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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