Global Journal of Science Frontier Research, H: Environment & Earth Science, Volume 22 Issue 1

Emission NOx (gram) 1555.19 1387.4 1179.8 Emission VOCs(gram) 1852.506 1652.58 1405.4 Fuel consumption (liter) 423.1 377.4 321 V. C onclusions The assessment of the intersection was performed by important parameters such as considering delay, queue, fuel consumption, amount of pollutantsand level of service. The analysis showed the existing condition at the intersection was stable flow with average delay of 21.61second/vehicleand LOS -C.Two alternatives to improve the intersection performance were investigated using Vissim 20: Alternative 1 and Alterative 2. The first alternative was re-designing green time of traffic light and the second alternative were consisted of re-designing green time and changed type of intersection. The 1 st alternative obtained LOS of F in 2030 year, average delay of 89 second/vehicle, queue 231.5 meters. The 2 nd alternative resulted performance was increasing with average delay of 81.1 second/vehicle, maximum vehicle queue 257.42meters and LOS of F in 2027 year. Alternative 1 is considered the best solution, this option will reduce delay by 25 %, queue by 21 %, decrease the congestion cost by 25 %, and increase LOS. In general, the 1st alternative improves the intersection performance significantly for long period of time. Based on the computer modeling results in all three conditions, the second scenario is the best option with optimization of traffic signals with the PTV vissim simulation software. R eferences R éférences R eferencias 1. “ Основные направления перехода к «  зеленой  » экономике в секторе городского транспорта ,” Tashkent, 2013. 2. B. Dadashova, X. Li, S. Turner, and P. Koeneman, “Multivariate time series analysis of traffic congestion measures in urban areas as they relate to socioeconomic indicators,” Socioecon. Plann. Sci. , 2020, doi: 10.1016/j.seps.2020.100877. 3. K. Zhang, S. Batterman, and F. Dion, “Vehicle emissions in congestion: Comparison of work zone, rush hour and free-flow conditions,” Atmos. Environ. , 2011, doi: 10.1016/j.atmosenv.2011.01.030. 4. W. Guan, S. He, and J. Ma, “Review on Traffic Flow Phenomena and Theory,” J. Transp. Syst. Eng. Inf. Technol. , vol. 12, no. 3, pp. 90–97, 2012, doi: 10.1016/S1570-6672(11)60205-5. 5. C. F. Daganzo and N. Geroliminis, “An analytical approximation for the macroscopic fundamental diagram of urban traffic,” Transp. Res. Part B Methodol. , vol. 42, no. 9, pp. 771–781, 2008, doi: 10.1016/j.trb.2008.06.008. 6. S. Jain, S. S. Jain, and G. Jain, “Traffic Congestion Modelling Based on Origin and Destination,” 2017, doi: 10.1016/j.proeng.2017.04.398. 7. A. de Palma and R. Lindsey, “Traffic congestion pricing methodologies and technologies,” Transp. Res. Part C Emerg. Technol. , vol. 19, no. 6, pp. 1377–1399, 2011, doi: 10.1016/j.trc.2011.02.010. 8. M. Barth and K. Boriboonsomsin, “Real-world carbon dioxide impacts of traffic congestion,” Transp. Res. Rec. , 2008, doi: 10.3141/2058-20. 9. F. He, X. Yan, Y. Liu, and L. Ma, “A Traffic Congestion Assessment Method for Urban Road Networks Based on Speed Performance Index,” Procedia Eng. , vol. 137, pp. 425–433, 2016, doi: 10.1016/j.proeng.2016.01.277. 10. A. Abadi, T. Rajabioun, and P. A. Ioannou, “Traffic Flow Prediction for Road Transportation Networks With Limited Traffic Data,” IEEE Trans. Intell. Transp. Syst. , vol. 16, no. 2, pp. 653–662, 2015, doi: 10.1109/TITS.2014.2337238. 11. X. Yang, Z. Gao, X. Zhao, and B. Si, “Road Capacity at Bus Stops with Mixed Traffic Flow in China,” Transp. Res. Rec. J. Transp. Res. Board , vol. 2111, no. 1, pp. 18–23, 2009, doi: 10.3141/2111-03 12. L. Zheng, T. Sayed, and F. Mannering, “Modeling traffic conflicts for use in road safety analysis: A review of analytic methods and future directions,” Anal. Methods Accid. Res. , 2021, doi: 10.1016/j.amar.2020.100142. 13. C. Caliendo and M. Guida, “Microsimulation approach for predicting crashes at unsignalized intersections using traffic conflicts,” J. Transp. Eng. , 2012, doi: 10.1061/(ASCE)TE.1943-5436.0000473. 14. S. E. Jabari, “Node modeling for congested urban road networks,” Transp. Res. Part B Methodol. , 2016, doi: 10.1016/j.trb.2016.06.001. 15. T. Saleem et al. , “Can Microsimulation Be Used to Estimate Intersection Safety? Case Studies Using VISSIM and Paramics,” Transp. Res. Rec. J. Transp. Res. , 2014. 16. Y. Wang and Y. Y. Chen, “Modeling the effect of microscopic driving behaviors on Kerner’s time- delayed traffic breakdown at traffic signal using cellular automata,” Phys. A Stat. Mech. its Appl. , vol. 463, pp. 12–24, 2016, doi: 10.1016/j.physa.2016. 06.126. 17. K. Kutlimuratov, S. Khakimov, A. Mukhitdinov, and R. Samatov, “Modelling traffic flow emissions at signalized intersection with PTV vissim,” e3s- conferences.org , doi: 10.1051/e3sconf/20212640 2051. 18. K. Hirschmann, M. Zallinger, M. Fellendorf, and S. Hausberger, “A new method to calculate emissions with simulated traffic conditions,” 2010, doi: 10.1109/ITSC.2010.5625030. 1 Year 2022 25 © 2022 Global Journals Global Journal of Science Frontier Research Volume XXII Issue ersion I VI ( H ) Assessing the Operational Impacts of Road Intersection using PTV Vissim Microscopic Simulation

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