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Estimating annual average daily traffic and transport emissions for a national road network: A bottom-up methodology for both nationally-aggregated and spatially-disaggregated results

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  • Fu, Miao
  • Kelly, J. Andrew
  • Clinch, J. Peter

Abstract

The regular and robust collection of traffic data for the entire road network in a given country will usually require high-cost investment in traffic surveys and automated traffic counters. This paper provides an alternative and low-cost approach for estimating annual average daily traffic values (AADTs) and the associated transport emissions for all road segments in a country. This is achieved by parsing and processing commonly available information from existing geographical data, census data, traffic data and vehicle fleet data. Ceteris paribus, we find that our annual average daily traffic estimation based on a neural network performs better than traditional regression models, and that the outcomes of our aggregated bottom-up road segment emission estimations are close to the outcomes from top-down models based on total energy consumption in transport. The developed approach can serve as a means of reliably estimating and verifying national road transport emissions, as well as offering a robust means of spatially analysing road transport activity and emissions, so as to support spatial emission inventory compilations, compliance with international environmental agreements, transport simulation modelling and transport planning.

Suggested Citation

  • Fu, Miao & Kelly, J. Andrew & Clinch, J. Peter, 2017. "Estimating annual average daily traffic and transport emissions for a national road network: A bottom-up methodology for both nationally-aggregated and spatially-disaggregated results," Journal of Transport Geography, Elsevier, vol. 58(C), pages 186-195.
    • Handle: RePEc:eee:jotrge:v:58:y:2017:i:c:p:186-195
    DOI: 10.1016/j.jtrangeo.2016.12.002
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    References listed on IDEAS

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    1. Lowry, Michael, 2014. "Spatial interpolation of traffic counts based on origin–destination centrality," Journal of Transport Geography, Elsevier, vol. 36(C), pages 98-105.
    2. Fu, Miao & Andrew Kelly, J., 2012. "Carbon related taxation policies for road transport: Efficacy of ownership and usage taxes, and the role of public transport and motorist cost perception on policy outcomes," Transport Policy, Elsevier, vol. 22(C), pages 57-69.
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    Cited by:

    1. Concettina Marino & Antonino Nucara & Maria Francesca Panzera & Matilde Pietrafesa, 2022. "Assessment of the Road Traffic Air Pollution in Urban Contexts: A Statistical Approach," Sustainability, MDPI, vol. 14(7), pages 1-16, March.
    2. Sfyridis, Alexandros & Agnolucci, Paolo, 2020. "Annual average daily traffic estimation in England and Wales: An application of clustering and regression modelling," Journal of Transport Geography, Elsevier, vol. 83(C).
    3. Weiheng Zhang & Yuvraj Gajpal & Srimantoorao. S. Appadoo & Qi Wei, 2020. "Multi-Depot Green Vehicle Routing Problem to Minimize Carbon Emissions," Sustainability, MDPI, vol. 12(8), pages 1-19, April.
    4. Pan, Yingjiu & Chen, Shuyan & Niu, Shifeng & Ma, Yongfeng & Tang, Kun, 2020. "Investigating the impacts of built environment on traffic states incorporating spatial heterogeneity," Journal of Transport Geography, Elsevier, vol. 83(C).

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