AI-driven Traffic Road Contion Monitoring & Analysis – Fintraffic

Why this project?

Traffic management is critical for safety, sustainability, and efficiency in Finland. With rapidly growing data sources (road sensors, video, IoT, etc.), Fintraffic wants to explore how AI can help detect incidents, monitor conditions, and improve decision-making.

Company Introduction

Fintraffic is a state-owned company responsible for managing and developing traffic services on roads, railways, airspace, and maritime routes in Finland. The company plays a central role in ensuring safe, smooth, and sustainable traffic across the country.

What we are doing in the project?

Experimenting with AI for road condition classification (icy, snowy, wet, dry).
Building a proof-of-concept pipeline for handling real-time traffic data, combining IoT data, video, and sensor feeds.
Using the AI Canvas methodology to define business needs, data requirements, and technical approach.

Connection to AI skalaajaat

This project is part of AI skaalaajat, which provides free development services for Uusimaa companies under de minimis support. Fintraffic’s project demonstrates how AI capabilities can be scaled from pilot experiments into broader organizational practices. The co-creation approach ensures that the results benefit not only Fintraffic but also other companies in Uusimaa through shared learnings and best practices.

Results/Benefits

The project is expected to improve traffic safety, flow, and sustainability through AI solutions. Results will be published at the end of 2025, providing more detailed insights into the benefits achieved.

Initial Mapping and Current State Assessment

In the first phase of the project, the current AI capabilities of the participating SMEs are assessed. This includes:

Defining the companies' AI-related goals and needs.

An AI maturity analysis to evaluate the company's readiness and capabilities to utilize AI in business.
Identifying the companies' business environment and technological challenges.

The goal is to form a clear picture of the companies' starting point and define the target state to be achieved with AI solutions.

AI Coaching and Workshops

In the second phase, companies participate in iterative training sessions and workshops. During these, the focus is on the following topics:

AI fundamentals and opportunities: How can AI create added value?
Proof of Concept (POC): Companies are provided with a concrete model for utilizing AI in a small-scale pilot project.
Multidisciplinary capabilities: How to combine expertise and technologies from different fields with the help of AI?

The workshops support companies in ideating and testing concrete AI solutions, while also providing an opportunity to network with other companies and experts.

Solution Concept Development and PoC Projects

At this stage, we move on to company-specific PoC projects, in which:

• The first AI solution models are put into practice in company business environments.

• The developed concepts are tested and validated in practice.

• Collaboration and expert support are utilized.

Minimum Viable Product (MVP): Each company will have a basic version of an AI solution developed, serving as the minimum viable product (MVP) to allow efficient testing before large-scale implementation.

Development of the Overall Concept and Scaling

At this stage, the lessons learned from the PoC projects are integrated into a tailored 'Whole AI Service' concept for each company, which includes:

Processes and models needed for the implementation and management of AI solutions.
Strategies for utilizing multidisciplinary networks.
Guidelines for scaling and integrating technologies into business processes.

The concept provides companies to scale and utilize AI solutions effectively in the long term.

Co-creation and Sharing Results

The last phase of the project highlights co-creation and learning:

Companies share the experiences gained during the PoC projects within their networks.
Workshops are organized for companies and experts to develop solutions together.
The project results are communicated and best practices are shared more broadly.
At the same time, future development opportunities are identified and it is ensured that the participating companies can continue to utilize AI solutions after the project ends.