AI Vehicle Recognition Helps Blackford Transit Cut Identification Time by 82%

Blackford Transit is a US based transportation agency managing the traffic operations, parking enforcement, and campus transit of a large university town. The organization faces sharp daily and seasonal shifts in vehicle flow due to academic calendars, events, and visitor influx, requiring dependable monitoring and rapid response.

To modernize its traffic enforcement and monitoring, Blackford Transit adopted our AI Enabled Vehicle Number Recognition (VNR) Platform, built on Azure’s Computer Vision and Custom Vision frameworks. The system uses machine learning models to detect and read license plates from images or live camera feeds, automatically verifies recognition confidence, and retrieves vehicle and owner information in seconds. This intelligent automation powered by a scalable cloud architecture helps staff and law enforcement manage high-traffic zones more effectively, strengthen compliance, and secure sensitive areas through real-time data, alerts, and analytics.

• Sector: Transportation | Security | Law Enforcement
• Project Type: AI-Powered Vehicle License Plate Recognition & Owner Data Retrieval
• Platforms: Angular | .NET 8 | SQL Database | Azure Blob Storage | Azure Computer Vision | Azure Custom Vision

• Manual and Time Heavy Verification – Officers manually entered plate numbers into separate systems, causing long queues and frequent input errors during peak hours.
• Unpredictable Traffic Peaks – Sudden spikes during university events and semester openings strained staff capacity and led to slower checks and delayed enforcement.
• Compliance Gaps – Limited personnel and manual monitoring allowed many vehicles to bypass parking and permit regulations, affecting both revenue and road discipline.
• Security Blind Spots – Sensitive areas like faculty parking, restricted research zones, and student residential entrances lacked proactive alerts for unauthorized entries.
• Disjointed Information Systems – Vehicle data was scattered across parking, transport, and security departments, creating delays in collaboration and cross verification.

• Automated Plate Recognition – AI algorithms detect license plates from uploaded images or live video streams, even under low light or partial obstruction.
• Confidence Based Validation – Each recognition result includes a confidence score, allowing auto approval of high confidence matches and manual review for uncertain cases.
• Instant Record Retrieval – Integrated data connectors automatically fetch vehicle and owner records, improving speed and decision accuracy.
• Real-Time Security Alerts – Alerts trigger when unauthorized or blacklisted vehicles are detected in restricted or high risk zones.
• Unified Multi-Agency Dashboard – A single cloud-based interface allows parking enforcement, security, and law enforcement teams to collaborate efficiently with shared access to logs and visual evidence.
• Scalable Cloud Infrastructure – Designed to handle sudden surges in data traffic during events without downtime or latency issues.

• Vehicle identification time reduced by 82% – Average verification dropped from minutes to seconds using automated recognition.
• Traffic compliance improved by 41% – Automated detection and faster enforcement reduced unauthorized parking and traffic violations across campus zones.
• Security response time cut by 67% – Unauthorized vehicle detection alerts enabled near instant response in sensitive areas.
• Administrative efficiency improved by 36% – Centralized dashboards eliminated duplicate work across transit and law enforcement teams.
• Revenue recovery increased by 24% – Higher enforcement accuracy reduced leakage from unpaid parking and violations, strengthening revenue collection.
• Annual time savings of 2,200 staff hours – Freed up officer capacity for proactive traffic management and safety initiatives.
• Improved citizen satisfaction – Faster enforcement, reduced congestion, and smoother parking management improved feedback from both residents and the student community.