Routing apps routinely capture detailed information as motorists use the GPS technology to plan and navigate routes. Making use of the crowd-sourced data from such apps could provide a low-cost and highly effective alternative to both high level and localised emissions modelling.
Current emissions data from road transport is collated from a number of different sources by the National Atmospheric Emissions Inventory and this is fed into annual reports to demonstrate compliance with emissions targets.
Many of these traditional air quality models rely on the assumption that traffic is freely flowing at the legal speed limit – whereas in many areas, traffic flow will vary through the day. These models also overlook finer-grained detail from individual roads or junctions that might be emissions hotspots at particular times of the day, explains a statement.
Although more detailed information might be available to city planners, it requires costly modelling by consultancies. Making use of the crowd-sourced data from routing apps could, the researchers argue, provide a low-cost and highly effective alternative to both high level and localised modelling.
Helen Pearce, a PhD researcher at the University of Birmingham who led the study, says: “In order to make decisions that really work ‘on the ground’, we need to be able to access and make use of this finer-grained detail.”
The approach suggested by the team was tested on roads in Birmingham’s busy city centre. Information on the time taken to travel a series of road links was obtained via a map provider’s API (application programming interface). This is conceptually similar to the approach that an individual would take to calculate the time of a journey, but using the API the researchers were able to obtain information for multiple roads and at multiple times of the day.
Following a successful preliminary study, the team scaled up their trial to include 920 major road links across Birmingham city centre, extracting information about these roads at hourly intervals. The researchers found they were able to clearly demonstrate the changes in traffic flow between typical weekdays, weekends, and also the effects of specific social events.
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