@phdthesis{Meissner2022,
  author    = {Katherina Mei{\"s}ner},
  title     = {Exploratory Road Accident Analysis - Identification of Interesting Relationships over Time to Support Road Safety Planning},
  doi       = {10.25528/152},
  url       = {https://nbn-resolving.org/urn:nbn:de:gbv:hil2-opus4-14602},
  pages     = {274},
  year      = {2022},
  abstract  = {Road accidents are one of the leading causes of death worldwide, particularly among young people. The police and local authorities therefore strive to reduce the risk of accidents through appropriate road safety measures. In order to plan these measures, the relevant types of accidents, i. e., accidents with certain features, must first be recognized. However, the variety of accident features and the amount of resulting feature combinations make it impossible to monitor all accident types manually. In this thesis, methods are proposed to automatically identify interesting accident types. Here, it is investigated whether combinations of features occur together and how the temporal pattern of the combined occurrence behaves. The change mining approach can then be used to determine whether structural changes in frequency occur during the period under consideration. For example, a feature combination that suddenly appears more frequently or exhibits a change in seasonality should be prioritized for further investigation so that appropriate road safety measures may be initiated for that combination. The implemented strategic, multi-stage data mining framework based on frequent itemset mining, time series clustering, forecasting methods, and a scoring process is able to detect interesting feature combinations. These are then processed on a map in a web interface suitable for the respective audience in order to support the strategic planning of road safety measures. The framework is applied to several accident data sets from different countries to determine suitable default parameter values for the respective data analysis methods and to carefully align the methods. It is shown that there exist only minor dependencies of the parameter selection on the database to be analyzed. For operational planning, it is necessary to consider small geographic areas and identify the features that have the greatest impact on accident occurrence there. Therefore, the developed operational framework analyzes and predicts the course of accident time series, taking into account the associated feature-specific time series. On the one hand, this makes it possible to increase the forecast performance, and, on the other hand, to determine which accident features have a significant influence on the course of the accident numbers over time. The insights gained can be used as a basis for short-term measures.},
  language  = {en}
}