Pacific Northwest Transportation Consortium

Permanent URI for this collectionhttps://digital.lib.washington.edu/handle/1773/43465

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An Animal Transfer Logistics Support Tool
(2024) Danna Moore; Jake Wagner
To support shelter animal logistics managers, an animal transfer logistics support tool was developed. The tool is composed of two components: 1) a shelter animal allocator and 2) a multi-pickup delivery route scheduler. Together they serve to identify and recommend potential transfer partners for both sending and receiving shelters and to schedule optimal routes for a multi-pickup and delivery transfer vehicle. The tool is still under development, but the current version is freely available online: shelter-logistics-92bc55bb5399.herokuapp.com.
Computer Vision for Traffic Monitoring
(2024) Ahmed Abdel-Rahim; Mike Lowry
This project examined computer vision applications for traffic monitoring and safety analysis. The focus was on evaluating the open-source computer vision code that we developed. Three case studies were completed using our computer vision code. The code was written in Python and uses a detection model called YOLOv8. The first case study demonstrated how user counts can be obtained from video feeds and provided examples of insights that can be drawn from these counts. The second case study used computer vision to create visualizations of user movements at intersections. The third case study developed and demonstrated the application of a new surrogate safety measure for pedestrian and bicyclist safety. Shortcomings and future opportunities of open-source computer vision systems are discussed.
Computer Vision for Traffic Monitoring
(2024) Ahmed Abdel-Rahim; Mike Lowry
This project examined computer vision applications for traffic monitoring and safety analysis. The focus was on evaluating the open-source computer vision code that we developed. Three case studies were completed using our computer vision code. The code was written in Python and uses a detection model called YOLOv8. The first case study demonstrated how user counts can be obtained from video feeds and provided examples of insights that can be drawn from these counts. The second case study used computer vision to create visualizations of user movements at intersections. The third case study developed and demonstrated the application of a new surrogate safety measure for pedestrian and bicyclist safety. Shortcomings and future opportunities of open-source computer vision systems are discussed
Extending the SR 522 signal Phase and Timing (SPaT) Challenge to Active Transportation Users
(2024) Yinhai Wang; Hao Yang; John Ash; Yifan Ling
Information and communication technologies offer significant advantages such as enhancing the efficiency, capacity, and reliability of traffic networks. Yet most improvements in signal management and connected vehicle interactions have concentrated on motorized traffic, neglecting non-motorized and vulnerable road users. Issues such as poor perception capabilities, outdated data gathering methods, unequal distribution of resources, and a lack of inclusivity have resulted in a challenging and hazardous environment for non-motorized users, particularly those with disabilities. To address these issues, we propose an innovative signal phase and timing (SPaT) services framework known as the Accessible Crossing Platform for Active Road Users (ACPARU) smart node. Equipped with cutting-edge computer vision algorithms and AI systems, the ACPARU smart node can collect essential data about active users such as location, category, movement direction, and mobility status, and it can create a real-time directional crossing request for each pedestrian and cyclist. The ACPARU smart node also enhances communication systems, serving as a dependable hub from which to distribute SPaT messages and manage interactions among the signal controller, connected vehicles, and users’ personal devices (like mobile phones and wearables) using various protocols. In comprehensive testing with 1,076 users across six intersections, ACPARU achieved 90.24 percent accuracy in generating directional-aware crossing triggers and 89.87 percent accuracy in estimating the mobility status of regular users and four categories of disabled individuals. The ACPARU smart node is fully compatible with connected vehicle environments and enhances the signal system affordably, primarily because of its flexibility and compatibility with existing infrastructure. The ACPARU smart node represents the first connected infrastructure system that combines traffic sensing, data processing, and information distribution to provide self-operating, unbiased signal services based on edge computing
Demand Inference for Free-Floating Micro-Mobility: Accessibility and Availability
(2024) Chiwei Yan; Ang Xu
Micro-mobility systems (such as bike-sharing or scooter-sharing) have been widely adopted across the globe as sustainable modes of urban transportation. To efficiently plan and operate such systems, it is crucial to understand the underlying rider demand --- where riders come from and the rates of arrivals into the service area. Estimating rider demand is not trivial as most systems only track trip data, which are a biased representation of underlying demand. In this report, we describe development of a locational demand model to estimate rider demand based on only trip and vehicle location and status data. We established conditions under which our estimators are identifiable and consistent. In addition, we devised an expectation-maximization (EM) algorithm with closed-form updates for efficient estimation. To scale the estimation procedures, this EM algorithm is complemented with a location-discovery procedure that gradually adds new locations in the service region that make the largest improvements to the log-likelihood. Experiments using both synthetic data and real data from a dockless bike-sharing system in the Seattle area demonstrated the accuracy and scalability of the model and its estimation algorithm.
CHARACTERIZATION OF UNDERSERVED POPULATION PERCEPTIONS AND MOBILITY NEEDS IN CONNECTED-VEHICLE AND SMARTER CITY ENVIRONMENTS
(2024) Ahmed Abdel-Rahim; Logan Prescott
This outreach effort aimed to better understand and characterize the mobility disadvantaged and underserved populations’ perceptions of, along with their safety-related mobility needs in, connected-vehicle and smart-city environments. It also aimed to educate and inform these communities about the opportunities for mobility and safety improvements that these technologies could provide. This report discusses the underserved populations and communities in both rural and urban areas in the Pacific Northwest (PNW). Overall, connected vehicle and smart mobility technologies, tools, and applications will play a crucial role in improving the mobility and safety for all underserved populations. These technologies will promote the independence, safety, and well-being of older persons, enabling them to maintain active and fulfilling lifestyles as they age. Similarly, the technologies will offer a range of benefits for people with physical and mental disabilities, enhancing their independence, safety, and overall quality of life. They also have the potential to address transportation challenges, promote cultural preservation, and improve access to essential services for Native American Tribes and communities. Oher mobility underserved populations, such as households with no car ownership, can have access to convenient, affordable, and sustainable transportation options that enhance mobility, connectivity, and quality of life for these underserved groups in both urban and rural communities. Examples of smart-city technologies that have the potential to assist underserved populations include adaptive technologies and personalized services for people with disabilities, AI-inclusive design, advanced driver assistance systems, real-time multimodal trip planning, enhanced safety features for older drivers, door-to-door mobility services, older population assistive technologies, age-friendly infrastructure, and on-demand transportation services.
DEVELOPMENT OF PACTRANS WORKFORCE DEVELOPMENT INSTITUTE
(2024) Yinhai Wang; Wei Sun; Muhammad Karim; Shane Brown; Kevin Chang; Billy Connor; Eric Jessup
With the recent emergence of technology and its applications in transportation engineering practice, the demand for continuing education and workforce development is growing. Being the Northwest regional University Transportation Center, PacTrans also carries the task of transportation workforce development for Federal Region 10. To fulfill this task and address regional workforce development challenges, PacTrans has seen a clear need to develop an institute that provides professional training and continuing education for Region 10’s transportation professionals. Bringing together decades of collective experience in educational research and continuing education, the research team established the PacTrans Workforce Development Institute (WDI) to address increasing workforce development needs. Each university offers its own strengths in transportation research and education and thus makes unique and meaningful contributions to this project. Through survey and outreach activities, the research team has identified the gaps between workforce training needs and existing training opportunities and has developed training courses to fill these gaps. To better accommodate working professionals' busy schedules, the PacTrans WDI offers demand-responsive and flexible training services in both on-site and online settings. Specifically, the WDI has developed and delivered several training courses, such as Understanding and Applying the Manual on Uniform Traffic Control Devices, Incorporating Human Factors into Roadway Design and Crash Diagnostics, Project Management and Key Skill Capability Building, etc. In addition, the WDI is scheduled to deliver several training courses, such as Data Analytics and Tools, Geospatial Analysis for Transportation Planners and Practitioners, and An Introduction to School Zone Safety.
Using Computer Vision Data to Evaluate Bicycle and Pedestrian Improvements: A Before and After Case Study of Separated Bike Lane Conversion
(2024) Don MacKenzie; Mike Lowry
The design of urban streets and sidewalks must balance the mobility and safety requirements of pedestrians, cyclists, transit users, freight vehicles, and auto drivers. Modern engineers and designers supplement traditional traffic engineering approaches that focus on metrics for automobiles and trucks with more formal consideration of people traveling by foot, bicycle, and bus. The advent of computer vision systems that record counts and pathways of all street and sidewalk users has created new opportunities for data collection, supporting insights that are not possible from the pneumatic tube counters or electromagnetic sensors that are commonly used to measure car and truck traffic. The resulting data can inform street designs that better accommodate all travelers and modes with greater safety. However, computer vision systems using emerging technology must demonstrate their ability to generate consistent, valid, and actionable data before their widespread adoption by urban designers and engineers.
ELUCIDATING SNOW HEIGHT FOR AVALANCHE ASSESSMENT THROUGH AUTOMATED DATA PROCESSING FROM A REMOTELY PILOTED AIRCRAFT SYSTEM AND AUGMENTED WITH AN ADVANCED ROAD WEATHER INFORMATION SYSTEM
(2024) Eyal Saiet; Billy Connor
Remotely piloted aircraft systems (RPAS) and a streamlined photogrammetry process intertwined with an advanced road weather information system (ARWIS) can provide continuous data and information about avalanche precursor conditions. A test case in Atigun Pass, Alaska, above the Arctic Circle, is presented. Wind events that generate large blowing snow quantities are believed to be a significant contributor to avalanches there. RPAS coupled with photogrammetry has been shown to have the potential to improve avalanche risk management. We streamlined the photogrammetry and data processing steps, thereby taking a step forward in unleashing this potential. From an operational and science standpoint, the ARWIS is informative during storms when RPAS cannot fly. Linking RPAS surveys and ARWIS data significantly improves tackling avalanche monitoring challenges. We showed that the ARWIS can monitor blowing snow events and has the potential to serve as a snowdrift monitoring system. Our findings revealed that RPAS surveys can tease out even small changes in snow cover, as well as capture cornice growth and collapse. By coupling RPAS surveys and ARWIS data, we compared two similarly significant blowing snow events; however, only one contributed to significant cornice growth. A comparison of measured temperatures and winds and visual inspection of the snowpack surface showed that extremely cold temperatures and scarred sastrugi surfaces might hinder cornice growth. We also showed that sizeable blowing snow events measured by the ARWIS correlated well with reported avalanches and snowdrifts on the Dalton Highway. Overall, this is the first insight into avalanche precursor conditions in Atigun Pass using an RPAS operated by a ADOT&PF and M&O avalanche forecaster
Magnetic Acceleration: Proof-of-Concept for an Experimental Propulsion System
(2024) Connor, Billy; Barnes, David; Dunham, London
The Magnetic Acceleration (MagAcc) system is a new concept for magnetic levitation and propulsion that uses a specialized configuration of permanent magnets to achieve acceleration without the use of additional electricity or fuel. A bench-level demonstration, a seven-foot straight track system of permanent magnets with a related load cart, was constructed at the Arctic Infrastructure Development Center (AIDC) High Bay on the University of Alaska Fairbanks campus. The demonstration track achieved acceleration using the MagAcc’s passive magnetic propulsion system as predicted. The MagAcc system accelerated the cart from a resting position onto the drive section of the track using solely permanent magnets. This demonstrated the actions of the linear polarity switching series of the magnetic fields and polarity orientation, as well as the effectiveness of the London Assemblage configuration for achieving acceleration. Iterative improvements were made to the initial design to reduce drag and improve acceleration and speed. Our research with the prototype provided concept validation and initial data for the potential applications and scale of the MagAcc system, and it suggested the potential for streamlined retrofitting of existing tracks. Additional possible implementations include smaller industrial applications, such as on a factory floor, and reductions to the complexity, expense, and emissions in regional and national transit systems. The system could also reduce operational costs associated with maglev systems currently in use.
Data-Driven Assessment of Post-Earthquake Bridge Functionality and Regional Mobility
(2024) Motter, Christopher; Phillips, Adam; Eberhard, Marc; Berman, Jeffrey; Maurer, Brett
The seismic performance of bridges is essential to post-earthquake mobility, as bridges are relied upon to carry goods and people into and out of urban centers after natural disasters. A 2019 Department of Homeland Security (DHS) report assessing the regional resiliency of Western Washington state to a Cascadia Subduction Zone (CSZ) earthquake predicted widespread and high levels of bridge damage. A primary objective of this study was to create an improved prediction of non-functional, partially functional, and functional bridges that will assist in post-earthquake emergency planning. The Washington State Department of Transportation (WSDOT) bridge database was expanded to include site class, properties of abutments and foundations, and additional bridges. Properties of bridges in the database were used to define the parameters for a parametric study on Western Washington bridges subjected to Magnitude 9.0 CSZ ground motions. Detailed multi-degree-of-freedom bridge models were developed with OpenSees. Models were formulated for a suite of representative bridges and used to conduct nonlinear time history analyses for synthetic ground motions that had been generated in previous studies. Results from the model analyses were used to provide a more detailed understanding of the likelihood of bridge damage and the likely service levels post-earthquake. Bridge response was limited in the longitudinal direction because of stiffness provided by the abutments and backfill soil. In the transverse direction, shear keys and bearings were found to limit the lateral deformation in columns as a result of participation of the bridge deck. The majority of bridges in the WSDOT inventory have shear keys and bearings and were predicted to be in full service following a CSZ earthquake. WSDOT has been retrofitting older bridge columns with steel jackets since 1991, and this retrofit has been shown to enhance ductility. Bridges without shear keys and bearings should be prioritized for retrofit. Shorter period bridges near the coast and longer period bridges in locations with sedimentary basins were also identified as being more prone to damage. Bridge functionality after a CSZ earthquake is likely to be considerably better than anticipated by the 2019 DHS report. Some bridges may require repair, but bridges are likely to remain useable for emergency vehicles and post-earthquake response. These conclusions were reached within the scope of the study, with several limitations noted in the report that will require further investigation.
AN RAI OF DATA: GENERALIZING THE DATA-DRIVEN ROCKFALL ACTIVITY INDEX (RAI) BASED ON LONG-TERM OBSERVATIONS OF WELL-CHARACTERIZED SLOPES
(2023) Darrow, Margaret; Herrman, Daisy; Wartman, Joe; Olsen, Michael; Leshchinsky, Ben
In previous PacTrans research, the research team developed the Rockfall Activity Index system (RAI), a point cloud-derived, high- resolution, morphology-based approach for identifying, assessing, and mapping rockfall hazards at a high resolution across the entire surface of a rock slope. Continued monitoring at sites in both Alaska and Oregon has shown that rockfall activity is variable as a function of geology and rock properties. In this research, we used geologic characterization, analysis of change from light detection and ranging (lidar) differencing, and collection of 4,800 Schmidt Hammer measurements to constrain relationships between geologic structure and rockfall activity at a variety of rock slopes. Generally, lower Schmidt hammer rebound measurements were observed in areas with higher activity rates, suggesting that rebound (especially when corrected per ASTM standards) may be an effective proxy for rockfall activity or susceptibility. However, the large variability in these measurements, particularly between sites, suggests that these measurements are best applied on a site-specific basis. Additionally, we computed rockfall volumes to analyze mobility impacts by using an empirical relationship derived from the Rockfall Impacts to Mobility (RIM) database, demonstrating the importance of linking rockfall activity and hazard. Future work could look at expanding databases of rockfall impacts to mobility, collect more Schmidt hammer measurements, collect more epochs of rock slope digital terrain models, and further connect rock slope weathering and structure to the RAI analysis.
Blockchain-based Smart Contracts for Transportation Infrastructure Project Funding
(2023) Louis, Joseph
The USDOT Strategic Plan highlights the importance of improving the mobility of people and goods through its focus on infrastructure. However, a significant portion of the transportation infrastructure in the United States–up to 173,000 miles and 45,000 bridges—is in poor condition, which causes a variety of mobility-related traffic concerns that are estimated to cost the taxpayer over $160 billion dollars per year. Much of the major transportation infrastructure projects that are funded with federal monies are typically executed through public-private partnerships (PPPs) in which a consortium of private contractors in partnership with relevant public agencies form a special-purpose project company. While PPPs are an effective means of sharing project risk across public and private entities for capital transportation projects, critics have noted that they suffer from disadvantages related to the complexity of project procurement and administration, susceptibility to cost and time overruns, and failure to account for uncertain events due to the long-term nature of the projects. This project investigated the use of decentralized financing methods enabled by blockchain technology to provide efficient and effective financial control for capital transportation infrastructure projects. This project created a prototype framework for the issuance of a “transportation infrastructure token” cryptocurrency to be issued by project owners (typically government agencies) for specific capital transportation assets. This cryptocurrency would provide a transparent and efficient means of funding and recovering costs from transportation infrastructure projects by using smart contracts for collecting tolls from the traveling public and issuing dividends to owners. The resulting prototype can serve as a template for use across multiple capital projects. It will also support non-traditional methods of financing such projects by making investments available to a wider and more diverse range of investors and enabling crowd-funding—thereby also increasing the equity of transportation funding mechanisms.
An Impact of Safety Rest Area Closures on Fatigue-Related Highway Crashes in the Pacific Northwest
(2023) Shrestha, Kishor
Safety rest areas (SRAs) enable highway users to rest during trips. This study examined how SRA facility closures affect highway crashes, particularly those due to driver fatigue. The closures of three SRA facilities in Washington and two SRAs in Idaho were examined. The project team gathered information on SRA closures, mileage points, annual average daily traffic (AADT) of highway sections, and highway crashes from states and media outlets. The Washington State Department of Transportation and Idaho Transportation Department provided crash data, and crash information in these two states was correlated with SRA shutdowns. The numbers of crashes per month and per 10,000 AADT were used to compare crash rates before, during, and after the closures of SRA facilities. The results indicated no significant increase in fatigue-related accidents during the shutdown periods. However, total crash rates and fatigue-related crash rates rose in one location, decreased in another location, and did not change in another location during the closure times. Some extant literature has shown that fatigue-related events increase during SRA facility closures. The study highlighted the importance of SRA facilities in reducing driver fatigue and ensuring safer roads by shedding light on the correlation between SRA closures and highway crashes. The results may help policymakers create strategies for minimizing accidents caused by weariness and formulate regulations for SRA closures.
Advanced energy storage system for electric vehicle charging stations for rural communities in the pacific northwest
(2023) Hess, Herbert
A rural electric vehicle charging system is envisioned with an energy source, e.g., solar panels on a car port, energy storage, e.g., a flywheel energy storage system, and an energy sink, e.g., electric vehicle charging. The focus of this project was on the hardware development of the sensors and actuator subsystems of the energy storage system. The energy storage device is a reluctance machine operated as either a motor or generator, depending on the direction of energy transfer. The rotor of the reluctance machine functions as a flywheel, storing energy in the rotating mass. The position of the rotating mass (rotor) is critical to the function and performance of the energy storage control system. Commutating electrical currents is necessary to energy transfer by the reluctance machine. Three types of sensors and actuators subsystems are used to control the reluctance machine: 1) position or displacement sensors, 2) electrical current sensors, and 3) electrical current actuators. The hardware interface of each sensor and actuator subsystem was developed, including functional testing of the sensors and actuator subsystems hardware with Simulink Real-Time hardware-in-the-loop. These subsystems can be integrated into the flywheel energy storage system.
Integrating Food Access with Transit Services in Urban Areas of the Pacific Northwest: The Case of Seattle, Washington
(2023) Liao, Felix; Lowry, Mike
This study examined how public transportation can help improve access to emergency food resources and lower the risk of food insecurity in American cities. For a case study of Seattle, Washington, we used the General Transit Feed Specification (GTFS) to measure the accessibility of food banks and food pantries at the census block group level. We found that approximately 40 percent of neighborhoods in the city of Seattle were within walkable distances or half a mile of the nearest food bank or pantry. However, general access to the food pantry network was highly constrained by pantry operating hours. We found Tuesday, Wednesday, and Thursday afternoons to be popular timeslots, and food banks were rarely open during weekends. Furthermore, transit access to the citywide food pantry network was unevenly distributed in that some neighborhoods associated with larger numbers of food insecure populations were simultaneously those with poor accessibility to emergency food resources. These neighborhoods were primarily located in South Seattle and near the city’s northern edge. The results of regression models further indicated that convenient access to food banks or food pantries remains important for vulnerable communities. Finally, our study suggested that on-demand transit services or additional mobile food pantries would help bring free food to vulnerable communities, especially when regular public transit services are constrained by catastrophic circumstances such as the onset of the COVID-19 pandemic.
USING THE SCHMIDT HAMMER TO IMPROVE THE FORECASTING ACCURACY OF THE ROCKFALL ACTIVITY INDEX (RAI)
(2023) Darrow, Margaret; Herrman, Daisy
The Schmidt hammer is a widely used and inexpensive instrument for estimating rock strength either in the lab or in the field. Our research team tested the accuracy and repeatability of the Schmidt hammer to estimate rock strength on six Alaskan rock slopes and four Washington/Oregon rock slopes. We determined in situ rock hardness by using two different Schmidt hammers (Types L and N); conducted unconfined compressive strength (UCS) testing for select Alaskan rock samples; and summarized the advantages and disadvantages of using the Schmidt hammer in the field. Parameters that potentially affect Schmidt hammer results include testing methodology, sample testing conditions, and data reduction. Our results indicated that major structures within a rock unit (such as bedding or foliation), variation in mineralogy, and moisture content will significantly affect Schmidt hammer results. After data collection, several correction methods can be used to process the Schmidt hammer results. The choice of method depends on the intent of the measurements (i.e., strength of the intact rock or the rock mass), and the application of any method can alter the final results. Our UCS results generally correlated to the Schmidt hammer rebound values (e.g., rock types with high rebound values also had high UCS values). Before utilizing the Schmidt hammer, we suggest users determine the final goal before selecting the Schmidt hammer and testing methodology; identify the rock type and potential discontinuities that can influence results; identify the bias in sample or site selection; and select the most applicable data reduction method for the identified goal.
Estimating County to County Trade Flow
(2023) Lowry, Mike
Estimating count to county commodity trade flows is important for understanding a multitude of transportation, regional planning, and economic problems. While no primary data track intranational trade flows, gravity models have often been used to estimate these flows. However, to properly specify a gravity model, data on total commodity supply and total commodity demand for a consistent set of commodities and for every county must be obtained. Because these data are not available through primary data sources, a systematic process for estimating these values must be generated. This report details a process for starting with primary government data, specifically the quarterly census of employment and wages (QCEW) and national input-output accounts from the US Bureau of Economic Analysis (BEA), and generating employment, output, and commodity supplies and demands across 409 commodities in 3,142 counties. These commodity supplies and demands are then used in a gravity model to estimate bilateral trade for each commodity between every county in the United States. Employment, commodity trade data, and county to county trade totals are available at: https://tapestry.nkn.uidaho.edu/
The Use of Artificial Intelligence in Pavement Engineering
(2023) Kassem, Emad; Mikels, Natalie; Murtah, Ahmed; Sufian, Abu
The performance of asphalt pavements decreases with time because of traffic loading and environmental conditions. Performance decay models are needed in pavement management systems to program pavement preservation and rehabilitation treatments to extend the service life and improve the performance of flexible pavements. Many factors affect pavement performance, including the material properties and thickness of each layer, applied traffic, and environmental conditions. Performance models, including those for rutting, cracking, roughness, are often developed and used to forecast the future conditions of pavements. Meanwhile, to develop reliable performance models, numerous variables are needed in such models, and historical performance data are required. This study investigated and developed multiple types of artificial intelligence models to predict pavement performance. The study results demonstrated that random forests regression was best suited for the data utilized in this study. Multiple random forests regression models were developed to predict various indicators of pavement performance, such as the International Roughness Index (IRI), rutting, and cracking. These models utilized a theoretical dataset generated with the Pavement ME software and field data collected from the Long-Term Pavement Performance (LTPP) Program. There were good correlations between all the theoretical and predicted performance indicators. In addition, the predicted performance decay curves were found to closely simulate the measured decay curves. In addition, the results for the models developed with the field dataset demonstrated good correlations between measured and predicted performance indicators for some of the investigated performance indicators.
Measuring the Impacts of COVID-19 on the Trucking Industry: A Spatial and Econometric Framework to Capture the Impacts of the Hours-of-Service Emergency Declaration and Congestion Effects on Truck Driver Safety (Phase 2)
(2023) Hernandez, Salvador
This project quantitatively studied the significant effects of the Coronavirus 2019 pandemic on truck drivers and the trucking industry. A stated-preference survey distributed to truck drivers collected data regarding changes in the demographic, socioeconomic, business, temporal, management, and truck configuration characteristics of the trucking industry. A total of 47 paired variables were generated from the driver survey responses. Their medians were tested for a statistically significant difference through a rank-sum procedure, through which 13 of the comparisons showed significant change during the pandemic. Of the 520 respondents, 243 (34 percent) indicated that roads were more safe during the pandemic. This study also revealed changes in trucking operations and driver behavior as a result of the relaxation of trucking hours-of-service limitations.

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