Trafik Yoğunluğunun Takibi için Zaman Serileri Analizi Tabanlı Karar Destek Sisteminin Uygulanması

Öz

İnternet veri aktarım ağ bant genişliği teknolojilerindeki gelişmelerle doğru orantılı olarak, internetin günlük hayatta kullanımı daha yaygın hale gelmektedir. Nesnelerin interneti (IoT) kavramı, bu teknolojilere eklemlenebilecek sayısız nesnenin oluşturduğu yeni teknolojik ekosistemi ifade etmektedir. IoT kavramının en önemli vizyonlarının başında akıllı şehir konsepti gelmektedir. Şehirlerde, iletişimden ulaşıma kadar her bir bileşenin internete bağlı şekilde, yapay zekaya dayalı bilgisayar algoritmalarıyla kontrol ve takip edilmesi anlamına gelen bu konsept, gittikçe daha kalabalık hale gelen şehirlerin, bir kaosa girmeden düzen içinde işlemesini sağlamayı vaat etmektedir. Bu çalışmada, şehir trafik akış yoğunluğunu takip eden ve geleceğe yönelik öngörüler sağlayan, zaman serisi analizine dayalı bir karar destek yöntemi önerilmektedir. Önerilen yaklaşım, yapay sinir ağı tabanlı bir zaman serisi karar destek yöntemidir. Çalışmada şehir trafiğinde belirlenen rastgele kavşaklardan, saatlik geçen araç sayıları veri olarak kullanılmıştır. Kavşakların sahip olduğu araç yoğunluğunun bağıl etkileri hesaplanmış ve trafik akışıyla ilgili modeller tasarlanmıştır. Oluşturulan modellerin sağladığı tahminsel verilerin doğruluk oranına göre en uygun trafik akış modeli belirlenmektedir. Veriler sabit kabul edildiğinde, yapay sinir ağı tabanlı zaman serisi modelleri J1 ve J2 için %93 ve J3 için %66 doğrulukla tahminler yapılabilmektedir. Dinamik veri modeli için, bağıl etkileşime sahip trafik akışı tasarımına göre, kavşaklar arasında seri bağlı trafik modelinin 0.86 ile en yüksek doğruluğa sahip olduğu bulunmuştur.

Anahtar Kelimeler

• Veri Madenciliği
• Karar Verme
• Zaman Serileri Analizi
• Karar Destek Sistemleri
• Yapay Sinir Ağları

Implementing of Time Series Analysis Based Decision Support System for Traffic Density Monitoring

Abstract

In direct proportion to developments in bandwidth technologies for Internet data transmission networks, the use of the internet in daily life is becoming more common. The concept of the Internet of Things (IoT) refers to the new technological ecosystem consisting of numerous objects that can be added to these technologies. One of the most important visions of the IoT is the concept of a smart city. This concept, which means that every component in cities, from communications to transportation, is connected to the internet and controlled and monitored by artificial intelligence-based computer algorithms, promises to ensure that increasingly crowded cities function in an orderly manner without descending into chaos. This study proposes a decision support system based on time series analysis that monitors traffic density in cities and makes future predictions. The proposed procedure is an Artificial Neural Network (ANN) based Time Series (TS) decision support technique. The study used the number of vehicles passing by three randomly selected junctions every hour as data. The relative effects of vehicle density at the junctions were calculated and traffic flow models were designed. The most appropriate traffic flow model is determined based on the accuracy of the forecast data provided by the models created. When the data are considered stable, predictions can be made with 93% accuracy for the ANN-based TS models J1 and J2 and 66% for J3. For the dynamic model, according to the design of the traffic flow, it was found that the model of serially connected traffic between the junctions has the highest accuracy with a joint mean value of 0.86.

Keywords

• Data Mining
• Decision-Making
• Time Series Analysis
• Decision Support Systems
• Artificial Neural Network

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