Case studies

Pilsen Safe Roads

The city of Pilsen gives a high priority to improving road safety in its streets. Road safety is one of the areas of interest for the wider ‘Safe Pilsen’ strategy of the city (https://www.bezpecnaplzen.eu/).
The Municipal Police only has access to the data on traffic accidents and traffic offences registered by the state Police in a form of a statistic reports or not detailed enough maps. When planning its daily operations and evaluating its efficiency, the Municipal Police would benefit of an analytical tool that would for the first time bring together traffic-safety related data from both the state and Municipal Police (MP). Having such a tool would allow the MP managers to
  • better plan the daily patrols of its officers by sending them to areas where their presence is most needed based on the traffic accidents data (e.g. locations where accidents with cyclists and pedestrian involvement happen, or accidents caused by driving on red light, accidents with a driver who used alcohol/drugs, or accidents nearby public transportation stops)
  • better evaluate its operations in the long term, based on the identification of trends in the big historical data over time
Challenges encountered:

  • ‘opening the door’ to state and Municipal Police
  • availability of the data on traffic accidents (State Police) and traffic offences (Municipal Police)
  • complexity of the above-mentioned datasets
  • combining the two datasets of different nature (accidents and offences) in a single and easy-to-understand user interface

Stakeholders:

  • City of Pilsen (GIS/IT unit, Safety and Criminality prevention department)
  • Municipal Police of Pilsen
  • State Police

Actions steps:

  • get the buy-in and collect requirements from the City and Municipal police (MP)
  • obtain approval to use the police data
  • data analysis and processing, identification of information contained in the data that is important for the MP operations
  • combining the datasets in the analytical web application while providing the data filtering functionalities

Lessons learned:

  • significant effort needs to be invested in obtaining, understanding and processing the data
  • effort needs to be invested to get the ‘buy-in’ from the police stakeholders and to explain the added value of the solution for their daily work


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