In the last post, we talked about moving from “smart” cities to “thinking” cities. The idea was simple. Collecting data isn’t enough. Running dashboards isn’t enough. A thinking city understands what it’s looking at and knows what to do next. Roads are a perfect example of that shift. Right now, a lot of road analysis […]

How Agentic AI Becomes the Urban Decision-Maker Over the past few articles, we progressively moved up the intelligence stack in urban analytics: Now we reach the natural next step: Cities don’t need more dashboards.Cities need systems that act. This article explains how advanced Agentic AI transforms urban planning from analytical to operational: from observing cities

At Jimatech, we spend a lot of time thinking about how to make roads safer. We recently looked at how AI uses “eyes” to see what is happening on the street through digital images. It’s a big step forward, but if we want to get serious about fixing our infrastructure, we have to admit something:

Introduction Road accidents remain one of the leading causes of injury and death worldwide. With increasing numbers of vehicles, pedestrians, and cyclists sharing the same roads, ensuring safety has become a complex challenge. Traditional traffic monitoring systems rely heavily on human observation and basic sensors, which are often limited in speed, accuracy, and scalability. This

Introduction The quality of road infrastructure has a direct impact on travel safety, vehicle operating costs, economic activity, and overall quality of life. Well-maintained roads reduce accidents, lower maintenance costs for vehicles, and support efficient transportation systems. However, determining the actual condition of a roadway network, and deciding when to perform maintenance or rehabilitation, requires

Urban planning is entering a period of profound transformation. Rapid urbanization, climate change, infrastructure strain, and population growth are placing unprecedented pressure on cities around the world. Traditional planning methods, reliant on historical data, manual analysis, and static forecasting models, are no longer sufficient to handle the complexity and scale of modern urban environments. Artificial