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MaaSSim
Agent-based simulator for two sided urban mobility marketsvisum_to_pandas
python scripts to parse visum .net and .dmd file to pandas and store as .csv filesPTVVisum_Python_Snippets
Set of free to use python code snippets for PTV Visum scriptsExMAS
Exact Matching of Attractive Shared rides (ExMAS) for system-wide strategic evaluationsComplexSocialSystemsCourse
Teaching materials for students of Simulating and analyzing complex social systems at Jagiellonian UniversityDataScience_for_TransportationResearch
from raw online data from bike rental to mobility analyses - teaching materialclustering_mobility_data
methods to cluster mobility dataModel_Ogolny_Miejskiej_Mobilnosci
Model ogólny mobilności miejskiej dla miast małych i średnich - do celów dydaktycznych, badawczych i innych (c) Rafal Kucharski, Politechnika Krakowska, 2018OptimalCountLocator_PTVVisum_AddIn
OCL tells you where to place counting locations in the transport model to get best results. Our tool employs acknowledged optimization technique to specify set of optimal counting locations catching as much flow and as many OD pairs as possible. OCL is the optimization procedure wrapped in intuitive, user friendly interface, which can quickly find optimal solution even for complex networks. User can define the budget (number of points that can be counted) and detectors which are already installed. It's also available to determine what kind of detectors we want to install: junction, link, directed link. Additional technical parameter is algorithm depth, being number of paths between origin and destination that are taken into calculation process. We propose various strategies of optimization. In our opinion, and due to our tests, the most useful is mixed maximization of both OD pairs coverage and flow coverage, however you can choose to maximize only flow, or only OD pairs. Running time depends on size of the network. On the average up-to-date PC it takes about 1 minute to download 300k paths (model for Kraków, Poland of ca. 350 zones), and then time of optimization itself depends on number of connectors and takes roughly 5s per detector. To see results visually, you can import prepared .gpa file. Additionally you can use our flow bundle generator, where you can clearly see which flows are covered with your detection. For detailed results and statistics you can see report including OD coverage, flow coverage, keys of detected elements, calculation time, etc. Screenshotsquery_PT
Query public transport connections for a set of trip requests (from given origin to a destination at given departure time)CAVe
Connected Autonomous Vehicles EquilibriumLove Open Source and this site? Check out how you can help us