How to Achieve Up to 99.9% Vehicle Classification Accuracy without Intrusive Sensors

EXECUTIVE SUMMARY:

For decades, vehicle classification in U.S. tolling has been closely tied to pavement-embedded roadside infrastructure. In earlier electronic toll collection environments, intrusive sensors were the industry standard because they offered reliable performance for tolling authorities. However, as tolling operations have scaled and modernized over the years, these classification systems are no longer sustainable and present several challenges ranging from costly installation and maintenance fees, to frequent repairs that necessitate lane closures. Additionally, sensor accuracy is subject to wear and tear over time as hardware deteriorates from heavy traffic loads and inclement weather conditions.¹

When intrusive sensors fail, tolling errors and customer disputes increase, and manual review teams become overwhelmed which can create inefficiencies across the entire back-office ecosystem.
To meet today’s expectations for accuracy and operational resilience, tolling agencies are leaning more on the AI-driven capabilities of their back offices and embracing non-intrusive classification technologies that lower maintenance burdens and do not require them to cut into the pavement.

These advanced solutions have made accuracy independent of physical infrastructure, and help agencies strengthen revenue assurance while offering a future-proof path towards more efficient tolling operations.

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