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Citation |
Morley DW, Gulliver J. Environ. Pollut. 2016; 216: 746-754. |
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Affiliation |
MRC-PHE Centre for Environment & Health, Department of Epidemiology & Biostatistics, Faculty of Medicine, Imperial College London, W2 1PG, London, UK. |
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Copyright |
(Copyright © 2016, Elsevier Publishing) |
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DOI |
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PMID |
27350039 |
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Abstract |
Address-level estimates of exposure to road traffic noise for epidemiological studies are dependent on obtaining data on annual average daily traffic (AADT) flows that is both accurate and with good geographical coverage. National agencies often have reliable traffic count data for major roads, but for residential areas served by minor roads, especially at national scale, such information is often not available or incomplete. Here we present a method to predict AADT at the national scale for minor roads, using a routing algorithm within a geographical information system (GIS) to rank roads by importance based on simulated journeys through the road network. From a training set of known minor road AADT, routing importance is used to predict AADT on all UK minor roads in a regression model along with the road class, urban or rural location and AADT on the nearest major road. Validation with both independent traffic counts and noise measurements show that this method gives a considerable improvement in noise prediction capability when compared to models that do not give adequate consideration to minor road variability (Spearman's rho. increases from 0.46 to 0.72). This has significance for epidemiological cohort studies attempting to link noise exposure to adverse health outcomes. Language: en |