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[Audio] Teaching Aids & Materials This training combines cutting-edge technology with practical field tools to ensure you're fully prepared for real-world MTA enforcement scenarios. Each device and resource has been carefully selected to support your learning and operational success. Field Tablets & Smartphones Projector System Respondent Description Cards Devices running the AI MTA Transit Enforcement App for real-time data access. These are crucial for accessing real-time data and the AI MTA enforcement app directly in the field, enabling immediate decision-making. For live dashboard demonstrations and group analysis. This allows for large-scale viewing of AI insights and interactive group discussions to enhance learning. Used for Respondent Alert simulations and identification practice. These cards facilitate realistic simulations, improving personnel's ability to identify and respond to various scenarios. Real Surveillance Footage MTA Route Maps For pattern recognition and behavior analysis training. Analyzing actual footage helps trainees develop critical skills in identifying evasion patterns and suspicious behaviors. Highlighting BX35, BX19, BX21, BX36, and other high-risk corridors. These maps are vital for understanding the geographical context of high-risk areas and planning strategic deployments effectively. Detailed Device Features This diagram illustrates the key functionalities of the field tablets, including the power button, laser scanner, volume controls, and pogo pin for power and data. Understanding these features is essential for efficient operation. Enforcement in Action Witness how these tools empower our team to ensure compliance and maintain MTA public order across the MTA transit system, demonstrating real-world application of the training. Enforcement in Action - Field Example This field photo captures an officer actively boarding an MTA bus, providing another real-world demonstration of the teaching aids and training materials in action..

[Audio] AI-Powered MTA Transit Enforcement Training This comprehensive training program equips MTA transit enforcement personnel with the cutting-edge skills needed to utilize artificial intelligence for enhanced MTA fare enforcement. By leveraging AI, traditional enforcement methods are transformed, enabling more proactive and data-driven strategies to improve efficiency and reduce fare evasion across the MTA transit system. By the end of this training, you will master the skills needed to leverage artificial intelligence for effective MTA fare enforcement across the MTA transit system. Interpret AI Maps Identify Evasion Hotspots Locate active MTA buses in real time using dynamic mapping technology. Use red-signal heatmaps to pinpoint high-frequency fare-evasion zones. Analyze Time Patterns Deploy Strategically Predict peak evasion hours through data-driven insights. Position inspectors based on AI-detected risk zones. Make Field Decisions Use tablets and mobile dashboards for instant tactical choices. Revenue distribution across regions, highlighting key areas for enforcement focus. This funnel chart illustrates the number of fare evasion incidents reported in different regions, providing a quick visual of where enforcement efforts might be most needed..

[Audio] AI-Powered MTA Transit Enforcement Training This comprehensive training program equips MTA transit enforcement personnel with the cutting-edge skills needed to utilize artificial intelligence for enhanced MTA fare enforcement. By leveraging AI, traditional enforcement methods are transformed, enabling more proactive and data-driven strategies to improve efficiency and reduce fare evasion across the MTA transit system. By the end of this training, you will master the skills needed to leverage artificial intelligence for effective MTA fare enforcement across the MTA transit system. Interpret AI-Generated Maps Identify Evasion Hotspots Analyze Time Patterns Predict peak evasion hours through data-driven insights. Locate active MTA buses in real time using dynamic mapping technology. Use red-signal heatmaps to pinpoint high-frequency fare-evasion zones. Deploy Strategically Make Field Decisions Position inspectors based on AI-detected risk zones. Use tablets and mobile dashboards for instant tactical choices. Map Interpretation Pattern Analysis Predict peak evasion hours via data insights. Locate active MTA buses in real time with dynamic maps. Hotspot Identification Strategic Deployment Use red-signal heatmaps to find evasion zones. Position inspectors based on AI risk zones. This diagram illustrates the five core steps in AI-powered MTA transit enforcement, from real-time mapping to field decision-making, ensuring a data-driven approach to reducing fare evasion. Revenue distribution across regions, highlighting key areas for enforcement focus. This funnel chart illustrates the number of MTA fare evasion incidents reported in different regions, providing a quick visual of where enforcement efforts might be most needed..

[Audio] Patrol Planning Activity: 8:00 AM Scenario This exercise teaches strategic resource positioning before high-risk MTA buses arrive to maximize interception using predictive AI data. Goal: Maximize interception by positioning agents before the high-risk MTA bus arrives. Scenario Context It's 8:00 AM on a Tuesday, rush hour. Your REDDWELL dashboard flagged three high-priority locations with peak fare evasion expected between 8:15-8:45 AM. Step-by-Step Tactical Planning Review AI predictions for red-zone clusters and confidence scores. Use MTA BusTime to identify high-priority MTA bus arrival times during peak evasion. Calculate travel time, factoring in positioning and potential delays, to arrive before the high-risk MTA bus. Prioritize deployment locations based on AI confidence, MTA bus volume, and historical evasion. Communicate your position to dispatch, activate tracking, and position for clear sightlines. Real-World Example Data Location A: Fordham Road & Grand Concourse - 91% confidence Location B: 125th Street & Lexington Ave - 87% confidence Location C: Gun Hill Road & White Plains - 82% confidence.

[Audio] The 3-Step AI Enforcement Cycle This systematic approach ensures every enforcement action is data-driven, targeted, and effective. By following these three steps consistently, you'll develop the instincts needed for successful field operations. 1. Map — "Find the MTA Bus" 2. Target — "Check the Heat Map" 3. Identify — "Match the Visual Description" Locate active MTA buses on the live map. Identify MTA routes with repeated evasion alerts and track real-time movement to pinpoint potential hotspots. Critical for understanding the dynamic environment. Red zones highlight high evasion frequency. AI identifies stops with repeated non-payment patterns, guiding resource focus. Targeting concentrates efforts for maximum predictive impact. Use Respondent Alert cards for visual comparison (clothing, behavior, boarding). Confirm identity with surveillance snapshots before action. Precise identification is crucial to avoid misidentification and ensure fairness. Consistent practice of this cycle refines your operational intuition and continuously improves the effectiveness of each enforcement action..

[Audio] Role Play Integration Role-playing scenarios bridge the gap between theory and practice, simulating real-world pressure and decision-making. This actively engages participants, fostering critical thinking and immediate skill application. Hands-on exercises allow for direct feedback and correction in a safe environment, building practical expertise and confident decision-making. Receive Alert Cards Use AI Matching Tools Instructor distributes detailed respondent descriptions. Trainees apply technology to match descriptions. Compare Camera Feeds Make Tactical Decision Cross-reference live footage with respondent traits. Decide whether to intervene, observe, or escalate. Each role play is debriefed immediately, allowing you to learn from decisions and refine your approach before facing real respondents in the field..

[Audio] Respondent Alert Simulation Assessment Be On the Lookout (Respondent Alert) alerts require quick thinking and precise execution. This simulation tests your ability to receive intelligence, interpret data, and deploy tactically within minutes. These alerts are inherently time-sensitive as situations can evolve rapidly, making a swift response crucial for public safety or incident resolution. Delays can lead to missed opportunities or escalating risks. Success in this simulation will be measured by your accuracy in identifying the respondent and the speed of your tactical deployment, ensuring effective and timely intervention. Receive Respondent Alert Alert Alert appears on your device with respondent details Review Description Analyze clothing, behavior, time, and route information Locate Target MTA Bus Use AI map to identify the MTA bus respondent is likely on Deploy Strategically Position yourself at next stop BEFORE MTA bus arrives Observe & Confirm Watch boarding patterns and verify respondent identity.

[Audio] Real-Time AI Dashboard The AI dashboard is your command center in the field. It consolidates multiple data streams into actionable intelligence, updating every 3-5 seconds to keep you informed of changing conditions. This integration of various sources, from GPS and traffic data to historical evasion metrics, provides a comprehensive overview crucial for making rapid, informed decisions in dynamic field environments. This unified view significantly enhances situational awareness, allowing for more effective and timely interventions. When multiple alerts appear, prioritize based on the severity of the evasion metrics and the potential for immediate impact on public safety or operational efficiency. The dashboard's real-time updates help identify the most critical situations, guiding your tactical focus. BX35 Example AI automatically flags MTA buses with unusually high evasion counts. MTA Bus #1061 and MTA Bus #1060 currently showing elevated evasion metrics, requiring immediate attention and strategic deployment. MTA Bus ETA Precise arrival time predictions for tactical positioning Evasion Metrics Bar graphs showing fare-evasion frequency by MTA route MTA Route Congestion Real-time traffic and passenger load data Deployment Zones Optimal inspector positioning recommendations.

[Audio] Reflection is a cornerstone of continuous improvement, especially in dynamic environments. By systematically reviewing field operations, we can identify areas for growth and refine our strategies. This critical feedback loop directly informs future training modules and enhances the accuracy of our AI tools, ensuring our MTA systems and personnel are always evolving and optimizing for peak performance. Reflection & Follow-Up Technical Mapping Challenges Descriptive Identification Issues Did trainees struggle with the AI map interface or color-coded signals? Were there difficulties matching descriptions to visual evidence or making confident identifications? Next Steps Assessment Criteria Assign field shadow-shift with experienced inspector Review real case studies using AI tools Schedule one-on-one coaching for struggling trainees Provide additional practice with mapping interface Evaluate confidence, decision-making, accuracy, and tech comfort before independent field work..

[Audio] Outcomes & Next Steps Upon successful completion of this training program, you will possess the comprehensive skill set required for effective AI-assisted MTA transit enforcement. These capabilities will transform how you approach daily operations and significantly improve your success rate. This certification means officers are now fully qualified to utilize advanced AI tools for real-time analysis, predictive intelligence, and efficient resource deployment in the field. They are equipped to handle complex enforcement scenarios with enhanced precision and data-driven insights. Ongoing support and resources, including regular software updates and access to a dedicated helpdesk, will be available to ensure continuous operational excellence post-training. AI Map Interpretation Tactical Deployment Skills Pattern Recognition Confidence in reading live data, identifying patterns, and making location-based decisions Ability to position resources optimally based on predictive intelligence Skill in identifying evasion patterns across time, location, and MTA routes Field Readiness Complete preparation for real-world enforcement scenarios.

[Audio] AI Device Interaction: Mastering the Software Interface Your field tablet is more than a display device—it's an intelligent partner that responds to your needs in real time. Understanding how to interact efficiently with this technology is crucial for operational success, focusing entirely on software interface and advanced user techniques. Mastery of the software interface is paramount, as it directly translates AI insights into actionable intelligence, making it just as critical as traditional MTA transit enforcement tactics. Proficient software interaction ensures that officers can leverage the full power of the MTA AI system for real-time decision-making and strategic advantage. Common interface mistakes often stem from rushing or not fully understanding gesture controls, leading to missed alerts or incorrect data interpretation. To avoid these, practice diligently with the training modules and always double-check inputs before confirming actions. Dynamic View Switching Split-Screen Monitoring Custom Alert Configuration Route Bookmarking Bookmark MTA routes for instant access. Navigate map, alerts, and surveillance views. Monitor multiple data sources with split-screen. Custom alerts for prioritized patrol information. Voice Commands Toolbar Customization Power Management & Offline Mode Cross-Device Data Sync Voice commands for hands-free operation. Customize toolbar for rapid task execution. Manage power, use offline for continuous operation. Cross-device data sync for team coordination. Swipe to Filter Pinch to Zoom Tap to Select.

[Audio] MTA Transit Map Deep Dive Advanced Map Interpretation Basic map reading involves understanding symbols and identifying locations. Advanced interpretation, however, delves deeper by analyzing real-time data overlays and predictive models to understand evolving situations. This means moving beyond static routes to dynamic threat assessments. Density & Heat Intensity Historical Overlays & Trend Analysis AI Predictions & Confidence Intervals Advanced Filtering & Layering Interpret cluster density and heat gradients to understand evasion hotspot severity. Use historical data overlays to detect emerging patterns and predict high-risk areas. Gauge AI prediction reliability using confidence intervals and real-time cross-referencing. Apply sophisticated filters and integrate external map layers to reveal correlated evasion patterns..

[Audio] Hotzone Color System & Tactical Response Hotzone Color Intensity Meanings: Red: Fare evasion detected on site within 5 minutes or less - Immediate response required Orange: Fare evasion detected within 10 minutes or less - Could be happening on the MTA bus or respondent may have already left Yellow: Fare evasion detected within 15 minutes or less - Priority patrol area Pulsing Red: Real-time active evasion happening now - Respond immediately Key Tactical Advantages: Predictive Positioning Resource Allocation Pattern Recognition Know exactly where to be 30-60 minutes before peak evasion times Deploy multiple officers to dark red zones, single officers to lighter zones Identify respondent behavior - do they target rear-door boarding? Cluster near exits? Time-Based Targeting Route Correlation Deploy during peak windows when hotzones are most active If BX35 shows red clusters at three consecutive stops, that route needs sustained attention Real-World Example: Dark red cluster at Fordham Road & Grand Concourse, 8:15-8:45 AM means: Arrive by 8:10 AM, position near rear doors of articulated MTA buses, coordinate with partner for front entrance coverage..

[Audio] Surveillance Feed Analysis Surveillance footage is a critical tool for confirming AI alerts and building cases. Learning to read these feeds quickly and accurately will significantly improve your MTA enforcement effectiveness. Surveillance feeds act as visual corroboration for AI-generated alerts, providing concrete evidence of fare evasion attempts on MTA transit. This synergy between AI detection and human visual confirmation strengthens the case for MTA enforcement actions. When utilizing surveillance footage, it's crucial to be aware of and adhere to all relevant privacy laws and regulations. Ensure proper data handling, retention, and chain of custody to maintain legal compliance within the MTA system. Key Behavioral Indicators Timestamp Reading Watch for passengers who board MTA buses through rear doors, avoid eye contact with drivers, position themselves near exits, or display nervous body language. These behaviors, combined with AI alerts, create high-confidence MTA enforcement opportunities. Correlate video time with AI alert times to verify incidents Camera ID Recognition Understand which camera covers which zone of the MTA bus Behavior Identification Recognize suspicious boarding patterns like rear-door entry or fare machine avoidance Evidence Linking Connect visual evidence to AI alerts for stronger MTA enforcement actions.

[Audio] REDDWELL Interface and Hardware Overview Login Interface Security is paramount when accessing sensitive MTA transit enforcement data. The REDDWELL login system ensures that only authorized personnel can view operational intelligence and passenger information. These robust measures are essential to safeguard sensitive operational intelligence and protect passenger privacy, preventing unauthorized access and potential data breaches. A secure login process ensures data integrity and maintains the trustworthiness of MTA enforcement operations. Device Hardware Diagram The REDDWELL handheld device is designed for intuitive use, featuring clearly labeled buttons and an integrated scanner for efficient operations. Familiarize yourself with its layout for quick access to key functions during MTA enforcement duties. Identity Verification Region Selector Secure login confirms inspector credentials and authorization level before granting MTA system access Ensures you're viewing data for the correct jurisdiction, preventing cross-district confusion Data Protection Encrypts sensitive MTA transit data and passenger information to maintain privacy and legal compliance Ergonomic Design Integrated Scanner Optimized for single-hand operation, reducing fatigue during extended shifts Rapidly process tickets and verify credentials with a high-accuracy scanning module Tactile Buttons Physical buttons provide immediate feedback and reliable performance in diverse conditions Important: Never share your login credentials. Each access is logged and audited. Unauthorized access or credential sharing will result in immediate MTA system lockout and disciplinary action. If you suspect your credentials have been compromised, report it immediately to the IT security team. Failure to do so could lead to significant security vulnerabilities and misuse of official data..

[Audio] Mobile Backup Options: When Tablets Are Down Flexibility in the Field Technology is powerful, but you need backup options when primary devices fail. REDDWELL provides multiple access points to ensure you're never without critical MTA transit enforcement tools. In demanding field operations, device redundancy is crucial to ensure uninterrupted service and maintain critical data access. Unexpected equipment failures can compromise public safety and operational efficiency, making reliable backup solutions non-negotiable for effective MTA transit enforcement. Option 1: Smartphone Access Your personal or department-issued smartphone can run the full REDDWELL application: Login Process: Open app, enter credentials. Full Functionality: Access tracking, alerts, feeds, heatmaps. Real-Time Sync: Data syncs instantly across devices. Smaller Screen, Same Power: Complete operational capability. Option 2: OVD Mobile Scanning Device The Optical Verification Device (OVD) is a specialized handheld scanner designed for rapid respondent identification: Real-Time ID Verification: Scan OMNY cards instantly. Respondent Database Access: Cross-reference alerts, history. Compact & Durable: Pocket-sized, ruggedized for field. Instant Data Capture: Records interactions for reporting. Seamlessly switch between these devices as operational needs change, knowing that all data is continuously synchronized to maintain continuity and accuracy in your fieldwork. When to Use Each Device Tablet: Primary for patrol, monitoring, analysis. Smartphone: Backup for alerts, quick checks. OVD Scanner: Rapid OMNY cards verification, summons scanning..

[Audio] Predictive Evasion Modeling The AI doesn't just report current evasion—it predicts where and when future incidents are likely to occur. This capability allows you to be proactive, positioning resources before problems develop. Real-Time Prediction Capabilities REDDWELL's AI analyzes complex patterns in real-time to forecast fare evasion: Pre-Incident Forecasting: AI predicts evasion 5-30 minutes before occurrence. Granular Forecasts: Specific dates, times, and hyper-local accuracy. Hyper-Local Accuracy: Pinpoints specific turnstiles, station gates, and bus stops. Confidence Scoring: Each prediction includes a confidence score for informed decisions. Temporal Analysis & Location Intelligence Detailed temporal analysis provides insights into evasion patterns across different periods: Leveraging spatial data, the MTA system identifies high-risk zones and vulnerabilities: Hour-by-Hour Likelihood: Likelihood curves across all hours. Day-of-Week Patterns: Distinct behavioral analytics for weekdays vs. weekends. Monthly & Pay-Cycle Trends: Identification of monthly trends, including pre-payday spikes. Ranked MTA Transit Stops: Dynamic ranking based on predicted evasion probability. MTA Bus Route Segments: Identification of specific high-risk segments. Station Entrance/Exit Patterns: Analysis of evasion behaviors linked to specific entry/exit points..

[Audio] MTA BusTime Integration Strategy Why Combine REDDWELL + MTA BusTime: REDDWELL tells you WHERE and WHEN evasion is likely. MTA BusTime tells you WHICH MTA buses are arriving and EXACTLY when. Together, they create perfect tactical timing. 4-Step Integration Process: Identify Hotzone Check MTA BusTime REDDWELL shows "125th & Lex - 87% probability, 8:15-8:45 AM" See M60 arriving 8:22 AM, M100 at 8:35 AM, M101 at 8:42 AM Calculate Positioning Adapt in Real-Time Arrive by 8:18 AM to observe M60 arrival during peak window If M60 is delayed, shift to M100 or relocate to secondary hotzone Practical Field Scenario: Tuesday 8:00 AM - REDDWELL Alert: "Fordham Road & Grand Concourse, 8:20-8:35 AM, 91% confidence" Your response: Check MTA BusTime → Bx12 (8:22 AM), Bx15 (8:28 AM), Bx41 (8:33 AM) → Position by 8:18 AM → Focus on Bx12 (arrives during peak prediction) → After Bx12, reassess for Bx15 Key Benefits: Eliminates wasted time at empty stops Allows coverage of multiple hotzones by timing movements Provides backup if tablet connectivity fails Demonstrates professional, data-driven methodology.

[Audio] Field Deployment Simulation: Bus Stop This simulation focuses on critical physical aspects of field deployment at an MTA bus stop, transforming AI insights into on-the-ground action. While digital intelligence provides invaluable insights, the physical positioning of personnel at an MTA bus stop is equally crucial for effective intervention and deterrence. It ensures that the predictive data from AI can be actioned efficiently, turning predictions into preventative measures. Optimal positioning also enhances visibility, allowing for proactive engagement while maintaining officer safety and maximizing the effectiveness of interventions within the MTA transit system. Strategic Positioning Optimize standing location for observation. Observe Targets Identify suspicious behavior, potential evasion methods. Maintain Situational Awareness AI data for initial deployment guidance Monitor surroundings, adapt to changing conditions. Real-world application at the MTA stop.

[Audio] Role Play: Respondent Alert Response Scenario: Urgent Alert A male respondent (25-30 years old, red jacket, black backpack) was last seen boarding MTA BX35 at 149th Street at 14:23. The MTA bus is approaching 161st Street. You have 4 minutes to intercept. Real-time alert on a mobile device Choose Interception Stop Board MTA Bus or Observe? Confirm Identity Discreetly Assess Need for Backup Role Play Exercise Structure Trainees pair: Officer and Respondent roles. Instructor manages tablet alerts, timing, and updates. Focus: Communication and de-escalation skills. Key Focus Areas: Interpersonal Skills Verbal approach techniques Reading body language De-escalation strategies Identification request procedures Handling responses and excuses Legal boundaries and rights. Effective communication is paramount during respondent encounters, enabling officers to gather crucial information, build rapport, and de-escalate potentially tense situations without resorting to force. Clear and respectful dialogue can significantly influence outcomes, ensuring both officer safety and public cooperation. Common failures include misinterpreting non-verbal cues or using overly aggressive language; these can be prevented through active listening and consistent application of de-escalation training..

[Audio] Feedback Loop & AI Learning Your direct observations and reports from the field are crucial. By detailing each encounter, including the nuances of suspect behavior and environmental factors, officers provide invaluable data that directly refines the AI's predictive capabilities, making its alerts more precise. It's important to provide honest and detailed reporting on every outcome, whether successful or not. This comprehensive feedback, even regarding false positives or unexpected situations, is vital for the AI to learn, adapt, and continuously enhance its effectiveness for future operations. Your role extends beyond MTA transit enforcement—you're also training the AI to become a better tool for all inspectors, ensuring MTA system improvement through quality reporting. Confirmed Evasion False Positives Behavior Patterns Validates AI predictions and strengthens pattern recognition Teaches AI to reduce incorrect alerts and improve accuracy Identifies new evasion tactics and emerging trends Timing Data Location Intelligence Refines predictive models for time-based deployment Improves hotspot identification and zone mapping.

[Audio] Ethics & Privacy With powerful AI tools comes significant responsibility. We must balance effective enforcement with respect for civil liberties and privacy rights. This isn't just about legal compliance—it's about maintaining public trust. Our commitment to privacy safeguards ensures that while we enhance security, we also uphold the fundamental rights of every passenger by deploying AI efficiently to identify genuine threats while scrupulously avoiding unwarranted intrusion. Privacy Respect Anonymized Data Legal Guidelines Public Transparency AI must respect individual privacy rights and avoid unnecessary surveillance Personal information is protected through anonymization and encryption All AI operations comply with federal, state, and local privacy laws Maintain transparency about AI use and enforcement practices Your Ethical Obligations Reporting Concerns Use AI tools only for legitimate MTA transit enforcement. Never access data out of curiosity or share information inappropriately. Treat all passengers with dignity and respect. Technology assists judgment—it doesn't replace it. If you observe potential misuse of AI tools, privacy violations, or ethical concerns, report them immediately to your supervisor. Failure to adhere to these standards can lead to severe consequences, including disciplinary action and legal penalties..

[Audio] Final Recap & Certification Congratulations on completing the AI-Powered MTA Transit Enforcement Training. You've mastered a sophisticated technology platform and developed the tactical skills needed for modern fare enforcement. 100% Training Modules Field Ready Core Skills Completed comprehensive curriculum Mastered essential AI enforcement capabilities Prepared for independent operations Complete Final Assessment Review All Skills Comprehensive review of mapping, targeting, identification, and deployment techniques Demonstrate proficiency through practical examination and scenario testing Earn Certification Badge Prepare for Field Deployment Receive official AI MTA Transit Enforcement certification for your personnel file Schedule your first supervised field shift and begin real-world operations You are now equipped to leverage artificial intelligence for more effective, efficient, and ethical MTA transit enforcement. Use these tools wisely, continue learning, and always prioritize both public safety and civil liberties. Immediately after certification, ensure your personnel file is updated with your new qualification and familiarize yourself with the scheduling for supervised field shifts. Take this time to review key protocols and scenarios from your training modules. During your first week of deployment, you will be paired with an experienced officer for practical guidance. For any operational questions or technical issues, please contact your team lead or the dedicated IT support hotline..

[Audio] About REDDWELL: Innovation in MTA Transit Safety Establishment & Origins The First Major Success: Provença Station Founded in 2014 in Barcelona, Spain, REDDWELL is the flagship product of Awaait, a company headquartered on the famous Avinguda Diagonal. The journey began in 2008 as a civil engineering consultancy, but following Spain's economic downturn in 2012, the team pivoted to focus entirely on Artificial Intelligence (AI) and research & development. The breakthrough at Provença station in Barcelona demonstrated the MTA system's effectiveness, with AI-powered cameras identifying fare evaders in real-time. This pilot led to an extraordinary 70% drop in fare evasion, proving the dramatic effectiveness of selective MTA enforcement. What REDDWELL Is Today The Genesis: The "DETECTOR" Project REDDWELL leverages cutting-edge AI to provide efficient and discreet MTA fare enforcement. Our flexible system supports various devices, ensuring officers have the right tools for the job. Traditional fare evasion controls were inefficient and frustrating for paying customers. In 2013, Awaait partnered with FGC to test "DETECTOR," a revolutionary system that would eventually evolve into REDDWELL. Real-Time Selective Alerts Visual Evidence Selective Inspection Mode REDDWELL connects to cameras above turnstiles. When AI detects an infraction—like tailgating or jumping—it triggers an alert to your smartphone within approximately 3 seconds. You receive images or short video clips of the infraction, allowing you to visually identify the offender and intercept them before they reach the platform. Instead of blocking stairways, officers intervene only when the app flags a specific target. Visual proof before approaching helps de-escalate disputes and reduces conflict. Heatmaps & Strategic Deployment The MTA system tracks live metrics showing high evasion rates at specific gates, helping supervisors deploy teams based on real-time activity heatmaps for optimal impact. Smartphone Access Optical Verification Officers can access the REDDWELL app on their smartphones, ensuring full functionality even if dedicated tablets are unavailable. This flexibility guarantees uninterrupted MTA enforcement operations. Equipped with Optical Verification Devices (OVD), field officers can swiftly identify individuals in real-time, cross-referencing visual evidence from the app with on-ground observations for accurate MTA fare enforcement. The Impact Our Commitment Today, REDDWELL serves MTA transit systems globally, reducing fare evasion by up to 40% while improving officer safety and public satisfaction. It has transformed MTA enforcement by making it smarter, safer, and more equitable. REDDWELL remains committed to ethical AI deployment, privacy protection, and continuous innovation, ensuring technology serves the public good while respecting individual rights..

[Audio] THE END Thank you for completing the AI-Powered MTA Transit Enforcement Training. You are now equipped with the knowledge and skills to leverage cutting-edge technology for effective, ethical, and strategic MTA fare enforcement. Stay safe, stay smart, and use these tools to make our MTA transit system better for everyone..