Russian Developer Creates Crowdsourced Gas Station Map with Real-Time Fuel Availability Using AI

A Russian resident independently created an online service — an interactive map of gas stations that shows in real time where fuel is available, where gasoline has run out, where it's free, and where long queues have formed. During development, the author used neural networks, and the service itself works directly in the browser and does not require installing a separate application.

How the gas station map works

Based on the name of the project — "people's map" — the service most likely relies on data added by drivers themselves: they mark whether fuel is available at a specific gas station, how long the queue is, or if the station is completely closed. Such a crowdsourcing approach allows you to update the situation much faster than the gas companies or official authorities do, which don't have the task of publishing operational data for each pump.

• Developer — a private individual, not a company
• The service is available directly in the browser, without installing an application
• On the map are marked gas stations with fuel, without fuel, and with queues
• When creating the service, the author used neural networks

Why such a service turned out to be in demand

Periodic gasoline shortages at individual gas stations in different regions of Russia are not uncommon: fuel demand unevenly increases during the vacation season, and supply logistics do not always manage to adapt. In such conditions, drivers have to visit several gas stations in search of a working pump, wasting time and fuel on the search. A map that shows the situation at each gas station in real time removes some of this uncertainty and saves both time and gasoline.

The role of neural networks in development

The story of the gas station map is a characteristic example of how modern AI tools lower the barrier to entry in development. A few years ago, creating such a service — with cartography, a database of gas stations, and a user interface — required a team of developers and months of work. Today, neural networks can generate a significant portion of the code, help with backend logic and map integration, so one person with an idea and basic skills is able to bring the project to a working state on their own.

How it differs from official services

Large mapping services like Yandex.Maps or 2GIS have been adding data about gas stations for years — prices, locations, reviews — but the task of tracking specifically fuel availability in real time and queue lengths is not their priority: this is a narrow, local problem that becomes acute only occasionally and in certain regions. Such niche tasks are convenient to solve independently: there is no need to coordinate the feature with a large product team, you can quickly assemble a minimally working version and refine it based on feedback from early users.

What this means

Such local services, made by one enthusiast with the help of AI, show: neural networks are turning from a tool of large IT companies into a tool for a specific person solving a specific everyday problem — and doing it faster and cheaper than before.