Noida is preparing an artificial intelligence-based traffic system that will identify where and when congestion regularly occurs, using historical and real-time mobility data linked to Google Maps and the city’s Integrated Traffic Management System (ITMS) in Sector 94. The proposed system is intended to help the Noida Authority detect recurring traffic bottlenecks and decide whether diversions, signal changes or road-capacity interventions are required.
According to the report, the AI software will analyse traffic conditions on selected roads and locations, including the number of vehicles using a route, the time taken to travel and the duration of congestion. It will also examine recurring patterns to identify the possible reasons behind traffic jams at particular times and places.
The system is expected to be connected to the ITMS facility in Sector 94. By comparing normal travel times with current conditions, the software could flag a route when the time required to travel through it rises significantly above the usual level. This would allow traffic managers to identify potential congestion while it is developing and examine whether the problem is recurring or linked to a particular set of conditions.
The proposed arrangement will use both live and stored data. The system may retain traffic information for one to two months, allowing officials to compare present conditions with previous patterns. This could help distinguish between an isolated slowdown and a regular congestion point that requires a more permanent traffic-management response.
The report said the platform could draw traffic information from Google Maps and use data from cameras already installed in the city. Satellite-based data from Google may also be used to obtain information about traffic conditions in particular areas. The stated objective is to reduce the need for new cameras, sensors and optical-fibre lines on every road.
The Authority’s officials said the data could support several types of intervention, including traffic diversions, changes to signal operations and improvements to road capacity. The system could therefore function as a decision-support tool for the existing traffic-management infrastructure rather than as a standalone replacement for field-level monitoring.
The proposal also reflects a shift from responding to traffic jams only after they occur to identifying recurring congestion through data analysis. However, the report does not specify a launch date, the roads that will be covered first, the technical arrangement for accessing external data or the process through which an AI-generated congestion alert will lead to an on-ground decision. The next steps are expected to involve integrating the proposed data system with the Sector 94 ITMS.
