Some time ago I constructed a cellular automaton (see wikipedia for explanation) for simulating birch pollen concentration in air during the flowering season in the southern Finland. After the serious work was finished, I had some fun re-writing the simulator using Processing, with some simplifications on the physics. This resulted in an applet called SneeFore. Continue reading Cellular automaton for simulating pollen transport
The public transport planning and execution within the Helsinki metropolitan area (Finland) is organized by the Helsinki Metropolitan Area Council (YTV). YTV has set up a very useful web interface for checking the public transportation timetables and routes, and some time ago I created a crawler for collecting traveltime data for Traveltime maps. With slight modifications, the crawler lends itself for plotting the public transport routes in the Helsinki metropolitan area (year 2009):
The crawler software also produces the tortuosities of the transportation routes. Tortuosity is a numerical value expressing how much the route differs from the straight line joining the endpoints of the route. Thus, tortuosity is an indicator of the effectiveness of the routing plan. The straight line between the endpoints is the most effective path and has the tortuosity value 1, and the tortuosity increases as the route deviates from the straight line: when the distance between points A and B is 1 km, a route of tortuosity 2 has the length of 2 km and a route of tortuosity 7 has the length 7 km. The value of tortuosity is ultimately bounded from below by the road network tortuosity.
Below is a plot combining the tortuosity data fetched for working days (blue dots) and Sundays (red dots), overlaid by the road network tortuosities (grey dots):