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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Data analytics for intermodal freight transportation applications

With the growth of intermodal freight transportation, it is important that transportation planners and decision-makers are knowledgeable about freight flow data to make informed decisions. This is particularly true with Intelligent Transportation Systems (ITS) offering new capabilities for intermodal freight transportation. Specifically, ITS enables access to multiple different data sources, but they have different formats, resolutions, and time scales. Thus, knowledge of data science is essential to be successful in future ITS-enabled intermodal freight transportation systems. This chapter discusses the commonly used descriptive and predictive data analytic techniques in intermodal freight transportation applications. These techniques cover the entire spectrum of univariate, bivariate, and multivariate analyses. In addition to illustrating how to apply these techniques manually, this chapter will also show how to apply them using the statistical software R. Additional exercises are provided for those who wish to apply the described techniques to more complex problems.

Huynh, Nathan↗
DOE OSTI2023

Emerging Trends in Freight [Slides]

Freight transportation accounts for only 5% of the vehicles on U.S. roads, but 10% of vehicle miles of travel and 27% of on-road energy use. With rail and water, freight accounts for 28% of U.S. transportation energy and emissions. Trucking is the primary mode for transporting goods, but rail is a significant mode for longer shipments. Economic, technological, and operational trends pose both challenges and opportunities to meet U.S. demand for goods movement as well as national decarbonization goals. This presentation provides a systems perspective for energy efficiency freight mobility and an overview of available NREL analytical tools to address these challenges.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

The Port of Los Angeles Zero- and Near-Zero-Emission Freight Facilities Shore to Store Project

The City of Los Angeles Harbor Department (Harbor Department, Port of Los Angeles) partnered with Equilon Enterprises LLC (d/b/a Shell Oil Products US) (Shell), Toyota Motor North America (Toyota), and Kenworth Truck Company (Kenworth) partnered with the Port of Hueneme, United Parcel Service, Total Transportation Services Inc., Southern Counties Express, Toyota Logistics Services, Air Liquide, National Renewable Energy Laboratory, Coalition For A Safe Environment, and the South Coast Air Quality Management District to introduce hydrogen fuel into the Southern California drayage truck market by demonstrating near-commercial heavy-duty hydrogen fuel cell electric trucks at and between freight facilities throughout the region, while continuing to lay the groundwork for battery-electric operations. The Shore to Store project built on project team experience to help realize our vision of zero-emission freight operations in the future. Ten Kenworth zero-emission Class 8 fuel cell electric trucks, integrated with Toyota’s fuel cell drive technology, were operated by United Parcel Service, Total Transportation Services Inc., Southern Counties Express, and Toyota Logistics Services in commercial service. The demonstration fleet fueled at the Shore to Store hydrogen fueling stations that were built in Ontario, California, and Wilmington, California. An additional station at the Port of Long Beach (Portal Station) was available for fueling the fleet. Portal Station was supported by grants from the California Energy Commission and South Coast Air Quality Management District and used as match funding for the Shore to Store project. The Port of Hueneme demonstrated two battery-electric yard tractors, and Toyota Logistics Services demonstrated two zero-emission forklifts at their warehouse facility, showcasing elements of the entire supply chain operating with zero emissions. This project showcased a snapshot of the zero-emission supply chain of the future, providing a model by which freight facilities can support zero-emission operations. The Shore to Store project: Demonstrated the technical feasibility of zero-emission hydrogen fueled Class 8 heavy-duty trucks and electric cargo handling equipment in rigorous goods movement operation throughout the Southern California region. Cumulatively completed 59,212 miles of zero-emission operation, with 21,650 miles driven in-service with the fleets, using hydrogen fuel cell electric Class 8 heavy-duty trucks. Operated zero-emission yard tractors for a total of 2,749.6 hours. Created direct localized emission reductions in designated disadvantaged communities, including those in zip codes 90220, 90247, 90248, 90731, 90744, 90802, and 91761. For the 59,212 miles of zero-emission operation, reduced emissions by an estimated total of 15.5 kg NOX (0.163 g/km), 1.01 kg SOX (0.0106 g/km), and 304.8 metric tonnes of CO2e (3.2 kg/km) compared to diesel baseline vehicles including both tailpipe emissions and the emissions from producing the fuel sources.

08 HYDROGEN↗
DOE OSTI2024

An OpenStreetMaps based tool to study the energy demand and emissions impact of electrification of medium and heavy-duty freight trucks

In this paper, we present the mathematical formulation of an OpenStreetMaps (OSM) based tool that compares the costs and emissions of long-haul medium and heavy-duty (M&HD) electric and diesel freight trucks, and determines the spatial distribution of added energy demand due to M&HD EVs. The optimization utilizes a combination of information on routes from OSM, utility rate design data across the United States, and freight volume data, to determine these values. In order to deal with the computational complexity of this problem, we formulate the problem as a convex optimization problem that is scalable to a large geographic area. In our analysis, we further evaluate various scenarios of utility rate design (energy charges) and EV penetration rate across different geographic regions and their impact on the operating cost and emissions of the freight trucks. Our approach determines the net emissions reduction benefits of freight electrification by considering the primary energy source in different regions. Such analysis will provide insights to policy makers in designing utility rates for electric vehicle supply equipment (EVSE) operators depending upon the specific geographic region and to electric utilities in deciding infrastructure upgrades based on the spatial distribution of the added energy demand of M&HD EVs. To showcase the results, a case study for the U.S. state of Texas is conducted.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Improving the accuracy of freight mode choice models: A case study using the 2017 CFS PUF data set and ensemble learning techniques

Here, the US Census Bureau has collected two rounds of experimental data from the Commodity Flow Survey, providing shipment-level characteristics of nationwide commodity movements, published in 2012 (i.e., Public Use Microdata) and in 2017 (i.e., Public Use File). With this information, data-driven methods have become increasingly valuable for understanding detailed patterns in freight logistics. In this study, we used the 2017 Commodity Flow Survey Public Use File data set to explore building a high-performance freight mode choice model, considering three main improvements: (1) constructing local models for each separate commodity/industry category; (2) extracting useful geographical features, particularly the derived distance of each freight mode between origin/destination zones; and (3) applying additional ensemble learning methods such as stacking or voting to combine results from local and unified models for improved performance. The proposed method achieved over 92% accuracy without incorporating external information, an over 19% increase compared to directly fitting Random Forests models over 10,000 samples. Furthermore, SHAP (Shapely Additive Explanations) values were computed to explain the outputs and major patterns obtained from the proposed model. The model framework could enhance the performance and interpretability of existing freight mode choice models.

42 ENGINEERING↗
DOE OSTI2026

Cascading economic losses from port disruptions under capacity constrained multimodal freight networks

This study quantifies how throughput disruptions at major seaports cascade through capacity-constrained multimodal freight networks and interregional production systems. We couple an agent-based model (ABM) multimodal freight simulation that resolves rerouting, terminal queueing, and inventory drawdown under binding modal and facility capacities with a multiregional output loss input-output (MRIIM) model that propagates realized delivery shortfalls across regions and sectors. The framework is demonstrated for the Port of Los Angeles using Freight Analysis Framework flows and Bureau of Economic Analysis input-output accounts and is evaluated over a 52-week horizon under deterministic sector targeted shocks and stochastic disruption realizations with uncertain severity and duration. Results indicate nonlinear amplification: realized national losses concentrate in manufacturing and transportation/warehousing even when exogenous port shocks are dispersed, suggesting that congestion spillback and limited short-run substitution can dominate the initial shock allocation. We further evaluate a tabular reinforcement-learning (Q-learning) intervention layer that selects among a small set of implementable system level levers (truck-to-rail and truck-to-barge shift settings) without overriding shipper routing, finding that such interventions reduce total losses for moderate disruptions but yield diminishing returns once substitute modes approach capacity. By linking operational freight behavior to system wide impacts under uncertainty, the proposed ABM-MRIIM pipeline provides a reusable workflow for port disruption stress testing, identification of structurally critical sectors/corridors, and evaluation of resilience interventions under realistic capacity limits.

42 ENGINEERING↗
DOE OSTI2020

SMART Mobility. Multi-Modal Freight Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019, and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Multi-Modal Freight Pillar. The Multi Modal Freight Pillar’s objective is to assess the effectiveness of emerging freight movement technologies and understand the impacts of the growing trends in consumer spending and e-commerce on parcel movement considering mobility, energy, and productivity. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant pillar’s Capstone Report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

BEAM CORE: A Flexible Ecosystem for Freight, Demographics, and Vehicle Analysis

The Behavior, Energy, Autonomy, and Mobility Comprehensive Regional Evaluator (BEAM CORE) is an open-source, modular ecosystem of highly refined, agent-based modeling tools developed by Lawrence Berkeley National Laboratory and the National Laboratory of the Rockies. Organizations such as metropolitan planning organizations, agencies, and companies can use BEAM CORE to analyze freight movement and optimize logistics solutions, assess the impacts of emerging freight technologies or e-commerce trends, model dynamic population growth and evolution, and understand the drivers and impacts of electric vehicle adoption across households in a given region. Users can choose from a flexible suite of modeling modules according to their needs and priorities. The modules integrate with travel demand models to support enhanced analysis of diverse scenarios involving freight movement, vehicle technologies, and other key factors relevant to regional planning.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

System-of-systems optimization of hydrogen infrastructure for heavy-duty freight corridors: The interstate 10 case study

Medium and heavy-duty freight transportation requires hydrogen energy infrastructure that is cost-effective, operationally reliable, spatially coherent, and resilient to demand variability along major corridors. This paper presents an integrated hydrogen corridor planning framework using Oak Ridge National Laboratory's OR-AGENT that couples freight-driven, route-resolved hydrogen demand modeling with optimized station siting, sizing, and station-level techno-economic analysis. The framework is demonstrated for the Interstate 10 freight corridor and the Houston-to-Los-Angeles region. Hydrogen demand is derived from high-resolution origin–destination freight data, duty-cycle characterization, and physics-based energy consumption modeling. Candidate refueling sites are selected from existing heavy-duty diesel fueling locations and optimized subject to onboard storage and station capacity constraints. Resulting station throughputs are evaluated using established techno-economic models for electrolytic hydrogen production and dispensing infrastructure. Results show that a regional, portfolio-level aggregation, average dispensed electrolytic hydrogen cost of $6.87–$7.26/kg is currently feasible, and is strongly influenced by demand density and utilization.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗
NASA NTRS1972

Current Problems and Issues in Air Freight Rates

Actions of the Civil Aeronautics Board in determining air freight rates are discussed. The tariff filings by domestic airlines for making basic changes in domestic fares and rates are reported. The roles of the carriers and the Civil Aeronautics Board in establishing freight rates are defined. Specific examples of areas of controversy in establishing freight rates are included. Methods for improving the air cargo and freight rate situation are proposed.

Stout, A. R.↗
DOE OSTI2022

Modeling Freight Traffic Demand and Highway Networks for Hydrogen Fueling Station Planning: A Case Study of U.S. Interstate 75 Corridor

The use of hydrogen as an alternative transportation fuel has gained much interest in recent years. Hydrogen can be utilized in electric vehicles equipped with hydrogen powertrains (including hydrogen internal combustion engines or fuel cells). Given that most of the freight in the U.S. is transported via diesel trucks, transition to hydrogen fuel would help achieve significant environmental benefits as well as accelerate the decarbonization of the freight transportation sector. This paper presents the methodology and results of a case study on modeling freight traffic demand and highway networks based on publicly available data for the Interstate 75 freight corridor. The purpose of this study is to prepare input traffic and network data that can support the planning of a hydrogen fueling station infrastructure. In particular, the data can be used for siting and characterizing an optimized framework of hydrogen fueling stations from candidate diesel stations along the Interstate 75 corridor. The methodologies developed and presented in this paper may be readily expanded and applied to any transport corridor given the data availability. This paper is the first in a series that will build out a comprehensive model to optimize a consolidated national hydrogen refueling infrastructure eco-system targeted at commercial vehicles.

Uddin, Majbah↗
DOE OSTI2025

Freight Electrification, Moving Goods While Managing Grid Impacts

Analyzing freight data from the Salt Lake City inland port demonstrates significant potential forintegrating battery-electric freight vehicles (BEVs) into key transportation corridors. Our partnership with freight operators like RSD and MWCS, and other companies moving freight around the port, provided valuable fleet telemetry and operationalinsights, highlighting critical corridors on and around I-15 and I-80.

Source record↗
DOE OSTI2026

Uncertainty quantification and reliability assessment for intermodal freight transportation

Intermodal freight optimization models support cost-effective, low-emission, and timely goods movement by coordinating trucks, rail, and barges. These models determine optimal flows, routing, and modal switches while respecting infrastructure and operational constraints. However, their real-world utility is often undermined by pervasive uncertainties-such as fluctuating transportation costs and emissions, variable terminal capacities, and uncertain freight demand-that distort key performance outcomes, including total system cost, carbon footprint, and transit time reliability. This study presents a structured framework for quantifying uncertainty in intermodal freight transportation (IFT) optimization. The framework evaluates how input uncertainty affects system performance and reliability, a critical need for ensuring that model-based decisions remain robust under real-world variability, especially amid volatile fuel prices, shifting demand, and growing disruptions. It integrates three complementary methods: (1) Sobol-based global sensitivity analysis to identify influential parameters affecting cost, emissions, and transit time, (2) Monte Carlo-based capacity perturbation analysis to assess robustness under probabilistic facility disruptions, and (3) Monte Carlo filtering with Bayesian inference to detect threshold-based performance vulnerabilities. The results highlight diesel truck unit cost as the dominant driver of variability. To improve system resilience, planners should prioritize uncertainty in fuel-related parameters when designing intermodal strategies.

Intermodal freight transportation↗
DOE OSTI2024

Abatement cost curve analysis of freight rail decarbonization alternatives

This paper investigates decarbonization alternatives for the freight rail industry, considering economic, environmental, and operational aspects. The study compares battery-electric and hydrogen fuel cell locomotives, drop-in fuels including biofuels and e-fuels, and overhead catenary electrification through abatement cost analyses. Scenario analysis identifies the cost-effectiveness of each of the technologies at different stages of decarbonization in the US Class 1 freight railroad network. We show that though battery locomotives offer a lower-cost decarbonization option in the considered scenarios, green hydrogen fuel-powered locomotives become more attractive when battery charging delays are considered. We introduce the concept of the technology margin to cater for uncertainties in battery charging operations and hydrogen fuel production for cases where carbon taxes are imposed on railroad operations. In conclusion, the study provides valuable insights for policymakers and rail operators, contributing to sustainable freight rail operations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

The Port of Los Angeles Zero- and Near-Zero-Emission Freight Facilities "Shore to Store" Project (Final Project Report)

The City of Los Angeles Harbor Department (Harbor Department, POLA) partnered with Equilon Enterprises LLC (d/b/a Shell Oil Products US) (Shell), Toyota Motor North America (Toyota) and Kenworth Truck Company (Kenworth) partnered with the Port of Hueneme (POH), United Parcel Service (UPS), Total Transportation Services Inc. (TTSI), Southern Counties Express (SCE), Toyota Logistics Services (TLS), Air Liquide, National Renewable Energy Laboratory (NREL), Coalition For A Safe Environment, and the South Coast Air Quality Management District (South Coast AQMD) to introduce hydrogen (H 2 ) fuel into the Southern California drayage truck market by demonstrating near-commercial heavy-duty H 2 fuel cell electric trucks at and between freight facilities throughout the region, while continuing to lay the groundwork for battery-electric operations. The "Shore to Store" (S2S) project built on project team experience to help realize our vision of zero-emission freight operations in the future. Ten Kenworth zero-emission Class 8 fuel cell electric trucks, integrated with Toyota's fuel cell drive technology, were operated by UPS, TTSI, SCE, and TLS in revenue service. The demonstration fleet fueled at the S2S hydrogen fueling stations that were built in Ontario, California and Wilmington, California. An additional station at the Port of Long Beach (Portal Station) was available for fueling the fleet. Portal Station was supported by grants from the California Energy Commission (CEC) and South Coast AQMD and used as match funding for the S2S project. POH demonstrated two battery-electric yard tractors, and TLS demonstrated two zero-emission forklifts at their warehouse facility, showcasing elements of the entire supply chain operating on zero-emissions. This project showcased a snapshot of the zero-emission supply chain of the future, providing a model by which freight facilities can support zero-emission operations.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Electrifying Terminal Trucks To optimize freight yards

The objectives of this project were to demonstrate the feasibility of electrification for freight yards’ diesel terminal fleets through pilot projects with two or more fleets, and to generate outreach material that can be used regionally and nationally to promote electrification in other terminal fleets. We planned to leverage the data into a model program for adoption of zero emissions freight handling and make information about this project, its outcomes, and the business case for zero emission deployments available to the larger freight marketplace.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Techno-Economic Analysis and Life Cycle Assessment of Alternative Fuels for Locomotives in the U.S. Freight Rail Sector

Freight rail is more energy-efficient than truck transport over long-haul distances, offering a low-energy and emissions-intensive option for transporting freight. This study evaluates techno-economic analysis and life cycle assessment of seven alternative unblended fuels for freight locomotive engines─biodiesel, renewable diesel (RD), bio-oils, methanol, dimethyl ether (DME), ethanol, and ammonia─across 16 fuel pathways utilizing soybean, corn, woody biomass, renewable hydrogen, and waste sources, e.g., sludge, manure, and industrial CO 2 , and compares these to conventional diesel. The minimum fuel selling price (MFSP) ranged from $\$2.05$ to $\$8.27$ per diesel gallon equivalent (2020 US dollars), with biocrude and RDs produced from hydrothermal liquefaction (HTL) of sludge having the lowest MFSPs due to coproduct credits and avoided waste treatment cost. Life cycle GHG emissions ranged from −41 to 53 g of CO 2 e/MJ. RD from waste via HTL achieves negative emissions by diverting sludge/manure from GHG-intensive conventional management. Few pathways such as biocrude, methanol, and DME require additional control for SO X emissions in the refinery, while ethanol, FT-diesel, and bio-oil require additional control for particulate matter emissions. Bio-oil and RD from sludge have lower marginal abatement cost or MAC (–$\$38$/tonne CO 2 lowest) while methanol and ammonia with renewable hydrogen have higher MAC ($\$490$/tonne CO 2 maximum).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Modeling freight mode choice using machine learning classifiers: a comparative study using Commodity Flow Survey (CFS) data

This study explores the usefulness of machine learning classifiers for modeling freight mode choice. We investigate eight commonly used machine learning classifiers, namely Naïve Bayes, Support Vector Machine, Artificial Neural Network, K-Nearest Neighbors, Classification and Regression Tree, Random Forest, Boosting and Bagging, along with the classical Multinomial Logit model. US 2012 Commodity Flow Survey data are used as the primary data source; we augment it with spatial attributes from secondary data sources. The performance of the classifiers is compared based on prediction accuracy results. The current research also examines the role of sample size and training-testing data split ratios on the predictive ability of the various approaches. In addition, the importance of variables is estimated to determine how the variables influence freight mode choice. The results show that the tree-based ensemble classifiers perform the best. Specifically, Random Forest produces the most accurate predictions, closely followed by Boosting and Bagging. With regard to variable importance, shipment characteristics, such as shipment distance, industry classification of the shipper and shipment size, are the most significant factors for freight mode choice decisions.

42 ENGINEERING↗