Using ANPR data to create an anonymized linked open dataset on urban bustle.

Area of Science:

• Urban planning and data science
• Traffic analysis and policy development
• Data anonymization and aggregation techniques

Background:

• Automatic Number Plate Recognition (ANPR) cameras are primarily used for law enforcement.
• ANPR systems generate valuable statistical data with potential for urban insights and policy-making.
• Current data sharing methods limit the full utilization of ANPR data for diverse applications.

Purpose of the Study:

• To develop a method for anonymizing and aggregating ANPR data.
• To enable the sharing of ANPR data for local decision-making, machine learning models, and open data portals.
• To demonstrate the value of anonymized ANPR data for urban policy development.

Main Methods:

• Geotemporal binning for data aggregation.
• Combining nine existing data specifications for data publication.
• Development of the ANPR Metrics tool for data analysis and visualization.
• Implementation in Kortrijk, Belgium, using data from 43 ANPR cameras.

Main Results:

• Successful anonymization and aggregation of ANPR data from 43 cameras.
• Generation of statistical data and dashboards providing useful visualizations.
• Mobility experts confirmed the deduction of valuable insights from the data.
• Demonstrated that anonymized ANPR data complements existing traffic analysis tools.

Conclusions:

• Anonymized and aggregated ANPR data provides a valuable source for data-driven policy-making.
• The proposed method enables effective sharing of ANPR data for urban insights.
• This approach enhances current traffic analysis tools, supporting local decision-making.