policy brief

New Insights from Satellite Data Show the Impact Trucks are Having on Communities in Southern California

Publication Date

March 1, 2025

Associated Project

Pilot Study: Use of High-Resolution Satellite Data to Identify Truck Traffic On the Roads and Near Warehouses Across Multiple Regions in Southern California

Author(s)

Jun Wu

Areas of Expertise

Freight， Logistics， & Supply Chain, Intelligent Transportation Systems， Emerging Technologies， & Big Data

View policy brief

Abstract

The rapid growth in freight transportation, particularly heavy-duty trucks, poses significant environmental and public health challenges for communities near major ports and freeways. In areas such as those near the Port of Los Angeles and the I-710 corridor, communities are exposed to elevated levels of air pollution, noise pollution, and associated health risks. Traditional traffic data collection methods primarily concentrate on gathering traffic volume data for freeway segments or smaller areas, often overlooking heavy-duty vehicles across roadway networks and in local communities.

To better understand the environmental impact and spatial distribution of heavy-duty truck traffic, this research employed a deep learning approach to analyze satellite imagery and publicly accessible spatial data. This approach allowed identification and categorization of heavy-duty trucks and shipping containers along critical freight routes and analysis of impacts on adjacent communities.

Related Publications

journal article preprint | March 13, 2025

A Deep-Learning Approach to Detect and Classify Heavy-Duty Trucks in Satellite Images

journal article preprint

A Deep-Learning Approach to Detect and Classify Heavy-Duty Trucks in Satellite Images

Publication Date

August 29, 2024

Associated Project

Pilot Study: Use of High-Resolution Satellite Data to Identify Truck Traffic On the Roads and Near Warehouses Across Multiple Regions in Southern California

Author(s)

Yiqiao Li, Langting Sizemore, Jun Wu

Areas of Expertise

Freight， Logistics， & Supply Chain, Intelligent Transportation Systems， Emerging Technologies， & Big Data

View journal article preprint

Abstract

Heavy-duty trucks serve as the backbone of the supply chain and have a tremendous effect on the economy. However, they severely impact the environment and public health. This study presents a novel truck detection framework by combining satellite imagery with Geographic Information System (GIS)-based OpenStreetMap data to capture the distribution of heavy-duty trucks and shipping containers in both on-road and off-road locations with extensive spatial coverage. The framework involves modifying the CenterNet detection algorithm to detect randomly oriented trucks in satellite images and enhancing the model through ensembling with Mask RCNN, a segmentation-based algorithm. GIS information refines and improves the model’s prediction results. Applied to part of Southern California, including the Port of Los Angeles and Long Beach, the framework helps assess the environmental impact of heavy-duty trucks in port-adjacent communities and understand truck density patterns along major freight corridors. This research has implications for policy, practice, and future research.

Related Publications

policy brief | April 23, 2025

New Insights from Satellite Data Show the Impact Trucks are Having on Communities in Southern California

research report

Development of a New Methodology to Characterize Truck Body Types along California Freeways

Publication Date

December 31, 2015

Associated Project

Development of a New Methodology to Characterize Truck body Types Along California Freeways

Author(s)

Stephen Ritchie, Andre (Yeow Chern) Tok

Areas of Expertise

Freight， Logistics， & Supply Chain, Intelligent Transportation Systems， Emerging Technologies， & Big Data

View research report

Abstract

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.

BEng/PhD/MS Thesis

Commercial vehicle classification system using advanced inductive loop technology

Publication Date

September 7, 2008

Associated Project

Measuring Commercial Vehicle Travel Through New High Fidelity Inductive Sensors

Author(s)

Andre (Yeow Chern) Tok

Areas of Expertise

Freight， Logistics， & Supply Chain, Intelligent Transportation Systems， Emerging Technologies， & Big Data

View BEng/PhD/MS Thesis

Abstract

Commercial vehicles typically represent a small fraction of vehicular traffic on most roadways. However, their influence on the economy, environment, traffic performance, infrastructure, and safety are much more significant than their diminutive numerical presence suggests. This dissertation describes the development and prototype implementation of a new high-fidelity inductive loop sensor and a ground-breaking commercial vehicle classification system based on the vehicle inductive signatures obtained from this sensor technology. This new sensor technology is relatively easy to install and has the potential to yield reliable and highly detailed vehicle inductive signatures for advanced traffic surveillance applications. The Speed PRofile INterpolation Temporal-Spatial (SPRINTS) transformation model developed in this dissertation improves vehicle signature data quality under adverse traffic conditions where acceleration and deceleration effects can distort inductive vehicle signatures. The axle classification model enables commercial vehicles to be classified accurately by their axle configuration. The body classification models reveal the function and unique impacts of the drive and trailer units of each commercial vehicle. Together, the results reveal the significant potential of this inductive sensor technology in providing a more comprehensive commercial vehicle data profile based on a unique ability to extract both axle configuration information as well as high fidelity undercarriage profiles within a single sensor technology to provide richer insight on commercial vehicle travel statistics.

working paper

Commercial Vehicle Classification using Vehicle Signature Data

Publication Date

August 30, 2008

Author(s)

Hang Liu, Shin-Ting (Cindy) Jeng, Andre (Yeow Chern) Tok, Stephen Ritchie

Areas of Expertise

Freight， Logistics， & Supply Chain, Intelligent Transportation Systems， Emerging Technologies， & Big Data

View working paper

Abstract

Knowledge of vehicle classes is especially useful for monitoring commercial vehicles (CVs). Accurate CV class information will enhance truck traffic surveillance and fleet management, such as in port areas by providing information for environmental impact investigations. From an implementation perspective, it is recognized that there are often significant advantages to use the existing inductive loop infrastructure. However, inductive loops are not always the most practical surveillance technology considering the required implementation effort and cost. In this regard, this study explored the potential of adopting a new vehicle signature detection technology – wireless magnetic sensors – for CV classification. The vehicle signature data used for the development of the wireless sensor based models was collected from the University of California, Irvine (UCI) Commercial Vehicle Study Test-bed in San Onofre, California. Vehicle signatures from round inductive loop sensors were also collected for refining an existing round loop based model and for comparison purposes. Significant dropped data was observed in the wireless sensor signatures, which required the implementation of a dual sensor data recovery procedure to reconstruct the signatures, which would otherwise have been unusable. The results indicate that the single wireless sensor vehicle classification model, which is based on multi-layer perceptron neural network, successfully distinguished single-unit and multi-unit trucks with 93.5% accuracy. The double wireless sensor vehicle classification model, which adopted a K-means clustering and discriminant function, achieved 73.6% accuracy, while the round loop based model produced even better performance (85%) in testing, both according to the FHWA scheme F with 13 classes.

working paper

Traffic Congestion and Trucking Managers' Use of Automated Routing and Scheduling

Publication Date

January 19, 2003

Author(s)

Thomas Golob, Amelia Regan

Areas of Expertise

Freight， Logistics， & Supply Chain, Intelligent Transportation Systems， Emerging Technologies， & Big Data

View working paper

Abstract

Using data from a 2001 survey of managers of 700 trucking companies operating in California, we tested competing hypotheses about the relationship between managers’ perceptions of the impact of traffic congestion on their operations and their companies’ adoption of routing and scheduling software. Demand for automated routing and scheduling was found to be influenced directly by the need to re-route drivers, and indirectly by the need, generated by customers’ schedules, to operate during congested periods. We were also able to identify which types of trucking companies are most affected by congestion and which types are more likely to adopt such software.

working paper

The Perceived Usefulness of Different Sources of Traffic Information to Trucking Operations

Publication Date

May 31, 2001

Author(s)

Thomas Golob, Amelia Regan

Areas of Expertise

Freight， Logistics， & Supply Chain, Intelligent Transportation Systems， Emerging Technologies， & Big Data

View working paper

Abstract

Managers in charge of the California operations of nearly 1,200 trucking companies were asked their opinions regarding how useful various sources of traffic information are to their dispatchers and to their drivers. They were also asked to evaluate the usefulness of improved traveler information systems. Nonlinear canonical correlation analysis was used to link company characteristics and perceptions of the value of the sources. Results showed that evaluations of sources such as Internet traffic information, in-vehicle navigation systems, and area-wide dedicated highway advisory radio are primarily related to the location of operations, whether a trucking operation is private or for-hire, the average length of the company’s load moves, and the provision of intermodal services.

journal article preprint

Freight Operators’ Perceptions of Congestion Problems and the Application of Advanced Technologies: Results from a 1998 Survey of 1200 Companies Operating in California

Publication Date

September 30, 2000

Author(s)

Amelia Regan, Thomas Golob

Areas of Expertise

Freight， Logistics， & Supply Chain, Intelligent Transportation Systems， Emerging Technologies， & Big Data

View journal article preprint

Abstract

Freight transportation plays a vital role in the economy of the nation and the state of California in particular. The value of total freight shipments originating in California in 1997 is estimated $638.5 billion, 10.6 percent of all US shipments by value. This represents 706.5 million tons of fright an amount equal to 7.2 percent of the freight move nationally, measured by weight. Measured by value and weight, respectively, 67.4 and 73.7 percent of this fright moved by tuck. An additional 15.4 and 2.4 percent (by value and weight) of the freight originating in California moved over more than one mode, most likely spending part of its journey over the road. The California Trucking Association estimates that trucking employs one out of twelve workers in California.