The input data consists of two parts: the street network, and the demand.
The street network that is used was originally developed for the Swiss regional planning authority (Bundesamt für Raumentwicklung). It has since been modified by Vrtic at the IVT, and again by us. The network has the fairly typical number of 10572 nodes and 28622 links. Also fairly typical, the major attributes on these links are type, length, speed, and capacity. As pointed out above, this is enough information for the queue simulation.
In order to test our set-up, we generated a set of 50000 trips going to the same destination. Having all trips going to the same destination allows us to check the plausibility of the feedback since all traffic jams on all used routes to the destination should dissolve in parallel. In this scenario, we simulate the traffic resulting from 50000 vehicles which start between 6am and 7am all over Switzerland and which all go to Lugano, which is in the Ticino, the Italian-speaking part of Switzerland south of the Alps. In order for the vehicles to get there, most of them have to cross the Alps. There are however not many ways to do this, resulting in traffic jams, most notably in the corridor leading towards the Gotthard pass. This scenario has some resemblance with real-world vacation traffic in Switzerland.
For a realistic simulation of all of Switzerland, the starting point for demand generation is a 24-hour origin-destination matrix, again from the Swiss regional planning authority (Bundesamt für Raumentwicklung). For this matrix, the region is divided into 3066 zones. Each matrix entry describes the number of trips from one zone to another during a typical 24-hour workday; trips within zones are not included in the data. The original 24-hour matrix was converted into 24 one-hourly matrices using a three step heuristic which uses departure time probabilities and field data volume counts. These matrices are then converted to individual (disaggregated) trips using another heuristic. The final result is that for each entry in the origin-destination matrix we have a trip which starts in the given time slice, with origin and destination links in the correct geographical area. More details can be found in Voellmy et al. (forthcoming).
In the long run, it is intended to move to activity-based demand generation. Then, as explained above one would start from a synthetic population, and for each population member, one would generate the chain of activities for the whole 24-hour period.