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Please note: The algorithm descriptions in English have been automatically translated. Errors may have been introduced in this process. For the original descriptions, go to the Dutch version of the Algorithm Register.

Forecasting crowded car parks (part of Crowd Safety Manager)

Municipality of The Hague

Predicting busy car parks is a European collaborative project aimed at developing a Mobility Analytics as a Service (MAaaS) toolkit. The aim is to efficiently manage, analyse and visualise large amounts of mobility data. Specifically, it involves predicting parking garage fill rates.

Last change on 8th of January 2025, at 11:24 (CET) | Publication Standard 1.0
Publication category
Impactful algorithms
Impact assessment
Field not filled in.
Status
In use

General information

Theme

  • Traffic
  • Space and Infrastructure

Begin date

2023-01

Contact information

datashop@denhaag.nl

Link to publication website

https://www.tudelft.nl/innovatie-impact/pioneering-tech/articles/emeralds-innovatie-mobiliteitsmanagement-via-data-science-technieken

Responsible use

Goal and impact

The algorithm is designed to assess crowds and accurately predict the filling rate of car parks, so we know what crowds to expect at Scheveningen.

Considerations

We want to use this application to predict the crowds at Scheveningen. This could also lead to wrong estimates, so it is important to consider the results as an experiment.

Human intervention

At this stage, it is just a support system, which can help with decision-making around the deployment of traffic controllers, etc.

Risk management

Focus on data privacy and accurate data analysis to avoid wrong conclusions and policy decisions.

Legal basis

The mayor has a statutory duty to maintain public order. The municipal council also has public order and security duties.

Links to legal bases

  • artikelen 172-176: https://wetten.overheid.nl/BWBR0005416/2025-01-01
  • Paragraaf 2 en 3: https://wetten.overheid.nl/BWBR0004318/2010-10-10

Elaboration on impact assessments

We do not work with personal data here, so a DPIA is not necessary.

Operations

Data

  • Resono data
  • Parking data (historical filling rate data of car parks)
  • Weather information (temperature, rainfall, wind)
  • Events (location, time, expected attendance).

Technical design

Currently, we analyse traffic data and use resono data, parking data, weather information and event data to predict parking garage fill rates. We make these predictions with rule-based algorithms.

External provider

Argaleo

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