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

Yıl 2019, Cilt: 3 Sayı: 2, 229 - 240, 30.09.2019

Emre Arabacı Recep Çağrı Orman Bayram Kılıç Kerem Hepdeniz Bekir Yitik

https://doi.org/10.31200/makuubd.570622

Cited By: 1

Öz

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.

Anahtar Kelimeler

kavşak, karbon ayak izi, emisyon, rölanti stop-start

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

Öz

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.

Anahtar Kelimeler

intersection, carbon footprint, emission, idle stop-start

Kaynakça

• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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). Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-25808-9_4
• Nigarnjanagool, S., & Hussein, D. I. A. (2005). Evaluation of a dynamic signal optimisation control model using traffic simulation. IATSS research, 29(1), 22-30. https://doi.org/10.1016/S0386-1112(14)60115-1
• Piecyk, M. I., & McKinnon, A. C. (2010). Forecasting the carbon footprint of road freight transport in 2020. International Journal of Production Economics, 128(1), 31-42. https://doi.org/10.1016/j.ijpe.2009.08.027
• Pignataro, L. J., Cantilli, E. J., Falcocchio, J. C., Crowley, K. W., McShane, W. R., Roess, R. P., & Lee, B. (1973). Traffic engineering: theory and practice.
• Polson, N. G., & Sokolov, V. O. (2017). Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies, 79, 1-17. https://doi.org/10.1016/j.trc.2017.02.024
• Pursula, M. (1999). Simulation of traffic systems-an overview. Journal of geographic information and decision analysis, 3(1), 1-8.
• Qi, L., Zhou, M., & Luan, W. (2016). A two-level traffic light control strategy for preventing incident-based urban traffic congestion. IEEE transactions on intelligent transportation systems, 19(1), 13-24. https://doi.org/10.1109/TITS.2016.2625324
• Sharma, S., & Mishra, S. (2013). Intelligent transportation systems-enabled optimal emission pricing models for reducing carbon footprints in a bimodal network. Journal of Intelligent Transportation Systems, 17(1), 54-64. https://doi.org/10.1080/15472450.2012.708618
• Sovacool, B. K., & Brown, M. A. (2010). Twelve metropolitan carbon footprints: A preliminary comparative global assessment. Energy policy, 38(9), 4856-4869. https://doi.org/10.1016/j.enpol.2009.10.001
• Sundar, R., Hebbar, S., & Golla, V. (2014). Implementing intelligent traffic control system for congestion control, ambulance clearance, and stolen vehicle detection. IEEE Sensors Journal, 15(2), 1109-1113. https://doi.org/10.1109/JSEN.2014.2360288
• Vallati, M., Magazzeni, D., De Schutter, B., Chrpa, L., & McCluskey, T. L. (2016). Efficient macroscopic urban traffic models for reducing congestion: a PDDL+ planning approach. In Thirtieth AAAI Conference on Artificial Intelligence.
• Zhao, D., Dai, Y., & Zhang, Z. (2011). Computational intelligence in urban traffic signal control: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), 485-494. https://doi.org/10.1109/TSMCC.2011.2161577