Does anyone have any experience or tips on handling spurious hits, when using a Metrocount Sensor on a bidirectional 2-lane carriageway? I have a MTE report which shows some spurious speed events when the traffic volume is high (i.e. 129 mph on a an urban road when the volume is greater than 100 vehicles in each direction in 15 mins). I suspect that these are caused by the near simultaneous A>B and B>A hits by the opposing lines of traffic. These high speed values are artificially increasing the average speed values. I am wondering, if there is some correction factor that can be applied based upon the traffic volume, which compensates for these spurious hits. Also have there been any experimental studies regarding this problem, which indicate how the percentage error varies as a function of traffic volume and how the reported results can be corrected or calibrated against other known data? Thanks for any info.
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As bi-directional volume increases, so does the likelihood of simultaneous bidirectional vehicles crossing your sensors. And as vehicles’ speeds are derived from the time it takes for a vehicle to cross the tubes, classification algorithms can become confused if vehicles from the opposite direction interfere with the “clean” recording of axles.
It would be great to get a copy of your dataset with the spurious speeds, as I could take a look at it and provide you with some more specific comments. To send me a copy, you can upload it using our support form or attach it to an email to email@example.com
Also, consider using Profile settings to exclude spurious high speeds from your analysis.
While it’s important that we understand the cause and effect of the spurious hits, if you’re looking at the dataset as a whole, the occasional spurious speed may not have much statistical impact, especially if you’ve gathered many thousands of good vehicle records. Again, I can comment further after examining your particular dataset.
As a time-stamping system, MetroCount uses advanced algorithms to determine vehicle speeds and classes. If the sequence of axles is interrupted by an oncoming vehicle, the sequence of vehicles may be tagged as “Coerced” in the Individual Vehicles report. You can modify the Algorithm to choose what to do with the Coerced Sequences, but the best solution is to avoid them when possible.
To do this, consider using a logger for each direction. As you probably know, the MTE analysis software supports multi-dataset analysis, so two datasets from opposite lanes can be processed together.
This reply was modified 3 years, 5 months ago by Vern Bastian.