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

Yıl 2025, Cilt: 20 Sayı: 72, 189 - 215, 12.12.2025

Hande Okutucu , Şükrü Mustafa Kaya

Öz

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

Anahtar Kelimeler

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

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

Öz

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.

Anahtar Kelimeler

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

Kaynakça

• Abu-Rayash, A., & Dincer, I., (2025). Development of an integrated model for environmentally and economically sustainable and smart cities. Sustainable Energy Technologies and Assessments, 73, 104096. https://doi.org/10.1016/j.seta.2024.104096.
• Ahmed, K., Dubey, M. K., Kumar, A., & Dubey, S., (2024). Artificial intelligence and IoT driven system architecture for municipality waste management in smart cities: A review. Measurement: Sensors, 36, 101395. https://doi.org/10.1016/j.measen.2024.101395.
• Alkhaldi, T. M., Darem, A. A., Alhashmi, A. A., Al-Hadhrami, T., & Osman, A. E., (2024). Enhancing smart city IoT communication: A twolayer NOMA-based network with caching mechanisms and optimized resource allocation. Computer Networks, 255, 110857. https://doi. org/10.1016/j.comnet.2024.110857
• Arachchige, K. G., Murtaza, M., Cheng, C. T., Albahlal, B. M., & Lee, C. C., (2024). Blockchain-enabled mitigation strategies for distributed denial of service attacks in IoT sensor networks: An experimental approach. Computers, Materials & Continua, 81(3), 3679–3705. https://doi.org/10.32604/cmc.2024.059378.
• Awan, N., Khan, S., Rahmani, M. K. I., Tahir, M., Alam, N. A., Alturki, R., & Ullah, İ., (2020). Machine learning-enabled power scheduling in IoT-based smart cities. Computers, Materials & Continua, 67(2), 1045– 1060. https://doi.org/10.32604/cmc.2021.014386.
• Azzalini, D., Flammini, B., Emanuele, C. A., Guadagno, A., Ragaini, E., & Amigoni, F., (2024). An empirical evaluation of deep autoencoders for anomaly detection in the electricity consumption of buildings. Energy and Buildings, 327, 115069. https://doi.org/10.1016/j. enbuild.2024.115069.
• Balcıoğlu, Y. S., & Sezen, B., (2024). K-en yakın komşu (KNN) ve MLP yöntemi ile hibrit bir sistem: Trafik akış tahmini. Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi, 26(4), 1801–1816. https://doi. org/10.32709/akusosbil.1255897.
• Collado-Montañez, J., Martín-Valdivia, M.-T., & Martínez-Cámara, E., (2025). Data augmentation based on large language models for radiological report classification. Knowledge-Based Systems, 308, 112745. https://doi.org/10.1016/j.knosys.2024.112745.
• Dahooie, J. H., Mohammadian, A., Qorbani, A. R., & Daim, T., (2023). A portfolio selection of Internet of Things (IoTs) applications for the sustainable urban transportation: A novel hybrid multi-criteria decision making approach. Technology in Society, 75, 102366. https://doi. org/10.1016/j.techsoc.2023.102366.
• Doğaroğlu, B., & Çalışkanelli, S. P., (2022). Akıllı otopark sistemlerinde kullanılan araç tanıma teknolojileri üzerine bir inceleme. Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi, 5(2), 85–98. https:// doi.org/10.51513/jitsa.1098978
• Donta, P. K., Srirama, S. N., Amgoth, T., & Annavarapu, C. S. R., (2022). Survey on recent advances in IoT application layer protocols and machine learning scope for research directions. Digital Communications and Networks, 8(1), 1–16. https://doi.org/10.1016/j.dcan.2021.10.004.
• Dui, H., Li, H., Dong, X., & Wu, S., (2025). An energy IoT-driven multi-dimension resilience methodology of smart microgrids. Reliability Engineering & System Safety, 253, 110533. https://doi.org/10.1016/j. ress.2025.110533.
• Idrissi, Z. K., Lachgar, M., & Hrimech, H., (2024). Blockchain, IoT and AI in logistics and transportation: A systematic review. Transport Economics and Management, 2, 100002. https://doi.org/10.1016/j. team.2024.09.002.
• İlyas, S., & Albayrak, Y., (2023). Comparison of machine learning models for traffic volume estimation at smart intersections. Journal of Engineering Sciences, 7(5), S14. https://doi.org/10.30855/ gmbd.0705S14.
• Kakız, A. T., Kakız, M. T., & Çoban, R., (2022). An evaluation of autoencoder neural network role in IoT edge computing. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 5(3), 1383– 1392. https://doi.org/10.47495/okufbed.1037534.
• Karri, C., Machado, J. J. M., Tavares, J. M. R. S., Dannana, S., Gottapu, S. K., Gandomi, A. H., & Jain, D. K., (2024). Recent technology advancements in smart city management: A review. Computers, Materials & Continua, 81(3), 2105–2130. https://doi.org/10.32604/ cmc.2024.058461.
• Katsigiannis, M., & Mykoniatis, K., (2024). Enhancing industrial IoT with edge computing and computer vision: An analog gauge visual digitization approach. Manufacturing Letters, 41, 1264–1273. https:// doi.org/10.1016/j.mfglet.2024.09.153.
• Kaya, Ş. M., İşler, B., Abu-Mahfouz, A. M. A., Rasheed, J., & AlShammari, A., (2023). An intelligent anomaly detection approach for accurate and reliable weather forecasting at IoT edges: A case study. Sensors, 23(5), 2426. https://doi.org/10.3390/s23052426.
• Kheder, M. Q., & Mohammed, A. A., (2024). Real-time traffic monitoring system using IoT-aided robotics and deep learning techniques. Kuwait Journal of Science, 51(1), 1–10. https://doi. org/10.1016/j.kjs.2023.10.017.
• Khemakhem, S., & Krichen, L., (2024). A comprehensive survey on an IoT-based smart public street lighting system application for smart cities. Franklin Open, 8, 100142. https://doi.org/10.1016/j. fraope.2024.100142.
• Moura, D. L. L., Aquino, A. L. L., & Loureiro, A. A. F., (2024). An edge computing and distributed ledger technology architecture for secure and efficient transportation. Ad Hoc Networks, 164, 103633. https://doi.org/10.1016/j.adhoc.2024.103633.
• Özcan, K. Y., (2023). Components of smart cities: Smart city applications and smart space management. Mediterranean Journal of Humanities, 13, 295–312. https://doi.org/10.13114/MJH.2023.607.
• Rahmani, A. M., Tanveer, J., Gharehchopogh, F. S., Rajabi, S., & Hosseinzadeh, M., (2024). Novel offloading strategy for multiuser optimization in blockchain-enabled mobile edge computing networks for improved Internet of Things performance. Computers and Electrical Engineering, 102, 109514. https://doi.org/10.1016/j. compeleceng.2024.109514.
• Rajagopal, S. M., Supriya, M., & Buyya, R., (2025). Leveraging blockchain and federated learning in edge-fog-cloud computing environments for intelligent decision-making with ECG data in IoT. Journal of Network and Computer Applications, 204, 104037. https:// doi.org/10.1016/j.jnca.2024.104037.
• Tekouabou, S. C. K., Alaoui, E. A. A., Cherif, W., & Silkan, H., (2022). Improving parking availability prediction in smart cities with IoT and ensemble-based model. Journal of King Saud University - Computer and Information Sciences, 34(3), 586–595. https://doi.org/10.1016/j. jksuci.2020.01.008.
• Tran-Dang, H., & Kim, D.-S., (2025). Digital twin-empowered intelligent computation offloading for edge computing in the era of 5G and beyond: A state-of-the-art survey. ICT Express, 11(1), 167–180. https://doi.org/10.1016/j.icte.2025.01.002.
• Utku, A., (2023). Derin öğrenme tabanlı trafik yoğunluğu tahmini: İstanbul için bir vaka çalışması. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 11(3), 1584–1598. https://doi.org/10.29130/dubited.1139534.
• Wei, J., & Ju, Y., (2024). Research on optimization method for traffic signal control at intersections in smart cities based on adaptive artificial fish swarm algorithm. Heliyon, 10(10), e30657. https://doi.org/10.1016/j.heliyon.2024.e30657.
• Yalli, J. S., Hasan, M. H., Jung, T. L., & Al-Selwi, S. M., (2024). Authentication schemes for Internet of Things (IoT) networks: A systematic review and security assessment. Internet of Things, 30, 101469. https://doi.org/10.1016/j.iot.2024.101469.
• Yıldırım, Ü., & Çataltepe, Z., (2007). Örüntü tanıma ve öznitelik seçme yöntemleri kullanarak kısa zaman sonraki yol trafik hız öngörüsü. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 3, 45–53.
• Yu, X., & Do, Y., (2024). Perceptual and featural measures of Mandarin consonant similarity: Confusion matrices and phonological features dataset. Data in Brief, 52, 109868. https://doi.org/10.1016/j. dib.2023.109868.
• Zhang, G., Jin, J., Chang, F., & Huang, H., (2024). Real-time traffic conflict prediction at signalized intersections using vehicle trajectory data and deep learning. International Journal of Transportation Science and Technology, 13(4), 122–133. https://doi.org/10.1016/j. ijtst.2024.10.009.
• Zhao, J., Gao, Y., Tang, J., & Zhu, L., (2018). Highway travel time prediction using sparse tensor completion tactics and K-nearest neighbor pattern matching method. Journal of Advanced Transportation, 2018, 5721058. https://doi.org/10.1155/2018/5721058.