Discover @RafalKucharskiPK Open Source projects

Rafal Kucharski (@RafalKucharskiPK)

RafalKucharskiPK

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Registered over 8 years ago
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Python
54.5 %

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45.5 %
Location 🇵🇱 Poland
Country Total Rank 2,868
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Jupyter Notebook
104

Python
401

MaaSSim

Agent-based simulator for two sided urban mobility markets

visum_to_pandas

python scripts to parse visum .net and .dmd file to pandas and store as .csv files

PTVVisum_Python_Snippets

Set of free to use python code snippets for PTV Visum scripts

ExMAS

Exact Matching of Attractive Shared rides (ExMAS) for system-wide strategic evaluations

Jupyter Notebook

ComplexSocialSystemsCourse

Teaching materials for students of Simulating and analyzing complex social systems at Jagiellonian University

Jupyter Notebook

DataScience_for_TransportationResearch

from raw online data from bike rental to mobility analyses - teaching material

Jupyter Notebook

clustering_mobility_data

methods to cluster mobility data

Jupyter Notebook

Model_Ogolny_Miejskiej_Mobilnosci

Model ogólny mobilności miejskiej dla miast małych i średnich - do celów dydaktycznych, badawczych i innych (c) Rafal Kucharski, Politechnika Krakowska, 2018

OptimalCountLocator_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. Screenshots

query_PT

Query public transport connections for a set of trip requests (from given origin to a destination at given departure time)

Jupyter Notebook

CAVe

Connected Autonomous Vehicles Equilibrium

PlateNumbers_PTVVisum_AddIn

Description Plate Number survey (ANPR) is always a chance to improve your model. However it always comes along with data processing problems - thousands of records stored in numbers of files and all need to be processed to gather information. That's why we integrated data processing within PTV Visum. Now all standard steps between APNR and OD. But not anymore: ANPR Support will support you with every step of ANPR data processing: Flexible data importer will create SQL database from your records. Powerful database engine will organize results Full Visum integration will import counting points' locations Filtering engine will show data you need (i.e. list of truck crossing two count locations during morning peak hour). Data processing machine will calculate OD matrices with several error detection procedures. You will be able to export travel time skim matrices, paths, OD matrices to Visum. You will see your results on histograms and charts. Summary take advantage of integrating ANPR data and Visum network model in one flexible Add-In speed up your calculations with highly efficient database engine work with user friendly GUI to simply see the data in tables, lists and plots or export ones to Visum and Excel. ANPR created by i2 runs as a script from Visum 12. It uses data from Visum network and ANPR data to provide not only advanced queries to ANPR database but also calculation of the characteristics based on Visum network and Count Locations.