Bengaluru digital twin plans are being presented as a way to move the city’s traffic management from reacting to congestion towards testing interventions before they are implemented. Proposed by Bharat1.ai, the system would combine mobility data from roads, buses, Metro services, private vehicles and other transport modes to create a continuously updated computational model of how people and vehicles move through the city.
The proposal comes as Bengaluru faces one of India’s most visible urban mobility failures. According to the 2025 TomTom Traffic Index, the city was ranked the world’s second-most congested. A 10-km journey takes an average of 36 minutes and nine seconds, while the city’s congestion level was recorded at 74.4 per cent. Commuters lost an estimated 168 hours to rush-hour traffic over the year.
Those numbers describe the scale of the problem, but they do not by themselves explain how congestion is produced across a city with several transport agencies, private mobility platforms and rapidly changing travel patterns. That institutional fragmentation is the central problem the proposed digital twin is intended to address. Its significance therefore lies less in creating another digital map and more in building a shared operating picture for agencies that currently hold separate parts of the mobility network.
The initiative was presented on September 24 at the 3rd National Conference on Women’s Safety and Security in Bengaluru. Umakant Soni, co-founder of Bharat1.ai, told The Indian Express that the initial focus would be on major roads and mobility corridors rather than modelling the entire city at once. One proposed output is a digital hotspot map identifying locations where congestion repeatedly occurs and allowing authorities to compare possible interventions.
That phased approach is important. Modelling an entire metropolitan system requires large volumes of consistent data, a common technical framework and agreement among the institutions that generate or control the information. Starting with major corridors could make the proposal more manageable, but it would also limit what the model can initially explain. Traffic on a major road is often shaped by activity on smaller streets, junctions, parking areas, bus stops and Metro stations. A corridor-level system can identify patterns, but its usefulness will depend on how much of the surrounding network it can represent.
A conventional digital map shows roads, locations and routes. A digital twin would attempt to model movement and test how the system responds to change. If passengers leaving a Metro station regularly face long waits for buses, autorickshaws or cabs, the model could help examine whether more frequent feeder services or changes to interchange arrangements might improve connectivity. If authorities propose moving a bus stop, restricting a U-turn or changing a traffic regulation, simulations could be used to assess whether congestion is reduced in one place but displaced to another.
The proposed system could also account for variations by time and day, as well as disruptions caused by rain, road closures and other incidents. This would allow authorities to compare recurring congestion with event-driven disruption. The distinction matters because a permanent bottleneck may require a network or design response, while a temporary blockage may require better incident management. The supplied proposal does not establish that these capabilities have already been deployed; they remain intended applications of the planned system.
Bharat1.ai’s longer-term target is to reduce travel time by about 30 per cent. That figure is an ambition associated with the proposal, not an outcome already demonstrated in Bengaluru. Whether such a reduction is possible would depend on the quality and coverage of the data, the interventions selected by authorities and the ability of multiple agencies to act on the model’s findings. A digital twin can test options, but it cannot by itself add bus capacity, redesign a junction or enforce a traffic restriction.
This distinction separates data visibility from administrative capacity. Bengaluru’s mobility information is currently spread across government agencies, public transport operators and private companies. Each may see a different part of the journey: a road authority may observe traffic movement, a bus operator may hold service information, Metro systems may record station use, and private platforms may possess trip or demand data. Without a mechanism to combine these sources, agencies may continue to diagnose congestion within institutional boundaries even when the underlying problem crosses them.
The proposal’s mobility lab is intended to address this coordination gap. Bharat1.ai has proposed bringing together startups, academic institutions, researchers, government agencies and private companies. Discussions have begun with some mobility and transport players, while the company is also engaging government officials and Metro representatives. The model is not only a technology project; it is also a proposed institutional arrangement for deciding who contributes data, who can access it and how the resulting analysis is used.
That arrangement will determine whether the digital twin becomes a planning tool or remains a demonstration project. The supplied material does not specify the final data-sharing agreements, governance structure, technical standards, procurement model or operating authority. It also does not establish which agency would be responsible for validating the model’s results or converting its simulations into traffic-management decisions. These unresolved questions are not peripheral. They are the conditions under which the technology would have practical value.
The public-safety dimension adds another layer. A consolidated view of mobility patterns could help identify areas that become isolated at particular times, locations with limited public transport connectivity and transport hubs where large numbers of people gather or disperse. Such information could assist police in considering patrols, emergency-response vehicles and other resources. In the context of women’s safety, it could complement measures such as CCTV surveillance and emergency-response systems by showing how movement and connectivity change across locations and times.
However, the proposal does not establish that a digital twin would directly improve safety outcomes. Its feasibility for policing would depend on the data available and on integration with existing police infrastructure. Mobility data can show patterns of movement and access, but it does not automatically explain why people avoid a location, whether a space is unsafe or which intervention would improve conditions. Those questions require operational knowledge and, potentially, information beyond transport datasets.
The connection between mobility planning and public safety nevertheless reflects how urban problems overlap in practice. A poorly connected transport hub can be a congestion point, a transfer difficulty and a location where people are left waiting for long periods. A road closure can affect travel times, emergency access and pedestrian movement at once. An integrated model could help agencies see these interactions, provided the relevant information is available and interpreted within the right institutional context.
Karnataka’s public-private partnership ecosystem has been identified by Bharat1.ai as a possible basis for collaboration. Mamatha Venkatesh, the company’s chief marketing officer and former head of Startup India, said the conference offered an opportunity for technology companies and the police to understand each other’s requirements. The Bengaluru Police Innovation Centre has indicated openness to collaborating with startups and technology companies developing public-safety solutions, while the Bengaluru Police Commissioner is planning a roundtable for cybersecurity startups to present their technologies and discuss policing applications.
The next formal step for the digital twin proposal is the submission of a concept note and further discussions with authorities. That means Bengaluru is not yet moving from an approved system to full implementation. The immediate test is whether the proposal can define a practical first phase, identify the agencies and datasets required, and establish how its outputs would inform decisions on the ground.
The evidence currently supports a clear conclusion: Bengaluru’s congestion problem is not only a shortage of road space or a failure at individual junctions. It is also a problem of fragmented information and fragmented responsibility. A digital twin could help expose how different parts of the network interact, but its success will depend on whether the city can build the institutional arrangements needed to share data and act on what the model shows. The concept note, future agency discussions and the proposed mobility lab will indicate whether the initiative can move beyond a technology proposal towards an operational planning system.
