RRRR (usually pronounced R4) is a C-language implementation of the RAPTOR public transit routing algorithm.
#BUS SIMULATOR 21 TIMETABLE FREE#
If you find something new that's not on the list, please feel free to add it to the wiki!Īs far as other open-source public transport routing libraries - there is also the RRRR project by Bliksem Labs: The routing bibliography above includes references for the following categories of algorithms and related work: Walk+Transit or Bike+Transit trips are planned using a variant of the MOA* algorithm with epsilon-dominance for path pruning and the Tung-Chew heuristic (a graph providing a lower bound on aggregate weight) for queue ordering. Walk-only and bicycle-only trips are generally planned using the A* algorithm with a Euclidean heuristic or contraction hierarchies. Currently, OpenTripPlanner uses a single time-dependent (as opposed to time-expanded) graph that contains both street and transit networks. This is a list of articles, dissertations, and books that have inspired and informed both the existing OTP routing engine and some ongoing experiments. OpenTripPlanner is multi-modal routing engine that also includes bike and walk - from the above link: There is an extensive list of publications (30+) on public transport routing algorithms that has been compiled over time by contributors to the open-source (Java) OpenTripPlanner project here: You go to point B and wait for the first guy to show up and take his card. They each have an index card to write down the choices they make as they go. Tails, stay on (or keep waiting, if off). Heads, get off (or get on, if already off).
You tell them to flip the coin every time they have a chance to get on or off a bus. You hire 1,000 people and arm them with a quarter to flip.
Picture the algorithm like this: You are trying to find the quickest route from stop A to stop B, starting at a certain time. Just to throw something out there you can try right away- choose thousands of random routes that start at your origin, and fish out the ones that work reasonably well at getting to the destination. Your other option would be to use simulation. In Deb's paper he proposes a genetic algorithm. So much so, that is has a name: the mixed integer nonlinear programming problem (MINLP). The problem you are working on is not a trivial task.
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