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Journals / Trafik ve Ulaşım Araştırmaları Dergisi / 2026 / Sayı: 1

A Review of Microscopic Traffic Simulation, Microscopic Emission Modeling, and Image Processing Techniques

Mikro Ölçekli Trafik Simülasyonu, Mikro Ölçekli Emisyon Modellemesi ve Görüntü İşleme Teknikleri Üzerine Bir Derleme

Elif İzol(HARRAN ÜNİVERSİTESİ)0000-0003-2826-6970
Abdulkadir Yıldırım(HARRAN ÜNİVERSİTESİ)0000-0002-0223-9221
Güzin Akyıldız Alçura(YILDIZ TEKNİK ÜNİVERSİTESİ)0000-0001-7424-2764
Halit Özen(İSTANBUL TEKNİK ÜNİVERSİTESİ)
Pages
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Publication Date
2026-01-09
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Furthermore, it explores how these integrated systems can be leveraged as part of intelligent decision-support mechanisms to enhance urban management and sustainability efforts.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Accurately estimating vehicle emissions at the urban scale is crucial for effective environmental management and traffic planning.
📊
Key Findings & Data:In recent years, integrated approaches that combine microscopic traffic simulation, microscopic emission modelling, and image processing-based vehicle detection techniques have gained significant attention due to their potential to enhance prediction accuracy.
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Conclusion & Impact:Furthermore, it explores how these integrated systems can be leveraged as part of intelligent decision-support mechanisms to enhance urban management and sustainability efforts.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Trafik Simülasyonu · Emisyon Modellemesi · Görüntü İşleme

Abstract

Accurately estimating vehicle emissions at the urban scale is crucial for effective environmental management and traffic planning. In recent years, integrated approaches that combine microscopic traffic simulation, microscopic emission modelling, and image processing-based vehicle detection techniques have gained significant attention due to their potential to enhance prediction accuracy. However, studies that holistically incorporate all three components within a single framework remain scarce in the literature. This review analyzes selected research that utilizes image processing methods (e.g., YOLO, Kalman Filter, Optical Flow) for vehicle detection, traffic simulation platforms (e.g., VISSIM, SUMO) for modelling traffic flow, and various emission models (e.g., PHEM, VERSIT+) for estimating pollutants such as CO₂, CO, NOₓ, and PM. The study particularly emphasizes real-time applications and the integration of heterogeneous data sources. By examining the strengths and limitations of existing hybrid approaches, the paper highlights current challenges and identifies future research opportunities for developing comprehensive, data-driven systems that integrate traffic dynamics with emission estimation tools. Furthermore, it explores how these integrated systems can be leveraged as part of intelligent decision-support mechanisms to enhance urban management and sustainability efforts.

Özet

Kentsel ölçekte araç emisyonlarının doğru tahmin edilmesi, etkili çevre yönetimi ve trafik planlaması için büyük önem taşımaktadır. Son yıllarda, mikroskobik trafik simülasyonu, mikroskobik emisyon modellemesi ve görüntü işleme tabanlı araç tespit yöntemlerini birleştiren entegre yaklaşımlar, tahmin doğruluğunu artırma potansiyelleri nedeniyle giderek daha fazla ilgi görmektedir. Bununla birlikte, her üç bileşeni bir çerçevede birleştiren çalışmalar literatürde oldukça sınırlı kalmaktadır. Bu derleme, araç tespiti için görüntü işleme tekniklerini (örn. Gerçek zamanlı nesne tanıma yöntemi olan You Only Look Once [YOLO]), Kalman Filtresi, optik akış), trafik akışı modellemesi için trafik simülasyon araçlarını (örn. Trafik mikro simülasyon yazılımı, Verkehr In Städten – Simulation [VISSIM]), kentsel hareketlilik simülasyonu (Simulation of Urban Mobility [SUMO]) ve karbondioksit (CO₂), karbonmonoksit (CO), azotoksit (NOₓ) ve partikül madde (PM) gibi emisyonların hesaplanması için çeşitli emisyon modellerini (örn. Passenger Car and Heavy Duty Emission Model (PHEM), Vehicle Emission Model [VERSIT+]) kullanan seçilmiş araştırmaları incelemektedir. Özellikle gerçek zamanlı uygulamalara ve farklı veri kaynaklarının bütünleştirilmesine odaklanmaktadır. Bu çalışma, mevcut hibrit yaklaşımların avantaj ve sınırlılıklarını ortaya koymakta ve trafik verilerini emisyon tahmin araçlarıyla bütünleştiren kapsamlı modellerin geliştirilmesine yönelik geleceğe dair öneriler sunmaktadır. Bununla birlikte, bu sistemlerin karar destek mekanizmalarına entegre edilmesiyle şehir yönetiminde etkinlik sağlanabilmesi konusunda fikirler sunmaktadır.

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Cite This Article

İzol, E., Yıldırım, A., Alçura, G. A., Özen, H. (2026). A Review of Microscopic Traffic Simulation, Microscopic Emission Modeling, and Image Processing Techniques. Trafik ve Ulaşım Araştırmaları Dergisi, Sayı: 1. https://doi.org/10.38002/tuad.1706324

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References (110)

  1. Abdelwahab, M. A. (2019). Fast approach for efficient vehicle counting. Electronics Letters, 55(1), 20–22. https://doi.org/10.1049/el.2018.6719
  2. Acuto, F., Coelho, M. C., Fernandes, P., Giuffrè, T., Macioszek, E., ve Granà, A. (2022). Assessing the environmental performances of urban roundabouts using the vsp methodology and AIMSUN. Energies, 15(4), 1371. https://doi.org/10.3390/en15041371
  3. Adamidis, F. K., Mantouka, E. G., ve Vlahogianni, E. I. (2020). Effects of controlling aggressive driving behavior on network-wide traffic flow and emissions. International Journal of Transportation Science and Technology, 9(3), 263–276. https://doi.org/10.1016/j.ijtst.2020.05.003
  4. Adi, K., Widodo, A. P., Widodo, C. E., Pamungkas, A., ve Putranto, A. B. (2018). Automatic vehicle counting using background subtraction method on gray scale images and morphology operation. Journal of Physics: Conference Series, 1025(1), 012025. https://doi.org/10.1088/1742-6596/1025/1/012025
  5. Ahn, K., ve Rakha, H. (2008). The effects of route choice decisions on vehicle energy consumption and emissions. Transportation Research Part D: Transport and Environment, 13(3), 151–167. https://doi.org/10.1016/j.trd.2008.01.005
  6. Ahn, K., Rakha, H., Trani, A., ve Van Aerde, M. (2002). Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels. Journal of Transportation Engineering, 128(2), 182–190. https://doi.org/10.1061/(ASCE)0733-947X(2002)128:2(182)
  7. Aimsun. (t.y.). Aimsun – Mobility intelligence for decisions that count. 12 Şubat 2025 tarihinde https://www.aimsun.com/ adresinden erişilmiştir.
  8. Akkaya, S., ve Engin, T. (2022). Trafik simülasyon yazılımlarına genel bakış. Akıllı Ulaşım Sistemleri ve Uygulamaları Dergisi, 5(2), 158–168. https://doi.org/10.51513/jitsa.1090209
  9. Alam, G. M. I., Arfin Tanim, S., Sarker, S. K., Watanobe, Y., Islam, R., Mridha, M. F., ve Nur, K. (2025). Deep learning model based prediction of vehicle CO2 emissions with eXplainable AI integration for sustainable environment. Scientific Reports, 15(1), 1–28. https://doi.org/10.1038/s41598-025-87233-y
  10. Barceló, J. (2010). Fundamentals of traffic simulation. Springer Press.
  11. Bari, C., Gangwal, A., Rahimi, Z., Srikanth, L., Singh, B., ve Dhamaniya, A. (2023). Emission modeling at toll plaza under mixed traffic condition using simulation. Environmental Monitoring and Assessment, 195(7), 11409. https://doi.org/10.1007/s10661-023-11409-0
  12. Barth, M., ve Boriboonsomsin, K. (2008). Real-world carbon dioxide impacts of traffic congestion. Transportation Research Record, 2058(1), 163–171. https://doi.org/10.3141/2058-20
  13. Beza, A. D., Maghrour Zefreh, M., ve Torok, A. (2022). Impacts of different types of automated vehicles on traffic flow characteristics and emissions: a microscopic traffic simulation of different freeway segments. Energies, 15(18), 6669. https://doi.org/10.3390/en15186669
  14. Birleşmiş Milletler. (1992). United Nations Framework Convention on Climate Change. https://unfccc.int/resource/docs/convkp/conveng.pdf
  15. Birleşmiş Milletler. (1998). Kyoto Protocol to the United Nations Framework Convention on Climate Change. https://unfccc.int/resource/docs/convkp/kpeng.pdf
  16. Birleşmiş Milletler. (2015). Paris Agreement. https://unfccc.int/sites/default/files/english_paris_agreement.pdf
  17. Blokpoel, R., Hausberger, S., ve Krajzewicz, D. (2017). Emission optimised control and speed limit for isolated intersections. IET Intelligent Transport Systems, 11(3), 174–181. https://doi.org/10.1049/iet-its.2016.0027
  18. Blomgren, M., ve Jungbjer, P. (2019). Emissions modeling of electric urban transit: Analysis of environmental effects of electric public transit in Johanneberg by using the software PTV Vissim and EnViVer [Yüksek lisans tezi]. Chalmers University of Technology.
  19. Caliper Corporation. (t.y.). TransModeler traffic simulation software. 12 Şubat 2025 tarihinde https://www.caliper.com/transmodeler/default.htm adresinden erişilmiştir.
  20. Charef, A., Jarir, Z., ve Quafafou, M. (2024). The impact of motorcycle positioning on start-up lost time: the empirical case study of signalized intersections in marrakech using VISSIM. Engineering, Technology and Applied Science Research, 14(3), 14313–14318. https://doi.org/10.48084/etasr.7141
  21. Chauhan, B. P., Joshi, G. J., ve Parida, P. (2019). Car following model for urban signalised intersection to estimate speed based vehicle exhaust emissions. Urban Climate, 29, 100480. https://doi.org/10.1016/j.uclim.2019.100480
  22. Chauhan, B. P., Joshi, G. J., ve Parida, P. (2024). Traffic simulation-emission modelling to evaluate impact of signal cycle on automobile emissions. European Transport / Trasporti Europei, 98, 1–15. https://doi.org/10.48295/et.2024.98.9
  23. Chawla, A., Khare, M., ve Khan, S. (2021). Evaluating the effect of speed variation on vehicular emission using an integrated modelling approach. S. S. Arkatkar, S. Velmurugan ve A. Verma (Ed.), Recent advances in traffic engineering içinde (ss. 299–315). Springer. https://doi.org/10.1007/978-3-030-51485-3_20
  24. Chen, J., Li, Y., Meng, Z., Feng, X., Wang, J., Zhou, H., Li, J., Shi, J., Chen, Q., Shi, H., ve Wang, S. (2022). Study on emission characteristics and emission reduction effect for construction machinery under actual operating conditions using a portable emission measurement system (PEMS). International Journal of Environmental Research and Public Health, 19(15), 9546. https://doi.org/10.3390/ijerph19159546
  25. Chhadikar, N., Bhamare, P., Patil, K., ve Kumari, S. (2019). Image processing based tracking and counting vehicles. Proceedings of the 2019 3rd International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE. https://doi.org/10.1109/ICECA.2019.8822092
  26. Chonchannavar, S. R., ve Arunkumar, T. (2022). Vehicle detection and counting using video frame. International Research Journal of Modernization in Engineering Technology and Science, 4(8). https://doi.org/10.56726/irjmets29427
  27. Csikós, A., ve Varga, I. (2012). Real-time modeling and control objective analysis of motorway emissions. Procedia - Social and Behavioral Sciences, 54, 1027–1036. https://doi.org/10.1016/j.sbspro.2012.09.818
  28. Dai, Z., Song, H., Wang, X., Fang, Y., Yun, X., Zhang, Z., ve Li, H. (2019). Video-based vehicle counting framework. IEEE Access, 7, 64460–64470. https://doi.org/10.1109/access.2019.2914254
  29. De La Rocha, E., ve Palacios, R. (2010). Image-processing algorithms for detecting and counting vehicles waiting at a traffic light. Journal of Electronic Imaging, 19(4), 043025–043028. https://doi.org/10.1117/1.3528465 Eclipse SUMO. (2025). SUMO (Simulation of Urban Mobility) user documentation. 10 Şubat 2025 tarihinde https://sumo.dlr.de/docs/ adresinden erişilmiştir.
  30. Ejercito, P. M., Nebrija, K. G. E., Feria, R. P., ve Lara-Figueroa, L. L. (2017). Traffic simulation software review. Proceedings of the 2017 8th International Conference on Information, Intelligence, Systems and Applications (IISA) (ss. 1–4). IEEE. https://doi.org/10.1109/iisa.2017.8316415
  31. Elshebli, E., ve Erdos, F. (2022). Combination of simulation and machine learning to mitigate traffic emissions. Proceedings of the 2022 13th IEEE International Conference on Cognitive Infocommunications (CogInfoCom) (ss. 41–46). IEEE.
  32. Essamlali, I., Nhaila, H., ve El Khaili, M. (2025). Impact of urban block shape on traffic and air quality: A SUMO-based comparative study of rectangular, radial, and triangular forms. Transportation Research Interdisciplinary Perspectives, 31, 101413. https://doi.org/10.1016/j.trip.2025.101413
  33. European Commission. (2016). EU reference scenario 2016: Energy, transport and GHG emissions trends to 2050. Publications Office of the European Union. https://doi.org/10.2833/9127
  34. Fachrie, M. (2020). A simple vehicle counting system using deep learning with YOLOv3 model. RESTI Journal (System Engineering and Information Technology), 4(2), 462–468. https://doi.org/10.29207/resti.v4i2.5474
  35. Fan, J., Gao, K., Xing, Y., ve Lu, J. (2019). Evaluating the effects of one-way traffic management on different vehicle exhaust emissions using an integrated approach. Journal of Advanced Transportation, 2019, 1–11. https://doi.org/10.1155/2019/6248796
  36. Fang, Y., Zhang, S., Yu, K., Gao, J., Liu, X., Cui, C., ve Hu, J. (2025). PM₂.₅ concentration prediction algorithm integrating traffic congestion index. Journal of Environmental Sciences, 155, 359–371. https://doi.org/10.1016/j.jes.2024.09.029
  37. Frey, H. C., Zhang, K., ve Rouphail, N. M. (2010). Vehicle-specific emissions modeling based upon on-road measurements. Environmental Science and Technology, 44(9), 3594–3600. https://doi.org/10.1021/es902835h
  38. Gao, J., Chen, H., Dave, K., Chen, J., ve Jia, D. (2020). Fuel economy and exhaust emissions of a diesel vehicle under real traffic conditions. Energy Science and Engineering, 8(5), 1781–1792. https://doi.org/10.1002/ese3.632
  39. Giechaskiel, B., Casadei, S., Rossi, T., Forloni, F., ve Di Domenico, A. (2021). Measurements of the emissions of a “golden” vehicle at seven laboratories with portable emission measurement systems (PEMS). Sustainability, 13(16), 8762. https://doi.org/10.3390/su13168762
  40. Gräbe, R. J., ve Joubert, J. W. (2022). Are we getting vehicle emissions estimation right? Transportation Research Part D: Transport and Environment, 112, 103477. https://doi.org/10.1016/j.trd.2022.103477
  41. Hausberger, S., Rodler, J., Sturm, P., ve Rexeis, M. (2003). Emission factors for heavy-duty vehicles and validation by tunnel measurements. Atmospheric Environment, 37(37), 5237–5245. https://doi.org/10.1016/j.atmosenv.2003.05.002
  42. Int Panis, L., Broekx, S., ve Liu, R. (2006). Modelling instantaneous traffic emission and the influence of traffic speed limits. Science of The Total Environment, 371(1–3), 270–285. https://doi.org/10.1016/j.scitotenv.2006.08.017
  43. Jia, T., Zhang, P., ve Chen, B. (2022). A microscopic model of vehicle CO2 emissions based on deep learning - a spatiotemporal analysis of taxicabs in Wuhan, China. IEEE Transactions on Intelligent Transportation Systems, 23(10), 18446–18455. https://doi.org/10.1109/tits.2022.3151655
  44. Karaçor, F., Ergül, H., Hatipoğlu, S. ve İzol, E. (2024). Yerel yönetimlerin sürdürülebilir kentsel hareketlilik planları yeterliliklerinin tespiti üzerine bir uygulama. Trafik ve Ulaşım Araştırmaları Dergisi, 7(1), 75–84. https://doi.org/10.38002/tuad.1388954
  45. Ko, M., Lord, D., ve Zietsman, J. (2012). Environmentally conscious highway design for crest vertical curves. Transportation Research Record, 2270(1), 96–106. https://doi.org/10.3141/2270-12
  46. Kwak, J., Park, B., ve Lee, J. (2012). Evaluating the impacts of urban corridor traffic signal optimization on vehicle emissions and fuel consumption. Transportation Planning and Technology, 35(2), 145–160. https://doi.org/10.1080/03081060.2011.651877
  47. Lee, G., You, S. I., Ritchie, S. G., Saphores, J.-D. M., Sangkapichai, M., ve Jayakrishnan, R. (2008). Environmental impacts of a major freight corridor: A study of the I-710 in California (UCTC-FR-2008-11). University of California Transportation Center. https://escholarship.org/uc/item/7kw9370n
  48. Lee, K. S., Eom, J. K., ve Moon, D. S. (2014). Applications of TRANSIMS in transportation: A literature review. Procedia Computer Science, 32, 769–773. https://doi.org/10.1016/J.PROCS.2014.05.489
  49. Lejri, D., Can, A., Schiper, N., ve Leclercq, L. (2018). Accounting for traffic speed dynamics when calculating COPERT and PHEM pollutant emissions at the urban scale. Transportation Research Part D: Transport and Environment, 63, 588–603. https://doi.org/10.1016/j.trd.2018.06.023
  50. Li, S., ve Yoon, H. S. (2023). Vehicle localization in 3d world coordinates using single camera at traffic intersection. Sensors, 23(7). https://doi.org/10.3390/s23073661
  51. Lin, Y. C., Lin, Y. T., Chen, C. R., ve Lai, C. Y. (2025). Meteorological and traffic effects on air pollutants using Bayesian networks and deep learning. Journal of Environmental Sciences, 152, 54–70. https://doi.org/10.1016/j.jes.2024.01.057
  52. Liu, R. (1994). DRACULA microscopic traffic simulator. (Working Paper 431). University of Leeds.
  53. Liu, R., ve Tate, J. (2004). Network effects of intelligent speed adaptation systems. Transportation, 31(3), 297–325. https://doi.org/10.1023/B:PORT.0000025394.78857.13/METRICS
  54. Lopez, P. A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., ve Wießner, E. (2018). Microscopic traffic simulation using SUMO. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) (ss. 2575–2582). IEEE. https://doi.org/10.1109/ITSC.2018.8569938
  55. Ma, X., Lei, W., Andréasson, I., ve Chen, H. (2012). An evaluation of microscopic emission models for traffic pollution simulation using on-board measurement. Environmental Modeling and Assessment, 17(4), 375–387. https://doi.org/10.1007/s10666-011-9296-9
  56. Maduro, C., Batista, K., Peixoto, P. ve Batista, J. (2008). Estimating vehicle velocity using rectified images. Proceedings of the Third International Conference on Computer Vision Theory and Applications (VISAPP 2008) (ss. 551–558). SciTePress. https://doi.org/10.5220/0001088205510558
  57. Mądziel, M. (2024). Instantaneous CO2 emission modelling for a Euro 6 start-stop vehicle based on portable emission measurement system data and artificial intelligence methods. Environmental Science and Pollution Research, 31(5), 6944–6959. https://doi.org/10.1007/s11356-023-31022-5
  58. Mądziel, M., ve Campisi, T. (2023). Investigation of vehicular pollutant emissions at 4-arm intersections for the improvement of integrated actions in the sustainable urban mobility plans (SUMPs). Sustainability, 15(3). https://doi.org/10.3390/su15031860
  59. Mądziel, M., Jaworski, A., Savostin-Kosiak, D., ve Lejda, K. (2020). The impact of exhaust emission from combustion engines on the environment: Modelling of vehicle movement at roundabouts. International Journal of Automotive and Mechanical Engineering, 17(4), 8360–8371. https://doi.org/10.15282/ijame.17.4.2020.12.0632
  60. MATSim. (t.y.). MATSim – Open-source framework for large-scale agent-based transport simulations. 12 Şubat 2025 tarihinde https://www.matsim.org/ adresinden erişilmiştir.
  61. McTrans Center. (2025). TSIS-CORSIM 2025 – Traffic software integrated system. 12 Şubat 2025 tarihinde https://mctrans.ce.ufl.edu/tsis-corsim/ adresinden erişilmiştir.
  62. Metil, P. D., ve Nigavekar, A. R. (2019). Review on traffic density count using ımage processing. International Journal for Research in Applied Science and Engineering Technology, 7(1), 285–290. https://doi.org/10.22214/ijraset.2019.1048
  63. Miyazaki, H., Witayangkurn, A., ve Kii, M. (2020). A review of MATSim: A pilot study of Chatuchak, Bangkok THAILAND. Journal of Intelligent Informatics and Smart Technology, 4, 1–6. https://ph05.tci-thaijo.org/index.php/JIIST/article/view/97/93
  64. Namekawa, M., Kanagawa, S. ve Shinkai, K. (2019). Image recognition for vehicle traffic analysis at intersections. Proceedings of the 23rd International Congress on Modelling and Simulation (MODSIM 2019) (ss. 484–490). Modelling and Simulation Society of Australia and New Zealand.
  65. Niroomand, N., Bach, C., ve Elser, M. (2021). Segment-based CO₂ emission evaluations from passenger cars based on deep learning techniques. IEEE Access, 9, 166314–166327. https://doi.org/10.1109/ACCESS.2021.3135604
  66. Noland, R. B., ve Quddus, M. A. (2006). Flow improvements and vehicle emissions: Effects of trip generation and emission control technology. Transportation Research Part D: Transport and Environment, 11(1), 1–14. https://doi.org/10.1016/J.TRD.2005.06.003
  67. Okebe, M. W., Abuodha, S. O., ve Ochieng, M. O. A. (2024). Evaluating emission reduction policies along an urban arterial highway using the AIMSUN model. Journal of Advanced Transportation, 2024. https://doi.org/10.1155/2024/6309854
  68. Overtoom, I., Correia, G., Huang, Y., ve Verbraeck, A. (2020). Assessing the impacts of shared autonomous vehicles on congestion and curb use: A traffic simulation study in The Hague, Netherlands. International Journal of Transportation Science and Technology, 9(3), 195–206. https://doi.org/10.1016/j.ijtst.2020.03.009
  69. Park, B. B., Yun, I., ve Ahn, K. (2009). Stochastic optimization for sustainable traffic signal control. International Journal of Sustainable Transportation, 3(4), 263–284. https://doi.org/10.1080/15568310802091053
  70. Park, B., ve Qi, H. (2004). Development and evaluation of a calibration and validation procedure for microscopic simulation models (Report No. VTRC 05-CR8). Virginia Transportation Research Council. https://doi.org/10.21949/1503647
  71. Patiño-Aroca, M., Parra, A., ve Borge, R. (2022). On-road vehicle emission inventory and its spatial and temporal distribution in the city of Guayaquil, Ecuador. Science of the Total Environment, 848. https://doi.org/10.1016/j.scitotenv.2022.157664
  72. Pignatta, G. ve Balazadeh, N. (2022). Hybrid vehicles as a transition for full e-mobility achievement in positive energy districts: A comparative assessment of real-driving emissions. Energies. 15, 2760. https://doi.org/ 10.3390/en15082760
  73. Prohanov, S., Bart, A., Fazliev, A. Z. ve Starchenko, A. V. (2021). Use of computer vision in assessment of air quality in city transportation system. Proceedings of the 27th International Symposium on Atmospheric and Ocean Optics (Cilt 11916, Madde 119168T). SPIE. https://doi.org/10.1117/12.2603497
  74. PTV Group. (t.y.). PTV Vissim: Multimodal traffic simulation software. 12 Şubat 2025 tarihinde https://www.ptvgroup.com/en/products/ptv-vissim/ adresinden erişilmiştir.
  75. Quaassdorff, C., Borge, R., Pérez, J., Lumbreras, J., de la Paz, D., ve de Andrés, J. M. (2016). Microscale traffic simulation and emission estimation in a heavily trafficked roundabout in Madrid (Spain). Science of the Total Environment, 416–427. https://doi.org/10.1016/j.scitotenv.2016.05.051
  76. Rahimi, M. (2023). Modeling and simulation of vehicle emissions for the reduction of road traffic pollution [Doktora tezi, University of Trento]. IRIS Institutional Research Information System. https://doi.org/10.15168/11572_365449
  77. Rajput, D., Singhai, T., Bhadauria, D., ve Relan, S. (2023). Comparative analysis for vehicle detection and counting system using machine learning. International Journal of Scientific Research in Engineering and Management (IJSREM), 7(3), 1–6. https://doi.org/10.55041/ijsrem17924
  78. Rakha, H., Ahn, K., ve Trani, A. (2004). Development of VT-Micro model for estimating hot stabilized light duty vehicle and truck emissions. Transportation Research Part D: Transport and Environment, 9(1), 49–74. https://doi.org/10.1016/S1361-9209(03)00054-3
  79. Ratrout, N. T., ve Rahman, S. M. (2009). A comparative analysis of currently used microscopic and macroscopic traffic simulation software. Arabian Journal for Science and Engineering, 121–133. https://www.researchgate.net/publication/228676772
  80. Romero, B., Coello, M., ve Cordero-Moreno, D. (2020). GPS data correction through the IVE model for use in vehicle emission estimates for congestion pricing. Proceedings of the 2020 IEEE ANDESCON (ANDESCON 2020). https://doi.org/10.1109/ANDESCON50619.2020.9272080
  81. Saadeldin, A., Rashid, M. M., Shafie, A. A., ve Hasan, T. F. (2024). Real-time vehicle counting using custom YOLOv8n and DeepSORT for resource-limited edge devices. Telkomnika (Telecommunication Computing Electronics and Control), 22(1), 104–112. https://doi.org/10.12928/TELKOMNIKA.v22i1.25096
  82. Sakhare, N. N., Tatale, S. B., Sakhare, S. R., Dusaane, H., Puri, M., Girme, P., Sankpal, R., ve Ghule, P. (2020). Image processing and iot based dynamic traffic management system. International Journal of Scientific Research in Science, Engineering and Technology, 7(2), 180–188. https://doi.org/10.32628/IJSRSET207230
  83. Shokrolah Shirazi, M., Chang, H. F., ve Tayeb, S. (2022). Turning movement count data integration methods for intersection analysis and traffic signal design. Sensors, 22(19), 7111. https://doi.org/10.3390/s22197111
  84. Smith, M., Duncan, G., ve Druitt, S. (1995). PARAMICS: Microscopic traffic simulation for congestion management. The Institution of Electrical Engineers. https://doi.org/10.1049/ic:19950249
  85. Song, H., Liang, H., Li, H., Dai, Z., ve Yun, X. (2019). Vision-based vehicle detection and counting system using deep learning in highway scenes. European Transport Research Review, 11(1), 51. https://doi.org/10.1186/s12544-019-0390-4
  86. Song, Z., Wang, H., Sun, J., ve Tian, Y. (2020). Experimental findings with VISSIM and TransModeler for evaluating environmental and safety impacts using micro-simulations. Transportation Research Record, 2674(8), 566–580. https://doi.org/10.1177/0361198120925077
  87. Sterzin, E. D. (2004). Modeling influencing factors in a microscopic traffic simulator [Yüksek lisans tezi, Massachusetts Institute of Technology]. DSpace@MIT. https://dspace.mit.edu/handle/1721.1/29398
  88. Su, S., Hou, P., Wang, X., Lyu, L., Ge, Y., Lyu, T., Lai, Y., Luo, W., ve Wang, Y. (2023). Evaluating the measurement uncertainty of on-road NOx using a portable emission measurement system (PEMS) based on real testing data in China. Atmosphere, 14(4), 702. https://doi.org/10.3390/atmos14040702
  89. Suparwito, H., Prakoso, B. G. H., Kumalasanti, R. A., ve Polina, A. M. (2025). Real-time vehicle detection and air pollution estimation using YOLOv9. Jurnal Sisfokom (Sistem Informasi dan Komputer), 14(1), 23–30. https://doi.org/10.32736/sisfokom.v14i1.2339
  90. SYSTRA. (t.y.). Paramics microsimulation – Designs and evaluates road transport network. 12 Şubat 2025 tarihinde https://www.systra.com/digital/solutions/transport-planning/paramics/ adresinden erişilmiştir.
  91. Tenekeci, G. (2019). Computation and assessment of environmental emissions resulting from traffic operations at roundabouts. European Journal of Science and Technology, (Special Issue), 130–145. https://doi.org/10.31590/EJOSAT.637594
  92. Travel Forecasting Resource. (t.y.). TRANSIMS. 12 Şubat 2025 tarihinde https://tfresource.org/topics/TRANSIMS.html adresinden erişilmiştir.
  93. Ullah, M. R., Khattak, K. S., Khan, Z. H., Khan, M. A., Minallah, N., ve Khan, A. N. (2021). Vehicular traffic simulation software: A systematic comparative analysis. Pakistan Journal of Engineering and Technology, 4(1), 66–78. https://journals.uol.edu.pk/pakjet/article/view/686/469
  94. Unzueta, L., Nieto, M., Cortés, A., Barandiaran, J., Otaegui, O., ve Sánchez, P. (2012). Adaptive multicue background subtraction for robust vehicle counting and classification. IEEE Transactions on Intelligent Transportation Systems, 13(2), 527–540. https://doi.org/10.1109/TITS.2011.2174358
  95. Viola, P., ve Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cilt 1, ss. 511–518). IEEE. https://doi.org/10.1109/CVPR.2001.990517
  96. Wei, Y., Yu, Y., Xu, L., Huang, W., Guo, J., Wan, Y., ve Cao, J. (2019). Vehicle emission computation through microscopic traffic simulation calibrated using genetic algorithm. Journal of Artificial Intelligence and Soft Computing Research, 9(1), 67–80. https://doi.org/10.2478/jaiscr-2018-0025
  97. Wu, J. D., Chen, B. Y., Shyr, W. J., ve Shih, F. Y. (2021). Vehicle classification and counting system using YOLO object detection technology. Traitement du Signal, 38(4), 1087–1093. https://doi.org/10.18280/ts.380419
  98. Xia, Y., Shi, X., Song, G., Geng, Q., ve Liu, Y. (2016). Towards improving quality of video-based vehicle counting method for traffic flow estimation. Signal Processing, 120, 672–681. https://doi.org/10.1016/j.sigpro.2014.10.035
  99. Xu, Z., Wei, T., Easa, S., Zhao, X., ve Qu, X. (2018). Modeling Relationship between Truck Fuel Consumption and Driving Behavior Using Data from Internet of Vehicles. Computer-Aided Civil and Infrastructure Engineering, 33(2), 209–219. https://doi.org/10.1111/MICE.12344
  100. Yang, H., ve Qu, S. (2017). Real-time vehicle detection and counting in complex traffic scenes using background subtraction model with low-rank decomposition. IET Intelligent Transport Systems, 12(1), 75–85. https://doi.org/10.1049/iet-its.2017.0047
  101. Yang, Y., Yang, Y., Zhang, B., Cheng, P., Lee, D. H. ve Hu, S. (2024). Traffic emission estimation and modelling at urban intersections using high-resolution UAV-based video. 2024 Forum for Innovative Sustainable Transportation Systems (FISTS) (ss. 1–6). IEEE. https://doi.org/10.1109/FISTS60717.2024.10485600
  102. Yavuz, E., Öztürk, A., Balkanlı, N. G. N., Engin, Ş. N., ve Kuzu, S. L. (2024). Development of artificial intelligent-based methodology to prepare input for estimating vehicle emissions. Applied Sciences (Switzerland), 14(23), 11175. https://doi.org/10.3390/app142311175
  103. Yu, Q., Yang, Y., Xiong, X., Sun, S., Liu, Y., ve Wang, Y. (2021). Assessing the impact of multi-dimensional driving behaviors on link-level emissions based on a Portable Emission Measurement System (PEMS). Atmospheric Pollution Research, 12(1), 414–424. https://doi.org/10.1016/J.APR.2020.09.022
  104. Zhai, Z., Song, G., Lu, H., He, W., ve Yu, L. (2017). Validation of temporal and spatial consistency of facility- and speed-specific vehicle-specific power distributions for emission estimation: A case study in Beijing, China. Journal of the Air and Waste Management Association, 67(9), 949–957. https://doi.org/10.1080/10962247.2017.1294545
  105. Zhang, K., Batterman, S., ve Dion, F. (2011). Vehicle emissions in congestion: Comparison of work zone, rush hour and free-flow conditions. Atmospheric Environment, 45(11), 1929–1939. https://doi.org/10.1016/J.ATMOSENV.2011.01.030
  106. Zhang, R., Wang, Y., Pang, Y., Zhang, B., Wei, Y., Wang, M., ve Zhu, R. (2022). A deep learning micro-scale model to estimate the CO₂ emissions from light-duty diesel trucks based on real-world driving. Atmosphere, 13(9), 1466. https://doi.org/10.3390/atmos13091466
  107. Zhang, S., Wu, G., Costeira, J. P., ve Moura, J. M. F. (2017). FCN-rLSTM: Deep spatio-temporal neural networks for vehicle counting in city cameras. Proceedings of the IEEE International Conference on Computer Vision (ss. 3687–3696). https://doi.org/10.1109/ICCV.2017.396
  108. Zhang, Y., ve Ioannou, P. A. (2016). Environmental impact of combined variable speed limit and lane change control: A comparison of MOVES and CMEM model. IFAC-PapersOnLine, 49(3), 323–328. https://doi.org/10.1016/j.ifacol.2016.07.054
  109. Zhao, H., Mu, L., Li, Y., Qiu, J., Sun, C., ve Liu, X. (2021). Unregulated emissions from natural gas taxi based on IVE model. Atmosphere, 12(4), 478. https://doi.org/10.3390/atmos12040478
  110. Zhao, Z.-Q., Zheng, P., Xu, S., ve Wu, X. (2019). Object detection with deep learning: A review. IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3212–3232. https://doi.org/10.1109/TNNLS.2018.2876865