Bağdat Araç Trafiği Sıkışıklığı: Vaka Çalışması

Year 2023, Issue: 48, 34 - 39, 28.02.2023

Salim Mohammed Ali , Emad Al-hemiary

https://doi.org/10.31590/ejosat.1256277

Abstract

Taşıtlar çevreye önemli miktarda yeşil gaz emisyonuna katkıda bulunur. Geleneksel ölçüm araçları kullanılarak bu tür emisyonları hesaplama yöntemleri, trafik gecikmeleri gibi tahmini etkileyen birçok faktör olduğundan, araç sayısı arttıkça doğru bir gelecek tahmini vermemektedir. Bu nedenle, özellikle trafiğin yoğun olduğu saatlerde araç sayısı önemli ölçüde değiştiğinde, belirli bir bölge için karayolu ağlarının trafik kapasitesinin ölçülmesinde farklı bir yaklaşım düşünülmelidir. Ayrıca, trafik gecikmelerinde yakıt sarfiyatı israfının miktarı sadece araç sayısına göre kolayca hesaplanamamaktadır. Bu bildiride, Irak'ın Bağdat şehrinde trafik sıkışıklığının kirlilik, yakıt tüketimi ve zaman maliyeti açısından etkisini SUMO'nun yol ağı simülatörü kullanılarak incelemek ve hesaplamak için kapsamlı bir çalışma yapılmıştır. Her araç için rastgele seçilen yollarla çeşitli senaryolar dikkate alınır. Bu çalışmada, simülasyon testi sonuçlarından çeşitli ampirik denklemler çıkarılmıştır. Sonuç olarak, Bağdat şehrinin tamamı için 100 bin araç kapasitesi aşılırken, yakıt tüketiminde ve trafik gecikmelerinde bir sapma gözleniyor. Bununla birlikte, enterpolasyonlu denklemler, aynı şehir için daha fazla sayıda araç için trafik ölçümlerini yaklaşık olarak ölçmek için kullanılabilir.

Keywords

Sıkışıklık, Karayolu Trafiği, SUMO, V2X, Gaz Emisyonu.

Baghdad Vehicle Traffic Congestion: Case Study

Abstract

Vehicles contribute a considerable amount of green gas emission to the environment. Methods of calculating such emission using conventional measuring tools do not give an accurate future estimation as the number of vehicles increases, since there are many factors that affect the estimation such as traffic delays. Therefore, a different approach is should be considered in measuring road networks traffic capacity for a specific region, especially when the numbers of vehicles change dramatically during rush hours. Furthermore, the amount of fuel consumption wastage during traffic delays cannot be easily calculated based of on the number of vehicles solely. In this paper, a comprehensive study is made to examine and to calculate the effect of traffic congestion in Baghdad city of Iraq in terms of: pollution, fuel consumption, and time cost, using the road network simulator of SUMO. Several scenarios are considered with randomly selected paths for each vehicle. In this study, several empirical equations are extracted from the simulation test results. As a result, an aberration is observed in fuel consumption and traffic delays while exceeding 100 thousand vehicle capacity for the whole city of Baghdad. However, the interpolated equations can be used to approximately measure the traffic metrics for higher number of vehicles for the same city.

Keywords

Congestion, Road Traffic, SUMO, V2X, Gas Emission

References

• Salim, A., Ali, M., & Al-Hemairy, E. H. (2021). Communication Module for V2X Applications using Embedded Systems. Journal of Physics: Conference Series, 1933(1), 012112. doi:10.1088/1742-6596/1933/1/012112.
• World Population (2023, February 1). Baghdad Population 2021. World Population Review. Retrieved February 1, 2023, from https://worldpopulationreview.com/world-cities/baghdad-population
• COSIT (2023, January 1). Private Vehicle’s Report 2019. Retrieved January 1, 2023, from http://cosit.gov.iq/ar/2015-11-23-08-05-11
• Mohammed Ali, S. A. ., & Al-Hemairy, E. H. (2020). MINIMIZING E2E DELAY IN V2X OVER CELLULAR NETWORKS: REVIEW AND CHALLENGES. Iraqi Journal of Information and Communication Technology, 2(4), 31–42. https://doi.org/10.31987/ijict.2.4.79
• Alshaya, S., Al-Saleh, A., & Hassan, M. (12 2019). USING SUMO TRAFFIC SIMULATOR: A REALISTIC TRAFFIC SIMULATION FROM THE CITY OF ROME.
• Schweizer, J., Poliziani, C., Rupi, F., Morgano, D., & Magi, M. (2021). Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data. ISPRS International Journal of Geo-Information, 10(3), 165. https://doi.org/10.3390/ijgi10030165
• Dian Khumara, M. A., Fauziyyah, L., & Kristalina, P. (2018). Estimation of Urban Traffic State Using Simulation of Urban Mobility(SUMO) to Optimize Intelligent Transport System in Smart City. 2018 International Electronics Symposium on Engineering Technology and Applications (IES-ETA), 163–169. doi:10.1109/ELECSYM.2018.8615508
• Barrachina, J., Garrido, P., Fogue, M., Martinez, F. J., Cano, J.-C., Calafate, C. T., & Manzoni, P. (2014). Reducing emergency services arrival time by using vehicular communications and Evolution Strategies. Expert Systems with Applications, 41(4, Part 1), 1206–1217. doi:10.1016/j.eswa.2013.08.004
• Benner, K. M., Feather, M. S., Johnson, W. L., & Zorman, L. A. (1993). Utilizing Scenarios in the Software Development Process. In N. Prakash, C. Rolland, & B. Pernici (Eds.), Information System Development Process (pp. 117–134). doi:10.1016/B978-0-444-81594-1.50013-1
• Fontaras, G., Ciuffo, B., Tsiakmakis, S., Anagnostopoulos, K., Marotta, A., Pavlovic, J., … Zacharof, N. (01 2015). Simplified Technology-Specific Simulation Approach for Estimation of CO₂ Emissions from Traffic Simulation Models. doi:10.13140/RG.2.1.1793.4562
• Strohmandl, J. (10 2015). Development of simulation model for light-controlled road junction in the program Technomatix Plant Simulation. doi:10.13140/RG.2.2.29892.63368
• Ma, X., Huang, Z., & Koutsopoulos, H. (2014). Integrated Traffic and Emission Simulation: a Model Calibration Approach Using Aggregate Information. Environmental Modeling & Assessment, 19(4), 271–282. doi:10.1007/s10666-013-9397-8
• Bieker-Walz, L., Krajzewicz, D., Morra, A., Michelacci, C., & Cartolano, F. (03 2015). Traffic Simulation for All: A Real World Traffic Scenario from the City of Bologna. Lecture Notes in Control and Information Sciences, 13, 47–60. doi:10.1007/978-3-319-15024-6_4
• Vent, R. (2015). Real traffic flow modelling with SUMO.
• Huang, B., Zhang, Y., Lu, L., & Lu, J. J. (2014). A new access density definition and its correlation with crash rates by microscopic traffic simulation method. Accident Analysis & Prevention, 64, 111–122. doi:10.1016/j.aap.2013.11.014
• OpenStreetMap (2017, January 1). Planet dump retrieved from. OpenStreetMap Contributers. Retrieved January 1, 2023, from https://planet.osm.org”, https://www.openstreetmap.org
• Lopez, P. A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., … Wiessner, E. (2018). Microscopic Traffic Simulation using SUMO. 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2575–2582. doi:10.1109/ITSC.2018.8569938