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The study location also includes an existing Automatic Traffic Recorder (ATR), which uses a
series of inductive loops to collect data on volume, speed, and vehicle classification. To provide
a comparison to technologies currently being used, the data from the ATR will also be
compared with the radar sensor data. It should be noted that the ATR’s classification loop in the
NB lane 3 was damaged before this study took place. The loop was classifying vehicles by
length, rather than by number of axles, which may have had an effect on the accuracy of the
data collected in this lane.
CASE STUDY
The location chosen for this study was Interstate-29 (I-29) south of 19
th
Ave. N. (Fargo, ND),
which is shown in Figure 3. This section of freeway consists of six lanes, and has a speed limit
of 55 mph. The average daily traffic (ADT) at this location was 26,000 when counted in 2006.
Figure 3. Case Study Location
This location provided easy access to the right-of-way on the east side of the interstate, and
was adjacent to a field where the TDCS could be deployed. This site was also the location of
an existing ATR data collection system, which was installed during the reconstruction of I-29. In
addition, there were several roadway signs in the vicinity, which could be used as sensor
mounts. The sensors were set up on July 25, 2008, with a 50 ft offset from the roadway to
maintain a safe clear zone.
Sensor Calibration
Most of the sensors allow users to perform speed calibration. The calibration procedures varied
greatly among the three sensors, with the SmartSensor 105 being the most time-consuming.
The speed calibration for the SmartSensor 105 required taking speed readings for each lane
and adjusting the sensor’s value up or down depending on the speed error. This was an
iterative process and required several attempts to produce accurate vehicle detection (Figure 4).
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