In News:

The recent visit of former Dutch Prime Minister Dick Schoof to the Bengaluru Traffic Management Centre has brought international attention to ASTraM (Actionable Intelligence for Sustainable Traffic Management) - an AI-driven traffic governance platform. The system represents a shift toward predictive, data-driven urban traffic management in India’s rapidly expanding metropolitan cities.

What is ASTraM?

ASTraM is an advanced AI-based big data platform designed for macro-level traffic management.

Unlike traditional GPS-based applications that only display real-time congestion to commuters, ASTraM functions as a centralised intelligence engine for city authorities. It provides holistic, real-time situational awareness and predictive insights to traffic managers.

Development and Institutional Collaboration

ASTraM was developed through collaboration between:

• Bengaluru Traffic Police
• Arcadis, a Dutch design and consultancy firm

The model reflects international cooperation in urban governance and technology deployment.

Objectives

The system aims to:

• Transform traffic policing from a reactive complaint-based approach to a proactive, data-driven model
• Reduce urban congestion
• Improve road safety
• Streamline incident reporting
• Enhance planning for large-scale public events

How ASTraM Works

1. Data Integration

The platform integrates multiple real-time data streams, including:

• CCTV camera feeds
• Automatic Number Plate Recognition (ANPR) systems
• Open-source and transport-related datasets

2. AI-Based Analysis

The AI engine processes large volumes of data to:

• Identify recurring congestion patterns (daily bottlenecks)
• Detect non-recurring disruptions (accidents, protests, roadblocks)
• Forecast potential traffic choke points

3. Automated Communication

• Issues are batched and communicated to field officers at 15-minute intervals
• Enables localised and timely intervention

Key Features

• Situational Awareness: A centralised dashboard provides a bird’s-eye view of city-wide traffic conditions.
• Predictive Analytics: The system anticipates congestion trends before gridlocks occur.
• Incident Reporting Bot: Automated bots log accidents, breakdowns, and obstructions, reducing manual reporting delays.
• Event Simulation: Supports traffic planning during major events such as processions, protests, and festivals by modelling potential disruptions.
• Dashboard Analytics for Urban Planning: Provides long-term data insights for infrastructure planning and policy adjustments.