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204 Big Data Analytics for Connected Vehicles and Smart Cities Practical Applications and Concepts for Transportation Data Analytics 205

Figure 10.5 Location and duration of bottlenecks.

The data set used to generate the graphic in Figure 10.5 consisted of
traffic in both directions over weekday peak periods. Another way to visualize
the location of bottlenecks is to identify the nearest major intersection to the
bottleneck. Figure 10.6 shows bottlenecks located to major intersections. The
data set used to generate this visualization was for all months of 2013 and 2014.
The size of the boxes is proportionate to the duration of the bottleneck.
In our last example of a bottleneck visualization, shown in Figure 10.7,
the bottlenecks are plotted on a map base of the freeway, with bottlenecks rep-
resented by a dot at the location of the bottleneck. The size of the dots corre-
sponds to the duration of the bottleneck in minutes.
The dataset used to generate Figure 10.7 is for the evening peak period
for a specific day in a single direction along the freeway. The horizontal axis
represents the distance along the freeway, denoted by mile markers. The verti-
cal axis represents the time of day. The size of the dots indicates the duration
of the incident in minutes. Therefore a comet trail of bottlenecks stretching
back from a higher mile marker to a lower one, as time of day increases, can
be interpreted as a chain or sequence of bottlenecks that are probably related.
Earlier bottlenecks are likely to be causing later ones. This data was selected to
enable the comet trail patterns to be made visible in Figure 10.7. The sequences
of bottlenecks illustrate the analysis constraint discussed earlier in this section.
Since the template assumes a single dip in speed below the reference speed, the
results of the nPath analytics provide a sequence of bottlenecks. It is likely that
the comet trails are not a sequence of bottlenecks but, in fact, a single bottle-