MT-ERBS
Manipulating Traffic for Effective Rescue by Bypassing the Signals
MT-ERBS introduces a revolutionary AI-integrated traffic management system specifically engineered for emergency vehicle priority. Unlike conventional smart traffic solutions, the system employs deep learning, computer vision, and IoT infrastructure to dynamically manipulate traffic signals, creating optimal rescue corridors that maximize golden hour utilization during critical medical emergencies.
2023 - 2024
Theoretical framework development and system architecture design
2024 - 2025
Prototype implementation, benchmarking tool development, and field testing
2025 - 2026
Research documentation, publication preparation, and optimization
Siddharth Magesh
AI Researcher & System Architecture Lead
Arjun V L
AI Model Development & Algorithm Implementation
Gokulramanan V
IoT Integration & Field Testing Engineer
Core algorithms successfully implemented with comprehensive benchmarking framework developed. System demonstrates significant performance improvements over baseline traffic management. Currently in publication preparation phase.
MT-ERBS addresses the critical challenge of emergency response optimization in congested urban environments, targeting the “golden hour” period that determines survival outcomes in medical emergencies. The system introduces an emergency-centric traffic management paradigm combining 360-degree AI-powered intersection cameras, centralized server infrastructure with aerial city network visualization, and a multi-layered dynamic AI algorithm. By integrating ambulance telemetry, real-time traffic signals, hospital capacity data, and emergency call processing, MT-ERBS creates optimal rescue pathways while maintaining overall traffic flow efficiency—a fundamental advancement over traditional smart traffic systems that lack emergency prioritization capabilities.
Technical Architecture
Centralized Server Infrastructure
High-performance computing cluster providing real-time decision-making capabilities with aerial city network visualization for comprehensive traffic state awareness.
IoT Vision Network
360-degree AI-powered cameras deployed at critical intersections, performing continuous traffic density analysis and vehicle detection at 10 Hz frequency.
Emergency Vehicle Fleet Integration
Ambulances equipped with GPS modules, LCD status panels, AI communication systems, and real-time telemetry transmission for dynamic route optimization.
Multi-Source Data Fusion
Integration layer combining emergency calls, traffic camera feeds, ambulance GPS, and hospital capacity systems for comprehensive situational awareness.
Computer Vision Module
Deep learning-based traffic analysis for vehicle counting, congestion detection, and emergency vehicle identification with real-time inference.
NLP Processing Engine
Natural language processing for emergency call transcription and severity assessment, enabling automated dispatch prioritization.
Reinforcement Learning Controller
Adaptive decision-making system optimizing signal timing and route selection through continuous learning from traffic patterns.
Model Predictive Control (MPC)
Route refinement and signal coordination using predictive modeling for optimal ambulance corridor creation with minimal traffic disruption.
Specialized Processing Modules
Multi-modal sensor data integration with traffic state forecasting capabilities
Optimal ambulance-to-emergency matching with intelligent hospital targeting
Time-dependent pathfinding with MPC-based continuous route refinement
Green wave generation with upstream traffic gating for corridor creation
Physical signal control interface with emergency indicator activation
Hospital, police, and public transport system integration layer
Technology Stack
Performance Validation
A comprehensive Python-based evaluation framework was developed to validate system performance under various urban traffic conditions and emergency scenarios.
Traffic Network Modeling
Urban traffic grid simulation with configurable intersection density
Multi-Metric Evaluation
Travel time, blocking time, spillback analysis, signal switching frequency
Scenario Testing
Multiple test cases with varying traffic conditions and ambulance routes
Real-time Visualization
System state monitoring dashboard with performance analytics
1st Place Winner
MT-ERBS received 1st Place recognition for its innovative emergency traffic management system at the Ideathon competition. The project was awarded for its groundbreaking approach to AI-integrated traffic manipulation with dynamic ambulance prioritization, demonstrating significant potential to revolutionize emergency response systems and save lives during critical golden hour periods.
Competition: Ideathon at Velammal Engineering College
Date: February 22, 2023
Organizer: Department of Physics, Velammal Engineering College
MT-ERBS addresses the critical challenge of emergency response optimization in urban traffic systems, specifically targeting the golden hour period that determines survival outcomes in medical emergencies. Unlike traditional smart traffic systems that prioritize general flow optimization, MT-ERBS implements an emergency-centric design philosophy that can potentially save lives through dramatically reduced ambulance response times.
The research establishes a foundation for next-generation intelligent transportation systems with integrated emergency response capabilities. Beyond ambulance prioritization, the system architecture supports extension to fire department rapid response, police emergency dispatch optimization, and large-scale disaster management scenarios. As smart city infrastructure continues to expand globally, MT-ERBS provides a blueprint for embedding life-saving emergency protocols into urban traffic management systems at scale.