Browsing by Author "Van Heerden, Shane Andrew"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    A framework for quantifying and characterising road accident risk : a data mining approach
    (Stellenbosch : Stellenbosch University, 2020-03) Van Heerden, Shane Andrew; Van Vuuren, J. H.; Grobbelaar, Sara; Stellenbosch University. Faculty of Industrial Engineering. Dept. of Industrial Engineering.
    ENGLISH ABSTRACT: According to the World Health Organisation, road accidents account for approximately 1:25 million deaths annually | the eighth leading cause of death worldwide. With the enormous losses to society resulting from road accidents, the prevention and severity reduction of road accidents has been an active area of research focus for many decades. Researchers frequently employ a variety of statistical learning techniques in an attempt to understand the factors contributing to higher levels of road accident risk. Such insights provide vital direction for governments with respect to safer road designs and the establishment of countermeasures aimed at reducing the number of road accidents. Furthermore, recent advances in machine learning have presented exciting new machine learning possibilities that were deemed far out of reach just over a decade ago. The tasks associated with data pre-processing in this context are, however, often daunting and immensely time-consuming. Moreover, the adoption of machine learning models in the road accident analysis literature has been relatively limited due to the uninterpretable nature of the majority of these models. A generic modular data mining framework is, therefore, proposed in this dissertation, aimed specifically at formalising and facilitating the tasks associated with road accident data preparation, and facilitating the interpretation of machine learning model output. This framework is designed to facilitate the configuration, enhancement and transformation of raw accident, vehicle, road and victim data into useful information which appropriately quantifies and characterises road accident risk. More specifically, this framework facilitates evaluation of road accident risk in terms of the rate and severity of being involved in a road accident along road segments and at road junctions based on historically recorded RAs. The configuration procedure in the proposed framework allows a user to format data attributes appropriately, as well as correct any missing or erroneous values that may exist in data sets. The enhancement procedure allows a user to merge vehicle and road records to a corresponding accident record for the purpose of creating an all-encompassing data set. It is also possible to construct new attributes based on current attribute values residing in the aforementioned data sets. After each of the individual data sets has been prepared appropriately and the data are deemed of a suficiently high quality, they may be stored in a database. Finally, the transformation procedure exploits these high-quality data to quantify the rate and severity of road accidents along road segments or at road junctions. These results serve as input to a standard supervised learning procedure in which road characteristics are used to predict these rate and severity measurements. In order to demonstrate the practical workability and usefulness of the proposed framework, a concept demonstrator of the framework is implemented in an existing data mining platform and applied to a real-world case study based on road accident data from Greater Manchester in the United Kingdom. Each of the individual data preparation components of the framework is tested in the context of this case study, while the effectiveness of the road accident risk evaluation approaches is demonstrated by means of multiple investigations.