As announced in Salzburg a few days ago, I’m happy to present the lastest enhancement to my IDF router for QGIS: travel time routing and catchment computation.
Travel times for pedestrians and cyclists are computed using constant average speeds, while car travel times depend on the speed values provided by the road network data.
Catchment computations return the links that can be traversed completely within the given time (or distance limit). The current implementation does not deal with links at the edge of the catchment area, which can only be traversed partially.
Loading the whole network (2.7GB unzipped IDF) currently requires around 10GB of memory. One of the next plans therefore is to add a way to only load features within a specified bounding box.
Plans to turn this into a full-blown plugin will most likely have to wait for QGIS 3, which will ship with Python 3 and other updated libraries.
Monday, January 4th 2016, was the open data release date of the official Austrian street network dataset called GIP.at. As far as I know, the dataset is not totally complete yet but it should be in the upcoming months. I’ve blogged about GIP.at before in Open source IDF parser for QGIS and Open source IDF router for QGIS where I was implementing tools based on the data samples that were available then. Naturally, I was very curious if my parser and particularly the router could handle the whole country release …
Some code tweaking, patience for loading, and 9GB of RAM later, QGIS happily routes through Austria, for example from my work place to Salzburg – maybe for some skiing:
The routing request itself takes something between 1 and 2 seconds. (I should still add a timer to it.)
So far, I’ve implemented shortest distance routing for pedestrians, bikes, and cars. Since the data also contains travel speeds, it should be quite straight-forward to also add shortest travel time routing.
The code is available on Github for you to try. I’d appreciate any feedback!