Parking violations cause numerous problems, thus affecting daily mobility. Nevertheless, there are no extensive statistics on illegal parking in Germany, which means that the causes of this misconduct are still unexplored. The objective of this paper is to present a count data modeling approach for parking violations based on video footage taken from windshields of driving vehicles incorporating spatial data from OpenStreetMap (OSM). The main benefit of this data source is the transferability of the data collection procedure just by installing a recording device in vehicles of municipal services like the waste collection. Moreover, the data from OSM is freely available for all cities. To account for excess zero counts in the street segments, a zero-inflated negative binomial distribution model is used to explain the number of violations per 100 meters. “Excess” zeros are modeled using the logit part of the model while the remaining counts of parking violations are fitted by the negative binomial model. Much effort is made in the paper to present the results of the count data models in an interpretable way. The most intuitive way seems to be predictions of parking violations per 100 meters (incidence rate) for different settings. Incidence rates are predicted for variations in explanatory variables holding all other variables constant. We find parking violations per 100 meters to be highest in main shopping streets. In addition, free parking spaces negatively and the number of POI (such as buildings, craft stores, and shops) positively affect illegal parking.


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    Titel :

    Modeling of Parking Violations Using Zero-Inflated Negative Binomial Regression – A Case Study for Berlin


    Beteiligte:
    Hagen, Tobias (Autor:in) / Reinfeld, Nicole (Autor:in) / Saki, Siavash (Autor:in)

    Erscheinungsdatum :

    2023


    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    380



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