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Promet - Traffic&Transportation journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

PUBLISHED
30.03.2021
LICENSE
Copyright (c) 2024 Livia Maglić, Tomislav Krljan, Neven Grubišić, Lovro Maglić

Estimating Urban Road Transport Vehicles Emissions in the Rijeka City Streets

Authors:

Livia Maglić
University of Rijeka, Faculty of Maritime Studies

Tomislav Krljan
University of Rijeka, Faculty of Maritime Studies

Neven Grubišić
University of Rijeka, Faculty of Maritime Studies

Lovro Maglić
University of Rijeka, Faculty of Maritime Studies

Keywords:microscopic SUMO model, COPERT Street Level model, vehicle emission estimation, sustainable urban transport

Abstract

The growing demand for private and public transport services in urban areas requires sophisticated approaches to achieve satisfactory mobility standards in urban areas. Some of the main problems in urban areas today are road congestions and consequently vehicle emissions. The aim of this paper is to propose a methodological approach for the estimation of vehicle emissions. The proposed methodology is based on two interrelated models. The first model is a microscopic simulation SUMO model which can be used to identify the most congested urban areas and roads with critical values of traffic parameters. The second model is the COPERT Street Level for estimating vehicle emissions. The proposed models were tested on the urban area of Rijeka. The results of the microscopic SUMO simulation model indicate six urban roads with the critical traffic flow parameters. On the basis of the six identified urban roads, an estimation of vehicle emissions was carried out for specific time periods: 2017, 2020, 2025, and 2030. According to the results of the second model, the urban road R20-21 was identified as the most polluted road in the urban district of Rijeka. The results indicate that over the period 2017–2030, CO emissions will be reduced on average by 57% on all observed urban roads, CO2 emissions by 20%, and PM emissions by 58%, while the largest reduction of 65% will be in NOx emissions.

References

  1. Liu J, Han K, Chen X, Ong G-P. Spatial-temporal inference of urban traffic emissions based on taxi trajectories and multi-source urban data. Transportation Research Part C: Emerging Technologies. 2019;106: 145-165. DOI: 10.1016/j.trc.2019.07.005

    The Intergovernmental Panel on Climate Change (IPCC). AR5 Climate Change 2014: Mitigation of Climate Change. Chapter 8 – Transport; 2014. Available from: http://www.ipcc.ch/ [Accessed 1st Nov. 2019].

    Wang H, Zeng W. Revealing Urban Carbon Dioxide (CO2) Emission Characteristics and Influencing Mechanisms from the Perspective of Commuting. Sustainability. 2019;11(2): 385. DOI: 10.3390/su11020385

    Bakker S, Haq G, Peet K, Gota S, Medimorec N, Yiu, A, Jennings G, Rogers J. Low-Carbon Quick Wins: Integrating Short-Term Sustainable Transport Options in Climate Policy in Low-Income Countries. Sustainability. 2019;11(16): 4369. DOI: 10.3390/su11164369

    Zhang W, Lu J, Zhang Y. Moving towards Sustainability: Road Grades and On-Road Emissions of Heavy-

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How to Cite
Maglić, L. (et al.) 2021. Estimating Urban Road Transport Vehicles Emissions in the Rijeka City Streets. Traffic&Transportation Journal. 33, 2 (Mar. 2021), 165-178. DOI: https://doi.org/10.7307/ptt.v33i2.3613.

SPECIAL ISSUE IS OUT

Guest Editor: Eleonora Papadimitriou, PhD

Editors: Marko Matulin, PhD, Dario Babić, PhD, Marko Ševrović, PhD


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