These smart cameras are not meant to stay in one place. They are mounted on municipal vehicles that traverse streets and yards, delivering a mobile layer of observation rather than fixed snapshots of the cityscape. The deployment strategy envisions cameras riding along with official TsODD vehicles as well as other municipal fleet units, creating a flexible network that can be redirected to where it is most needed in real time. The initiative aims to capture a broad field of view as the vehicles move, helping to monitor traffic conditions and detect violations with increased efficiency on the go.
According to Kommersant, the Traffic Management Center (TsODD) has issued a tender for the rental of 55 smart video systems that will operate in concert with an intelligent neural network. The plan is to equip official TsODD transport along with municipal fleets with these systems, enabling continuous data collection and processing as part of a broader smart transportation program. The lease is valued at 70 million rubles, signaling a substantial commitment to upgrading the city’s digital infrastructure and its capability to supervise road usage in a more automated, centralized manner.
All data gathered by the cameras — including vehicle counts and the faces of bystanders within a radius of about five meters — is transmitted to an automated system designed to document and analyze traffic violations within the Moscow intelligent transport framework. This approach is being emphasized at a moment when concerns about stole vehicle parts and related theft are rising, as noted by industry observers. The streamline aims to produce faster, more consistent enforcement records, potentially reducing delays in identifying infractions and supporting quicker responses by authorities.
Beyond enforcement, the camera network is also tasked with verifying the proper installation and placement of road signs and other traffic indicators. In addition, the captured data is used to identify damage to the road infrastructure, enabling timely maintenance and reducing the risk of hazards caused by deteriorating conditions. This broader scope underlines a shift toward a more proactive, data-driven approach to urban mobility, where real-time video feeds inform both everyday operations and long-term planning for safer, more reliable streets.