Akıllı Şehirlerde IoT Uç Bilişim ile Trafik Yoğunluğu Tahmini: Bir Vaka Çalışması

Abstract

Dijitalleşen dünyada akıllı şehir yönetimi ve uygulamaları son yıllarda birçok alanda hayatımıza girmiştir. Üretilen inovatif sensör tabanlı cihazların artmasıyla birlikte akıllı şehirlerde akıllı çevre, enerji, ulaşım, sağlık, trafik gibi kavramlar ortaya çıkmış ve akıllı şehir yönetimi ile vatandaşların hayat kalitesi artmaya başlamıştır. Çalışmada akıllı şehirlerin alt başlığı olan akıllı ulaşım ve trafik yönetimi kavramına odaklanılmaktadır. Akıllı şehirlerde trafik yönetimi kavramı IoT (Nesnelerin İnterneti) ve Edge Computing (Uç Bilişim) teknolojisinin entegrasyonu sayesinde gelişme kaydetmektedir bu sistemle trafik yoğunluğu tahmini daha gerçekçi bir sonuç ortaya koymaktadır. IoT cihazları, trafik sensörleri, kameralar ve GPS destekli cihazlar aracılığıyla akıllı şehirlerde trafik yoğunluğu, araç hızları ve yol koşulları gibi eş zamanlı veriler toplamaktadır. Çalışmamızda belirli bir günde, bir saatlik araç yoğunluğu tahminlemesi hedeflenmiştir. 3 yıllık veri setinden yararlanılmış ve yılın her günü 24 saatlik araç yoğunluğu verileri kullanılmış olup, zaman serisi algoritmaları kullanılarak ileri bir tarih için saatlik araç yoğunluğu tahmini yapılmıştır. Zaman serisi analizlerinden ANN (Artificial Neural Network), KNN (K-Nearest Neighbors), LSTM (Long Short-Term Memory), Random Forest, Prophet, XGBoost (Extreme Gradient Boosting) gibi algoritmalar kullanılarak tahminleme yapılmıştır. Algoritmaların hata oranlarına incelenip en doğru tahmin hangi algoritma ile bulunduğu ortaya koyulmuş ve bu algoritmayla yapılan araç yoğunluğu tahmin verisi gerçeğe en yakın kabul edilmiştir. Çıkan sonuçlar makalenin son kısmında tartışılmış ve değerlendirilmiştir

Keywords

• Nesnelerin İnterneti
• Uç Bilişim
• Akıllı Şehirler
• Trafik Yoğunluğu
• Makine Öğrenmesi

Traffic Density Estimation with IoT Edge Computing In Smart City: A Case Study

Abstract

In the digitalizing world, smart city management and applications have become an integral part of our lives in recent years. With the increase in innovative sensor-based devices, concepts such as smart environment, energy, transportation, healthcare, and traffic have emerged within smart cities, improving the quality of life for citizens through smart city management. This study focuses on the concept of smart transportation and traffic management, which is a subcategory of smart cities. The concept of traffic management in smart cities has advanced thanks to the integration of IoT (Internet of Things) and Edge Computing technologies. This system provides more realistic traffic density predictions. IoT devices, traffic sensors, cameras, and GPS-enabled devices collect real-time data such as traffic density, vehicle speeds, and road conditions in smart cities. In our study, we aim to predict hourly vehicle density for a specific day. A three-year dataset was utilized, consisting of 24-hour vehicle density data for each day of the year. Using time series algorithms, hourly vehicle density predictions were made for a future date. Algorithms such as ANN (Artificial Neural Network), KNN (K-Nearest Neighbors), LSTM (Long Short-Term Memory), Random Forest, Prophet, and XGBoost (Extreme Gradient Boosting) were employed for predictions. The error rates of the algorithms were analyzed to identify the most accurate prediction method. The vehicle density prediction data produced by this algorithm was considered the closest to reality. The results were discussed and evaluated in the final section of the article.

Keywords

• Internet of Things
• Edge Computing
• Smart City
• Traffic Density
• Machine Learning

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