Predicting the speed of a car
There are some visual indicators of speed that should be present even in one frame. An easy example to think of is "blur", looking at a static image you could still determine "movement" (and therefore velocity) if a picture is blurred vs. absolutely sharp.

Additionally perspective, depth and shape of other objects might differ depending on velocity.

So it certainly could be possible to train a model based only on static images but it certainly should not outperform a model using both features of static images and differences between frames.

Edit:

I also looked at the repo and the presented method a bit. In the used method there could be a huge over-fitting problem if the video is only one single drive!

In this case the image detection would only identify "where" the picture was taken and since any point along the drive is associated with a single speed it could work from there i.e.: when the truck drove past a palm tree he was driving at 30 mph so identifying a palm in the picture should predict 30mph which works for a single drive data set but obviously is not able generalize.