Project at a Glance
Title: Development of a new methodology to characterize truck body types along California freeways
Principal Investigator / Author(s): Richie, Stephen
Contractor: UC Irvine
Contract Number: 11-316
Topic Areas: Health Effects of Air Pollution, Mobile Sources & Fuels
The purpose of this project was to develop a new methodology to characterize truck body types along California freeways. With new information on truck activity by body types, results from this study are expected to improve heavy-duty vehicle classification in the Emission Factors (EMFAC) model and the California Vehicle Activity Database (CalVAD), and provide critical data that is required for the analysis of freight movement that will benefit the California Statewide Freight Forecasting Model (CSFFM) and other freight- or truck-related studies.
This study sought to develop two types of classification models: the first from the combination of inductive loop signature and weigh-in-motion (WIM) data, and the second from standalone inductive loop signature data. The key benefit of these models is their readiness for implementation at existing traffic detector infrastructure such as inductive loop detector (ILD) and WIM sites. It was demonstrated through this study that the modifications to existing inductive loop detector and WIM sites were minimal, and did not compromise existing operations. The standalone inductive signature classification model (designed for implementation an existing ILD sites) demonstrated the ability to distinguish over 40 truck configurations, while the combined inductive loop signature and WIM classification model was able to identify over 60 truck types. These models were subsequently deployed at sixteen selected sites in the California San Joaquin Valley. A prototype web interface called the Truck Activity Monitoring System (TAMS, http://freight.its.uci.edu/tams ) was designed to generate dynamic reports of the results via an interactive web-based user interface.
Other models developed in this study include a method for estimating truck volumes by a reduced number of body types from standalone WIM data, an optimal site selection model for determining the optimal sites for deployment of the advanced classification system developed in this study, and a method for estimating gross vehicle weight distributions at inductive loop detector sites instrumented with inductive signature technology by using data obtained from affiliated WIM sites.
The project was separated into three phases: proof-of-concept truck body classification models were developed in Phase 1; model enhancement was performed in Phase 2; and system deployment took place as Phase 3.
For questions regarding research reports, contact: Heather Choi at (916) 322-3893
Stay involved, sign up with ARB's Research Email Listserver