VelociCity OptiRoute
Revolutionizing Traffic Management with AI
Overview
Delhi's bustling metropolis faces significant challenges in traffic congestion. Our Agent-based Traffic Signal Management System leverages Model-free Reinforcement Learning (RL) to revolutionize urban mobility, creating an adaptive and intelligent solution for modern cities.
Key Features
Real-Time Optimization
Adaptive traffic control with dynamic signal phase adjustments based on current traffic density.
Multi-Agent Collaboration
Coordinated traffic management through inter-agent communication.
Deep Neural Networks
Advanced DQN and actor-critic models for robust decision-making.
Environmental Impact
Reduced carbon emissions through optimized traffic flow.
Technical Workflow
System Flow Chart
Data Collection
Traffic sensors, cameras, IoT devices
Processing
Real-time data analysis & state representation
AI Decision Making
DQN agent evaluates optimal actions
Signal Control
Dynamic adjustment of traffic signals
Performance Monitoring
Continuous evaluation & optimization
1. Environment Interface
Development of traffic simulation using MATLAB's MARTO.
2. Problem Formulation
Definition of RL environment with state space and rewards.
3. Agent Design
Implementation of deep Q-network (DQN) agent.
4. Training & Validation
Agent training using realistic traffic data.
System in Action
Training Stage
Trained Agent
Future Vision
Smart City Integration
Seamless connection with IoT devices and Edge AI-based decision-making.
V2I Communication
5G-enabled vehicle-to-infrastructure communication for proactive traffic management.
Sustainable Impact
70% reduction in wait times and significant decrease in carbon emissions.