How SL Live Map uses open APIs to visualize Stockholm traffic

A Swedish developer has built a real-time “live map” of Stockholm’s public transport that lets users watch metro trains, commuter rail and buses move across the city. The project, called SL Live Map, pulls open transit data via Trafiklab.se and has drawn reactions ranging from train operators’ praise to users saying they watch it for hours—part utility, part digital mindfulness.

What SL Live Map shows—and why people keep watching

SL Live Map visualizes Stockholm’s transit network as a stream of moving, color-coded dots. Instead of static schedules, it emphasizes motion and system behavior: where vehicles cluster, where gaps open up, and how service patterns change through the day.

That “always-on” quality is a big part of the appeal. Real-time maps turn public infrastructure into something legible and even hypnotic—similar to flight trackers and ship trackers that have become popular consumer products. For riders, it can be practical (checking whether a bus is actually coming). For others, it becomes a calming, ambient window into the city’s pulse.

The developer behind the project

The site was created by Gunnar R Johansson, who works as a product owner in the web team for SVT Nyheter and Sport. He describes the map as intentionally soothing, and the feedback reflects that: professionals who operate vehicles appreciate seeing their work represented, while many users treat it like a screen they can leave open and return to.

Johansson also runs a personal site, gunnar.se, which he calls his “digital allotment,” where he aims to publish frequent projects. SL Live Map fits a broader pattern in modern software culture: small, focused tools built quickly, shared publicly, and improved through community reaction.

The data layer: Trafiklab and the power of open APIs

To keep the map continuously updated, Johansson relies on API calls to Trafiklab.se, a platform that publishes open data from Swedish public transport organizations. This is the technical backbone that makes “live” possible: frequent polling or streaming-style updates, normalized data formats, and stable endpoints that independent developers can trust.

Open transit APIs matter because they lower the barrier to innovation. Agencies rarely have the bandwidth to build every niche interface riders might want, but they can enable an ecosystem by exposing reliable data. When that data is accessible, a single developer can create a product that changes how thousands of people understand a network.

For AI and data-driven applications, open mobility data is also a training and evaluation resource. Even when a project is primarily visual, the same feeds can support:

• Delay detection and anomaly monitoring
• Demand and crowding inference (when combined with other signals)
• Route optimization experiments and simulation
• Accessibility tools that predict disruptions and suggest alternatives

Where AI fits in real-time transit visualization

SL Live Map itself is described as a visualization project rather than an AI system, but it sits close to where AI is increasingly used in transportation. In many cities, agencies and vendors apply machine learning to predict arrival times, detect incidents, and optimize dispatching. The quality of those models depends on data coverage, latency, and consistency—exactly the properties that open-data platforms try to improve.

Generative AI can also change how people interact with transit data. Instead of interpreting a map, riders may increasingly ask a conversational interface: “Will I make my connection at T-Centralen if I leave now?” or “What’s the least crowded route to Slussen?” Those experiences still rely on the same underlying real-time feeds, but add a natural-language layer on top.

At the same time, AI raises new expectations. If consumers get used to predictive, personalized answers, raw real-time maps may evolve into hybrid products: visualization plus forecasting, with confidence intervals and explanations. That shift would require careful design to avoid overpromising precision—especially during disruptions when models are least reliable.

Why this matters for the AI and developer ecosystem

The success of a small project like SL Live Map highlights a broader trend: high-impact software can be built by individuals when public data is accessible and well-documented. For the AI industry, it is a reminder that not all innovation starts with massive models and GPU clusters. Some of the most widely used tools begin as lightweight interfaces to existing systems.

It also underscores the strategic value of open data governance. When transit agencies publish data through platforms like Trafiklab, they are not just improving transparency—they are enabling experimentation. That experimentation can later feed into more advanced analytics, including AI-based operations support, passenger information systems, and city-scale mobility planning.

Johansson’s broader portfolio points to the same dynamic. He recently built biljettkoll.se, a site designed to help people find tickets to sold-out theater performances across Sweden. Different domain, similar pattern: take fragmented information, structure it, and make it usable. In an era where AI is often framed as the primary interface to information, projects like these show the enduring value of clean data pipelines, thoughtful UX, and a clear purpose.