U.S. Department Of Transportation Research Articles

Use of Hydrogel Electrolyte in Zn-MnO2 Rechargeable Batteries: Characterization of Safety, Performance, and Cu2+ Ion Diffusion.

Achieving commercially acceptable Zn-MnO2 rechargeable batteries depends on the reversibility of active zinc and manganese materials, and avoiding side reactions during the second electron reaction of MnO2. Typically, liquid electrolytes such as potassium hydroxide (KOH) are used for Zn-MnO2 rechargeable batteries. However, it is known that using liquid electrolytes causes the formation of electrochemically inactive materials, such as precipitation Mn3O4 or ZnMn2O4 resulting from the uncontrollable reaction of Mn3+ dissolved species with zincate ions. In this paper, hydrogel electrolytes are tested for MnO2 electrodes undergoing two-electron cycling. Improved cell safety is achieved because the hydrogel electrolyte is non-spillable, according to standards from the US Department of Transportation (DOT). The cycling of "half cells" with advanced-formulation MnO2 cathodes paired with commercial NiOOH electrodes is tested with hydrogel and a normal electrolyte, to detect changes to the zincate crossover and reaction from anode to cathode. These half cells achieved ≥700 cycles with 99% coulombic efficiency and 63% energy efficiency at C/3 rates based on the second electron capacity of MnO2. Other cycling tests with "full cells" of Zn anodes with the same MnO2 cathodes achieved ~300 cycles until reaching 50% capacity fade, a comparable performance to cells using liquid electrolyte. Electrodes dissected after cycling showed that the liquid electrolyte allowed Cu ions to migrate more than the hydrogel electrolyte. However, measurements of the Cu diffusion coefficient showed no difference between liquid and gel electrolytes; thus, it was hypothesized that the gel electrolytes reduced the occurrence of Cu short circuits by either (a) reducing electrode physical contact to the separator or (b) reducing electro-convective electrolyte transport that may be as important as diffusive transport.

Development of track component health indices using image-based railway track inspection data

The primary role of the US Department of Transportation (USDOT) Federal Railroad Administration (FRA) is ensuring the safe operation of railway rolling stock and infrastructure by way of regulatory oversight. FRA regulations require US railroads to conduct visual track inspections as often as twice per week depending on a specific track segment’s FRA track class, which also governs maximum train operating speed. Such inspections are often subjective due to the inherent limitations of human visual inspection and cognition. Additionally, human visual inspections require some level of risk given the need for inspectors to be on track while also consuming valuable network capacity. As a result, and the desire to collect objective data to improve both safety and maintenance planning, railroads are pursuing new means and methods to assess track condition and evaluate track component health. This paper presents a numerical method to define track component health using field data collected on the High Tonnage Loop (HTL) at the Transportation Technology Center (TTC) in Pueblo, Colorado, USA. Line scan laser and image data of the track were captured using a 3D Laser Triangulation system and were subsequently processed using Deep Convolutional Neural Networks (DCNNs). The track heath quantification method proposed establishes benchmarks that were developed based on the understanding of railway track mechanics, high axle load (HAL) railroad engineering instructions, and FRA regulations. The novel metrics presented are referred to as Track Component Heath Indices (TCHIs) and are quantitative values that objectively assess track condition and provide a means to monitor condition change with time and tonnage. These data can be used in conjunction with traditional track geometry and other forms of track heath data (e.g. GPR and rail profile) to more holistically assess the condition of the track structure and its components and ultimately predict its future state.

Understanding Active Transportation to School Behavior in Socioeconomically Disadvantaged Communities: A Machine Learning and SHAP Analysis Approach

Active Transportation to School (ATS) offers numerous health benefits and is considered an affordable option, especially in disadvantaged neighborhoods. The US Centers for Disease Control and Prevention (CDC) advises 60 min of daily physical exercise for children aged 6 to 17, making ATS a compelling approach to promote a healthier lifestyle among students. Initiated in 2005 by the US Department of Transportation (DOT), the Safe Routes to School (SRTS) program aims to foster safe and regular walking and biking to school for students. This paper examines students’ travel behavior using SRTS survey data and assesses the program’s effectiveness in promoting ATS in Chula Vista, California. Employing machine learning algorithms (random forest, logistic regression, and support vector machines) to predict students’ likelihood to walk to school, it utilizes SHAP (SHapley Additive exPlanations) to pinpoint significant variables influencing ATS across all models. SHAP underscores critical factors affecting transportation choices to school, highlighting the importance of home-to-school distance, with shorter distances positively impacting active transportation. However, only half of students within schools’ walking distance opted to walk to school, underscoring the necessity of addressing parental safety concerns, including factors such as crime rates and traffic speed along the route.

Real-Time Big Data Analytics and Proactive Traffic Safety Management Visualization System

Big data and data-driven analysis could be utilized for traffic management to improve road safety and the performance of transportation systems. This paper introduces a web-based proactive traffic safety management (PATM) and real-time big data visualization tool, which is based on an award-winning system that won the US Department of Transportation (USDOT) Solving for Safety Visualization Challenge and was selected as one of the USDOT Safety Data Initiative (SDI) Beta Tools. State-of-the-art research, especially for real-time crash prediction and PATM, are deployed in this study. A significant amount of real-time data is accessed by the system in order to conduct data-driven analysis, such as traffic data, weather data, and video data from closed-circuit television (CCTV) live streams. Based on the data, multiple modules have been developed, including real-time crash/secondary crash prediction, CCTV-based expedited detection, PATM recommendation, data sharing, and report generation. Both real-time data and the system outputs are visualized at the front end using interactive maps and various types of figures to represent the data distribution and efficiently reveal hidden patterns. Evaluation of the real-time crash prediction outputs is conducted based on one-month real-world crash data and the prediction results from the system. The comparison results indicate excellent prediction performance. When considering spatial-temporal tolerance, the sensitivity and false alarm rate of the prediction results [i.e., high crash potential event (HCPE)] are 0.802 and 0.252, respectively. Current and potential implementation are also discussed in this paper.

Deep learning based on connected vehicles for icing pavement detection

Slippery road conditions, such as snowy, icy or slushy pavements, are one of the major threats to road safety in winter. The U.S. Department of Transportation (USDOT) spends over 20% of its maintenance budget on pavement maintenance in winter. However, despite extensive research, it remains a challenging task to monitor pavement conditions and detect slippery roadways in real time. Most existing studies have mainly explored indirect estimates based on pavement images and weather forecasts. The emerging connected vehicle (CV) technology offers the opportunity to map slippery road conditions in real time. This study proposes a CV-based slippery detection system that uses vehicles to acquire data and implements deep learning algorithms to predict pavements' slippery conditions. The system classifies pavement conditions into three major categories: dry, snowy and icy. Different pavement conditions reflect different levels of slipperiness: dry surface corresponds to the least slippery condition, and icy surface to the most slippery condition. In practice, more attention should be paid to the detected icy and snowy pavements when driving or implementing pavement maintenance and road operation in winter. The classification algorithm adopted in this study is Long Short-Term Memory (LSTM), which is an artificial Recurrent Neural Network (RNN). The LSTM model is trained with simulated CV data in VISSIM and optimized with a Bayesian algorithm. The system can achieve 100%, 99.06% and 98.02% prediction accuracy for dry pavement, snowy pavement and icy pavement, respectively. In addition, it is observed that potential accidents can be reduced by more than 90% if CVs can adjust their driving speed and maintain a greater distance from the leading vehicle after receiving a warning signal. Simulation results indicate that the proposed slippery detection system and the information sharing function based on the CV technology and deep learning algorithm (i.e., the LSTM network implemented in this study) are expected to deliver real-time detection of slippery pavement conditions, thus significantly eliminating the potential risk of accidents.

Social Inequities in Exposure to Traffic-Related Air and Noise Pollution at Public Schools in Texas.

Although children are particularly vulnerable to the adverse impacts of vehicular pollution and spend significant portions of their time at school, previous studies have not examined or compared school-level social inequities in exposure to both traffic-related air and noise pollution in the same study area. We addressed this gap through a case study in Texas-the second-largest US state based on total population and number of children. Vehicular pollution exposure was measured using: (1) outdoor concentrations of nitrogen dioxide (NO2), a widely used proxy for traffic-related air pollution; and (2) road noise estimates from the US Department of Transportation's National Transportation Noise Mapping Tool. These variables were linked to data on locations and sociodemographic characteristics of children enrolled in Texas public schools. We found children attending schools with the highest exposure to both NO2 and road noise (top 25%) were significantly more likely to be Black, Hispanic, and eligible for free/reduced lunches (socioeconomically deprived). Results from multivariable generalized estimating equations that control for spatial clustering and other relevant factors revealed that schools with greater NO2 exposure were significantly more likely to serve racial/ethnic minority and younger students, while schools with greater exposure to road noise were significantly more likely to serve socioeconomically deprived and older students. These findings underscore the urgent need to reduce both air pollution and noise exposure at school locations, especially in schools attended by higher proportions of socially disadvantaged children that are often additionally burdened with other challenges.

The Role of the Top 50 US Cargo Airports and 25 Air Cargo Airlines in the Logistics of E-Commerce Companies

Background: The purpose of this study is to analyse the role of the main 50 US cargo airports and 25 air cargo airlines in the logistics of e-commerce companies from 2000 to 2020, to highlight the importance of airports in the logistics and e-commerce industries. Methods: A review of the relevant literature on airports, air cargo carriers, logistics, and e-commerce sectors was undertaken to understand the link between them. The data were collected using four criteria: airport category, airport location, top 25 air cargo carriers, and other relevant data from the Federal Aviation Administration, International Air Transport Association, Organization for Economic Co-operation and Development, US Department of Transportation, amongst many others. Results: The findings reveal that there is a consolidated relationship between airports, air cargo airlines, and e-commerce, which has been especially evident during the COVID-19 pandemic. Airports and air cargo carriers are identified as the most relevant partners in the e-commerce industry. This is because of the e-commerce sector and its users’ demand for speed and reliability in the interaction between the demand for and supply of products and services. Conclusions: The pandemic has changed the way in which organizations operate and is likely to create new demand from companies and users in the aviation and e-commerce industries. E-commerce companies are highly dependent on the quality and efficiency of air cargo airlines and airports because they need to provide a good shipping service for their products to customers.

Treatment of Highway Stormwater Runoff Using Sustainable Biochar: A Review

Human activities and atmospheric deposits on highways have led to an increase of pollutants in highway stormwater runoff. The reduction of these pollutants to a minimum acceptance level has been a great subject of concern for the US Department of Transportation and US Environment Protection Agency. Researchers have investigated several methods; however, biochar utilization, an environmentally and economically favorable way for pollutant removal from highway stormwater runoff, has not been studied extensively. Biochar has been intensively used for soil improvement and water treatment. Its unique carbon structure, large surface area, and higher pore structure have made it a potential candidate for water retention and contaminant removal. This literature review focused on the removal of highway stormwater runoff pollutants such as suspended solids (dust, sludge, and leaves), heavy metals (Ni, Zn, Pb, Cr, Cd, and Cu), nutrients (nitrate and phosphate), and organic contaminants (paraffin, nitrobenzene, and p-nitrotoluene) using biomass-derived biochar. This comprehensive literature review introduces biochar production and the effect of the production process operating temperature on biochar’s structure and performance. It also analyzes the impact and efficiency of biochar on pollutant removal and illustrates its potential in the treatment of highway stormwater runoff.

Opportunities beyond CO2 for climate mitigation

Opportunities beyond CO2 for climate mitigation

Impact of Slope Orientation on Inlet Spacing: Gutter Flow Analyses

A roadway’s capacity to drain itself is of utmost importance for the safety and comfort of its users. Standing water and any amount of channelized flow on roadways create nuisances to the users, and the extent of encroachment into the lanes and the water-film thickness over the lanes are crucial for motorists with relatively high speed. Guidelines cover a wide range of subjects from size and type of inlets, which capture the channelized flow for conveyance into enclosed drains, to the decision for slope orientation, but the guidelines seem to lack in checking the depth of channelized flow. HEC-22 (the urban drainage design manual of US Department of Transportation) endorses limiting the flow depths to curb height (as if the concern is no longer the roadway users) and fixes the criterion for the inlet spacing (restricted to 90 to 150 m) to maximum allowable flow spreads. This study analyzed the maximum allowable inlet spacing via setting three criteria: fixed maximums to flow depth, spread for the channel flow, and to over-lane water-film thickness. The impact of slope orientation on inlet spacing is tested along with some other factors for roadways of two types (local and highway). The results were graphed for various uniform slope orientations under a wide range of rainfall intensities for the determined inlet spacing values. This was performed by combining a kinematic wave equation solution to dismiss the conditions that lead to hydroplaning depths when using the Rational Method and Manning’s equation to obtain water depths and inlet spacings for an inlet of full capture capacity. It is found that the allowable spacing values do not constitute any major restrictions in highway setting (3 m shoulder) in terms of recommended spacing. In the local setting, however, with a maximum spread of 1.8 m, maximum allowable inlet spacing becomes a limitation in many orientations, and slope optimization under such conditions becomes crucial at times when providing the same spacing for two orientations.

Performance Rating of National Highways Based on Road User Perspective – A Case Study of Kalka–Shimla Highway

The road network plays the most important role in the development of India’s infrastructure. Through the initiative of rating national highways, we will be able to set standards for quality highway services, and as a result, road users will receive excellent value for their money. Moreover, better quality Indian Highways will lead to safer travel experiences and consequently result in fewer road accidents and fatalities. The effort for evaluating the roads was done by US Department of Transportation in 1950s as has been highlighted in the Highway Sufficiency Rating Manual of 1952. However, these ratings were based only on efficiency and safety parameters, completely neglecting the user perspective and services. As the road users gain access to information about products and services in real time over the internet, they are more empowered than ever before. Thus, the performance evaluation of a highway stretch must include additional aspects associated with the user experience. This paper examines the real-time problems they face along the journey. National Highway authority of India (NHAI) and Ministry of Road Transport and Highways (MORTH) of India have framed Standard Operating Procedure (SOP) for assessment and rating of 4-6 lanes divided carriageway NHs based on road user perspective. Road user perspective carries a 20 per cent weightage in overall highway efficiency. A questionnaire survey was taken as part of the road user perspective study on the Kalka-Shimla NH No. 5 in the state of Himachal Pradesh in India. The highway scored 17.1 marks out of 20 based on users’ response, representing an 85.5% score, and indicating minor deficiencies regarding the selected highway facilities.

Multiagent Reinforcement Learning-Based Signal Planning for Resisting Congestion Attack in Green Transportation

Inefficient signal control will not only exaggerate traffic congestion, but also increase the fuel consumption and exhaust emissions. Thus, signal planning is highly important in green transportation. As the Connected vehicle (CV) technology has transformed today’s transportation systems by connecting vehicles and the transportation infrastructure through wireless communication, the CV-based signal control system has seen significant studies recently. Unfortunately, existing signal planning algorithms in use are developed for the signal-intersection, showing low traffic efficiency in the multi-intersection collaborative planning due to ignoring the traffic correlation among the neighboring intersections. In this work, we target the USDOT (U.S. Department of Transportation) sponsored CV-based traffic control system, and implement a multi-intersection traffic network. We model the multi-intersection collaborative signal planning problem as a multi-agent reinforcement learning problem, and present an actor-attention-critic algorithm to improve transportation efficiency and energy efficiency in green transportation, as well as resist congestion attack. Experiment results on the multi-intersection traffic network indicates that 1) compared to the baseline, our approach reduces the total delay by as high as 44.24%; 2) our method transports more vehicles passing the intersections meanwhile reduces the total CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions by 2.40%; 3) under the congestion attack, our approach shows robustness and reduces the total delay by as high as 64.33%.

Legislation Supports Autonomous Vehicles But Not Connected Ones [Connected and Automated Vehicles

It is inspiring to notice that legislation that helps incorporate autonomous vehicles on our roads in the near future is moving forward. The National Highway Traffic Safety Administration (NHTSA), which is part of the U.S. Department of Transportation (USDoT), has integrated automated vehicles into the existing safety standards such that the text in the standards do not rely on the existence of steering wheels and driver’s seats, which may not exist in fully autonomous vehicles. In addition, the NHTSA has incorporated lane-keeping support, pedestrian automatic emergency braking, blind spot detection, and blind spot intervention into its Five-Star Safety Ratings program. Such driver-assistance technologies are the first steps toward fully autonomous vehicles but, more importantly, toward safer ones. Developing safer vehicles requires significant investments, so it is important that the legislation provide a framework that is predictable, reducing the risk of long-term commitment.

The impact of predicted quality and customer cost on quality assurance behavior

PurposeBased on cost of quality (CoQ) research, this study aims to highlight the importance of incorporating the costs to customers in contributing to service quality when examining how customers respond to possible service failures [quality assurance behavior (QAB)]. Consequently, this study also aims to show how the CoQ framework can be a useful tool to the service industry in determining enhancements in quality and related expenditures.Design/methodology/approachUsing the airline industry as a case example, this study empirically tests the impact of predicted service quality and its associated costs on an individual’s QAB (wait time spent at the departing airport) through revealed preferences. The study uses survey data from more than 4,000 passengers matched with travel-specific quality information archived by the US Department of Transportation (DOT).FindingsThis study finds that customers are willing to increase their level of QAB when informed of an increased probability of service failure. This study also finds that the level of QAB varies depending on anticipated customer costs of avoiding or responding to service failures.Practical implicationsFindings of this study emphasize the need for shared responsibility between service providers and their customers in making decisions on the provision of service quality, as helping customers adjust the appropriate level of QAB may result in greater efficiency and higher quality of service.Originality/valueThis study conceptualizes and empirically tests causal relationships between expected quality and customer efforts (QAB), thus contributing to operations literature examining CoQ in a service setting. This study argues that it is critical to consider shared responsibilities between co-producers (service providers and customers) in service operations studies.

Motor Vehicle Crash Testing Regulations for More Inclusive Populations

There is a stark disparity in motor vehicle crash deaths and injuries between male and female drivers. Female drivers are 13% more likely to be killed than their male counterparts in similar motor accidents. However, vehicle safety test practices do not account for diverse body proportions when assessing safety outcomes. Vehicle crash testing standards only require testing of two variations of adult-sized crash test dummies: a 50th percentile male and a 5th percentile female. Automotive companies are not required to test safety outcomes in crash test model’s representative of average female proportions or of non-average body sizes and physiological compositions. Current crash test standards are regulated by the National Highway Traffic Safety Administration (NHTSA) under the US Department of Transportation. This memo proposes three actions for the NHTSA and the Department of Transportation to address disparities in vehicle safety outcomes: 1) update safety standard requirements to include a 50th percentile female crash test dummy, 2) implement a federal tax incentive program for companies to include a greater diversity of vehicle occupant models, and 3) allocate funds for research and development of virtual crash testing models. These proposed initiatives seek to raise the minimum safety requirements and prioritize wider representation of vehicle occupants to improve parity in vehicle safety outcomes.

A gender-based analysis of workforce promotion factors in U.S. transportation agencies

The gender-based factors that impact job promotions in various engineering fields have not been widely studied. In this study, a descriptive analysis was performed on the employees of the U.S. Department of Transportation (USDOT) from 2011 to 2017 to determine the factors, including education, age, and years of service, that make men and women eligible for a promotion. The results indicated that women are elevated to high-level positions less often than men, primarily because of their lower level of education, age, and fewer years in service. The results of this study will provide guidance and assistance to DOT managers as they develop policies that motivate women to become eligible for higher level positions and will assist employers in private and public engineering organizations as they move toward a more gender-diverse working environment.

Military Transportation in State Freight and Defense Community Plans

The US Department of Defense (DoD) makes extensive use of the US multimodal transportation network to move material for operational support, keep facilities supplied, and move personnel. Federal agencies, especially the US Department of Transportation, work closely with the DoD. States and localities also play a critical role in defense infrastructure. This research examines state freight and defense community plans to identify how the transportation needs of the DoD are being addressed in state planning. The findings are 72% of the 50 state freight plans mention defense transportation, while 34% considered defense transportation needs in-depth. About 6 of 19 identified state defense community plans mention transportation, but only 3 have specific defense transportation goals. Nationally, the DoD and federal agencies work closely together to maintain the nation’s defense transportation network, but at the local and state level closer cooperation on transportation issues should be developed. Military readiness remains high, but efficiencies in the system can be improved.

Construction Project Management Dimensions in Transportation Agencies: Case Study of the US Department of Transportation

AbstractTransportation projects require unique and specific management processes, making an investigation of the particular dimensions of project management relevant to such projects necessary. The...

Drug presence in driving deaths in Maryland: Comparing trends and prevalence in medical examiner and FARS data

Accurate and reliable information on drug use by road users is essential to inform safety policy development but the reliability of national data has been questioned. There are two primary repositories of drug test information from fatal motor vehicle crashes in Maryland: (1) the Fatality Analysis Reporting System (FARS), which is a national crash database managed by the US Department of Transportation, and (2) the Maryland Medical Examiner (ME). In this study, we compared drug test information for people killed in crashes in Maryland between the FARS system and ME from 2006 – 2018. As ME records are the primary source for the FARS data from Maryland, these two data sets should be closely correlated. We used probabilistic linkage to match FARS and ME cases and compared matched cases by individual drug group. Matching was achieved on 83 % of cases (N = 4803 matched pairs). ME data consistently indicated higher overall incidence and trends in the presence of depressants, narcotics, and stimulants in crash deaths. Sensitivity analysis using both strict and relaxed matching criteria did not change this result. Road safety policy and prevention efforts for crashes involving drugs and alcohol require an accurate understanding of both long-term trends and year-to-year changes in drug prevalence. These findings demonstrate the potential value of using ME data as source of drug test information for crash deaths in the United States.

Evaluation of shortwave and longwave radiation models for mechanistic-empirical pavement analysis

ABSTRACT Pavement temperature is one of the most important factors influencing the performance of flexible pavements. The major components of the Earth's heat balance system are downwelling shortwave radiation (D-SWR) from the sun, downwelling longwave radiation (D-LWR) from the atmosphere, and upwelling longwave radiation (U-LWR) emitted by the warm pavement surface. The Pavement ME Design (PMED) software (AASHTO, 2015. Mechanistic-empirical pavement design guide–a manual of practice. Washington, DC: AASHTO) computes temperature distributions in the pavement over depth and time using algorithms originally proposed by Dempsey et al. (1985. Environmental effects on pavements: theory manual. US Department of Transportation, Federal Highway Administration). These algorithms are largely empirical, depend heavily on imprecise corrections for cloud cover, and in some parts contradict atmospheric physics. Improved models for the major radiation components proposed here include: (1) D-SWR values modelled by the MERRA-2 climate re-analysis product from NASA (Rienecker et al., 2011. MERRA: NASA’s modern-era retrospective analysis for research and applications. Journal of Climate, 24, 3624–3648); (2) D-LWR modelled using the Idso (1981. A set of equations for full spectrum and 8-to 14-μm and 10.5-to 12.5-μm thermal radiation from cloudless skies. Water Resources Research, 17, 295–304) empirical parameterisation with a physics-consistent adjustment for cloud cover; and (3) U-LWR calculations without the physics-inconsistent cloud cover adjustment embedded in PMED. The D-SWR and D-LWR radiation models were evaluated via comparisons against ground-based radiation observations at 21 locations in the Solar Infrared Radiations Stations (SIRS) database. The MERRA-2 estimates of D-SWR, although not perfect, were found to agree substantially better than the current PMED models with the ground truth SIRS. The D-LWR fluxes from the Idso (1981. A set of equations for full spectrum and 8-to 14-μm and 10.5-to 12.5-μm thermal radiation from cloudless skies. Water Resources Research, 17, 295–304) parameterisation were also found to agree substantially better than the current PMED with the ground truth SIRS, with particularly striking improvements for the all-sky (i.e. including clouds) condition. A limited series of flexible pavement performance analyses were conducted to illustrate the potential practical impact of these radiation model changes. The new radiation models produced significantly higher pavement temperatures that lead to substantially higher total rutting, asphalt rutting, and bottom up fatigue cracking. These changes in distress magnitudes must be evaluated in qualitative terms only, as all analyses used the same field calibration coefficients for the distress models. The primary conclusion from this work is that the current radiation models incorporated in PMED are inaccurate and, in some cases, inconsistent with fundamental atmospheric physics. This can have significant impacts on predicted pavement performance. It is recommended that the current PMED radiation models be replaced by the new D-SWR, D-LWR, and U-LWR. New field calibration of the empirical distress models would be required after incorporating the new radiation models.