I have done some tests to compare OD-level impedance results from doing a Dijkstra_m on the connected, non-treated network (ie /NetworkSetup/Base_Analysis/NetwerkSpec/CreateInitialWorkingNetwork/LinkSet) and the treated, compressed network (ie /NetworkSetup/Base_Analysis/NetwerkSpec/CreateMoreEfficientNetwork/FinalLinkSet)
This is done in the /Analyses/Compression_Verification module I just pushed (db8f6e9)
Approach:
- Select a random sample of 5,000 inhabited grid cells
- Apply the dijkstra_m64 function on the above mentioned networks.
- Maximum search parameter = 360 minutes.
- Measure error as impedance with compressed network - impedance with uncompressed network
Then combine the two matrices to the cartesian product of the random sample (n x n) and export to csv file. Analyse.
Results:
- All errors are > 0, so compression leads to a structural overestimation of travel times
- Average discrepancy is 3.6 seconds on all OD links
- Errors increase somewhat with higher uncompressed travel times
- Limited outliers even up to 3 minutes

Fig above: errors on all simulated OD relations as a function of travel times with uncompressed network. Errors are compressed network travel times - uncompressed network travel times. The blue line is a floating average, see geom_smooth() in ggplot2. Black dots are individual measures.
While the discrepancies are minute, it is still worthwhile to look into them in detail to understand what the cause is.
I have done some tests to compare OD-level impedance results from doing a Dijkstra_m on the connected, non-treated network (ie /NetworkSetup/Base_Analysis/NetwerkSpec/CreateInitialWorkingNetwork/LinkSet) and the treated, compressed network (ie /NetworkSetup/Base_Analysis/NetwerkSpec/CreateMoreEfficientNetwork/FinalLinkSet)
This is done in the /Analyses/Compression_Verification module I just pushed (db8f6e9)
Approach:
Then combine the two matrices to the cartesian product of the random sample (n x n) and export to csv file. Analyse.
Results:
While the discrepancies are minute, it is still worthwhile to look into them in detail to understand what the cause is.