Project Updates

While traffic counts are a fundamental and straightforward freeway monitoring
measure, they tell only part of the story. Heavy vehicles, such as trucks and buses, impose a much higher burden on bridges and highways than small individual vehicles.

The impact of heavy vehicles on traffic is significant because they are slower
and occupy more space than lighter vehicles. It is of consequence that pavement wear and tear increases exponentially with vehicle weight. Therefore, the ability to classify vehicles at traffic monitoring stations adds valuable information for transportation operators and planners.

The vehicle mix has implications for demand management, congestion thresholds, corridor safety, and, above all, design and maintenance requirements. Therefore, vehicle classification is an integral component of a comprehensive systems management strategy. The Federal Highway Administration (FHWA) requires regional classification studies to be performed periodically, but they’re usually done manually or with temporary setups. CCIT and Berkeley Transportation Systems, Inc. (BTS) partnered with Inductive Signature Technologies (IST) to enhance PeMS and
to provide automated vehicle classification at selected vehicle detection stations in the
San Diego area.

Funded by the San Diego Association of Governments (SANDAG), the project has enabled improvements to the web-based PeMS graphical user interface, allowing practitioners to directly access vehicle classification data along with other freeway measurements. IST’s inductive-loop technology provides automatic vehicle classification data that is streamed to PeMS and can now be stored and retrieved permanently. The classification engine identifies no fewer than 16 different types of vehicles, and this information can be readily used for planning, safety analyses, and project studies.

How Does it Work?