I don't think this type of data is well suited for analysis in Geostatistical Analyst. Geostatistics more or less assume a continuous phenomenon with 2D data, not 1D line data highly depended on something like a road surface type (e.g. ordinary asphalt / bitumen, open asphalt, double layered open etc...).
What assumptions for any spatial model do you have? And what are you trying to achieve by modelling the friction?
I would suggest trying to correlate friction with road surface data first (if available), in an ordinary (non-spatial) statistical software package, and see if there are any statistical relationships at all.
but my goal is not to determine these factors but rather to see/verify that the data collected under similar conditions (so do the fricton readings) would exhibit similar spatial patterns.
I talked this over with a few people, and none of us are completely sure exactly why this is happening, but we have a few ideas.
First, our semivariogram estimation algorithms implicitly assume that the data can, in fact, be accurately modeled with a semivariogram. When that is true, it does a good job of estimating the parameters. Unfortunately, when the data cannot be accurately modeled with a semivariogram, the calculations can produce unintuitive results. Even small changes in the input data can manifest in big changes in the semivariogram parameters because it's trying to fit something that fundamentally doesn't fit.
Second, your two datasets aren't as similar as you might think. The line graphs do look similar and mostly honor the same highs and lows, but their variances are quite different (Day 2 has twice the variance of Day 1). This explains the big differences in the sill estimation.
I wish I could give more helpful feedback on this issue, but you may want to rethink how you're quantifying the spatial structure of this phenomenon because it looks like comparing estimated semivariogram parameters is not going to work well.
Why aren't you simply graphing the two datasets against each other, since the data seems to be measured at about equal locations for each data point? The spread around the line day2=day1 will tell you quite a lot about "similarity".
This is where the semivariogram analyses come into play that, for instance, the range of spatial autocorrelation is used such that the first sensor location would also affect adjacent locations (i.e., road conditions would improve) to the same distance of the "range" at a decreasing rate, represented by its empirical semivariogram model. If so, then the second sensor location could possibly be changed to another location. Thus, it is important to analyse and characterize the model parameters for several different road condition types (e.g., icy, snowy, wet, and dry), and if there are some similarities, I can determine potential sensor locations. I anticipated that the under similar weather conditions (so do the friction values), their "range" would also be similar but there were a few exceptions (like the examples I used).
Just looking at the first graph you provided, showing the two data-series graphed against distance, tells me there are significant differences in the "spatial autocorrelation" over the entire distance measured, ranging from gradually changing friction values to steeply / abruptly changing friction over short distances consistent with a major change in conditions like a change in road surface type.
These results will make it hard to estimate a single semivariogram or "range" for a given set of conditions. Semivariogram estimation works best with the kind of data you also pointed out: gradually changing continuous phenomena like groundwater levels in sediments, concentrations of pollutants in air or water etc.
Here in the Netherlands, which probably has one of the highest road sensor networks of any country in the world, I think sensor placement is mainly determined by the desire to cover crucial road network links, get data for each section in between two highway intersections, and to cover notorious locations based on road traffic accident data which is registered in a country wide database. There is also lot of modelling going on for stuff like road traffic noise, particulate matter etc. I haven't heared of the actual modelling / optimization of sensor placement on road networks though. I have the feeling that is still largely done by pragmatic decisions / expert knowledge.
Anyway, let's see what Eric has more to say.