We collect and integrate data from sensors, cameras and city infrastructure to build AI-based predictive models — helping decision-makers plan safer, smoother and more sustainable mobility.
Before we propose a solution, we name the problem plainly — the way road authorities, planners and residents actually see it.
Cameras, sensors, GPS systems and city platforms operate in isolation, so no one sees the full, real-time picture of traffic in the city.
Road investments and traffic-scheme changes are still more often based on experience than on models predicting their real effect.
Polish cities are today among the most congested in Europe — costing residents time, fuel and air quality.
Without predictive monitoring, a road incident is noticed only once congestion is already a fact, not a risk.
Public authorities often lack clear tools to show residents and investors why a given spatial decision was made.
Urban mobility is today one of the most measurable problems in Polish urban planning.
of Poles live in cities (GUS, 2024 data) — more than 22 million people relying every day on urban transport infrastructure.
peak-hour congestion level recorded in Łódź — the most congested city in Poland (TomTom Traffic Index 2026).
the average time to cover 10 km at peak hour in Wrocław today — the longest among Polish cities covered by the TomTom report.
lost every year by the average driver in Poznań sitting in traffic — more than a working week spent in the car (TomTom Traffic Index 2025/26).
From data collection to deployment — a complete toolchain supporting urban mobility decisions.
Advanced systems combining data from cameras, traffic sensors, GPS and city platforms into one coherent picture of mobility — the foundation for predictive analytics and real-time monitoring.
We forecast traffic patterns, the impact of urban investments and residents’ reactions to new transport solutions — supporting scenario simulation and strategic planning.
We integrate city data, AI and expert knowledge into tools for real-time traffic management, scenario evaluation and long-term planning.
Our interdisciplinary team delivers practical, data-backed recommendations that support evidence-based urban policy.
We verify the effectiveness and scalability of solutions under real city conditions before they move to full deployment.
For public institutions and companies — we build competencies in data analysis, digital urbanism and the practical use of AI to solve real urban challenges.
They support informed decision-making and clear communication with authorities, residents and city planners through intuitive browsing of complex data.
We test different urban-planning options and assess their impact on traffic flow, accessibility and city function — for better, long-term spatial decisions.
We don’t sell off-the-shelf software — we build tools tailored to a specific city, street and problem.
Bridge and road engineers, GIS specialists, AI researchers and geotechnical experts work on the same problem in parallel, not in sequence.
We combine knowledge of urban planning, traffic engineering and artificial intelligence, running research that later feeds directly into our deployed tools.
We train and verify our models on data collected directly on the street — as with the measurement section on Al. Jana Pawła II in Kraków.
The team grew out of a company delivering large-scale road, bridge and rail infrastructure projects — we understand the constraints of real-world construction.
Real-time data acquisition from sensors, cameras and traffic-detection technology.
Fast, reliable communication between field devices and processing systems.
AI algorithms for traffic-pattern recognition, event detection and predictive modelling.
Cleaning, standardising and integrating data into a coherent analytical base.
Monitoring, scenario simulation and decision-making within the city’s digital twin.
Five engineering and scientific disciplines that, at most companies, work separately — here they meet on a single project.
Design and construction of large-scale infrastructure: bridges, viaducts, flyovers, motorways.
GIS systems, LiDAR remote sensing, environmental documentation and analysis for transport infrastructure.
Numerical modelling with the Finite Element Method (FEM), engineering software development.
Machine learning, neural networks, vehicle classification, image analysis.
Running multi-disciplinary, multi-year infrastructure investments from concept to completion.
Six people, six disciplines, one goal: measurably better mobility in Polish cities.
In 1993 he graduated from the Faculty of Civil Engineering at Kraków University of Technology. He began his career as a designer at Mostoprojekt in Kraków, gained construction experience at Dromex Cieszyn, and then worked as a site engineer at Budimex-Dromex, where he was gradually promoted to Regional Director of the Southern Road Division. He founded Grupa MP Mosty Sp. z o.o. in 2005 and has been President of the Management Board of the Group since April 2015.
Civil engineer, designer and manager, graduate of the Faculty of Civil Engineering at Kraków University of Technology (1991). He developed his career at, among others, the Transprojekt design office, and since 2002 has co-built the engineering structures of the MP Mosty holding, where he currently serves as Vice-President of the Management Board of both the MP Mosty Group and Mosty Kraków S.A. As an experienced author and co-author of infrastructure projects, he has been responsible for key engineering structures in Poland, including the Kraków Fast Tram tunnel, flyovers at road interchanges in Kraków, and bridges and viaducts along the A4 motorway.
Manager and specialist in geoinformatics and environmental protection, graduate of the Faculty of Forestry at the University of Agriculture in Kraków and the Faculty of Applied Mathematics at AGH University of Kraków. For nearly 20 years he has been actively involved in investment processes, specialising in advanced geographic information systems (GIS), LiDAR remote sensing and environmental documentation. As founder and President of the Kraków-based company Geomind, he delivers key environmental projects and analyses for, among others, transport infrastructure and nature conservation, working as a specialised subcontractor on major engineering projects, including in the road and bridge sector.
An experienced manager and entrepreneur focused on business development, new technologies and delivering innovative ventures. He combines business experience with a practical approach to building and growing new projects. At Urbidyn he is involved in developing the company, shaping its business model and implementing solutions that use digital technologies, data and artificial intelligence in the Smart City field.
Civil engineer, scientist and academic teacher, affiliated since 2016 with AGH University of Kraków as an assistant professor in the Department of Geomechanics, Civil Engineering and Geotechnics at the Faculty of Civil Engineering and Resource Management. Graduate of the Faculty of Civil Engineering at Kraków University of Technology (1992), where in 2001 he earned his PhD in technical sciences, and spent over two decades at the Institute of Structural Mechanics. He specialises in structural dynamics, engineering programming, structural mechanics and advanced numerical modelling using the Finite Element Method (FEM).
Engineer, scientist and research-teaching staff member at the AGH University of Kraków. He serves as an assistant in the Department of Applied Computer Science and Modelling at the Faculty of Metals Engineering and Industrial Computer Science. His scientific work focuses on technical computer science and telecommunications as well as materials engineering. His main research interests include Industry 4.0, artificial intelligence (machine learning and neural networks) and advanced analysis of material structures, including optimising the digital-image segmentation of high-strength steel microstructures. He is the author and co-author of scientific publications indexed in international databases, covering topics such as vehicle classification and metallurgical process modelling.
We start with measurement, not assumptions — a model is only as good as the data it stands on.
We build adaptive tools that learn together with the city, instead of freezing it at a single point in time.
We don’t describe the past — we predict the effects of decisions before they are made.
We explain every model in the decision-maker’s language — no black box, no empty jargon.
We test under real conditions before proposing full deployment — small steps, measurable results.
Technology serves residents — every metric ultimately translates into people’s time and safety.
AI in traffic management isn’t an empty promise. Here are results from deployments in Poland, Europe and China.
Data-driven adaptive traffic signals cut travel time by 11% at peak and 8% off-peak, with estimated societal benefits of USD 31.8 billion a year against an implementation cost of USD 1.48 billion.
Source: Nature Communications, 2025An AI- and big-data-based urban traffic analytics system (Alibaba City Brain) led to a noticeable reduction in congestion and air pollution in the city.
Source: industry analyses of City Brain deploymentsAn AI-driven adaptive signal system cut travel time by over 25% and reduced vehicle idling time by 40% in pilot areas.
Source: literature on Surtrac deploymentsAdaptive traffic signals cut intersection delays by 22%, translating into a measurable drop in vehicle emissions.
Source: smart-mobility deployment reviewsAI-based bus-route optimisation improved fleet fuel efficiency by 12% across the European deployments analysed.
Source: smart-mobility deployment reviews, 2025In the most congested Polish cities (Łódź, Lublin, Poznań, Wrocław), drivers lose several days a year sitting in traffic — these are exactly the corridors that benefit most from predictive traffic management.
Source: TomTom Traffic Index 2025 / 2026It depends on scope and data availability — a typical pilot on a single measurement section takes 6 to 12 weeks, from sensor installation to the first predictive reports.
Not always. We can use existing city cameras and sensors, supplementing them with our own measurement points where data is incomplete — as with the section on Al. Jana Pawła II in Kraków.
Data remains the property of the city or client. We work with aggregated and anonymised data wherever possible, in line with GDPR requirements and critical-infrastructure guidelines.
No. We scale our models proportionally to the size of the road network — we’ve worked on single sections and intersections as well as analyses covering entire districts.
Yes — we offer training for internal teams, ongoing care for predictive models, and recurring reports that update forecasts as new data comes in.
On engineering and environmental projects we work with, among others, Mosty Kraków (formerly MP Mosty) and Geomind — combining construction and geoinformatics expertise.
Briefly describe the challenge and we’ll get back to you within 2 business days with a proposed scope for a call or pilot.
Office:
ul. prof. M. Życzkowskiego 12
31-864 Kraków, Poland
pawel.samek@urbidyn.pl
robert.wanczyk@urbidyn.pl
