Research Team: Stephen G. Ritchie (lead) and Andre Tok
UC Campus(es): UC Irvine
Problem Statement: Traffic counts are a major source for traffic volume data, which is needed for highway project development; financing considerations; project cost-benefit comparisons; analyzing, monitoring and controlling traffic movement on the highways; traffic accident surveillance; research purposes; highway maintenance; public information; highway legislation and many other purposes. Investments in detector infrastructure has been significant. Currently, there are approximately 3,000 Traffic Census stations in California that are used to report traffic count and classification data for State and Federal programs. The Truck Activity Monitoring System (TAMS) leverages existing in-pavement traffic sensors to provide truck activity data in California. This was accomplished by updating existing inductive loop detector sites with inductive signature technology, and implementing advanced truck classification models to provide detailed truck count data with over 40 detailed truck body configurations. The initial system was originally developed for the CARB for implementation at sixteen locations in the San Joaquin Valley and subsequently deployed statewide to over 90 detector locations in California with funding from California Department of Transportation (Caltrans).
Project Description: This study will improve the capabilities of the TAMS in two key areas. The first enhancement will be to design and develop the TAMS Traffic Census Reporting Dashboard – a centralized axle-based classification data reporting module within TAMS for the Caltrans Traffic Census Program, which reports traffic count data to the Highway Performance Measurement System (HPMS) to support the analysis of highway system condition, performance, and investment needs, and for apportioning Federal-aid Highway Funds. The second TAMS enhancement will leverage existing TAMS field infrastructure at strategic locations to further enhance the ability to monitor truck activity patterns on larger spatial and temporal scales. This will be accomplished through the integration of Bluetooth and inductive signature data.
Status: In Progress
Project Partner(s): California Air Resources Board (CARB)