| Title | Decision Support for Road Safety: Development of Key Performance Indicators for Police Analysts |
| Authors | Meißner, Katherina and Rieck, Julia |
| Year | 2020 |
| Volume | 6(2) |
| Abstract | In 2017, five out of 100,000 people were killed by road accidents in Europe. In order to reduce this number with appropriate measures, the police nowadays manually defines combinations of accident attributes (e. g., accidents on slippery road surfaces at night), which then form the basis for tracking the number of accidents over time. The aim of this paper is to combine the following data analysis approaches in order to detect interesting attribute combinations, also referred to as “itemsets”, relevant for current and future observations. The resulting combinations are proposed to the police as new key performance indicators and can also be used directly for planning police measures to increase road safety. A four-stage decision support system is introduced that employs frequent itemset mining in the first stage. The temporal aspect of traffic accident data is illustrated by time series containing, for each itemset, the relative frequencies of accidents with the corresponding attribute combination. In the second step, the time series are grouped according to their shape by time series clustering and classification. In the third step, we determine In 2017, five out of 100,000 people were killed by road accidents in Europe. In order to reduce this number with appropriate measures, the police nowadays manually defines combinations of accident attributes (e. g., accidents on slippery road surfaces at night), which then form the basis for tracking the number of accidents over time. The aim of this paper is to combine the following data analysis approaches in order to detect interesting attribute combinations, also referred to as “itemsets”, relevant for current and future observations. The resulting combinations are proposed to the police as new key performance indicators and can also be used directly for planning police measures to increase road safety. A four-stage decision support system is introduced that employs frequent itemset mining in the first stage. The temporal aspect of traffic accident data is illustrated by time series containing, for each itemset, the relative frequencies of accidents with the corresponding attribute combination. In the second step, the time series are grouped according to their shape by time series clustering and classification. In the third step, we determine the optimal forecasting method for each generated cluster of time series. Based on the prediction of future frequencies, we identify the most interesting attribute combinations in the last step. These are displayed geographically so that a police analyst can easily identify current and developing hot spots. |