AI-Powered Disruption Recovery System
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Illustrative preview (sample data, not verified outcomes): Real-Time Incident Detection
Illustrative preview (sample data, not verified outcomes): Recovery Plan Approval Interface
Illustrative preview (sample data, not verified outcomes): Plan Execution Monitoring
Illustrative preview (sample data, not verified outcomes): Incident Resolution Summary
AI Agents
Specialized autonomous agents working in coordination
Sentinel Orchestrator
## Coordination Gap Multiple specialist analyses must be synthesized into a coherent recovery decision. Without central coordination, agents may produce conflicting recommendations, causing delays and suboptimal outcomes.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Numerical examples are scenario inputs or illustrative outputs, not validated performance results. ## Orchestration Architecture Powered by **GPT-4-Turbo** with 128K context window, this supervisor agent: - Receives incident detection alerts and deploys specialist agents in parallel - Uses chain-of-thought reasoning with confidence scoring - Builds consensus across agent recommendations - Synthesizes final recovery decisions with 94%+ confidence - Queries Agent Registry to optimize task delegation - Manages escalation to human operators when confidence thresholds aren't met
Incident Classifier
## Classification Complexity Incidents vary widely—mechanical failures, accidents, weather events—each requiring different response protocols. Manual classification is slow and inconsistent, delaying appropriate response initiation.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Numerical examples are scenario inputs or illustrative outputs, not validated performance results. ## Pattern-Based Classification Powered by **Claude-3-Opus** with 200K context window: - Executes 3-step reasoning: OBSERVE → ANALYZE → DECIDE - Queries Incident History DB (847KB historical data) for pattern matching - Integrates Weather API data for environmental context - Invokes Pattern Matcher ML endpoint for anomaly detection - Outputs incident class, severity level, and 96% confidence score - Generates immediate action recommendations - Uses ~1,800 tokens per classification cycle
Impact Analyzer
## Impact Visibility Gap Disruptions create cascading effects—missed connections, revenue loss, SLA breaches—that are difficult to quantify quickly. Operators lack real-time impact assessment for informed decision-making.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. Numerical examples are scenario inputs or illustrative outputs, not validated performance results. ## Cascade Impact Modeling Powered by **GPT-4** with comprehensive data integration: - 4-step analysis: passenger count → connection mapping → revenue calculation → SLA assessment - Queries Passenger Analytics DW (1.2MB), APC System (156KB), Connection Database (89KB) - Invokes Network Simulator ML model for cascade prediction - Calculates direct/indirect affected passengers (e.g., 205/234) - Identifies at-risk connections (e.g., 83 total, 68 guaranteed) - Estimates revenue impact and SLA breach probability (78%) - Uses ~1,900 tokens per analysis
Resource Optimizer
## Resource Allocation Challenge Finding the optimal replacement vehicle requires evaluating location, ETA, driver availability, qualifications, and vehicle specifications simultaneously—a complex multi-constraint optimization problem.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. ## Constraint-Based Optimization Powered by **Claude-3-Sonnet** with optimization capabilities: - Uses linear programming and constraint satisfaction algorithms - 4-step process: fleet query → filtering → ETA calculation → selection - Integrates Fleet Management (567KB), Crew System (234KB), Depot Status (45KB) - Calculates ETAs via Routing Engine API - Recommends optimal vehicle with driver qualification verification - Ranks alternative vehicles by suitability score - Delivers recommendations in ~1,700 tokens with 7-minute average ETA
Route Strategist
## Route Recovery Complexity Disruptions require dynamic rerouting decisions—short-turns, replacements, diversions—that must balance passenger coverage, delay minimization, and operational cost.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. ## Multi-Strategy Route Planning Evaluates short-turn, replacement-bus and diversion options using available network, traffic and passenger data. Compares estimated coverage, delay and cost, and presents alternatives for dispatcher approval. Validate model assumptions and operating constraints before execution.
Communication Director
## Passenger Information Gap Affected passengers need timely, personalized notifications through their preferred channels. Manual communication is slow, generic, and fails to reach passengers effectively.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. ## Multi-Channel Personalized Messaging Powered by **Claude-3-Sonnet** with NLG capabilities: - 4-step process: identification → preference analysis → generation → dispatch - Queries Passenger Profiles (234KB) for channel preferences - Uses Template Engine (67KB) for message personalization - Routes through Channel Router (12KB) for optimal delivery - Generates push notifications (168), SMS (37), emails (186) - Updates 8 stop displays with real-time information - Personalizes messages based on journey and language preferences - Uses ~1,500 tokens per communication cycle
Compliance Monitor
## Regulatory Compliance Risk Transit disruptions trigger regulatory obligations—passenger rights, SLA commitments, incident reporting—that must be tracked and fulfilled to avoid penalties and maintain operating licenses.
Core Logic
Illustrative workflow design. Validate capabilities, performance, controls and integration requirements before implementation. ## Automated Compliance Validation Powered by **GPT-4** with regulatory knowledge: - 4-step reasoning: regulation check → SLA analysis → rights assessment → recommendation - Queries Regulation DB (123KB) for applicable rules - Validates against SLA Engine (45KB) for contract compliance - Logs all decisions via Audit Logger (2KB) - Determines compliance status (GREEN/YELLOW/RED) - Calculates compensation requirements and deadlines - Sets incident report deadline (24 hours) per regulations - Uses ~1,400 tokens per validation cycle
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
6 technologies
Architecture Diagram
System flow visualization