The package can be installed via pip. To install the package, you can use
pip install hybrid_vector_model
Please note that a compiler may be needed to install the package. Please refer to the section Installation on the main page for details.
Some algorithms implemented in this package rely on share memory libraries that work on Unix systems only. If the hybrid vector model shall be applied to large sytems, it is strongly encouraged to execute the code on Linux, as some tasks are not implemented to run in parallel on Windows.
The hybrid vector model is implemented in the HybridVectorModel class. To create a hybrid model for a system of interest, it is convenient to use the HybridVectorModel.new(…) method, which takes several data files as input and creates and fits a hybrid vector model. The model is then returned.
Since the covariates used to model traffic incentive (repulsiveness of donors and attractiveness of recipients) may vary from system to system,
it is necessary to provide a class representing the travel incentive model (here called ‘traffic factor model’) to the
The class for the traffic factor model must inherit from BaseTrafficFactorModel, and provides
a method returning a factor proportional to the traffic between each donor and recipient and a list of all the required covariates.
In this package, an example for such a traffic factor model is implemented with respect to boater traffic from jurisdictions to lakes. The implemented traffic factor model can be found in the module boater_movement_model, and may be helpful as a reference when building a custom model.
In conclusion, a model could be created and fitted as follows:
import os # Import the class implementing the hybrid vector # model from hybrid_vector_model import HybridVectorModel # Import the class implementing the traffic factor model # Instead of this import, it may be better to implement # your own class tailored to your system. from boater_movement_model import TrafficFactorModel # Reuse earlier results if possible restart = False # Declare the file names. Because we assume here that the # files are in a subdirectory 'Example', we need to merge # the file names accordingly. # See the documentation for HybridVectorModel.new for a # detailed description of the files and their contents. folder = "Example" fileNameEdges = os.path.join(folder, "Edges.csv") fileNameVertices = os.path.join(folder, "Vertices.csv") fileNameOrigins = os.path.join(folder, "PopulationData.csv") fileNameDestinations = os.path.join(folder, "LakeData.csv") fileNamePostalCodeAreas = os.path.join(folder, "PostalCodeAreas.csv") fileNameObservations = os.path.join(folder, "SurveyData.csv") # Set the compliance rate of travellers. This is the fraction of # travellers who would stop at a survey location and comply with a survey. # Typically, this rate cannot be computed directly from # survey data and must therefore be specified independently. complianceRate = 0.8 # File name of the model fileNameSave = "Example" # These parameters define which routes are deemed likely. # The first parameter is the factor by how much an admissible # route may be longer than the shortest route. # The second parameter specifies the length of subpaths of the # route that are required to be optimal (length given as fraction # of the total length). 0: no restrictions, 1: only optimal paths # are considered. # The last two parameters control internal approximations. Choosing # 1 in both cases yields exact results. routeParameters = (1.4, .2, 1, 1) # create and fit a hybrid traffic model model = HybridVectorModel.new( fileNameBackup=fileNameSave, trafficFactorModel_class=TrafficFactorModel, fileNameEdges=fileNameEdges, fileNameVertices=fileNameVertices, fileNameOrigins=fileNameOrigins, fileNameDestinations=fileNameDestinations, fileNamePostalCodeAreas=fileNamePostalCodeAreas, fileNameObservations=fileNameObservations, complianceRate=complianceRate, routeParameters=routeParameters, restart=restart )
Example data files are provided with the package in the subfolder
hybrid_vector_model/Example. Alternatively, these files
can be downloaded from the github repository.
There you can also find a graphical image of the example road network.
The theory behind the model implemented in this package is explained in the paper “A hybrid gravity and route choice model to assess vector traffic in large-scale road networks”. The algorithm for optimizing inspection stations is described in “Managing aquatic invasions: Optimal locations and operating times for watercraft inspection stations”. Please cite the corresponding publication(s) if you have used the package in your own research.