Modern electronic toll collection requires fast, accurate vehicle identification in unpredictable real-world conditions. TransCore, an ST Engineering company, developed the Neural Network Solutions System (N2S2) — an AI-powered video analytics platform that advances Automated License Plate Recognition, reduces misreads, minimises manual back-office reviews, and protects revenue integrity across global smart mobility networks.
Modern tolling depends on fast, accurate vehicle identification. As expressways, bridges, and express lanes handle rising traffic volumes, tolling agencies require systems that recognise vehicles reliably despite poor lighting, harsh weather, high-speed movement, and varied plate formats.
The primary challenge goes beyond simply capturing a clear image. Tolling systems must accurately interpret license plates across diverse operating environments. When reads are missed or misinterpreted, agencies face:
Traditional camera-based recognition systems perform well in controlled environments, but struggle against real-world factors like rain, fog, snow, glare, motion blur, and obstructed plates. At high transaction volumes, these limitations hamper operational scalability.
AI-powered video analytics leverages machine learning and deep neural networks to evaluate visual information dynamically. Instead of relying solely on static image capture or fixed rules, the technology analyses continuous video streams to recognise patterns in image quality, motion, plate structure, and environmental lighting.
To address these demands, TransCore developed the Neural Network Solutions System (N2S2)--an AI-driven platform built to enhance Automated License Plate Recognition (ALPR) precision, improve transaction processing, and scale tolling operations efficiently.
1. Enhancing ALPR Precision
N2S2 combines internal AI tools, advanced model-development capabilities from TransCore’s R&D teams, and production-ready software deployed in live customer environments. By reducing dependency on ideal image conditions, N2S2 improves ALPR performance in challenging real-world environments.
2. Strengthening Revenue Integrity
By automating image validation and vehicle classification, N2S2 minimises misreads and ensures transactions are processed accurately. This reduces the administrative effort needed to resolve billing disputes and audit exceptions.
3. Generating Traceability with Unique Digital Neural Arrays
For every processed license plate image, N2S2 can generate a Unique Digital Neural Array. This digital identifier ensures complete data traceability, supports downstream analytics, strengthens audit trails, and offers operators detailed visibility into long-term system performance.
4. Supporting Scalable, Interoperable Mobility Architecture
Hosted on a flexible cloud-based architecture, N2S2 supports multi-agency interoperability across North America, Asia Pacific, and the Middle East. It operates seamlessly alongside TransCore’s SmartPass® RFID readers to enable integrated credential-based tolling and account management.
With over 80 years of experience in transportation technology, TransCore brings deep domain expertise across tolling, congestion pricing, intelligent transportation systems (ITS), back-office management, and RFID technologies. As part of ST Engineering, TransCore’s capabilities contribute to a broader Smart Mobility portfolio aimed at building connected, efficient, and scalable transportation ecosystems worldwide.
AI-powered video analytics is redefining vehicle identification by overcoming the limitations of traditional camera-based recognition. By delivering high ALPR accuracy under severe weather, poor lighting, and high speeds, solutions like TransCore’s N2S2 reduce manual back-office reviews, safeguard revenue integrity, and provide a scalable foundation for modern, interoperable smart mobility networks.
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