Data-driven planning for traffic and parking

Show traffic volumes per road segment and track changes over time using modeling and official data sources.

Provides a shared evidence base for planning, follow-up, and dialogue with residents and businesses without one-off manual studies.

Monitor current speeds, traffic flows, and network changes to quickly spot deviations and congestion.

You can respond earlier to disruptions and bring a live situational picture into meetings and operations.

See where the road network approaches capacity limits and identify bottlenecks before problems grow.

Prioritize measures and investments where pressure actually increases—not only where complaints are loudest.

Compare actual driving speeds with posted limits and find stretches where targeted measures are needed.

Supports road safety work with geographic precision and follow-up over time.

Analyze where heavy traffic passes, how it varies across the day, and which streets are most affected.

Supports environmental and livability issues, freight access, and infrastructure that must handle heavier loads.

Get an overview of congestion, roadworks, closures, and traffic incidents in one place.

A unified situational picture reduces fragmentation across sources and makes it easier to communicate impacts.

Use AI-based forecasts to assess how new development or land-use changes affect traffic flows.

Build early scenarios for detailed plans and strategic choices—before changes show up in reality.

Bring the city's parking spaces into one overview with capacity, type, fees, and time limits.

Creates a shared source of truth for planning, operations, and policy.

Track how occupancy varies by area and hour to see where pressure is highest.

Avoid guessing from one-off measurements—patterns become visible over time.

Visualize occupancy on the map to quickly see over- or under-utilized areas.

Makes it easy to show the situation for managers, elected officials, and partners in a way everyone understands.

Analyze historical patterns and compare days, times, and areas for better planning and follow-up.

Tie measures to actual change afterwards—not only to intentions.

Identify when demand is highest and when capacity goes unused.

Evidence for pricing, signage, communication, and future expansion.

Understand differences between e.g. retail, residential, and hospital areas and how they affect occupancy.

Different neighbourhoods get different logic—the analysis reflects reality instead of one city-wide average.

Complement the analysis with observations from the field via a mobile-friendly workflow.

Human insight and models meet so decisions do not rest on a single data source.

Export data for reporting, follow-up, and further processing in the city's own systems.

Fits your regular reporting chain without locking knowledge into one tool.