Chennai is entering 2026 with a major shift towards artificial intelligence–enabled urban transport, integrating driverless metro trains, adaptive traffic signals and digital transit platforms to deliver faster, safer and more efficient mobility across the city.

Chennai Metro Rail Limited plans to deploy 32 driverless train sets across its Phase II corridors as part of this transition. The metro operator has awarded a contract worth Rs 15.38 billion to Alstom for the supply of 32 automated train sets, comprising 96 coaches, to operate over 118.9 km across three Phase II corridors. The trains will use unattended train operation technology (GOA-4), allowing automated control of movement, speed, station stops and door operations, with oversight maintained at the operations control centre to manage emergencies and service disruptions.

Alstom India is deploying AI across train design, signalling and predictive maintenance. Continuous data feedback enables optimisation of schedules and maintenance cycles, reducing downtime while improving reliability. The company said AI models are continuously tested and retrained to respond to unforeseen conditions, while retaining the ability for human intervention when required.

AI is also transforming road transport management in the city. The Greater Chennai Traffic Police has implemented adaptive traffic signals at key junctions, enabling real-time adjustments based on vehicle density and helping ease congestion during peak hours. Meanwhile, the Metropolitan Transport Corporation has rolled out GPS-based fleet management across its bus network, reducing vehicle bunching and providing commuters with accurate, real-time arrival information.

At the planning and integration level, the Chennai Unified Metropolitan Transport Authority is digitally unifying multiple transport modes. Fare collection, season tickets and route information are being integrated on a single platform, reducing manual processes and generating data to guide infrastructure planning. Predictive analysis of commuter movement has already informed projects such as the Kilambakkam skywalk and upgrades at Tambaram and Guindy stations, improving intermodal connectivity and pedestrian access.

The city’s experience in 2025 also highlighted the limits of automation. During heavy monsoon flooding, human intervention was essential for traffic diversion, bus route adjustments and extended metro operations. Officials noted that while AI enhances efficiency, it does not replace manpower. Future plans include intelligent traffic corridors, such as the Alandur–Airport stretch, and signal systems capable of detecting emergency vehicles, underscoring Chennai’s strategy of combining AI, predictive analytics and human oversight to build a resilient urban transport system.