policy brief

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

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.

Preprint Journal Article

Charging Infrastructure Decisions by Heavy-duty Vehicle Fleet Operators: An Exploratory Analysis

Abstract

Insufficient charging/fueling infrastructure poses a major challenge to achieving U.S. policy goals for transitioning the heavy-duty vehicle (HDV) sector to zero-emission vehicles. Addressing the infrastructure needs of HDV fleet operators, who are key demand-side stakeholders, is crucial for developing effective solutions and strategies. This study investigates these needs through a fleet survey of California’s drayage sector, focusing on battery electric trucks. Key aspects examined include preferences for charging locations, access types, charging duration, time-of-day for charging, and innovative solutions like Truck-as-a-Service. Analyzing responses from 53 companies with varying fleet sizes, annual revenues, and operational characteristics, the study employed a comprehensive exploratory approach, utilizing descriptive analysis, thematic analysis, and hypothesis testing. Findings reveal that while most fleets preferred on-site charging, about a quarter, primarily smaller fleets with five or fewer trucks, preferred both on-site and off-site options. Private access was often favored for on-site facilities, though some respondents recognized the benefits of shared access for expanding operational coverage. The study also identified a need for faster charging solutions at both off-site and on-site locations, particularly for long-haul or mixed operations. Time-of-day preferences varied widely, driven by the need for efficient operations. Furthermore, a small proportion of participating fleets preferred Truck-as-a-Service over traditional procurement, predominantly among smaller fleets or those with lower revenues. The comprehensive research findings contribute to a deeper understanding of charging infrastructure needs and offer practical insights for policy practitioners and industry stakeholders committed to advancing zero-emission infrastructure. ​​

published journal article

Real-Time Truck Characterization System: A Pilot Implementation of the Freight Mobility Living Laboratory (FML2)

Publication Date

September 5, 2024

Abstract

California possesses multiple major freight gateway and logistics facilities that serve both the state and the entire U.S. But the economic, environmental, and local community impacts of trucks, especially heavy-duty trucks that are currently essential to our supply chains and freight transportation system remain poorly measured due to the lack of comprehensive and detailed truck activity data. This paper describes the pilot implementation of the real-time, scalable, and cost-efficient Freight Mobility Living Laboratory (FML2). This system provides truck characterizations across multiple attributes, such as truck body types, axle-based and Gross Vehicle Weight Rating (GVWR)-based classification and is currently deployed at 30 detection locations in Southern California along major freight corridors to support freight modeling and analysis needs. This paper details the design of the FML2 from edge data processing, predictive model development, communication architecture, and backend data storage to the real-time data dashboard to visualize the classification results. Three case studies have been presented at the end of the paper to demonstrate the potential of FML2 for use by both researchers and practitioners to gain further insights on truck activities.

policy brief

Shifting Future Electric Vehicle Trips to e-Bikes Could Help Reduce Electricity Demand at Critical Times in California

Abstract

California aims to replace gasoline and diesel light-duty vehicles (LDVs) with zero-emission LDVs, many of which will be plug-in battery electric vehicles (BEVs) and achieve 100% zero-carbon electricity by 2045. Large-scale plug-in BEV deployment will substantially increase electricity demand, particularly during peak hours (4:00pm to 9:00pm) when renewable energy is in short supply. Popular strategies for charging BEVs with electricity produced from renewable energy include smart charging and creating more energy storage that soaks up renewable energy during the day and dispenses it later when needed. These strategies, however, may not be enough. Consumer acceptance limits smart charging, and increased energy storage capacity is expensive. Another potential strategy involves lowering the overall demand for electricity by shifting BEV trips to electric-powered bicycles (e-bikes). While e-bikes cannot entirely replace BEV trips, they are ideal for short trips (five miles or less). Currently, 64% of US vehicle trips fall into the short trip category. Using synthetic travel pattern data from the San Diego region, we quantified the electric grid cost savings of shifting future BEV trips to e-bikes. For our analysis, we determined the passenger LDV trips that e-bikes could potentially replace. To provide an upper bound on replaceable trips, we considered trips that met the following criteria: LDV trips within home-based tours (a sequence of trips starting and ending at the home location) made by no more than two household members (between 16 and 70 years old), with less than five stops, under four hours in travel duration, and with individual trip distances up to seven miles long. We also created three scenarios that differ in terms of the tour purposes:
• Scenario 1: All purposes (e.g., work, recreation, eating out, etc.) except escort (i.e., transporting someone else to their activity) and shopping tours
• Scenario 2: All purposes except escort tours
• Scenario 3: All purposes

published journal article

Dynamic Pricing for Maximizing Performance of High-Occupancy Toll Lanes Along a Freeway Corridor

Abstract

Single-occupancy vehicles (SOVs) are charged to use the high-occupancy-toll (HOT) lanes, while high-occupancy-vehicles (HOVs) can drive in them at no cost. The pricing scheme for HOT lanes has been extensively studied at local bottlenecks or at the network level through computationally expensive simulations. However, the HOT lane pricing study on a freeway corridor with multiple origins and destinations as well as multiple interacting bottlenecks is a challenging problem for which no analytical results are available. This paper attempts to fill the gap by proposing to study the traffic dynamics in the corridor based on the relative space paradigm. In this new paradigm, the interaction of multiple bottlenecks and trips can be captured with Vickrey’s bathtub model by a simple ordinary differential equation. The paper considers three types of lane choice behavior and analyze their properties. Then, it proposes a distance-based dynamic pricing scheme based on a linear combination of I-controllers. This closed-loop controller is independent of the model and feeds back the travel time difference between HOT lanes and general-purpose lanes. Given the mathematical tractability of the system model, this study analytically studies the performance of the proposed closed-loop control under constant demand and show the existence and stability of the optimal equilibrium. Finally, the results were verified with numerical simulations considering a typical peak period demand pattern.

policy brief

Automated Vehicles and Transportation Network Companies Will Likely Impact the Efficacy of Transportation Pricing Strategies

published journal article

Fleet Operator Perspectives on Alternative Fuels for Heavy-duty Vehicles

Abstract

Despite the deployment of alternative fuel vehicles (AFVs) being one of the promising measures to reduce air pollutants and greenhouse gas emissions, AFVs still represent a very small share in the heavy-duty vehicle (HDV) sector. Understanding HDV fleet operator perspectives on alternative fuels is critical to developing effective demand-side strategies to facilitate wider and more rapid adoption of heavy-duty AFVs. This study explored California HDV fleet operator perspectives on viable alternative fuel options in the next 10–20 years, along with motivators for, and barriers to, such adoption. Eighteen in-depth qualitative interviews were conducted, after which thematic analysis was employed to analyze the interview data. Electric, hydrogen, compressed natural gas (CNG), and hybrid options were commonly perceived as viable in the 2030s by the participating organizations. Various optimistic aspects were addressed, including advanced technologies and emission reduction benefits (electric/hydrogen), continued fuel commitments due to their fleet or infrastructure investments already made (CNG), and lower complexity in fleet routing along with favorable driver acceptance (hybrid options). However, many concerns and uncertainties were also reported, including functional unsuitability (electric), uncompetitive upfront costs (hydrogen), unready infrastructure, perceived unavailability of vehicles, uncertain return on investment (electric/hydrogen), and unpromising support from state government (CNG). The study findings help fill a key knowledge gap in AFV fleet adoption research regarding HDV fleet operator perspectives, and contribute to developing demand-side strategies to aid the success of AFV diffusion throughout the HDV market.

working paper

Seamless Travel: Measuring Bicycle and Pedestrian Activity in San Diego County and its Relationship to Land Use, Transportation, Safety, and Facility Type

Abstract

This paper provides the data collection and research results for the Seamless Travel project. The Seamless Travel Project is a research project funded by Caltrans and managed by the University of California Traffic Safety Center, with David Ragland, PhD., as the Principal Investigator and Michael Jones as the Project Manager. The project is funded by Caltrans Division of Innovation and Research and is being conducted by the Traffic Safety Center of University of California Berkeley and Alta Planning + Design.

Measuring bicycle and pedestrian activity is a key element to achieving the goals of the California Blueprint for Bicycling and Walking (the Blueprint). Meeting these goals, which include a 50% increase in bicycling and walking and a 50% decrease in bicycle and pedestrian fatality rates by 2010, and increases in funding for both programs, will require a quantifiable and defensible base of knowledge. This research helps meet two of the Blueprint’s major strategic objectives: (1) collecting data on volumes and facilities, and (2) determining the most cost-effective methods of estimating bicycle and pedestrian collision rates.

research report

Development of an Adaptive Corridor Traffic Control Model (PATH TO 5323)

Abstract

This research develops and tests, via microscopic simulation, a real-time adaptive control system for corridor management in the form of three real-time adaptive control strategies: intersection control, ramp control and an integrated control that combines both intersection and ramp control. The development of these strategies is based on a mathematical representation that describes the behavior of traffic flow in corridor networks and actuated controller operation. Only those parameters commonly found in modern actuated controllers (e.g., Type 170 and 2070 controllers) are considered in the formulation of the optimal control problem. As a result, the proposed strategies easily could be implemented with minimal adaptation of existing field devices and the software that  controls  their  operation.  Microscopic  simulation  was  employed  to  test  and  evaluate  the performance of the proposed strategies in a calibrated network. Simulation results indicate that the proposed strategies are able to increase overall system performance and also the local performance on ramps and intersections. Prior to testing the complete model, separate tests were conducted to evaluate the intersection control model on: 1) an isolated intersection, and 2) a network of intersections along an arterial. The complete model was then tested and evaluated on the Alton Parkway/I-405 corridor network in Irvine, California. In testing the optimal control model, we simulated a variety of conditions on the freeway and arterial subsystems that cover the range of demand from peak to non-peak, incident to non-incident, conditions. The results of these experiments were evaluated against full-actuated operation and found to offer improved performance.