Research Philosophy

My research bridges the gap between theoretical advancement and practical implementation, focusing on creating real-world solutions for modern transportation challenges.

Innovation-Driven

Developing cutting-edge technologies that push the boundaries of what's possible in transportation engineering and connected vehicle systems.

Application-Focused

Ensuring research outcomes have practical applications that can be implemented to solve real-world transportation challenges and improve urban mobility.

Collaborative Approach

Working with industry partners, government agencies, and academic institutions to create comprehensive solutions for transportation systems.

Connected & Automated Vehicles

Developing next-generation connected and automated vehicle technologies that enhance safety, efficiency, and sustainability.

  • Vehicle-to-Vehicle (V2V) Communication
  • Vehicle-to-Infrastructure (V2I) Systems
  • Autonomous Vehicle Decision Making
  • Mixed Traffic Environment Modeling

Smart Cities & ITS

Creating intelligent transportation systems that integrate seamlessly with smart city infrastructure to optimize urban mobility.

  • Adaptive Traffic Signal Control
  • Real-time Traffic Management
  • Smart Infrastructure Integration
  • Multi-modal Transportation Systems

Traffic Safety & VRU

Developing safety-critical systems and technologies to protect vulnerable road users and reduce traffic accidents.

  • Pedestrian & Cyclist Safety Systems
  • Collision Avoidance Technologies
  • Emergency Response Optimization
  • Accident Prediction & Prevention

AI & Machine Learning

Applying artificial intelligence and machine learning techniques to solve complex transportation problems.

  • Traffic Flow Prediction
  • Dynamic Route Optimization
  • Behavioral Pattern Analysis
  • Intelligent Decision Support Systems

1. Problem Identification & Literature Review

Identifying critical transportation challenges through stakeholder engagement and comprehensive literature analysis.

Systematic Reviews Stakeholder Interviews Gap Analysis

2. Theoretical Framework Development

Developing mathematical models and theoretical frameworks that capture transportation phenomena.

Mathematical Modeling Game Theory Optimization Theory

3. Simulation & Modeling

Creating comprehensive simulation environments to test and validate theoretical concepts.

SUMO VISSIM Python/MATLAB

4. Real-World Validation

Validating simulation results through field studies, data collection, and pilot implementations.

Field Studies Data Collection Pilot Testing

5. Implementation & Technology Transfer

Translating research outcomes into practical applications through industry partnerships.

Industry Collaboration Patent Filing Commercialization

Simulation Software

  • SUMO: Open-source traffic simulation for large-scale network modeling
  • VISSIM: Microscopic traffic simulation for detailed vehicle behavior
  • MATLAB/Simulink: Mathematical computing for algorithm development
  • Custom Frameworks: Proprietary simulation frameworks for specific applications

Programming & Analytics

  • Python: Primary language for data analysis and ML
  • R: Statistical computing for advanced data analysis
  • TensorFlow/PyTorch: Deep learning frameworks for AI applications
  • Apache Spark: Big data processing for large-scale analysis

Data & Infrastructure

  • GIS Systems: Geographic information systems for spatial analysis
  • IoT Sensors: Real-time traffic data collection and monitoring
  • Cloud Computing: Scalable computing for large-scale simulations
  • Edge Computing: Distributed computing for real-time processing
Ongoing

AI-Powered Traffic Signal Optimization

Developing machine learning algorithms that adapt traffic signal timing in real-time based on connected vehicle data and traffic patterns.

Expected Impact: 25-30% reduction in urban traffic delays
Collaborators: Ford Motor Company, City of Mumbai
Ongoing

V2X Communication in Adverse Weather

Investigating how connected vehicle communication systems perform under various weather conditions and developing adaptive protocols.

Expected Impact: Enhanced safety in challenging weather conditions
Collaborators: Northwestern University, Leidos Corporation
Planning

Autonomous Vehicle Integration in Mixed Traffic

Studying the integration of autonomous vehicles in mixed traffic environments typical of Indian road conditions.

Expected Impact: Smoother transition to autonomous vehicle adoption
Collaborators: IIT Bombay SeDriCa Team, Industry Partners

Workshop Organization

"Advancing CAV Technologies for Indian Ecosystems" (2024)

Peer Review

Active peer reviewer for leading transportation research journals

Academic Committees

Member of various academic committees at IIT Bombay