National government should support municipalities with a data-driven approach to parking policies.
“There needs to be a better approach to parking within urban development projects in order to address the combined challenges of mobility and limited space. This is essential if we are to deliver the large-scale housing developments planned for both new neighbourhoods and the densification of existing urban areas.” That is the view of Ernst Bos and Menno Verschuur of Monit Data, who believe the national government has an important role to play by supporting municipalities with a broader range of data-driven tools and practical policy guidelines.
According to Ernst Bos, Managing Director of Monit Data, discussions about parking and housing too often focus on reducing parking requirements as the primary solution. “There’s a great deal of wishful thinking involved. It also fails to reflect societal changes. An ageing population and the increase in remote working have significantly changed how parking spaces are used. Spaces remain occupied for longer periods, making shared use—for example between residents and shoppers—far less feasible.”
Bos also believes that Mobility as a Service (MaaS) and shared mobility, while valuable concepts, have their limitations.“ They’re excellent initiatives, but in practice they solve only a small percentage of the parking challenge.” When combined with the reality that public transport and cycling are not always viable alternatives, and that constructing parking garages is extremely expensive—requiring an estimated €12 billion in construction costs to provide sufficient parking capacity—it becomes clear that a different approach is needed.
“The national government can play an important role by providing municipalities with a broader policy toolkit.”
A Broader Toolkit for Municipalities
Bos believes parking policies should not be viewed solely as a municipal responsibility. “Municipalities currently have only limited options for regulating on-street parking. With paid parking and residential parking permits as their main instruments, it’s difficult to implement targeted policies for different groups of parkers.” A broader range of policy instruments would allow municipalities to ensure that valuable on-street parking is primarily available for those who genuinely need to park there, while directing other users to alternative facilities such as parking garages. Looking further ahead, Bos believes municipalities may even need to consider partially restricting vehicle access to city centres. “We’ve seen this approach work successfully in several countries.”
More Knowledge Sharing
Menno Verschuur, Marketing Manager at Monit Data, adds that municipalities need more than traditional parking standards to solve the parking challenges associated with large-scale housing development. “The national government can support municipalities by facilitating knowledge sharing. This includes demographic data and guidance on how to combine it with current parking standards. That would enable municipalities to move towards truly data-driven parking standards that take demographic characteristics into account, rather than relying primarily on housing types.”
Another important part of the solution is closer collaboration between municipalities, architects and property developers. Bos explains: “Parking should be treated as an integral part of housing development. Instead of solving parking separately for every individual project, developers should consider shared neighbourhood parking facilities that serve multiple developments.”
Predictive Models for Future Parking Demand
As municipalities increasingly seek to understand future parking demand as part of urban development, the need for predictive data is growing. Bos explains: “Where municipalities previously used Monit’s data mainly to monitor parking policies, there is now growing demand for predictive models. For urban development projects, we’ve developed a parking balance model that combines current parking usage with projected demand from new developments and organic growth.” These models operate at a highly detailed level, taking into account factors such as neighbourhood, time of day and day of the week.
“We’re currently running pilot projects with our customers to identify where AI truly delivers added value.”
AI: verrijken in plaats van vervangen
Monit also sees significant opportunities for applying Artificial Intelligence (AI) to forecast parking demand. Bos says: “We’re exploring Machine Learning models that identify patterns in large datasets and use them to generate accurate forecasts. At the same time, it’s important to remain critical about AI. Model outcomes must be explainable and verifiable, and data security is essential. Most importantly, AI is only as good as the data it learns from.” “For more than twenty years, Monit has specialised in transforming raw data from a complex mix of parking systems into a single, validated dataset. That’s why we don’t see AI replacing our work—we see it enhancing it.”
Verschuur adds: “To better understand where AI creates real value, we’re currently conducting pilot projects with several customers. One example is research into the variables that influence expected parking demand. These differ from one area to another, but in coastal tourist cities our AI models identified weather as one of the most significant factors.” By incorporating weather forecasts into AI-driven parking demand models, municipalities can better predict parking demand and plan staffing levels accordingly.
This article was produced in collaboration with the ‘Trendboek Mobiliteit’ (Trendbook Mobility), a Dutch publication that reflects on current developments while exploring the future of mobility.
For the Trendboek Mobiliteit 2026, industry experts were invited to share their perspectives on a range of key mobility topics. Additional interviews from the publication are available at www.mobiliteit.nl.
