Predictive Analytics for Traffic Safety
Assessing the applicability of predictive policing and predictive analytics for improving traffic safety
The objective of PreASiSt is to assess whether general concepts of predictive policing can be adopted and applied to the field of traffic safety. The motivation of predictive policing is to predict crimes before they occur. In terms of traffic safety, a model should help to understand and capture conditions that could trigger an accident. Forecasts of these variables can be used to predict and evaluate the risk of accidents. Based on these risks, the police can plan and take measures to increase traffic safety in these areas. In order to identify relevant parameters, this project approaches the problem from a theoretical and data driven view point, using the Cross Industry Standard Process for Data Mining (CRISP). The city of Bremen provided a detailed history of traffic accidents and other relevant data sets and their local police contribute expert knowledge.
52°North focuses on data analytics. We investigate both the suitability of available data sources for predictive data analysis and the use of machine learning methods for
creating accident risk prediction models. However, data understanding can only be achieved by understanding the underlying processes. Hence this work is done in close
collaboration with the German University of the Police (DHPOL) and the local experts from the Bremen Police.
During 2019, 52°North put a large effort into acquisition and preparation of the data. Work included the examination of different data sources to determine their potential
use for predictive policing. This resulted in the integration of a subset of promising data sets (traffic counts in Bremen, weather data, accident data based on EUSKA, traffic
data from HERE, OSM road network) in a common database for analysis. 52°North also developed a model based on machine learning algorithms that extracts the information on accident occurrences from this huge data set.