Digital Twin of Ulyanovsk
Ulyanovsk has adopted an advanced predictive tool that can model the movement of every bus and identify congestion before it forms. The city’s transport digital twin is expected to drive a major shift in urban mobility decision-making over the next fifteen years.
Launch of the Digital Framework
Ulyanovsk has completed the development of a long-term plan for enhancing its urban road and transportation system through 2039. At the core of this initiative is a city‑scale digital twin — a detailed three‑dimensional mobility model created by SIMETRA, a St. Petersburg–based transport‑planning firm. The model marks a major shift in how cities can approach network‑wide mobility governance.
The digital twin spans 595 transport‑analytical zones and 262 public‑transport routes, including municipal and intermunicipal lines. Its initial focus is the analysis and optimization of passenger‑transport demand. The system evaluates peak‑hour conditions, daily route loads, bottlenecks and segments prone to overload. Using these projections, the city can plan mobility infrastructure in five‑year cycles through 2040, anticipating population growth, demand fluctuations, and the emergence of new economic clusters.
Two strategic planning documents accompany the project: a Comprehensive Transport Infrastructure Development Program and a Road Traffic Management Scheme. Together, they outline the creation of intermodal hubs, dedicated bus lanes, modernization of tram and trolleybus systems, and optimization of bus routes.
From Diagnostics to Prediction
The Ulyanovsk digital model forms the basis of a full Intelligent Transportation System (ITS). This represents not merely automation, but a transition toward proactive, data‑driven predictive management — replacing reactive responses with anticipatory decision‑making.
The ITS allows the city to adapt to dynamic shifts in transport demand. When new housing districts appear, the system can propose optimal routes, determine required fleet size, and project loads on nearby infrastructure. It can also adjust routes in real time, redistributing resources during unexpected events such as roadworks, accidents, or passenger surges caused by sports or cultural events. Long‑term infrastructure upgrades through 2040 will follow recommendations generated by the digital model.
The technology also has export potential. Russia is expanding domestic mobility platforms such as RITM³, a fully Russian system for transportation and mobility management. The methodology used in Ulyanovsk can be adapted for cities with similar infrastructure challenges and congestion patterns.
Evolution of Transport Digitalization
Digital tools in transport management are not new, but their scale and pace have increased significantly in the past five years. Moscow became an early pioneer of integrated ITS. Since implementation, traffic accidents have dropped by half, while average vehicle speeds rose by thirteen percent despite a growing vehicle fleet. Globally, Japan was among the first to develop intelligent transport systems: Tokyo’s Vehicle Information and Communication System (VICS), launched in 1995, provides GPS‑based congestion data, route recommendations, and emergency alerts.
Russia’s experience with digital twins is also expanding. In 2023, a digital twin of a segment of the M‑11 “Neva” expressway was deployed in the Leningrad region. In 2024, Rostelecom introduced a City Digital Twin module in Perm. Unlike earlier projects focused on individual elements — road‑network diagnostics, route optimization or passenger‑flow management — Ulyanovsk is implementing an integrated system where all components interact to produce a unified picture of the transport environment. This reflects the evolution of urban digitalization from auxiliary tools to strategic engines of city development.
A Continuous-Improvement Mode
Experience shows that proactive mobility governance requires not only technology but also organizational change. Cities need specialists capable of interpreting simulation outputs and converting them into actionable planning decisions. Effective coordination among municipal departments — road management, transport agencies, and urban‑planning offices — is also essential, as is securing investment for infrastructure projects derived from model recommendations.
The next stage involves connecting the digital twin to real‑time monitoring systems. When linked with road cameras, motion sensors, passenger‑counting tools, and other IoT data streams, the system enters a continuous‑improvement cycle. Real‑time data enables ongoing calibration, exposes blind spots, and refines predictive accuracy. Ulyanovsk may become a demonstration site for such advanced integration.
From Local Success to System‑Wide Transformation
Completion of Ulyanovsk’s digital twin marks the start of a new phase in Russia’s urban transportation governance. The project will likely encourage similar initiatives across the country. Regions with persistent transport challenges may pursue their own digital models, increasing demand for companies like SIMETRA and for transport‑modelling software — fostering market growth and innovation.
Strategically, Ulyanovsk’s transport digitalization is not only a local investment but also a step toward realizing a broader smart‑city vision. Cities that lead in deploying digital mobility tools will gain competitive advantages in attracting investment, creating jobs and improving quality of life. The combined technological and socioeconomic impact of such systems will help define the trajectory of Russian urban development for decades.
