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Estimating emissions on vehicular traffic based on projected energy and transport demand on rural roads: Policies for reducing air pollutant emissions and energy consumption


  • Ozan, Cenk
  • Haldenbilen, Soner
  • Ceylan, Halim


This study deals with the estimation of emissions caused by vehicular traffic based on transport demand and energy consumption. Projected transport demand is calculated with Genetic Algorithm (GA) using population, gross domestic product per capita (GDPPC) and the number of vehicles. The energy consumption is modelled with the GA using the veh-km. The model age of the vehicles and their corresponding share for each year using the reference years is obtained. The pollutant emissions are calculated with estimated transport and energy demand. All the calculations are made in line to meet the European standards. For this purpose, two cases are composed. Case 1: Emissions based on energy consumption, and Case 2: Emissions based on transport demand. The both cases are compared. Three policies are proposed to control demand and the emissions. The policies provided the best results in terms of minimum emissions and the reasonable share of highway and railway mode as 70% and 30% usage for policy I, respectively. The emission calculation procedure presented in this study would provide an alternative way to make policies when there is no adequate data on emission measurement in developing countries.

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  • Ozan, Cenk & Haldenbilen, Soner & Ceylan, Halim, 2011. "Estimating emissions on vehicular traffic based on projected energy and transport demand on rural roads: Policies for reducing air pollutant emissions and energy consumption," Energy Policy, Elsevier, vol. 39(5), pages 2542-2549, May.
  • Handle: RePEc:eee:enepol:v:39:y:2011:i:5:p:2542-2549

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    References listed on IDEAS

    1. David Banister, 2000. "Sustainable urban development and transport -a Eurovision for 2020," Transport Reviews, Taylor & Francis Journals, vol. 20(1), pages 113-130, January.
    2. Haldenbilen, Soner & Ceylan, Halim, 2005. "The development of a policy for road tax in Turkey, using a genetic algorithm approach for demand estimation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 39(10), pages 861-877, December.
    3. Haldenbilen, Soner & Ceylan, Halim, 2005. "Genetic algorithm approach to estimate transport energy demand in Turkey," Energy Policy, Elsevier, vol. 33(1), pages 89-98, January.
    4. Ceylan, Halim & Bell, Michael G. H., 2005. "Genetic algorithm solution for the stochastic equilibrium transportation networks under congestion," Transportation Research Part B: Methodological, Elsevier, vol. 39(2), pages 169-185, February.
    5. Soner Haldenbilen & Halim Ceylan, 2005. "Transport Demand Management in Turkey: A Genetic Algorithm Approach," Transportation Planning and Technology, Taylor & Francis Journals, vol. 28(6), pages 403-426, August.
    6. Ceylan, Halim & Bell, Michael G. H., 2004. "Traffic signal timing optimisation based on genetic algorithm approach, including drivers' routing," Transportation Research Part B: Methodological, Elsevier, vol. 38(4), pages 329-342, May.
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    Cited by:

    1. Tseng, Po-Hsing & Lin, Dung-Ying & Chien, Steven, 2014. "Investigating the impact of highway electronic toll collection to the external cost: A case study in Taiwan," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 265-272.
    2. Mahlia, T.M.I. & Tohno, S. & Tezuka, T., 2012. "History and current status of the motor vehicle energy labeling and its implementation possibilities in Malaysia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 1828-1844.
    3. Sonmez, Mustafa & Akgüngör, Ali Payıdar & Bektaş, Salih, 2017. "Estimating transportation energy demand in Turkey using the artificial bee colony algorithm," Energy, Elsevier, vol. 122(C), pages 301-310.


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