Target network preprocessing
We pre-processed the input simple geometry to make it even simpler as shown below.
The initial merged result was as follows (original data on left)
Speed-up the results by transforming to a projected coordinate system:
Let’s check the results:
We can more reduce the minimum segment length to ensure fewer NA values in the outputs:
As shown in the results, some sideroad values have unrealistically high values:
Let’s see the results again:
The good news: the number of NAs is down to only 21 compared with the previous 100+. Bad news: sideroads have been assigned values from the main roads.
We can fix this with the max_angle_diff argument:
As shown in the results, the sideroad values are fixed:
Let’s see the results again:
Now let’s testing on 3km dataset
Read columns from rnet_y to assign functions to them
Read 3km_exmaple_merged from github
Now let’s testing on large dataset
Read columns from rnet_y to assign functions to them
Read large_exmaple_merged from github