Vehicle Classification Schemes Used in Traffic Survey Systems
How consistent AI-based vehicle classification improves data quality for engineering decisions.
Accurate traffic data depends on consistent vehicle classification — and “consistent” is the operative word, since classification schemes only add value if the same vehicle gets classified the same way every time it’s counted, regardless of who or what is doing the counting.
Typical schemes cover categories like two-wheelers, three-wheelers/autos, cars, light commercial vehicles (LCVs), buses, and heavy commercial vehicles (trucks) — sometimes subdivided further by axle count, which matters specifically for pavement-design purposes, since axle loading is a direct input to pavement thickness calculations.
Manual classification introduces exactly the inconsistency that undermines this data: one counter’s judgment call on whether a vehicle is an LCV or a light truck may differ from another’s, and the same counter’s criteria can drift over a long shift. AI-based classification applies identical criteria to every vehicle, every time — which matters most precisely when the resulting data feeds an engineering decision like pavement loading design, where classification errors compound into real infrastructure cost.
