This is the project I did for the Social and Technological Networks course I took as part of my MSc Artificial Intelligence at the University of Edinbrugh. Using data from the Nederlandse Spoorwegen, I modeled which cities are best to commute from if you work near Amsterdam Central Station (ASD) but don't want to pay Amsterdam rent, based on the following factors:
- Crowdedness c: How busy the train is
- Duration d: How much time it takes to get to ASD
- Flexibility f: How many ways there are to get to ASD
- Punctuality p: Whether the train(s) will arrive on time
- Switches l: How many times the commuter has to switch trains
I combined these factors into a single stress measure S as follows, where αs are weights for each factor and O is the set of possible options:
S=min∀o∈O(αcco+αddo+αp(1−po)+αsso)+αf(1−f)
By scraping data from the Dutch national railway operator Nationale Spoorwegen (NS), I could then make the following plots of how “stressful” a commute to Amsterdam would be for each train station in the Netherlands, for different α settings.
Stress maps with different settings for αs; green indicates low stress and red indicates high stress. Amsterdam Central Station (ASD) is marked in black. The mixed model uses my preferences as α settings.
Using settings from the bottom-right mixed model, the best cities to commute from are mostly in the Randstad and the area north-west of Amsterdam. See Appendix A of the report for a list of the top-thirty stations according to this metric.
More info: