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The observed data are samples of traffic flow on a road network.
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Open image in new window Fig. 2 Sample of traffic record.
Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network.
Our study was based on a sample of traffic officers and indoor office workers with different degrees of exposure to traffic pollutants.
The ETH Zurich team scoured a massive sample of internet traffic between 2004 and 2012, finding more than 19 million unique active subnet addresses.
The 1-h sample of network traffic was sufficient to capture connections from almost all the different clients observed over a longer period of time.
Therefore, we assigned a User-Agent to almost all observed HTTPS connections using a combined dictionary based on the data from a single server and the correlations from a 1-h sample of network traffic.
These samples of possible future traffic accidents are created according to the distribution parameters determined from historical data, including the temporal and spatial distribution.
By exploiting Infinitesimal Perturbation Analysis to capture the network performance sensitivity, we obtain an adaptive control law suitable for on-line control on the basis of traffic samples acquired during the network evolution.
In many of these studies, only a percentage of traffic stream the sampling rate or penetration rate is captured and used as the traffic data source.
Differences in NO2 between locations were most strongly associated with density of built space within 1 kilometer of the sampling site and the amount of traffic within 100 meters.
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