Washington State Department Of Transportation Research Articles

Greenhouse Gas Emissions Inventory from Roadway Construction: Case Study for the Washington State Department of Transportation

Recent emphasis on actions to reduce greenhouse gas (GHG) emissions has pushed many state departments of transportation (DOTs) to develop carbon accounting practices compatible with their current standard data collection and storage guidelines. Once accurate and reliable accounting of GHG emissions is established, strategies can be formed that could help mitigate the adverse environmental impacts of materials used by state DOTs. To date, the Washington State Department of Transportation (WSDOT) has not conducted comprehensive research on the embodied carbon within its construction material usage (i.e., upstream Scope 3 emissions inventory of procured materials) with most previous carbon accounting practices being focused on Scope 1 and Scope 2 emissions (i.e., the carbon footprint of direct and indirect energy usage). This paper summarizes the results of a life cycle assessment on the agency-wide material procurements and construction operations that emit GHGs at WSDOT as a case study. This study uses several data sources from WSDOT in conjunction with publicly available life cycle emissions factor data to estimate GHG emissions attributed to the materials used to build and maintain roadways under WSDOT’s jurisdiction. Results indicate that upstream Scope 3 emissions for WSDOT as an agency is a significant contributor to its overall GHG emissions inventory. Specifically, between 2017 and 2022, this paper estimates an average annual upstream Scope 3 emissions of 310,000 metric tons of CO2 equivalents, which translates to 56% of the total annual GHG emissions including Scope 1 and 2 emissions.

Developing a multi-criteria prioritization tool to catalyze TOD on publicly owned land areas

Public agencies can take a leading role in catalyzing TOD by making land available to developers (selling or leasing land, potentially below market prices). In particular, park-and-ride areas that are publicly owned can be leveraged to support TOD uses, such as affordable housing, office space, small businesses, and mixed-use buildings given their convenient access to transit systems and often large land areas. However, few previous studies have discussed the use of publicly owned park-and-rides, which are an important component of publicly owned land, as a catalyst for TOD. To fill the gap in the literature and effectively support TOD planning, this research developed a multi-criteria prioritization tool to identify the most promising locations for TOD and tested it at three park-and-ride sites owned by the Washington State Department of Transportation. The tool was developed through the Delphi process, which is an effective and inexpensive approach to evaluate relevant indicators by synthesizing the opinions of experts from various backgrounds. Five categories with a total of 14 TOD indicators, including transit supportive land-use zoning, job accessibility, land price, land-use mix, and household income, were selected as measures of TOD suitability. The importance of these indicators varied with three different TOD scenarios: (1) emphasis on affordable housing, (2) emphasis on market-rate housing, and (3) emphasis on mixed-use development. Using the calculated suitability scores, this tool can prioritize potential TOD sites for further review.

Collisions and cannabis: Measuring the effect of recreational marijuana legalization on traffic crashes in Washington State

Objective Washington State was among the first states in the US to legalize recreational consumption and retail sales of marijuana. Recreational use of cannabis was legalized December 6, 2012, following the passage of Initiative 502 30 days prior. Roughly 19 months later the first retail cannabis stores opened their doors for public sales (“commercialization”). I measure the impact of cannabis legalization and commercialization on traffic collisions in Washington State. Methods With county-level vehicle crash data from the Washington State Department of Transportation collected monthly, I utilize an interrupted time-series framework with Poisson estimation to compare traffic collisions with recreational retail cannabis sales revenue from 2011 (three years pre-commercialization) through 2017 (three years post-commercialization). First, I measure the shift in collisions brought about by Washington’s 2012 cannabis legalization. Then, I compare retail cannabis sales—a measure of commercialization—to traffic collisions based on severity of injury (fatal, severe injury, minor injury, non-injury, and all). Results After controlling for confounding factors, evidence suggests that recreational cannabis legalization led to fewer fatal and serious injury collisions. Retail cannabis sales generally correlate with more traffic collisions, particularly for less severe (minor injury) crashes. These findings are robust to the inclusion of additional control variables pertaining to county-level cannabis usage and driving behavior while intoxicated. Conclusions Cannabis legalization led to fewer fatal, serious, and minor injury collisions. Commercialization (cannabis sales) correlated with an increase in less severe crashes. Although cannabis use generally increased in Washington State following legalization/commercialization, survey data suggest that driving behavior while under the influence of cannabis did not change significantly over the post-commercialization period. Future research should focus on measuring the dose-dependent impact of cannabis consumption on traffic collisions. This should include recognition of the importance of cannabis dosing, timing, and route of consumption. Lastly, the dangers of poly-drug driving—particularly cannabis and alcohol—are well established and should be high priority for further research.

Cooperative and Comprehensive Multi-Task Surveillance Sensing and Interaction System Empowered by Edge Artificial Intelligence

Numerous sensors were introduced to intelligent transportation systems (ITS) in the past decade. Consequently, new sensing technologies and the generated data attracted more and more attention, which brought new challenges, such as redundant sensors, huge maintenance costs, and data explosion, to ITS. To satisfy the core demands of traffic agencies in a more effective and efficient way, therefore, this paper proposes the idea of “Sensing as a Service” (SaaS) and implements it in the Cooperative and Comprehensive Smart Edge Node for Sensing and OpeRation (COCO SENSOR) system for practical deployments. COCO SENSOR is an innovative multi-task sensing system, which was developed to address key practical applications, including real-time vehicle counting and recognition, road-surface-condition classification, visibility estimation, and live communication among traffic controllers and road users in a single unit. COCO SENSOR introduced customized cooperative sensing and a parallel computation mechanism to increase perception accuracy and efficiency with limited computation resources on the edge device. In collaboration with the Washington State Department of Transportation and the City of Bellevue, a field experiment was conducted to test the system’s performance. The results of the experiment show that the COCO SENSOR effectively fills the gap between sensing and traffic services and that it successfully executed four practical applications, including traffic-volume counting by vehicle type, traffic-status detection, road-visibility estimation, and road-surface-condition classification, with high accuracy. Additionally, a mobile app was developed for both traffic managers and users to access comprehensive traffic information and live warning messages.

Study of Measures to Design Asphalt Mixes Including High Percentages of Recycled Asphalt Pavement and Recycled Asphalt Shingles

With the increased use of recycled materials and various additives, the performance of asphalt mixes, especially their cracking resistance, has become a primary concern. Researchers have developed performance test methods and parameters to simulate field performance in the laboratory. When these performance tests are included in specifications, contractors must adjust their mix design parameters accordingly. To aid contractors, this study evaluated rut depths obtained from Hamburg wheel-tracking tests of 261 asphalt mixes and cracking test (CT) index values from IDEAL-CT tests of 69 mixes designed by contractors and verified by the Washington State Department of Transportation. These mixes had different gradations; binder contents; air void contents; recycled asphalt pavement (RAP); recycled asphalt shingles (RAS); recycling agents; binder performance grades; and other volumetric properties. The results show that (1) rut depth is highly sensitive to binder content, performance grade, aggregate angularity, and RAP/RAS content and (2) the CT index is sensitive to binder content, air void content of the mixture, aggregate gradation, and RAP/RAS content. The low-RAP mixes also showed worse cracking performance than the mixes without RAP. Therefore, the performance grade of blended binder may need to match the target binder grade even for low-RAP mixes. It was found that the design air void of 3.5% is optimal in respect of cracking resistance. The use of softer virgin binder or rejuvenator may not be effective in completely mitigating the cracking potential of high-RAP/RAS mixes.

Truck Parking Pattern Aggregation and Availability Prediction by Deep Learning

With the significant increase of e-commerce, freight transportation demand has surged significantly over the past decade. Most of the demand has been served by trucks in the United States. One major problem commonly identified across the country is the worsening truck parking availability because the increase of truck parking facilities has lagged behind the growth of trucking activities. The lack of parking spaces and real-time parking availability information greatly exacerbate the uncertainty of trips, and often results in illegal and potentially dangerous parking or overtime driving. This paper elaborates on pilot research on improving truck parking facilities cooperated with the Washington State Department of Transportation (WSDOT), building and testing the advanced Truck Parking Information and Management System (TPIMS) with the real-time user visualization and prediction function empowered by artificial intelligence. Furthermore, by analyzing the activities of truck drivers, the researchers aggregated the regularity of truck parking patterns by a customized sequential similarity methodology. A Truck Parking Occupancy Prediction (TPOP) neural network for time-variant occupancy prediction by deep learning and attributes embedding is proposed and integrated into the TPIMS. The TPOP achieves 5.82%, 5.07%, 4.84%, and 4.19% mean average percentage error (MAPE) for 16, 8, 4, and 2 minutes ahead of occupancy prediction respectively, significantly outperforms other state-of-the-art methods. Clearly, the proposed solutions can benefit both the truck drivers and government agencies by a more efficient and smart TPIMS.

Analysis of Washington State Department of Transportation Risks

Risk management is an important part of ensuring the successful delivery of major transportation projects. The first step in the risk management process is to identify the risks facing the project team so they can proactively complete the remaining steps: analyzing the severity; choosing how to respond; and controlling the level of risk throughout the lifecycle of the project. Although some state agencies have been practicing risk management for nearly two decades, little analysis of the risks identified during project development has been done. This study analyzes the risk registers pertaining to 51 major transportation projects undertaken by the Washington State Department of Transportation valued at approximately $7.7 billion to determine the distribution of risks among categories in the risk breakdown structure and find the most common risks in each category. The findings were then explored through interviews with industry professionals to gain insight into the results, add context, and illustrate the importance of identified risks. The results contribute to a better understanding of the risks facing transportation departments and lay the foundation for a more comprehensive risk identification step at the project level.

Toward a real‐time Smart Parking Data Management and Prediction (SPDMP) system by attributes representation learning

Managing and estimating the availability information of parking lots is of great importance to travelers and managers. However, the task is very challenging since the occupancy rate is affected by various factors, including spatial-temporal features, parking lot attributes features, and environmental changes. Previous studies mostly focus on the short-term prediction by capturing the historical sequential dependencies among inputs and outputs, which leads to low estimation accuracy for long-term prediction and limited scalability for real deployment in parking lots. To address the challenges, a comprehensive framework for real-time Smart Parking Data Management and Prediction (SPDMP) system is proposed. Three types of data sources, including historical sequential data, real-time sequential data, and attributes category data, are sufficiently integrated into a customized Parking Availability Prediction (PAP) neural network by representation learning and heterogeneous feature embedding. Specifically, instead of using parking lot property information and environmental data directly, the authors design an Attributes Tensor Embedding Component (ATEC) to integrate the intra-class affection and interclass representations and correlations by two steps of customized embedding process. To balance the impact of the indiscriminate features for various prediction targets, this study proposes a multi-factor attention mechanism to learn the weights and help the PAP achieve a stable performance for time-various tasks. From extensive experiments on two real-world large-scale data sets collected in China and USA (including 19 urban parking and 2 truck parking lots), PAP achieves superior prediction results on both urban parking (6.69% average MAPE) and truck parking prediction (7.63% average MAPE) for five prediction time slots (5 min, 10 min, 30 min, 1 h, and 2 h). Furthermore, even with limited training data, PAP still shows better transferability and adaptability for various types of lots. The proposed SPDMP is selected and used as a pilot test bed by the Washington State Department of Transportation (WSDOT) for truck parking information promotion.

Using Large Linked Field Data Sets to Investigate Density’s Impact on the Performance of Washington State Department of Transportation Asphalt Pavements

Large data sets of Washington State Department of Transportation (WSDOT) pavement construction and condition data are linked together and used to investigate an implemented change in in-place density to lower specification limit (LSL) from 91% to 92%. This serves as a test case for using such large in-service data sets to create analysis value for a state DOT. Findings include: (1) WSDOT field density has remained relatively steady at 93% for over 20 years; (2) raising the density LSL to 92% will likely result in more contractor effort to achieve higher densities; (3) no clear trend links density with better pavement condition; (4) raising the density LSL will likely result in fewer problematically low densities; and (5) there is no evidence of differing pavement performance based on asphalt content, gradation, or nominal maximum aggregate size.

Field Performance of 9.5-mm Nominal Maximum Aggregate Size Asphalt Pavement in Washington State

AbstractThis paper compares Washington State Department of Transportation (WSDOT) 9.5- and 12.5-mm nominal maximum aggregate size (NMAS) mixtures using cost, mix design, field quality assurance, an...

Improved methodology for benefit estimation of highway pavement projects

This paper offers an improved process for evaluating the benefits and economic tradeoffs associated with a variety of highway pavement projects at the Washington State Department of Transportation (WSDOT). It first analyzes the WSDOT current evaluation process and their utilization of the Highway Economic Requirement System (HERS-ST) developed by the Federal Highway Administration (FHWA). The assessment identifies some limitations. As a supplement, this study develops the HERS-ST Benefit Application Tool (HERS-ST-BAT) within Excel to improve the HERS-ST software and enhance the capabilities of evaluating highway project analyses. By combining HERS-ST-BAT and HERS-ST, the analyst is able to provide estimates for a variety of regional-level agency and user costs associated with pavement programs and more effectively consider different investment alternatives. Two separate pavement project case studies are selected to apply the HERS-ST-BAT. The results indicate that the improved process is generally applicable to various highway pavement projects. Other transportation agencies, especially for those without a statewide travel demand model, can incorporate this method for evaluating pavement improvement decisions.

Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks.

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

Tidal current observations through Admiralty Inlet from ferry-mounted current profilers

Admiralty Inlet is a narrow sill located at the northern end of Puget Sound (WA, USA). Circulation through Admiralty Inlet is complex, with tidal currents exceeding 3 $$\hbox {ms}^{-1}$$ , large variations in fresh water input to the system, and seasonal ocean water intrusions. Long-term observations of the currents across the entire inlet are crucial for understanding circulation through Puget Sound. In this context, the Washington State Department of Transportation (WSDOT) Ferries, which run year round through Admiralty Inlet, provide a cost-effective platform to mount instruments and obtain long time series of currents distributed across the inlet. Through the Ferry-Base Monitoring of Puget Sound Currents project, two down-looking acoustic Doppler current profilers (ADCPs) are installed on board two WSDOT ferries, providing depth profiles of velocities across the inlet since May 2014. All data are quality controlled and organized in an horizontal and vertical grid across the inlet. Data within each grid cell are analyzed to capture tidal current harmonic components. Results agree well with data from fixed bottom-mounted ADCPs, and show large spatial variability in the amplitude of harmonic components, probably related to the bathymetric features of the inlet. Further analysis provides estimates of tidal asymmetry and residual currents through the inlet, which are relevant to water quality within the Puget Sound.

Factors Influencing Adoption of Information Technologies for Public Transportation Project Inspection: A WSDOT Case Study

This paper presents a case study of the organizational change process associated with the Washington State Department of Transportation (WSDOT) year-long research program that implemented a cloud-based mobile project inspection application to 18 project engineering offices (PEO) across the state. Ultimately, four out of the 18 PEOs decided to adopt the new technology. Data from semi-structured interviews and a user study conducted two months after implementation are used to identify organizational change strategies used by WSDOT, and how those relate to ideas from the general literature on change management. The loss of upper management program leaders, inadequate communication and training to prepare personnel for the change, and policy and procedural uncertainties in integrating the change with other systems and operations were found to be factors that may have influenced the outcome of the program. While this paper focuses on one DOT’s efforts, other DOTs may have similar organizational structures and implementation efforts, and the findings and lessons learned could serve as a representative model for how such implementation might best be accomplished in a DOT and how that might differ from traditional change management guidance.

Comparison of DB to DBB on highway projects in Washington State, USA

Comparative evaluations of design-build (DB), to design-bid-build (DBB), aredocumented in literature. However, a recent study suggests that even though several studies have been completed to compare DB and DBB, there are few statistically significant comparative results. Comparative analyses of highway projects of the same scope, size, and type could provide the basis to make the argument for the use of either DB or DBB on highway projects. The objective of this research is to compare DB to DBB on highway projects. The basis of comparison includes project cost, contract duration, number and type of contract change orders. Projects used for this research were obtained from the Washington State Department of Transportation (WSDOT). The projects were subsequently selected based on project scope,size, and type, and then analysed using quantitative methods. The research found that there is enough evidence to support the use of DB over DBB on highway projects. The findings of this study have significant implications for practitioners and policymakers on highway projects and should inform decisions on the choice of project delivery method. The main limitation of the research is that the study used only 14 projects due to the difficulty of finding matching projects,and as such the results could not be generalized. However, the findings add to the body of research on criteria for comparing DB to DBB. To enhance understanding of how project scope, size, and type might affect or be affected by project delivery methods,the research recommends the standardization of project types classification for highway projects

Assessing Transportation Impacts to Alkali Bees (Hymenoptera: Halictidae) and Alfalfa Seed Production in Southeastern Washington State

The Washington State Department of Transportation (WSDOT) is proposing safety improvements to U.S. Highway 12 near the town of Touchet, Washington. The improvements include realignment to the north in order to accommodate a wider roadway. The proposed realignment route is within the Touchet-Lowden-Gardena alfalfa seed-growing district, an area critical to alfalfa seed production. The most effective and efficient pollinator for alfalfa is Nomia melanderi Cockerell, a native ground-dwelling bee that is raised in bee beds by area farmers. Relocation of the highway will bisect several bee beds and alfalfa fields. The alfalfa leafcutting bee, Megachile rotundata F., is also an important pollinator in the area. A study was conducted regarding potential impacts to these bees from the highway improvements. One of the objectives of the study was to determine pollinator flight heights over county roads. The “vehicular bee sweeper” was designed to capture insects at specific heights over the roadway and provided data regarding flight height. The majority of N. melanderi flew at heights below 2.1 meters when no other factors were considered, though most M. rotundata flew at or below 0.9 meters. Environmental conditions such as temperature and wind speed had an effect on flight behavior of both pollinators. Due to the low-flying nature of these solitary bees, vehicle strikes can be expected to cause mortality in bisected populations. WSDOT will be able to understand the impacts of the proposed highway to these important alfalfa pollinators and make recommendations to minimize the road’s effects.

Reliance on and Reliability of the Engineer’s Estimate in Heavy Civil Projects

To the contractor, the engineer’s estimate is the target number to aim for, and the basis for a contractor to evaluate the accuracy of their estimate. To the owner, the engineer’s estimate is the basis for funding, evaluation of bids, and for predicting project costs. As such the engineer’s estimate is the benchmark. This research sought to investigate the reliance on, and the reliability of the engineer’s estimate in heavy civil cost estimate. The research objective was to characterize the engineer’s estimate and allow owners and contractors re-evaluate or affirm their reliance on the engineer’s estimate. A literature review was conducted to understand the reliance on the engineer’s estimate, and secondary data from Washington State Department of Transportation was used to investigate the reliability of the engineer’s estimate. The findings show the need for practitioners to re-evaluate their reliance on the engineer’s estimate. The empirical data showed that, within various contexts, the engineer’s estimate fell outside the expected accuracy range of the low bids or the cost to complete projects. The study recommends direct tracking of costs by project owners while projects are under construction, the use of a second estimate to improve the accuracy of their estimates, and use of the cost estimating practices found in highly reputable construction companies.

Booster Seat Effectiveness Among Older Children: Evidence From Washington State

The American Academy of Pediatrics has recommended that children as old as 12 years use a booster seat when riding in motor vehicles, yet little is known about booster seat effectiveness when used by older children. This study estimated the association between booster use and injuries among children aged 8-12 years who were involved in motor vehicle crashes. Researchers analyzed data on all motor vehicle crashes involving children aged 8-12 years reported to the Washington State Department of Transportation from 2002 to 2015. Data were collected in 2015 and analyzed in 2016. Children who were in a booster seat were compared with children restrained by a seat belt alone. Logistic regression was used to adjust for potential confounders. In unadjusted models, booster use was associated with a 29% reduction in the odds of experiencing any injury versus riding in a seat belt alone (OR=0.709, 95% CI=0.675, 0.745). In models adjusted for potential confounders, booster use was associated with a 19% reduction in the odds of any injury relative to riding in a seat belt alone (OR=0.814, 95% CI=0.749, 0.884). The risk of experiencing an incapacitating/fatal injury was not associated with booster use. Children aged 8-12 years involved in a motor vehicle crash are less likely to be injured if in a booster than if restrained by a seat belt alone. Because only 10% of U.S. children aged 8-12 years use booster seats, policies encouraging their use could lead to fewer injuries.

Cost-Effective Performance Management for Washington State Pavement Assets

The Washington State Department of Transportation (DOT) manages 18,230 lane miles (29,332 lane kilometers) of main-line roadway. In the past several years, the Washington State DOT has gone through an evaluation and modification of its pavement management practices. This paper presents the historical perspective on past Washington State DOT pavement practices and several strategies that were developed to improve the management of pavement assets. The Washington State DOT has been successfully preserving the long life of flexible pavements by applying thin hot-mix asphalt (HMA) mill and inlays on the basis of the concept that cracking in thicker HMA sections will primarily be top-down. Furthermore, using the framework developed by the evaluation of cost-effectiveness, several strategies have been implemented to improve the management of Washington State DOT pavement assets. These strategies are ( a) converting 3,000 lane miles (4,827 km) from asphalt to chip seal resurfacing before the year 2025, ( b) planning $850 million to $1.0 billion for rehabilitation and reconstruction for the aging concrete pavement network for the next 10 years with 50% of the concrete slated for reconstruction to be converted to flexible pavement using a crack and seat plus asphalt overlay process, and ( c) applying strategic maintenance to reduce the annual cost by extending the service life of flexible pavements. These cost-effective strategies are expected to generate an overall annual cost savings of $80 million per year by 2025.

Washington State Evaluation of Performance-Based Concrete for Bridge Decks

The Washington State Department of Transportation (DOT) revamped its bridge deck concrete specifications in an effort to eliminate or reduce early-age restraint cracking in bridge decks. Restraint cracking is caused by length changes resulting from shrinkage or temperature effects that are restrained by girders and internal reinforcement and show up primarily as transverse through cracks. Bridge decks constructed with the revised concrete specification are commonly referred to as “performance-based bridge decks” because the revised specification removes prescribed requirements and adds performance criteria such as shrinkage and permeability limits. Contractors are required to submit test results to prove their concrete mix design meets the specified performance requirements. The performance-based specification was first implemented in mid 2011 and is now included in the 2014 Standard Specifications. To evaluate the effectiveness of the revised concrete specification, bridges recently constructed with the performance-based specification and the traditional specification were visually inspected for cracks. The cracking severity was used to compare the bridges and can be used to draw conclusions on the effectiveness of the revised specification to prevent or reduce early-age restraint cracking in the bridge decks. This study evaluated and documented the effectiveness of the changes made to the Washington State DOT concrete specification for bridge decks and sought to identify any improvements that could be made to the current performance-based specification. The study found that the current performance-based concrete specification resulted in fewer restraint cracks than the traditional concrete specification and worked better for precast girder bridges than for steel girder bridges.