This field study was conducted in Quanzhou, China, where two primary schools were chosen based on traffic density and monitoring data of ambient air pollutants.
If all of this data was more widely available to data scientists, a data science challenge could help determine how many lanes to have open at a given time of day, how to fluctuate tolls dynamically based on traffic needs, how to monitor and rate the changing of traffic signals and much more.
Individual-level exposure was assessed using complicated dispersion models based on traffic, meteorology, roadway geometry, vehicle emission, air quality monitoring, and land use databases [ 16, 17].
For starters, sites set rates based on traffic -- the more traffic, the higher the rates they can command for space.
Hence, the contribution of traffic was scaled using an average hourly traffic intensity pattern (based on traffic counts), thereby deriving hourly intensities.
This work presents an adaptive routing architecture, named Monitored NoC (MoNoC), which is based on a traffic monitoring mechanism and the exchange of high priority control packets.
Recently, a number of location privacy protection methods have been developed for sensor networks, to resist the various types of traffic analysis attacks (e.g., those based on monitoring traffic patterns, traffic rates, and traffic volumes).
First results on traffic monitoring based on remote sensing SAR systems have been already shown in e.g. Bethke et al. [2], or Suchandt et al. [19].
It aims on traffic monitoring in case of mass events and disasters.
The expected traffic load is accurately estimated using a sample mean estimator based on previously monitored traffic in each cell.
