile harcanan ekstra zaman ve yakıt hem kullanıcılar hem de ülkeler için büyük bir gider kalemidir. Büyükşehirlerde yaşayan vatandaşların trafik yoğunluğunun zaman bazlı değişimini tahmin etmek ve buna göre planlama yapmaları bir zorunluluk haline dönüşmüştür. Trafik sıkışıklıkları genelde tüm şehirde aynı anda gerçekleşmez. Bölgesel olarak yaşanan trafik sıkışıklıkları diğer yolları da etkilemesi ile yaygınlaşır. Bu çalışma da yapay sinir ağları (YSA) kullanılarak önerilen yöntem ile geçmiş trafik verileri kullanarak bölgesel yoğunluklar tahmin edilmeye çalışılacaktır. Çalışma birçok benzer çalışmadan farklı olarak hava durumu gibi çevresel etkenleri de alarak tahmin modellemesinin başarısını arttırılmıştır. İstanbul Büyük Şehir Belediyesi Açık Veri Portalından toplanan 75 farklı noktaya ait 150.000 veri kullanarak önerilen model test edilmiş ve yaklaşık %90 başarı ile bölgesel trafik yoğunluğu tespit edilebilmiştir.

Keywords

Trafik Tahmini, YSA, SVM, Ortalama Hız, Büyük Veri.

PREDICTION of REGIONAL TRAFFIC INTENSITY with ARTIFICIAL NEURAL NETWORKS and SUPPORT VECTOR MACHINES

Abstract

As the density of people per kilometer increases in big cities, traffic congestion increases and passengers spend more time in traffic. The extra time and fuel spent due to traffic congestion is a big expense item for both users and countries. It has become a necessity to predict the time-based change in the traffic density of citizens living in metropolises and to plan accordingly. Traffic jams don't usually happen in the whole city at once. Regional traffic jams become widespread as they affect other roads. In this study, it will be tried to predict regional congestions by using historical traffic data with the proposed method using artificial neural networks (ANN). The study increases the success of forecasting modeling by taking environmental factors such as weather conditions apart from many equivalent studies. Using 150,000 data from 75 different points collected from the Istanbul Metropolitan Municipality Open Data Portal, the proposed model was tested and the regional traffic density could be determined with 90% success.

Keywords

Traffic Prediction, ANN, SVM, Average Speed, Big Data

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