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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

Miao Fu, J Andrew Kelly and J. Peter Clinch

Journal of Transport Geography, 2017, vol. 58, issue C, 186-195

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.

Keywords: Transport; Traffic; Road network; Emissions; Neural network; GIS (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1016/j.jtrangeo.2016.12.002

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