Transportation Asset Management Research Articles

Equity in Transportation Asset Management: A Proposed Framework

Transportation asset management has historically overlooked equity considerations. However, recently, there has been a significant increase in concerns about this issue, leading to a range of research and practices aimed at achieving more equitable outcomes. Yet, addressing equity is challenging and time-consuming, given its complexity and multifaceted nature. Several factors can significantly impact the outcome of an analysis, including the definition of equity, the evaluation and quantification of its impacts, and the community classification. As a result, there can be a wide range of interpretations of what constitutes equity. Therefore, there is no single correct or incorrect approach for equity evaluation, and different perspectives, impacts, and analysis methods could be considered for this purpose. This study reviews previous research on how transportation agencies are integrating equity into transportation asset management, particularly pavement management systems. The primary objective is to investigate important equity factors for pavement management and propose a prototype framework that integrates economic, environmental, and social equity considerations into the decision-making process for pavement maintenance, rehabilitation, and reconstruction projects. The proposed framework consists of two main steps: (1) defining objectives based on the three equity dimensions, and (2) analyzing key factors for identifying underserved areas through a case study approach. The case study analyzed pavement condition and sociodemographic data for California’s Bay Area. Statistical analysis and a machine learning method revealed that areas with higher poverty rates and worse air quality tend to have poorer pavement conditions, highlighting the need to consider these factors when defining underserved areas in Bay Area and promoting equity in pavement management decision-making. The proposed framework incorporates an optimization problem to simultaneously minimize disparities in pavement conditions between underserved and other areas, reduce greenhouse gas emissions from construction and traffic disruptions, and maximize overall network pavement condition subject to budget constraints. By incorporating all three equity aspects into a quantitative decision-support framework with specific objectives, this study proposes a novel approach for transportation agencies to promote sustainable and equitable asset management practices.

Automated traffic sign change detection using low-cost LiDAR scans and unsupervised machine learning

ABSTRACT Current practices in traffic sign monitoring heavily rely on manual inspections, a method that is both time-consuming and prone to human error. This leads to inefficiencies in the management and maintenance of these critical roadside assets. The objective of this work is to overcome these limitations by proposing a method for automated change detection in traffic signs using low-density LiDAR data. The proposed solution integrates noise elimination, point cloud restructuring, and cross-scan KD-tree generation, followed by the application of unsupervised machine learning techniques for change identification. The effectiveness of this method was verified by testing across three different highways with varying point cloud resolutions. For robust testing, an algorithm was also designed to simulate a broad range of different damage scenarios in traffic signs of different types, sizes, and placements. Testing in different scenarios along almost 15 km of the road revealed impressive results with accuracy and F1 score metrics ranging from 92% to 100%. Moreover, the algorithm was also extremely efficient with an average runtime of just 115” per km of fully automated unattended processing. The change detection potential of the proposed algorithm extends beyond traffic signs, as it could be adapted for many highway elements, enhancing the efficiency of transportation asset management and highway maintenance programmes. The findings indicate that this approach not only fills a significant gap in the current traffic sign monitoring and asset management practice but also offers a promising, comprehensive solution towards automated, cost-effective, and precise monitoring and maintenance of traffic signs, thus addressing a major challenge in this area.

Ensemble Machine Learning Classification Models for Predicting Pavement Condition

Forecasting pavement performance condition is essential within the pavement management system to optimize decisions with regard to planning maintenance and rehabilitation projects. Accurate forecasts facilitate timely interventions and assist in formulating cost-effective asset management plans. Data-driven machine learning models that utilize historical data to improve forecasting precision have gained attention in the field of asset management. Although numerous studies have employed regression-based models to forecast pavement condition, transportation asset management often operates according to condition index ranges rather than exact values. Therefore, classification models are suitable for predicting pavement condition grades and determining the appropriate maintenance type for pavement assets. This research focuses on developing five machine learning classification models to predict pavement condition: random forest; gradient boost; support vector machine; k-nearest neighbors; and artificial neural network. To enhance prediction performance, these models are integrated using ensemble methods, including voting and stacking. The classification models are developed using a dataset from the Georgia Department of Transportation that documented the condition of asphalt pavements for predefined maintenance sections between 2017 and 2021. A voting ensemble model constructed with the two best-performing individual classification models reached the highest accuracy rate at 83%. Although the performance of individual models fluctuates, ensemble models consistently produce a top-tier performance regardless of the variations in data sampling. Therefore, ensemble methods are recommended for developing pavement condition prediction models to improve accuracy and achieve a more consistent quality of predictions. The findings of this research will provide transportation agencies with information to help them strengthen their forecasting practices in relation to pavement condition, thereby improving their maintenance planning and cost savings.

Application of Satellite-Based Remote Sensing for the Management of Pavement Infrastructure Assets

The long-term monitoring of transportation infrastructure assets at a lower cost and with short mobilization time is of significant interest to both state and federal transportation agencies in the U.S. Because of the significant improvement in spatial and temporal resolution of synthetic aperture radar (SAR) remote sensing systems and a notable reduction in the cost of data acquisition, SAR has now become a viable method to provide economic and rapid condition assessment of transportation assets. A research study was developed and performed to comprehensively perform the inspection and characterization of a pavement surface based on the amplitude of backscattering of an X-band radar. In situ characterization of the test site was first performed using traditional inertial profilers and aerial photogrammetry with unmanned aerial vehicle (UAV) surveys. The results from these in situ methods were compared with the corrected amplitude of the SAR data, which indicated that the distribution of surface roughness values computed from the inertial profiler, UAV, and SAR exhibited similar probability densities at various segmental lengths considered in this study. This suggested that the problematic areas that are evident during in situ characterization can be delineated and quantified based on the normalized radar cross section of the pavement surface. Overall, the outcome of this research exhibits the potential of SAR for future transportation asset management undertakings, and the systematic framework developed as a part of this research could be of significant interest to engineers and transportation practitioners.

Practical Application of Digital Twins for Transportation Asset Data Management: Case Example of a Safety Hardware Asset

Digital Twins have emerged as a transformative technology with immense potential for enhancing asset data management in various industries. The transportation industry relies heavily on properly functioning and maintaining safety hardware assets, such as crash barriers, guiderails, and road signs, among others, to ensure the safety of road users. However, traditional asset data management practices for ancillary assets often fall short of providing real-time, comprehensive insights into the condition and performance of these critical assets. Conversely, Digital Twins can analyze data for predictive and preventive maintenance using cutting-edge technologies. This will enable asset managers to proactively address potential safety concerns, plan maintenance schedules efficiently, and optimize resource allocation. This can be achieved by leveraging historical asset data and real-time condition updates and providing the Digital Twin with an information-rich analytical model. The key steps to creating an information-rich analytical model are identifying the asset data that should be collected, understanding data architecture throughout the asset lifecycle, and generating asset data governance and stewardship frameworks. As such, this paper investigates the current state of practice for ancillary asset data collection and management and investigates the generation of information-rich analytical models for guiderails, a critical roadway safety hardware. Moreover, this study demonstrates how Digital Twins can be integrated and affect the development of transportation asset management systems. The findings of this article emphasize the significance of adopting Digital Twins in transportation asset data management. The case example showcases a compelling roadmap for other transportation agencies to consider integrating digital twins into their asset management strategies.

Assessing social equity considerations within transportation asset management

The impacts of transportation systems on people and communities are substantial but challenging to quantify. It requires significant effort and attention for transportation agencies to improve how social concepts such as equity are effectively incorporated into transportation asset management (TAM) plans and practices. This study, therefore, looked at TAM plans and practices to assess the current application of equity and suggested ways to improve equity incorporation into TAM at the state level. A content analysis was done on the 2019 TAM plans from all U.S. states, D.C., and Puerto Rico, and the results showed that equity-related words were rarely mentioned. This indicated that TAM is one of the sectors where equity is still marginalized, and there is a strong need to improve the incorporation of equity into TAM. The study then identified several equity definitions and measures that fit the context of pavement management and, as a result, TAM. Pavement management was used for demonstration purposes as pavements are a major type of asset managed by all state Departments of Transportation (DOTs). Definitions and measures were identified by reviewing documents from the USDOT and FHWA and three sustainability rating systems widely used in transportation: Envision, Greenroads, and INVEST. In addition to these definitions and measures, this study discussed several available guides and tools that would help agencies bring equity considerations into TAM, specifically at the state level. The results indicated that although some resources provide equity definitions and measures that can be used in TAM, more work is needed to develop them to match the practices and approaches used in this sector.

Transportation Asset Management Decision Support Tools: Computational Complexity, Transparency, and Realism

Asset management decision support tools determine which action (maintenance, rehabilitation, or reconstruction) is applied to each facility in a transportation network and when. Sophisticated tools recognize uncertainties and consider emerging priorities. However, these tools are often computationally complex and lack transparency, the models are difficult to evaluate, and the outputs are challenging to validate. This paper explores computational complexity, transparency, and realism in transportation asset management decision support tools to better understand how to select the right tools for a particular context. Descriptions of how state departments of transportation in the United States make use of optimization in their mandated transportation asset management plans to make decisions are used to understand the needs of states. This qualitative analysis serves as a review of the goals and practices of state agencies. An existing asset management tool is then used to demonstrate the tradeoffs involved in accurately capturing the decision-making process and complexity. The results provide examples of strategies that agencies can use when selecting decision support tools and for researchers and tool developers working toward developing the right tool for an application.

Understanding Everyday Travel Experiences and Transportation Infrastructure Challenges of People with Disabilities in Denver, CO

A common perception is that the transportation experiences of people with disabilities have improved dramatically since the passing of the Americans with Disabilities Act (ADA) in 1990. Though much progress has been made, many aspects of the current transportation system still limit people with disabilities from traveling safely and efficiently. Moreover, transportation planning and design efforts consistently lack representation of people with disabilities. This research seeks to address this disconnect via semi-structured interviews with (i) 28 stakeholders who self-identify as having a disability; and (ii) 9 government officials whose work intersects with the supply of infrastructure regulated under the ADA. All interviewees reside or work in the Denver region in Colorado. One objective is to understand how attributes of transportation infrastructure affect the daily mobility of people with disabilities. A second is to comprehend how the programming and management of transportation assets affects navigation for people with disabilities. Through a thematic and content analysis of the interview corpus, we reveal the challenges faced by people with disabilities through hard infrastructure issues and the programming or management of transportation facilities. Together, these act to diminish the quality of mobility options for people with disabilities. When working to create a transportation network that is universally accessible, it is critical for planners and engineers to work with people with disabilities when considering the design needs of individuals with different types of disabilities.

Consideration of Information Loss in the Transportation Asset Management Process

Transportation asset management (TAM) relies on data to inform decisions on the maintenance or improvement of a given asset. Two of the essential data elements in the TAM process include the inventory, and the condition of an asset over time. Often, the condition is described using several factors, such as the amounts and types of cracking, and those factors are then combined to form a condition index. The index is frequently the basis for developing performance prediction models. However, when the condition of an asset is defined using a measure comprised of several metrics, the risk exists that considerable information could be lost or distorted from index-related calculations. Although the importance of the loss of information depends on the decision framework in which the asset is being managed, it does distort the relationship between the asset deterioration and the factors that drive that deterioration. This paper describes information loss using data and indexes from the U.S. National Park Service pavement network. The effect of the loss of information on the ability to develop performance prediction models is demonstrated. The aim was to present an approach for calculating information loss along with strategies for minimizing that loss. The results showed how loss of information can affect the ability to develop performance prediction models. The primary conclusion of this study is that information loss and -distortion should be evaluated for all condition indexes in an effort to minimize that loss and distortion.

Departments of Transportation Efforts to Digitize Ancillary Transportation Asset Data: A Step Toward Digital Twins

The mission of state Departments of Transportation (DOTs) has evolved, and their perception has shifted from focusing on constructing new assets to managing and maintaining existing transportation assets and optimizing asset performance. In response to this change, state DOTs began adopting a “transportation asset management” approach, an intensive data-driven decision-making process to maintain and extend the serviceability of transportation assets throughout their lifecycle. However, state DOTs continue to face challenges in conducting cross-asset system analysis and integrating data across systems throughout the asset lifecycle. Conversely, emerging technologies, namely Digital Twins, have the potential to leverage the value of asset data and transform data into valuable insights to inform decision-making. The definition of Digital Twins in the infrastructure industry is inconsistent, and the transition toward a digitized built environment—a preliminary step required for a successful implementation of Digital Twins—has not been investigated. Thus, this paper presents the burning platform for the need for Digital Twins and defines the concept. Additionally, this study investigates the current practices of state DOTs toward the digital transition of their transportation asset data and contextualizes the DOTs’ maturity in the advancement of digital processes. This paper focuses on ancillary asset systems, particularly Roadside, Electronic, and Drainage Asset systems. A web-based survey was developed and distributed to state DOTs for data collection. For each asset, the perceived timeline for digitization was investigated, and three variables related to data digitization were explored: data format, data level of detail, and data collection technique.

Machine learning to enhance the management of highway pavements and bridges

The adoption of machine learning in transportation asset management is hindered by the perception of it being a black box, the natural resistance to change and the challenges of integration with existing management systems. This paper aims to enhance the understanding of machine learning and provide guidance for the development and implementation of machine learning to support decision making in the management of highway pavements and bridges. The paper identifies successful research efforts using machine learning, identifies opportunities and challenges in adopting machine learning and derives recommendations on when and how to apply different machine learning algorithms to support asset-management decisions. Four main challenges were identified: the trade-off between accuracy and interpretability, the shortage of machine learning engineers, data quality and the limitations of machine learning algorithms. Although the complexities associated with training machine learning algorithms challenge short-term implementation, machine learning offers a wide range of opportunities when compared with traditional approaches. The development of hybrid systems combining machine learning algorithms with expert opinions and traditional approaches seems a reasonable step forward to support agencies’ asset-management decisions.

Evaluation of Pavement Performance Models Using Historical Condition Data for the U.S. National Parks Pavement Network

Pavement management, a subdivision of transportation asset management, seeks to use finite and limited resources to maintain or improve the condition of a pavement network for a defined time horizon. Pavement performance prediction is integral to pavement management, and among other uses, it contributes to budget projections and allows scenario analyses to be conducted. This paper documents the evaluation of the original pavement performance models developed for the U.S. National Park Service (NPS) pavement network using historical condition data. The Federal Lands Highway Program began to use a pavement management system for NPS pavements in 2005, and the results in this paper represent an evaluation of those models with an emphasis on developing recommendations for improvement. The main finding of the work presented in this paper is that the current NPS models result in significant overprediction of deterioration in practically all cases, although the effect is more pronounced for those measures associated with structural failure. This means that the NPS pavement network is in better condition than the models predict. The performance prediction models for those segments that received preservation treatments are also investigated in this paper, and it is found that the model bias is less for these than for the models predicting condition without a preservation treatment. However, this is attributed to the segments that received preservation treatments being in good condition. Overall, the findings point to a need to recalibrate or redevelop the models to improve their prediction capabilities.

Evaluation of Flood-Vulnerable Pavement Network in Support of Resilience in Pavement System Management

Transportation systems play a pivotal role in the nation’s economic and societal development. Transportation assets, such as pavements and bridges, are inevitably subject to the effects of climate and environment. These stressors mainly include flood, precipitation, heat, wildfire, and wind. Specifically, more extreme weather events, such as hurricanes and snowstorms, have occurred in recent years. This requires stakeholders to better prepare transportation infrastructure resilience in planning, design, construction, and management. Its importance is manifested through nationwide policy and state-level practice. For example, in Bipartisan Infrastructure Law, the FHWA directs all state DOTs to incorporate resilience in their transportation asset management plans. A critical element in resilience is to evaluate the effect of climatic or environmental stressors on facilities’ performance. This study examines the impact of flood on the TxDOT-managed pavement network. It proposes a method to evaluate the flood-vulnerable pavement network in the context of resilience. First, the GIS tool is used to overlap a 100-year frequency flood with the road network to identify weak pavement sections subject to flood risk. Second, a simulation is run to determine sections affected by flood over a 10-year analysis horizon. Then, different deterioration models are used to predict the network-level pavement performance, to reflect (1) normal deterioration without flood, (2) optimistic accelerated deterioration under flooding, and (3) pessimistic accelerated deterioration under flooding. It is found that the network-level pavement will experience varying levels of performance reduction, owing to a 100-year flood impact. The quantified performance change can serve to enhance infrastructure resilience preparation for more reliable pavement system management.

Incorporation of National Pavement Performance Measures Into Decision-Making Process

State highway agencies (SHAs) are required by Federal Highway Administration (FHWA) to develop Transportation Asset Management Plans (TAMP) in which the performance targets for roadway assets are determined. FHWA also developed national performance metrics to use in determining performance targets for pavements. However, for pavement assets, most SHAs employ their own performance metrics for pavement condition data reporting and performing maintenance and rehabilitation (M&R) analysis. Therefore, there is a need to incorporate national performance measures into SHAs’ pavement management system to support the development of TAMP. In this study, new pavement performance metrics were proposed to assist SHAs in conducting M&R analysis. The relationships between the new metrics and existing pavement metrics used in the State of Tennessee were investigated. Comparison between the new metrics and the State’s metrics was made with reference to network-level condition summary and performance prediction models. Results indicated that the new performance metrics appeared to be stable and less sensitive to sectioning method. Similar time-series patterns of network-level performance deterioration were found between the State’s metrics and the new metrics. It is recommended that performance models be established based on the new metrics, as the deterioration rates from the two metrics were not well correlated.

Patrol Regimes for Traffic Officers in Transportation Asset Monitoring

In this paper, we investigate the feasibility of using highway traffic officers (TOs) for transportation asset management (TAM) alongside their primary role of incident response. Asset data, typically captured via highway surveys on an annual basis, are unsuitable for those assets whose condition might rapidly change, such as vegetation, streetlights, guardrails, or drainage systems. Therefore, we considered as a proof-of-concept, whether data collected from dashboard cameras installed in TO vehicles might provide analysts with near real-time asset data across an entire highway network. We considered a case study of a dedicated TO fleet deployed on the strategic road network (SRN) in England, UK, and developed a simulation based on publicly available data sets. Within the simulation, TOs patrolled under two distinct regimes and responded to dynamically generated incidents. The first regime aimed to minimize the the fleet’s incident response time, and the second aimed to maximize the fleet’s coverage, with the aim of capturing asset data across the entire highway network. Overall, our simulations showed that the TOs deployed for TAM reduced the SRN junction-to-junction section intervisit time by around 1 h 45 min, whereas their incident response time only increased by about 4 min. Moreover, 17% of SRN sections were not visited at all when the TOs prioritized fast incident response, which was reduced to 2% when the TOs prioritized the capture of asset data.

Integrating Vulnerable Road User Safety Criteria into Transportation Asset Management to Prioritize Budget Allocation at the Network Level

Safety is a major worldwide concern due to the increasing number of fatalities of vulnerable road users (VRUs). VRUs are pedestrians, bicyclists, and motorcyclists, and their safety is a priority when making infrastructure management decisions. Traditionally, transportation agencies have adopted transportation asset management (TAM) practices based on performance measures to assess the physical condition of transport infrastructure. This paper describes a framework to incorporate VRU’s safety into the TAM decision-making process. The main objective of the VRU-TAM framework is to mitigate pedestrian fatalities by improving the decision-making process at the strategic and operational management level. The VRU-TAM framework is composed of four phases: assessment, prioritization, scenario analysis, and results. It includes a prioritization methodology using a safety-weighted effectiveness ratio (SWER) to consider pedestrian safety factors and costs in the decision-making process. In a broader perspective, pedestrian safety is affected by driver, demographic, pedestrian, infrastructure, and policy related factors. The decision-making criteria reflected in SWER considers the asset importance, location, pedestrian safety risk, costs, and remaining life in the budget allocation process, and adopts the dynamic bubble up (DBU) technique for budget prioritization at the network level. An example is presented for crosswalks to demonstrate the applicability of the methodology to evaluate different budget-driven scenarios for funding allocation. As a conclusion, it is demonstrated that the outcomes of the budget-driven scenarios following the method that incorporates safety criteria, with tangible metrics, offer a deeper understanding of the effects of budgetary constraints on backlog costs and the remaining life of infrastructure assets.

STATE DEPARTMENTS OF TRANSPORTATION’S VISION TOWARD DIGITAL TWINS: INVESTIGATION OF ROADSIDE ASSET DATA MANAGEMENT CURRENT PRACTICES AND FUTURE REQUIREMENTS

Abstract. Transportation Asset Management (TAM) is a data-driven decision-making process to maintain and extend the serviceability of transportation assets throughout their lifecycle. TAM is an extensive data process that requires accurate and high-quality information for better decision-making. A significant challenge faced by state Departments of Transportation (DOTs) is the need to allocate their limited funds to optimize their assets’ performance. The criticality of this challenge increases when state DOTs need to manage a wide variety of assets distributed along with a vast network. To address this challenge, a new paradigm of digitizing the management of the built environment is emerging and is perceived to highly depend on the integration of several technologies namely on Digital Twins. Digital Twins, by definition, are the connection between the physical and the digital aspects of an asset, thus, aligning with the overarching objective of asset management of leveraging the use of the asset information (i.e., digital aspect of the asset) to improve the asset’s performance throughout its lifecycle (i.e., physical aspect of the asset). At the core of implementing Digital Twins is having the right data collected for use throughout the lifecycle of the asset. Thus, realizing the potentials of Digital Twins in supporting state DOTs to manage their transportation assets and the anticipated benefits, this paper investigated the current practices of state DOTs in digitizing the Data Collection for Roadside Asset Systems by developing and distributing a web-based survey. Five major Data Collection variables and seven Roadside Asset Systems were considered. Furthermore, this paper presents a case study from a leading DOT in digitizing the management of the built environment to further understand the requirements of implementing Digital Twins to support transportation asset data management.

Forecasting Federal Transportation Performance Management Bridge Condition Measures for Bridge Management

Many common processes of bridge management can benefit from network-level analysis of long-term costs and condition, on a time frame of about 10 years. Such processes include development and implementation of Transportation Asset Management Plans, long-range needs analysis, capital budgeting and programming, and policy analysis. The ability to forecast federal Transportation Performance Management (TPM) condition measures would provide managers with a way of evaluating the possible outcomes of funding, programming, and policy decisions. A model for this purpose has been developed as a part of StruPlan, an open-source spreadsheet for long-range renewal planning for transportation structures. Element condition state data are found to be highly exponential in distribution, while the federal measures “Percent Good” and “Percent Poor” are categorical when applied to specific bridges. Element data, providing more detail about the type, severity, and extent of defects, are valuable for deterioration modeling, while the TPM measures are simpler for reporting to stakeholders. A set of models was developed to bridge the gap between these measures. Thus far, the models have been calibrated and pilot tested using Idaho, South Dakota, and Kentucky data. The model is a novel approach that has not been attempted elsewhere, that may simplify important parts of bridge management and provide some valuable new ideas for researchers and developers.

Element-Based Multi-Objective Optimization Methodology Supporting a Transportation Asset Management Framework for Bridge Planning and Programming

The U.S. Moving Ahead for Progress in the 21st Century Act (MAP-21) mandates the development of a risk-based transportation asset management plan and the use of a performance-based approach in transportation planning and programming. This paper introduces a systematic element-based multi-objective optimization (EB-MOO) methodology integrated into a goal-driven transportation asset management framework to improve bridge management and support state departments of transportation with their transition efforts to comply with these MAP-21 requirements. The methodology focuses on the bridge asset class and is structured around five modules: data processing, improvement, element-level optimization, bridge-level optimization, and network-level optimization modules. It relies on a leading-edge forecasting model, three separate screening processes (i.e., the element deficiency, alternative feasibility, and solution superiority screening processes) to overcome computer memory and processing time limitations, and a simulation arrangement to generate life-cycle alternatives (series of improvement actions). Additionally, the EB-MOO methodology consists of three levels of optimization assessment based on the Pareto optimality concept: element-level, bridge-level, and network-level (following either a top-down or bottom-up approach). A robust metaheuristic genetic algorithm handles the different multi-objective optimization problems. A prototyping tool was developed for the implementation of the methodology through several examples of unconstrained and constrained (by budget, performance, or both) scenarios. Results reveal the capability of the methodology to generate Pareto optimal or near-optimal solutions, predict performance, and determine funding requirements and short- and long-term intervention strategies detailed at the bridge-element level for planning and programming. The EB-MOO methodology can also be expanded to accommodate other asset classes or modes.

Database Design and Integration Framework for Risk Management for State Highway Agencies

Risk-based transportation asset management program (TAMP) gives transportation agencies the ability to have a mechanism for documenting and measuring risks to their operations, as this will help drive the potential mitigation activities. The monitoring and updating of the risk management process is essential to TAMP as it will ensure that the financial plan and investment strategy components of the TAMP are suitable to ensure that agencies continue to fulfill their primary responsibilities. However, a review of the initial TAMP documents submitted by U.S. transportation agencies showed that although risks are acknowledged, there does not exist a clear line of sight between risk management and agencies’ programming. This is the result of a lack of cross-asset risk integration. As a result, the paper proposes a data integration framework for developing a cross-asset comprehensive database for risk management that integrates many of the common risks that state highway agencies have identified in the initial TAMP documents. In addition, the paper proposes modifications to the risk identification methodology that leverage the collaborative aspects of risk management to quantify risk in monetary terms.