Typically, when DPW puts out a traffic counter, we end up with data that provides an overall speed profile, something similar to this:
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Typical Speed profile for a road |
By itself, this chart gives you high-level data such as average speeds, and percentage of speeders. While this data is useful in determining if a road has an
overall speeding problem, it doesn't provide the micro-level details that tells you if a problem exists during
certain times. For example, last year we presented speed data to residents on Main Street that showed that, on a relative basis, instances of speeding were low. However, the data didn't break down the speed distribution as a function of time-of-day. For example: what if all the speeding occurred at night? Or maybe they're all morning commuters?
To try and resolve this, DPW has a borrowed a new counter for the summer that outputs more manageable data that can be graphed into something like this:
The 3-D graph above shows how the speed distribution varies throughout the day. In the above street, for example, we can see that there is a spike of speeding in the 7:00 hour and the 8:00 hour - meaning that these likely are morning commuters exiting the neighborhood.
Also, we can drill down and see if there are individuals that may speed at the
same time every day.
The above graph is a scatterplot of all the vehicles going over 30mph, broken down by time-of-day and by day-of-week. From this data, we can see that if there are speeders who speed every day at roughly the same time. Of course, there's no guarantee that it is the same driver, but the data still provides the opportunity for:
- Selective and efficient radar enforcement; using radar enforcement only when the data shows it to be useful.
- Micro-targeting speeders using radar enforcement on small time intervals to target likely repeat offenders.