Actually, there will be many intervals when you have no observations... namely, all the intervals between two recorded detections. You could ask the exact same question about a detection-less interval of one second as for one of two days.
I presume you are asking the way you do because you are aggregating detections into some buckets (e.g., hourly), and some of your buckets have zero counts. But even buckets with nonzero counts may have missed cars because the sensors were down for part of the bucket.
There is really no way to go about this without assumptions. Ideally, get some data of known good quality, e.g., by standing in the spot for two hours and counting cars yourself, then comparing to what your sensor detected. If this yields data that is close enough to the sensor reading, your sensor is presumably working well enough at least part of the time.
If so, I would start building a model for counts, with features time of day (spline transformed) in an interaction with day of week (since you almost certainly have multiple-seasonalities; this may be helpful), possibly holidays or weather. Calculate "backward-looking" prediction intervals. Any count that comes in low is suspect, especially if there are multiple low or zero counts in succession. (But there may also have been roadwork.) Look at all these issues, and decide either case by case, or by using some rule, what to do.
You can replace problematic values by sampling from the model's predictive distribution. Just using the fitted value will underestimate the variability (though if you only replace a small amount of data, this may not be a major issue).
