ARB Research Seminar

This page updated June 21, 2016

Development and Demonstration of the Truck Activity Monitoring System

Photo of Stephen Ritchie

Stephen Ritchie

Photo of Andre Tok

Andre Tok

Stephen Ritchie, Ph.D., Director, and Andre Tok, Ph.D., Assistant Project Scientist, Institute of Transportation Studies, University of California, Irvine

July 13, 2016
Cal EPA Headquarters, 1001 "I" Street, Sacramento, CA

Research Project


As an independent state, California's economy would rank as the sixth largest in the world. It possesses key freight transportation gateways to Mexico and the Asia Pacific, and is home to the most productive agricultural region in the United States. The San Pedro Bay Ports in Southern California is the busiest port complex in the United States, while several international and domestic air freight hubs are also located within the state. Hence, understanding freight movement activity is an essential component to ensure that the strategic plans and policies for transportation infrastructure investment in the State of California can adequately support the expected growth in freight activity.

Recent studies have revealed a significant void in detailed truck activity data necessary to understand the activity of trucks within the transportation infrastructure. While current technologies such as truck GPS data can provide truck travel information, they are not adopted uniformly across all truck fleets, with a tendency biased towards larger corporate fleets. Existing detector infrastructure is capable of providing truck counts by axle configuration at over 100 Weigh-In-Motion detector locations within the State of California. However, axle-based data provide limited association with the industries and facilities served by trucks.

The Truck Activity Monitoring System was initially developed by the University of California, Irvine Institute of Transportation Studies and funded by the California Air Resources Board (ARB) as a research study to develop a new advanced tool that will better characterize truck travel in the State of California by providing truck counts by detailed body configuration using inductive signature technology. The original study focus was to provide detailed truck data at sixteen locations in the California San Joaquin Valley, also known as the Central Valley - the agricultural food basket of the United States - where truck activity patterns vary seasonally and are not well understood. In recognition of its potential and contribution, this continuation effort is now being funded by California Department of Transportation to enhance and expand the system to over ninety locations along major truck corridors at the California state borders, regional cordons and within metropolitan area.

The output from this study can be used to distinguish the proportion of long haul and short haul trips in major corridors and vocation which informs vehicle duty cycle. This can improve the heavy-duty vehicle classifications in ARB's EMFAC (EMission FACtor) motor vehicle emissions model and to predict the effectiveness of various emissions control programs. Further, the information from this study can also be used to calibrate and validate the statewide freight-forecasting model and can help inform freight models under development by metropolitan planning organizations (MPOs). The data from this study can be used as a key input to the California Vehicle Activity Database (CalVAD) to estimate the truck activities. Ultimately, the results from this study can help to develop strategies to reduce emissions from California's trucks for use in the State Implementation Plan, Scoping Plan, Short Lived Climate Pollutant Plan, and Sustainable Freight Action Plan.

Speaker Biography

Stephen G. Ritchie, Ph.D., is Director of the Institute of Transportation Studies and Professor of Civil Engineering at the University of California, Irvine. Professor Ritchie's research interests focus on intelligent and sustainable transportation systems planning and engineering.

Particular interests include modeling and assessing the GHG, energy, air quality and health impacts of transportation operations and alternative fueled vehicles; new and innovative approaches for statewide freight transportation and commodity flow modeling for infrastructure investment and environmental analysis; development of data sources, sampling frameworks and new survey instruments to estimate statewide commercial vehicle utilization; collection and integration of high-resolution sensor data for advanced real-time modeling and classification of heavy duty truck characteristics and movements; and real-time freeway and arterial traffic performance measurement based on inductive signature vehicle reidentification. Professor Ritchie received his doctorate degree in civil engineering-transportation from Cornell University, Ithaca, New York.

Andre Y.C. Tok, Ph.D., is Assistant Project Scientist at the University of California, Irvine Institute of Transportation Studies. Dr. Tok is currently the project manager and lead researcher involved in the Caltrans-funded advanced truck count study that will enhance the truck classification system and deploy this new system to over ninety major truck corridors in the California to provide detailed truck data that is expected to serve the needs of state and regional agencies.

Dr Tok is passionately interested in the area of advanced detection technologies and freight transportation data. Of his recent research efforts, his was the lead researcher in the development of the California Statewide Freight Forecasting Model, the California Vehicle Inventory and Use Pilot Survey, and the integration of weigh-in-motion and inductive signature technologies to develop advanced truck classification models that will improve truck activity and freight movement data. Dr. Tok obtained his Bachelors in Civil and Environmental Engineering at the National University of Singapore in 2002. He subsequently earned his Ph.D. at UC Irvine in 2008 where his research was the first investigation in obtaining detailed truck body classification using a prototype inductive sensor called the Blade.

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