The U.S. Federal Highway Administration (FHWA) conducts Global Benchmarking Program (GBP) studies to assess, evaluate, and implement global innovations in key technology areas to help the nation respond to 21st century highway transportation challenges. FHWA initiated the GBP study on the “Use of Unmanned Aircraft Systems (UAS) to Enhance the Design, Construction, Inspection, and Maintenance of Transportation Infrastructure” in 2019. The purpose of the study was to learn about mature UAS applications in advanced digital project delivery and contactless practices conducted by international experts. The goal of the study was to identify opportunities to adopt UAS to enhance the design, construction, inspection, and maintenance of transportation infrastructure, thereby benefiting the U.S. highway transportation and digital construction sectors. In this report, UAS refers to unmanned and remotely piloted aircraft systems weighing less than 55 lbs. in the U.S. or less than 25 kg in the United Kingdom (U.K.) and Germany. The study team identified diverse uses of UAS as a supplemental or enhancing tool (rather than a replacement) for human workers performing critical structural inspections, conducting construction site surveys, working at high or difficult-to-reach locations, etc. Most importantly, the study team found that mature UAS use cases internationally have improved safety along with one or more of the following benefits: improved efficiency, improved data quality, greater data quantity, cost savings, and time savings compared to traditional methods. This report summarizes findings from virtual meetings and in-person visits conducted with subject matter experts (SMEs) in the U.K. and Germany. The U.S. study team visited the U.K. in June 2022 and Germany in September 2022. The team and international SMEs agreed that, at a time when transportation infrastructure in each country is nearing the end of anticipated design life or approaching the need for major maintenance/rehabilitation, UAS serve as an important tool to supplement and enhance transportation agencies’ and construction organizations’ day-to-day operations. Advances in the processing and managing of UAS-collected data present opportunities to “digitize” common practices in the construction sector, saving time and money in the initial construction design phase and allowing SMEs to focus their attention on high-risk or unusual issues. Data collected using UAS and digital models created using that data can accelerate the construction design process, increase safety during construction, and increase efficiency in construction inspection and maintenance tasks. UAS also provide economic growth and workforce development opportunities. This report organizes findings from the U.K. and Germany by (1) UAS use cases and best practices benefiting the highway transportation and digital construction sectors; (2) digital models and data management supported by UAS, and (3) asset lifecycle management supported by UAS. The study supports the U.S. Department of Transportation and FHWA’s Safety, Economic Growth, and Transformation priorities.
Mobility hubs (MHs) can facilitate seamless transitions between various transportation modes, such as transit, ridehailing, and micromobility, enhancing multimodal travel and delivering socioeconomic benefits. To inform mobility hub planning and development in Florida cities and beyond, this project develops a multi-criteria, geographic information system (GIS)-based MH site selection tool. The tool prioritizes factors such as multimodal travel needs, first-/last-mile connectivity, and socio-demographic considerations in the MH site selection process. Through an application of the tool in Gainesville, Florida, the authors have identified 17 potential MH sites, categorized into neighborhood, district, and regional levels. Survey findings reinforce the significance of MH to facilitate multimodal connectivity and reveal preferences for hub locations that align with residents’ travel patterns. Key mobility hub features strongly desired by Gainesville travelers include parking, bike/e-scooter racks, and non-transportation amenities such as safety features and digital displays. The authors have further tested the tool in the City of West Palm Beach, and the results demonstrate its broader applicability. This research offers a strategic roadmap and practical guidelines for cities to enhance transportation networks and support urban development through the implementation of well-planned MHs. The research team’s extensive collaboration with the City of Gainesville and subsequent funding from the U.S. Department of Energy to support the development of climate-controlled shelters at selected MH sites underscores the practical impact of this research.
To meet the expectation of multi-modal distributions from customers for last-mile delivery, a sustainable two-echelon E-Grocery delivery system is studied, which is motivated by the adaption of autonomous delivery robot (ADRs), parcel lockers in E-Grocery distribution industry. The authors formulate the multi-modal last mile system as a two-echelon location-routing problem with mixed vehicles and mixed satellites (2E-LRP-MVMS). A multi-objective optimization model is developed to capture the characteristics of cost components and environmental impact. The goal of the 2E-LRP-MVMS is to determine the location facilities, to optimize the number of parcels delivered to two echelons and routes at each level, also to reduce costs caused by carbon emissions. A hybrid immune algorithm is proposed, and two improved steps, vaccination and immunization are introduced in the algorithm. This research contributes to the last-mile delivery network design domain by modeling the environmental and economic cost of E-Grocery distribution adopting mixed fleets and mixed satellites.
This report summarizes four Shared Transportation Goals Workshops held by the Center for Transportation Studies in April through June 2024. These included the Equity Workshop, the Climate Change and Natural Systems Workshop, the Our Region is Dynamic and Resilient Workshop and the Our Communities are Healthy and Safe Workshop. Participants were representatives of the University of Minnesota and local, regional, and state transportation professionals. Metropolitan Council's Transportation Policy Plan 2050 (TPP) was used as the scaffolding for the discussion.
Zero-group velocity (ZGV) modes in rails are studied through simulation and experiments. Local resonances associated with ZGV modes appear as distinct, sharp peaks in the frequency amplitude spectrum, whose resonant frequencies can serve as indicators of the local structural integrity condition of the rail itself, assuming that one can excite, detect, and identify wave mode type with confidence. To better understand these interesting modes, semi-analytical finite element (SAFE) analysis is implemented to compute dispersion curves of a standard rail cross-sectional shape and to identify potential ZGV points and backward waves. Experimental rail dynamic data are collected from a 25-meter free rail sample with multiple excitation-sensor configurations to understand the detectability and excitability of specific resonances associated with ZGV and cutoff frequency points in rails. Spatial sampling of wave disturbance is performed to calculate the dispersion relations experimentally via two-dimensional Fourier transforms (2D-FFTs). The excellent agreement between simulation and experimental results confirms the existence of ZGV modes and cutoff frequency resonances in rails and verifies the feasibility of using impulse-based dynamic tests and piezoelectric devices for the promotion of ZGV modes.
The Transit Bus Electrification Tool is a Microsoft Excel-based spreadsheet tool that allows users to estimate the partial lifecycle greenhouse gas emission savings associated with replacing standard bus fleets with low-emission or zero-emission transit buses.
A comprehensive understanding of shippers’ preferences can help transport freight forwarders create targeted transport services and enhance long-term business relationships. This research proposes an integrated approach to learn shippers’ preferences in synchromodal transport operations and optimize transport services accordingly. A preference learning method was developed to capture shippers’ preferences through pairwise comparisons of transport plans. To model the underlying complex nonlinear relationships and detect heterogeneity in preferences, artificial neural networks (NNs) were employed to approximate shippers’ utility for a specific plan. Leveraging the learned preferences, a synchromodal transport planning model with shippers’ preferences (STPM-SP) was proposed, with the objectives of minimizing the total transportation cost and maximizing shippers’ satisfaction. A case study based on the European Rhine-Alpine corridor was conducted to demonstrate the feasibility and effectiveness of the proposed approach. The results demonstrated that artificial NNs have the capacity to identify complex (i.e., nonlinear and heterogeneous) relationships in shippers’ preferences. The planning results showed that the STPM-SP effectively found solutions with a significant satisfaction improvement of 37%. This research contributes to learning shippers’ preferences in the transport operation process and highlights the importance of incorporating these preferences into the decision-making process of synchromodal transport planning.
Since the early 2000s, over 1,600 transportation agencies in the United States have adopted Complete Streets policies. Recently, the Kentucky Transportation Cabinet (KYTC) published its Complete Streets, Roads, and Highways Manual, which aims to implement a safe and equitable transportation system throughout the state, as well as a Complete Streets policy. The manual and policy offer guidance on integrating Complete Streets principles into road design, however, KYTC currently lacks tools or methods to evaluate how well specific projects address Complete Streets goals. This is problematic because systematic assessments are needed to effectively prioritize projects and allocate scarce funding. Based on a review of Complete Streets initiatives in North America and around the world, as well as input from subject-matter experts, this report proposes a KYTC Complete Streets Scorecard that evaluates project benefits across seven categories — mobility, accessibility, connectivity, equity, safety, and effectiveness. Because the scorecard contains metrics focused on different user types (i.e., pedestrians, bicyclists, transit user), project teams have the flexibility to evaluate either an entire project or the impact of a project on specific user groups. Statewide implementation of the scorecard will help the Cabinet allocate funding and prioritize projects based on the extent to which they contribute to Complete Streets policy goals.
The purpose of this playbook is to identify the processes and activities necessary to create a stakeholder-administered, voluntary specification development and maintenance process for traffic regulation data to support automated driving systems. The playbook envisions a process whereby stakeholders convene as a working group to collaborate on specifications for capturing, managing, and disseminating traffic regulations data with an eventual transition of a mature specification to a standards development organization for formalization. Activities include stakeholder engagement, specification development, and administration of the specification. Appendices describe similar specification development efforts and alternatives.
Driver assistance systems that facilitate economical driving (eco-driving) aim to reduce greenhouse gas emissions and improve vehicle efficiency. These systems reduce idling time at intersections and smooth acceleration and deceleration patterns. Eco-driving has the potential to improve driving comfort by smoothing speed profiles. This study explores the behavior of drivers who followed a lead vehicle that demonstrated eco-driving strategies, such as reducing speed ahead of a signal change to minimize idling time at the intersection. The participants received cooperative driving automation (CDA) messages about the upcoming signal change or that shared the intent of the lead vehicle. The participants drove under one of four conditions: without adaptive cruise control (ACC) or CDA enabled, with ACC enabled, with CDA messages enabled, or with CDA messages and ACC both enabled. The four levels of CDA messages were no message, vehicle-to-vehicle (V2V) lead vehicle intent sharing, vehicle-to-infrastructure (V2I) signal status message, and both messages. The field research vehicle recorded speed, braking variability, and following distance from each participant’s vehicle. After each trial, the researchers asked the participants about their trust in the vehicle they had been following. The team analyzed speed and acceleration profiles to assess differences in fluctuation in different signal phasing conditions. Statistical analyses aimed to understand the impact of ACC status and CDA messages on driver behavior. The team used mid- and post-experiment questionnaires to evaluate the drivers’ experiences and acceptance of lead vehicle, CDA messaging, and vehicle automation technology. The study’s findings suggest that drivers in ACC-enabled vehicles could follow the eco-driving vehicle with more ease than drivers in conventional vehicles. The impact of CDA messages was not unanimous for all the scenarios. V2V messages helped ACC-enabled vehicle drivers, and V2I messages helped conventional vehicle drivers to some degree. The study found the participants had high trust ratings on lead vehicles and CDA messages. Overwhelming evidence of acceptance of vehicle automation was not observed, although the study showed slightly higher perceptions of safety gain than loss.
