Journals / Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi / 2019 / Cilt: 3 - Sayı: 2

Environmental Impact of Vehicles Waiting at the Signalized Intersections: A Case Study of a Four-Phase Intersection

Sinyalize Kavşaklarda Bekleyen Taşıtların Çevresel Etkileri: Dört Fazlı Bir Kavşak Üzerinden Durum Değerlendirmesi

Pages
229–240
Publication Date
2019-09-30
DOI
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AI Summary & Key Takeaways

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

However, with the use of the idle stop-start system, it has been observed that CO2 equivalent emissions can be reduced.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:With the increase in the number of vehicles in traffic, there are many scenarios for traffic flow.
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Key Findings & Data:In this study, CO2 equivalent emission values (carbon footprint) were calculated in order to examine the environmental effects in a four-phase intersection.
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Conclusion & Impact:However, with the use of the idle stop-start system, it has been observed that CO2 equivalent emissions can be reduced.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:kavşak · karbon ayak izi · emisyon

Abstract

With the increase in the number of vehicles in traffic, there are many scenarios for traffic flow. On the other hand, the waiting times of the vehicles in traffic are constantly increasing. A misplaced traffic plan leads to traffic congestion and environmental problems. In this study, CO2 equivalent emission values (carbon footprint) were calculated in order to examine the environmental effects in a four-phase intersection. Equations were derived to calculate CO2 equivalent emission at the intersection. The effect of the idle stop-start system and the number of electric vehicles was also considered as a future scenario. As a result of the study, it was observed that the small number of electric vehicles decreased the CO2 equivalent emission at the intersection significantly. However, with the use of the idle stop-start system, it has been observed that CO2 equivalent emissions can be reduced.

Özet

Trafikteki taşıt sayısının artması ile birlikte trafik akışı için birçok senaryo oluşturulmaktadır. Bununla birlikte taşıtların trafikteki bekleme süreleri de sürekli artmaktadır. Yanlış tasarlanmış bir trafik planı trafik sıkışıklığına ve çevresel problemlere neden olmaktadır. Bu çalışmada dört fazlı bir kavşaktaki çevresel etkilerin incelenebilmesi için CO2 eşdeğeri emisyon değerleri (karbon ayak izi) hesaplanmıştır. Kavşaktaki CO2 eşdeğeri emisyonunun hesaplanabilmesi için eşitlikler türetilmiştir. Bu hesaplamalar yapılırken rölanti stop-start sisteminin ve elektrikli taşıt sayısının etkisi de bir gelecek senaryosu olarak ele alınmıştır. Yapılan çalışma neticesinde elektrikli taşıt sayının az miktarda değişimi, kavşakta oluşan CO2 eşdeğeri emisyonunu önemli derecede azalttığı görülmüştür. Bununla birlikte rölanti stop-start sisteminin kullanılması ile birlikte CO2 eşdeğeri emisyonun az da olsa azaltılabileceği görülmüştür.

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Cite This Article

ARABACI, E., ORMAN, R. Ç., KILIÇ, B., HEPDENİZ, K., YİTİK, B. (2019). Environmental Impact of Vehicles Waiting at the Signalized Intersections: A Case Study of a Four-Phase Intersection. Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi, Cilt: 3 - Sayı: 2, 229–240. https://ajindex.com/articles/790597

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References (9)

  1. Cao, Z., Jiang, S., Zhang, J., & Guo, H. (2016). A unified framework for vehicle rerouting and traffic light control to reduce traffic congestion. IEEE transactions on intelligent transportation systems, 18(7), 1958-1973. https://doi.org/10.1109/TITS.2016.2613997
  2. Choy, M. C., Srinivasan, D., & Cheu, R. L. (2003). Cooperative, hybrid agent architecture for real-time traffic signal control. IEEE Transactions on Systems, Man, and Cybernetics-Part A: systems and humans, 33(5), 597-607. https://doi.org/10.1109/TSMCA.2003.817394
  3. Daganzo, C., & Daganzo, C. F. (1997). Fundamentals of transportation and traffic operations (Vol. 30). Oxford: Pergamon. D'Andrea, E., & Marcelloni, F. (2017). Detection of traffic congestion and incidents from GPS trace analysis. Expert Systems with Applications, 73, 43-56. https://doi.org/10.1016/j.eswa.2016.12.018 EPA. Greenhouse Gas Equivalencies Calculator. Accessed: 24.05.2019, https://www.epa.gov/energy/greenhouse-gas-equivalencies-calculator
  4. Ewing, R., & Dumbaugh, E. (2009). The built environment and traffic safety: a review of empirical evidence. Journal of Planning Literature, 23(4), 347-367. https://doi.org/10.1177%2F0885412209335553
  5. Fouladgar, M., Parchami, M., Elmasri, R., & Ghaderi, A. (2017). Scalable deep traffic flow neural networks for urban traffic congestion prediction. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 2251-2258). IEEE. https://doi.org/10.1109/IJCNN.2017.7966128
  6. Kumar, P., Ketzel, M., Vardoulakis, S., Pirjola, L., & Britter, R. (2011). Dynamics and dispersion modelling of nanoparticles from road traffic in the urban atmospheric environment—a review. Journal of Aerosol Science, 42(9), 580-603. https://doi.org/10.1016/j.jaerosci.2011.06.001
  7. Li, Z., Shahidehpour, M., Bahramirad, S., & Khodaei, A. (2016). Optimizing traffic signal settings in smart cities. IEEE Transactions on Smart Grid, 8(5), 2382-2393. https://doi.org/10.1109/TSG.2016.2526032
  8. Malakorn, K. J., & Park, B. (2010). Assessment of mobility, energy, and environment impacts of IntelliDrive-based Cooperative Adaptive Cruise Control and Intelligent Traffic Signal control. In Proceedings of the 2010 IEEE International Symposium on Sustainable Systems and Technology (pp. 1-6). IEEE. https://doi.org/10.1109/ISSST.2010.5507709
  9. Mannion, P., Duggan, J., & Howley, E. (2016). An experimental review of reinforcement learning algorithms for adaptive traffic signal control. In Autonomic Road Transport Support Systems(pp. 47-66). Birkhä