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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Suggested Citation:"Appendix B. Emerging PMR Practice Summaries." National Academies of Sciences, Engineering, and Medicine. 2020. Existing and Emerging Highway Infrastructure Preservation, Maintenance, and Renewal Definitions, Practices, and Scenarios. Washington, DC: The National Academies Press. doi: 10.17226/25795.
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Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

B-1 EMERGING PMR PRACTICE SUMMARIES HYPER-PERFORMANCE MATERIALS Description High performance roadway materials are designed to have better strength, durability and/or workability properties than corresponding traditional materials. High performance roadway materials include many variants, with newer variations expected with advancements in nanotechnology. The notable innovations in commonly used roadway materials are: • Ultra-high performance concrete – also known as reactive powder concrete, is a mixture of portland cement, steel or organic fibers for reinforcement, silica fume, quartz sand and super plasticizers with very low water cement ratio. • Self-healing asphalt – a porous asphalt concrete containing steel wool fibers. The crack healing potential of porous asphalt concrete is activated through induction heating of steel wool. Higher stiffness and crack healing properties of asphalt concrete provide longer fatigue life and better resistance against moisture damage and low-temperature cracking. • Ferrite-bainite steels – dual-phase micro-alloyed steels that have lighter weights, higher yield strengths, better fatigue resistance and improved stretchability with shearing than traditional steels. Transformative Aspects and Benefits High performance materials available today were little known just a decade or two ago. The research into improved materials is never ending, which is why the high performance materials we now have, which provide lighter designs and better durability due to improved engineering properties, will continue to evolve into the “hyper-performance” materials of tomorrow. Hyper- performance materials will: • Be stronger and more durable reducing the need for frequent maintenance, major structural rehabilitation and reconstruction activities. • Possess improved properties that will result in greater resiliency and adaptability in light of the impacts of climate change and extreme weather, as well as growing traffic and heavier vehicles. • Provide greater sustainability benefits, including reductions in the use of depleting natural resources, reduced energy consumption and lower emissions, and lower overall environmental footprints over the life cycle of an asset. • Result in significant reductions in life-cycle costs from reduced PMR requirements, as well as resiliency, adaptability, and sustainability. Scale of Impact / Discipline Applications Pavement: The key hyper-performance materials for pavement applications include ultra-high performance concrete and new variants of polymerized asphalt binders.

B-2 Ultra-high performance concrete materials are cement-based, high-strength, ductile materials that have compressive strengths up to 29,000 psi and flexural strengths up to 7,000 psi. These materials have excellent durability properties exhibiting very little deterioration after standardized freeze-thaw tests and lower permeability. Together, these properties of high performance concrete materials provide additional resistance against cracking, freeze-thaw damage, corrosion, abrasion and impacts. Similarly, new variants of polymerized asphalt binders are likely to possess higher resistance against cracking and deformation to extend the life expectancy of pavements. Not only do these materials extend the life expectancy of newly constructed, replaced or rehabilitated pavements, but they also extend the period of time before maintenance and preservation activities will be needed. Structures: Hyper-performance materials such as engineered concrete, composites, and specialty steels used in highway structures will result in a reduction in life-cycle costs due to greater durability and resiliency in the face of higher traffic levels and heavier freight loads. Improvements in high-strength concrete, self-healing materials and lighter weight, higher strength and less corrosive steel will provide for more economical designs in new structures, longer life and substantially less PMR activities. Other possibilities for the future include use of nanotechnology materials in structures. The incorporation of nanofibers and nanotubes into construction materials intentionally introduces cracking on a controlled nano- to micro-scale to reduce tensile stresses and mitigate cracking in concrete due to shrinkage, thermal changes, and corrosion. Advancements in nanotechnology is also expected to enable self-sensing, self-healing technologies that greatly reduce or eliminate the need for PMR activities. Drainage & Roadside: Key innovations related to D&R include porous friction course (PFC) overlays and porous pavements. The former uses specialized aggregates that works at the molecular level to chemically bond with and remove targeted pollutants from stormwater. The latter is capable of capturing runoff and associated pollutants, and storing and treating the runoff under roadway and roadside surfaces. Polymer composites may evolve to serve as alternative abrasive materials for winter maintenance and drainage pipes. These materials tend to be non-corrosive and possess adequate levels toughness, fracture resistance, and material resilience. Such materials will improve the asset performance of snow plowing hardware and pipe materials, improve safety and lower life-cycle costs. Plausibility The emergence of hyper-performance materials will be an incremental advancement for agencies, many of which are already using advanced materials such as ultra-high performance concrete and less corrosive steel. From the plausibility perspective, breakthroughs in the performance of highway materials are currently in exploratory research and development stages.

B-3 As with any innovation, there will be a cost premium in the initial stages of deployment; however, with larger economies of scale, the material costs will decrease over time. In addition, there will be research needs to investigate the impacts of such materials on structural design, maintenance, repair and renewal needs, quality assurance requirements, standards, specifications and testing protocols, and long term performance.

B-4 STRUCTURAL HEALTH MONITORING Description Structural health monitoring involves real-time continuous collection and monitoring of mechanistic responses (e.g. stresses, strains, displacements, etc.), structural damage, asset usage and condition. New generations of structural health monitoring systems involve wireless enabled, self-calibrating, compact-sized sensor packs with artificial intelligence capabilities, high‐fidelity hardware, and low power requirements. Next generation systems may evolve into materials/structures with response, shape or vibration control and damage mitigation with large- scale use of sensitive, controllable and auto-adaptive materials. Transformative Aspects and Benefits Current state-of-the-art structural health monitoring systems contain dense packs of expensive and wired sensors to collect engineering responses of structures. However, traditional wire-based systems require significant time and cost for cable installation, and unrealistically long times to transmit data to service centers. Although in many structures, “locked-in” stresses overwhelm transient ones, there is currently no reliable, non-destructive approach to estimating them. Owing to these limitations, a centralized asset condition monitoring center is infeasible. With opportunities including wireless enabled systems, ease of installation, renewable energy sources and low cost of sensors, a centralized monitoring center connected with a vast network of sensor systems appears to be a plausible option. When integrated with the “Internet of Things” (IoT) and artificial intelligence applications, there is a possibility for a self-diagnosing, self-reporting and work ordering infrastructure system. Benefits include improved asset management applications informed through significantly greater data availability collected without risky onsite inspections. Scale of Impact / Discipline Applications Structures: Structural health monitoring provides extensive data on structure deterioration for improved analytical and predictive models that lead to better decision making on preventative measures. Sensors will continue to provide better and more real-time data for short-term and long-term asset management and planning of PMR activities. New structures can be “smart” with embedded, self-diagnosing, non-destructive sensing for continuous measurement and data collection. Critical estimates of remaining service life for bridges can be made from structural health monitoring data collection. Structural health monitoring systems will also be able to monitor corrosion, corrosion rate, and section loss. Sensors can also inform emergency response systems like a tunnel emergency fire response or response to critical bridge structure damage from collision or earthquake. Structural health monitoring sensors may not only collect external data but be able to communicate directly with vehicle sensors aboard connected and automated vehicles, providing information on hazardous conditions on a bridge or in a tunnel. Sensor operations may capitalize on energy-harvesting opportunities from vibrations in bridges to drive low-voltage sensors.

B-5 Drainage & Roadside: Structural health monitoring will facilitate accurate and easy monitoring and testing of stormwater conveyance systems and treatment/storage facilities and allow for automatic sampling of stormwater treatment facilities. Plausibility Networks of wireless structural health monitoring systems will be a radical advancement for highway agencies. Mostly experimented in the research domain, the installation of miniature, wireless enabled sensors with artificial intelligence capabilities in the U.K. Crossrail project for continuous asset condition monitoring is an encouraging first step for asset management. However, more research and pilot studies are required to evaluate the feasibility of implementing a network of wireless structural health monitoring systems. At a minimum, the greater number of data collecting sensors and devices will require more training of staff, and a maintenance program dedicated to inspection and sensor equipment.

B-6 CUSTOMER EXPERIENCE MANAGEMENT (CXM) ANALYTICS Description Wikipedia defines customer experience management (CXM) as the process that companies use to oversee and track all interactions with a customer. CXM analytics entails a wide range of techniques to understand, influence and measure road user preferences and experience, and use this information in guiding PMR decision making and processes. Using predictive analysis, highway agencies can synthesize data obtained through multiple mobile-based sources, including smart phones and connected vehicles, to understand travel behavior and measure road user satisfaction with work zones and maintenance. Transformative Aspects and Benefits Traditionally, highway agencies have depended on individual customer complaints, stakeholder meetings, focus group responses, and statistical surveys to monitor and comprehend the range of preferences, experiences and responses of road users. More recently, many highway agencies have become adept at engaging social media toward the same goals. While such techniques will clearly remain, with advancements in acquiring mobile-based data and predictive analytics, highway agencies can measure road user experience in real-time and use such data to identify or adjust components of PMR management plans and activities, such as construction phasing, lane closure timing, network level project coordination, alternative route advisories and generalized motorist information. From a safety benefit perspective, CXM analytics might suggest a region, corridor, or point location where incidents or the potential for incidents related to roadway assets is a concern. CXM analytics can achieve a customer-oriented approach to maintenance needs identification, prioritization, and budgeting. By perceiving and having the capability to respond to customer experiences, CXM analytics has the potential to assist in strengthening relationships with stakeholders and monitoring and enhancing the credibility and support of a customer-driven highway agency. Scale of Impact / Discipline Applications Pavement, Structures, Drainage & Roadside: CXM analytics will help agencies provide better levels of service associated with pavement condition (e.g. roughness) to road users and achieve better project delivery outcomes with pavement construction and PMR activities. Similarly, customer feedback on the ride quality of bridge decks or perceptions of safety and security in tunnels can lead to potential corrective or mitigating actions. CXM analytics can help better schedule lane closures for inspection and PMR activities. They will also help agencies provide better levels of service associated with D&R activities to road users and nearby property owners by providing information on user experience relating to debris control on roadway surface, aesthetic considerations of landscaping and litter control, safety and visibility considerations of roadside mowing and edging, maintenance of roadside rest areas picnic spots, and detecting stormwater flooding.

B-7 Transportation Systems Management and Operations (TSMO): CXM analytics can be a valuable input into a winter maintenance decision support system or otherwise help to tailor a winter storm response to real-time public sentiment. The quality, extent, and pace of clearing and treating roads of snow and ice can use real-time customer input (as well as historical preferences) to identify both a priority of roads to treat and satisfaction with those already addressed. Work zone monitoring and near-real-time adjustment can be facilitated through CXM analytics. In addition to hard data from ITS devices, CXM data can suggest modifications to work zone characteristics based on customer feedback. Despite planning on paper or through advanced simulation methods, real-world experience can provide valuable feedback on work zone design and operation. Plausibility Most highway agencies have advanced considerably in the quantity and quality of customer engagement. CXM analytics will represent a significant further advancement in the ability of highway agencies to connect with their customers. While highway agencies are currently interested in expanding the use of mobile data for transportation planning and traffic management applications, related exploratory studies will help them to understand and tap into the potential of CXM analytics.

B-8 MACHINE LEARNING – ARTIFICIAL INTELLIGENCE FOR ASSET MANAGEMENT Description Machine learning is a type of artificial intelligence-based algorithms used in data analysis that allows computers to automatically learn from data. In the transportation realm, machine learning can be used to recognize patterns and trends, and gain insights from asset performance data that may otherwise have been lost in statistical variability, without the explicit need to program where and how to look for such patterns and trends. Transformative Aspects and Benefits Highway agencies spend significant resources to acquire a large amount of performance data for critical assets, particularly pavements and bridges, and analyze them to better understand their behavior under a wide variety of environmental and traffic-related conditions in order to improve engineering practices and design methodologies. Most highway agencies use performance data- based statistical models in their structural design and deterioration forecasting methodologies; however, these models are mostly designed to analyze trends based on formalized, pre- established, “deductive” knowledge of variables. There are opportunities to improve the state-of- the-practice with better understanding of asset behavior, improve current analytical methodologies by minimizing empirical risks, and guide the process toward robust decision making for PMR activities. Using automatic and inductive learning capabilities, machine learning applications can analyze complex data sets, investigate recent and long-term trends in asset behavior, and use this information to build more reliable, robust and data-driven decision support systems. With such data-driven decision support systems, highway agencies will benefit through better asset management practices, lower assets life-cycle costs, optimized resource allocation of funds, and associated benefits. Scale of Impact / Discipline Applications Pavement: Machine learning offers automated methods that can be invaluable to the development of pavement-related forecasting models as they analyze large volumes of highway- related data faster and more accurately. Not only can machine learning help to reinforce and refine current knowledge, but it can also discern previously unknown patterns. Using this knowledge, appropriate decisions can be made at the project level about the scoping of the next PMR activity. Such decision making can be scaled and customized to every project at a network level to facilitate prioritization and resource allocation decisions. For instance, machine learning can help identify when to schedule crack filling on non-working cracks to optimize the life-cycle sequence of PMR activities, whose effects on pavement performance cannot be analyzed otherwise using current mechanistic or empirical models. Structures: Automatic and inductive learning capabilities applied to the analysis of complex datasets can lead to further innovations for designing new structures that can anticipate and thereby reduce downstream PMR requirements. These capabilities can also provide models for the more efficient design of specific bridges through an improved ability to analyze complex datasets that, just as an example, could make calibrating load resistance factors more accurate.

B-9 Benefits of adaptive design based on data-driven models and utilizing Artificial intelligence (AI) learning will result in efficient, lighter weight structures that are more resilient and durable. TSMO: Machine learning algorithms a critical component of applying proactive methods to optimizing TSMO maintenance regimes, as discussed under the innovation, Advanced TSMO Device and Communications Systems Maintenance. These maintenance regimes can result in near-zero or zero device or system downtime and optimized use of resources and supply chain management. Plausibility Machine learning applications will be a significant improvement to a highway agency. Machine learning applications are growing in other areas, such as online retailing, genetics, finance and health informatics. There is vast amount of knowledge and lessons learned available from machine learning applications in other application areas to overcome many technical barriers, if any, in the transportation realm. Highway agencies can consider investments in research to explore the applications of machine learning to build more reliable, robust and data-driven decision support systems that have potential applications beyond asset deterioration modeling.

B-10 INTEGRATED BUILDING INFORMATION MODELING (IBIM) FOR HIGHWAYS Description According to the U.S. National Building Information Model Standard Project Committee, “Building Information Modeling (BIM) is a digital representation of physical and functional characteristics of a facility. A BIM is a shared knowledge resource for information about a facility forming a reliable basis for decisions during its life cycle; defined as existing from earliest conception to demolition.” Importantly, BIM applies not just to buildings but to any form of facility or infrastructure. BIM involves a variety of integrated technologies and business processes all focused on collecting, organizing, and accessing all asset related data and information of a facility during its life cycle, including PMR activities. BIM is currently state-of- the-art for complex, mega projects but not for every day work, and certainly not for an entire network of streets and highways, which means the advantages to PMR activities of continuously tracking and updating the physical form of a highway system are still a long way off. Transformative Aspects and Benefits Currently, for most of their projects, highway agencies typically use paper-based or electronic documents to manage information relating to the physical form of the asset. The electronic information is managed in commercial standalone systems, typically siloed along the lines of asset life-cycle functions, with no or little integration, connectivity or interoperability. Some agencies have begun to “federate” these standalone systems to acquire some automated connectivity using customized interfaces and middleware in a geospatially enabled environment. In the future, these standalone systems will evolve into iBIM, an integrated electronic system with rich vendor independent, interoperable data governed by common data standards, supported by a secured cyber infrastructure of full automated connectivity and web or cloud based applications. As multiple datasets obtained from different technologies are integrated into a common data environment, an iBIM system will become a one-stop way of storing, retrieving and archiving all asset related information, and thus, breaking down the traditional information silos among various internal divisions of an agency. Not only does this approach save the cost of collecting and processing the same data multiple times, it provides a platform for better organization and tracking of asset information, consistent interpretation of data, and streamlined workflow. Further, there are benefits associated with each life-cycle phase of a facility/asset that will collectively produce efficiencies in business processes, and thus lead to better asset management outcomes, lower total cost of asset ownership and associated user benefits. Scale of Impact / Discipline Applications Pavement: iBIM can allow decision-makers to readily access historical information related to design and construction, such as pavement design features and construction quality outcomes. This will support decision making relating to PMR activities, and utilizing “use-phase” information, such as asset conditions, maintenance and preservation histories, and renewal events, for pavement design and life-cycle modeling purposes. iBIM can also allow integration of information from performance monitoring systems to support holistic decision making, such

B-11 as undertaking safety improvements during pavement preservation or undertaking capacity improvements during pavement renewal. Structures: iBIM (or BrIM – Bridge Information Modeling) will facilitate the use of 3D modeling that can span the life cycle of structures from initial concept and visualization to 3D design with the digitized data being transferred to fabricators (possibly using 3D printers) and then to construction (including construction robotics and GIS systems) and ultimately to asset management systems that capture the “as-built” as well as the “as-preserved” and “as- maintained” changes to the structure over its serviceable life. BrIM provides for better organization and tracking of the structure’s historical data and facilitates streamlined workflows. Designers, construction managers and asset managers may one day put on a headset and “see” a new or renewed bridge virtually imposed onto the existing site, and virtually be able to explore construction phasing before any dirt is moved. TSMO: An iBIM platform represents the ultimate evolution of an enterprise asset management system that can manage the complex datasets generated from the ubiquitous deployment of TSMO device sensors. This platform would support data mining and use of algorithms that consider component conditions and failure modes to support advanced asset management strategies. Maintenance & Construction Equipment: iBIM will facilitate the connectivity, integration, and interoperability of data related to construction plans, asset information, and other worksite data for sharing among equipment in a manner sufficient to automate the use of vehicles and machines during PMR. Worksite safety, efficiency, and quality would improve. iBIM would be the overarching management tool for intelligent machine control, which governs the use of heavy construction equipment through software and 3D construction drawing data. Manual, labor- intensive, and error-prone steps would be eliminated. The iBIM platform would manage the complexity of a highway construction worksite and enable advances in automation and robotics to take hold. Plausibility An iBIM-like system covering all an agency’s highway assets will take many years, but in its fruition, will represent a huge leap in facilitating PMR activities. Despite the many necessary incremental steps required, there is a certain inevitability to the outcome. Many notable trends are currently emerging in allied areas of geospatial and surveying technologies, e-construction, digital engineering designs and intelligent construction machines and information technology that would facilitate the emergence of implementation of integrated BIM like systems in the future. There are industry-wide efforts currently underway to develop data standards specifically for highway applications and to ensure data interoperability among various in-house or vendor- supplied systems. In the U.S., some state highway agencies, including the Utah DOT, have federated their standalone information systems at the enterprise level, and are already realizing the benefits. In Europe, there is a growing number of transportation construction projects, including Crossrail and High Speed Two in the U.K, Rijkswaterstaat N33 in the Netherlands, where project level BIM systems were developed for asset management purposes. To implement project level BIM systems successfully, highway agencies will require a holistic asset

B-12 information practice, and pertinent business processes and standards. Highway agencies should also recognize that iBIM systems are considered foundational to enable information transfers to and from other innovations, such as the IoT and Structural Health Monitoring systems.

B-13 ENTERPRISE INFORMATION SYSTEMS – PMR APPLICATIONS Description An enterprise information system is a unified system of computer applications specifically designed for an organization that provides a platform to integrate and streamline their business processes. Enterprise information systems allow highway agencies to organize their business requirements and processes toward a delivery-oriented structure to help achieve their organizational objectives efficiently. Many highway agencies still labor under antiquated and disparate legacy systems. Given the extensive volume of PMR activities undertaken by highway agencies, the upgrading and integrated migration to an enterprise information system is central to the overall efficiency of any highway agency. Transformative Aspects and Benefits Highway agencies are organized as internal divisions along their functional activities and asset types, such as planning and programming, financial management and budgeting, real estate, environmental services, procurement, construction, maintenance, asset management, bridges, pavements, traffic operations, human resources, and legal services. Each internal division of the agency has a set of business processes to achieve their functional objectives through independent information systems. Most highway agencies have evolved from paper-based systems toward first generation electronic systems. Most of these electronic systems are still standalone along the lines of the “siloed” sets of business processes they serve. For example, a typical highway agency still maintains electronic versions of their legacy systems with little or no integration, as in management systems for right-of-way, environmental process, bridge assets, pavements, maintenance, ancillary assets, procurement, construction inspection and testing etc. An enterprise system, on the other hand, provides a platform to integrate all standalone systems into a single unified system which facilitates streamlining of business processes and information handling. The benefits of an enterprise system include opportunities to seamlessly integrate processes among a variety of responsibility centers, while avoiding duplication or gaps, as well as fragmentation and bottlenecks in workflows. Such a comprehensive framework also precludes costly investments in multiple solutions, which may or may not interact successfully, thus resulting in better management, improved decision making, increased organizational efficiency, and related cost savings. Scale of Impact / Discipline Applications Pavement, Structures, Drainage & Roadside, TSMO: Enterprise information systems will streamline business processes relating to life-cycle management of pavement, structures, drainage and roadside, and TSMO assets. Examples include the scheduling of pavement or bridge condition data collection, identification of PMR needs that align with strategic goals, estimation of resource needs, planning, procurement, control and closure of PMR activities, updating of information systems, and supporting data analytics. Such systems streamline the business process functions of various information systems, relating to procurement and project

B-14 management, maintenance, pavement management, bridge management, safety and mobility, to mitigate efficiency bottlenecks in workflows. Plausibility An enterprise information system is a big advancement to highway agencies. Given that the highway agencies are moving toward paperless systems, there are fewer barriers to implement an enterprise system in the future. For successful implementation, the buy-in and commitment from top management is a must to support the change management process. Since the primary purpose of an enterprise system is to support the agency’s business process, the system architecture should be designed and tailored to the needs of the agency. There will also be a need to continuously evaluate, revise and modernize existing support systems even after implementation. Additionally, agencies should devise implementation and change management strategies, including user engagement in needs assessment, and training. The potential for mismanagement of the process required to develop and implement such systems in agencies lacking the necessary in-house skills to plan and manage the process is significant. Of paramount importance is the engagement of technical management resources, independent of the selected vendor/system developer capable of defining up-front in the pre-procurement stage the requirements of such a system, and then overseeing the specifications, procurement, development, deployment, testing, transition, and full-scale operational phases.

B-15 CONNECTED VEHICLE APPLICATIONS TO SUPPLY REAL-TIME CONDITIONS INFORMATION Description The current principal focus of connected vehicle applications is on capitalizing on safety and mobility applications. But unlike other vehicle-to-infrastructure (V2I) applications, the focus for PMR related applications is not on the vehicle but on the infrastructure conditions data it can provide—the connected vehicle acting as a probe. Probe-based V2I communications can be used to capture and communicate data on an individual vehicle’s response to operating conditions from onboard sensors such as accelerometers, inertial sensors, and suspension motions detectors. Transformative Aspects and Benefits A promising aspect of connected vehicle probe-based information is the ability to combine conventional passive infrastructure measurements with probe-based, real-time information reflecting current system status and conditions. While the use of vehicles as probes and the communication of real-time probe data on infrastructure conditions do not provide direct asset condition data, data from individual vehicles can be aggregated and analyzed in terms of inferential relationships between such data and actual physical conditions. The fuzziness of direct correlations will be offset, to some degree, by the scale of the “big” datasets that can be created. In addition to provision of data for asset management, V2I-based data may also support correlation of this “crowdsourced” data with customer-related serviceability perceptions. Benefits include access to substantially greater amount of real-time condition data with greater fidelity to better plan and manage appropriate PMR decisions, both longer-term (e.g. preservation treatments) and immediate (e.g. emergency repairs or real-time adjustments to work zones). Scale of Impact / Discipline Applications Pavement: Similar to remote sensing systems but in closer proximity, probe-based connected vehicle-to-infrastructure (V2I) applications will provide faster and real-time pavement condition data to facilitate rapid responses to urgent and emergency pavement needs. (And working somewhat in “reverse” (i.e. I2V), pavements whose condition will be monitored with sensors can communicate such urgent and emergency conditions to motorists in their vehicles.) With onboard sensors on connected vehicles, such as accelerometers, inertial sensors and suspension motions detectors, probe-based V2I communications can serve as “crowd sources” of data relating to pavement surface condition, such as roughness, potholes, friction, rutting, cracking, deflection, and flooding. The dedicated short-range communications (DSRC) frequency bands associated with connected vehicles also enable transmission of collected data. When installed on public fleets and/or through private commercial data providers, probe-based V2I technologies, requiring less resources to collect large-scale, real-time information, will provide a significant breakthrough in pavement condition detection capabilities of highway agencies.

B-16 Structures: Connected vehicle applications for long-term structures’ PMR provide an opportunity for detecting certain deficiencies from the vehicle (such as deck cracking or roughness of pavement and joints, deck deflection, slippery conditions due to weather or skid resistance problems). Over the long run, the need for some embedded health monitoring devices may be reduced by using connected vehicles as substitute probes for data capture on the physical condition of bridges. TSMO: Within work zones, connected vehicles can supply a continuous stream of V2I telematics for optimizing smart work zone operations and incident response. Condition data, such as speed and queue information, will have far greater fidelity and can be shared and analyzed instantaneously to make real-time adjustments to work zone operations. Connected vehicles as probes also have the potential to eliminate the need for certain TSMO devices, as they may substitute for data capture systems such as road weather information system sensors or vehicle detection equipment (e.g. loop detectors). Plausibility Some ride quality data (from acceleration and suspension) can be collected currently using existing technologies (even mobile devices) if externally organized but will still require major data management and modeling. Some other potentially valuable data (ABS, traction control status) is typically not available from vehicles directly. Capitalizing on sensor data on a large- scale is likely to depend on the market penetration of onboard DSRC and transportation agencies’ uptake on accommodating V2I data collection. Further advances are dependent on external change, both technological and institutional, such as additional onboard sensor technology, together with related industry standards to support uniformity in the “crowd sourcing” nature of the data. The use of public fleets and/or private commercial data providers might provide a bridge to such as systems.

B-17 ARTIFICIAL INTELLIGENCE – PMR TRAFFIC MANAGEMENT APPLICATIONS Description Artificial intelligence (AI) deals with the use of computer algorithms to solve real-world problems with an ability to analyze, reason, and learn from different situations, to acquire and retain knowledge, and to respond quickly and successfully to a new situation. AI algorithms are particularly adept in finding optimal solutions for finite but very large numbers of possibilities, applying inductive reasoning in learning from observed information. While there are many facets to artificial intelligence, among PMR activities, AI is ideally suited to real-time intelligent transportation systems (ITS) operations, where providing rapid, optimized solutions in response to complex dataset and dynamic conditions is required. Transformative Aspects and Benefits Various studies have attempted to include artificial intelligence in traffic management systems. Early AI applications were mostly done in small “proof of concept” projects that had limited scope. With recent advances in connected vehicle technologies, better algorithms, data mining capabilities and high performance computing (e.g. parallel and distributed computing), AI has the potential to offer innumerable solutions, particularly for complex tasks that are traditionally beyond human capabilities. In the traffic operations realm, with the introduction of connected vehicles, the transportation industry is expected to experience a new wave of data explosion producing very large and complex digital data sets. Given the strength of machine learning algorithms, AI can digest large volumes of data, analyze and provide solutions relating to traffic control, congestion management, motorist information and incident/emergency management, such as detecting queuing conditions, dynamic adjustment of signal times in response to congestion, recommendations on best route and transportation mode to take from a given origin to destination, and auto-enforced traffic control. AI capabilities will facilitate faster, adaptive and dynamic responses to traffic conditions during PMR activities as well as during normal operations. Scale of Impact / Discipline Applications TSMO: Data in work zones, such as speed and queue information, will be available from ubiquitous sensors and connected vehicles, with far greater fidelity, instantly shareable and analyzed. Artificial intelligence (and other innovations like the IoT) will enable seamless interconnectivity among devices and systems to rapidly optimize the performance of smart work zone ITS equipment and systems. Work zone traffic will be actively managed using congestion- reducing algorithms to maximize throughput. Plausibility AI applications will be an incremental advancement for a highway agency but radical from the perspective of myriad possibilities of solutions that AI can provide. The evolution of AI is heavily dependent on the advancements in computer and cognitive sciences. While developing infrastructure for AI will not be expensive, there will be a future investment needed to enhance

B-18 the capacity of agency workforces and to integrate AI into business processes. Furthermore, there is a need for research to tap the full potential of AI applications beyond traffic operations, especially in structural modeling, travel forecasting, and resource allocation decision making.

B-19 PREDICTIVE-PROACTIVE MAINTENANCE REGIME FOR ROADWAY ASSETS Description The value of preventive maintenance is in avoiding the need for more costly, reactive maintenance. The often overlooked “other side of the coin” is that preventive maintenance done too early wastes resources by not utilizing the full potential of an asset that is still in good condition. Predictive-Proactive maintenance is a proactive, dual source assessment and intervention process that optimizes maintenance regimes for assets, taking into account their criticality and potential consequences of asset failure. Predictive-Proactive maintenance draws upon both asset condition/performance models and corroborating time series field data (often streamed from sensors) that can track actual versus predicted condition and performance so as to validate where in the time-based deterioration curve the asset actually is at any point in time, and thereby optimize the timing of a preventive maintenance action. Defining just where on the deterioration curve a maintenance intervention should occur can vary according to the criticality and risk tolerance associated with the asset. By recognizing that similar, or even virtually identical asset elements will have different failure rates due to differences in materials and construction or fabrication, as well as differences in local conditions including weather and traffic impacts, Predictive-Proactive maintenance can provide reliable information to produce customized, “just-in-time” preventive maintenance work programs that minimize post- construction life-cycle costs. Transformative Aspects and Benefits Many highway agencies are currently using a mix of reactive (i.e. when triggered by failure) and, to a lesser degree, preventive maintenance (i.e. at regular intervals based on time or usage) regimes for most asset types. State-of-the-art practice emphasizes preventive maintenance or preservation concepts because of the higher costs of reactive interventions. However, even preventive maintenance, performed at intervals that are based on time and/or usage with no due consideration for actual condition and performance of an asset, can needlessly cost more by spending money before the optimum point of intervention, thereby failing to exploit useful life remaining in an asset. Predictive-Proactive Maintenance takes preventive maintenance or preservation to the next level by incorporating predicted or quantified condition of the asset in maintenance decision making— a practice usually reserved for more substantive rehabilitation actions. This will allow agencies to proactively identify and address maintenance requirements in an optimized manner, thereby minimizing life-cycle maintenance costs. Use of predictive analytics in condition forecasting, corroborated by field measurements, also allows agencies to adjust the timing of maintenance activities at a reliability level commensurate with the criticality of assets and the performance goals of an agency. Implementation of Predictive-Proactive maintenance will result in greater savings and efficiency improvements for an agency through better allocation of resources, robust life-cycle management of assets, improved resiliency of assets, and associated user benefits, including lower traffic disruption and crash risks.

B-20 Scale of Impact / Discipline Applications Pavement, Structures, Drainage & Roadside: The application of robust performance prediction models and availability of current time series field data, driven by expanding and innovative sources of mega-data (structural health monitoring, connected vehicles, non-destructive testing, remote sensing, etc.) can facilitate a more proactive preservation regime for pavement, structure, and drainage and roadside assets. The scale of this application is widespread and can have profound beneficial impacts on the timing and extent of PMR activities from an asset management perspective. TSMO: A predictive-proactive approach to TSMO device maintenance is covered under the innovation, Advanced TSMO Device and Communications Systems Maintenance. Plausibility Predictive-Proactive maintenance is a next generation advancement in the maintenance practices of highway agencies. The feasibility of implementation within an agency typically grows with the maturity of its asset management systems. Advancements in the non-transportation domains, such as information technology, geomatics and geophysical systems and sensor technologies for highway condition assessment, will accelerate the implementation of Predictive-Proactive maintenance.

B-21 THE INTERNET OF THINGS (IOT) – PMR APPLICATIONS Description The Internet of Things (IoT) represents a network of physical objects containing embedded technology connected seamlessly across platforms through a unified information technology framework to create, communicate, aggregate, and analyze information. IoT devices may range from simple smartphones and digital boards to intertwining webs of sensors and actuating devices. Transformative Aspects and Benefits Highway agencies undertake data collection and analysis efforts using traditional techniques for countless reasons through concerted efforts with various entities. Often the same data is collected more than once, formatted differently, obscured by organizational silos, or residing in compartmentalized data repositories. With the IoT, there is an opportunity to collect massive volumes of various types of data, share them instantaneously and seamlessly across groups, and put them into immediate effective use. The IoT will provide an agency with a smart, reliable, efficient and cost-effective system to collect, analyze and respond to real-time information. A primary benefit of an IoT is to provide information relating to the performance of roadway assets in a coordinated and connected manner that can broaden the purview of asset managers in real-time, and most importantly, integrate with other innovations. The IoT will provide the “wiring” (much of it in wireless form, of course) that in 50 years portends self-monitoring, self- alerting, self-analyzing, and self-managing PMR, all under the watchful eyes of discipline professionals, perhaps fewer in numbers but more advanced in their technical proficiency as well as in their capability to make the critical decisions that guide the system toward optimal, cost- effective outcomes. Scale of Impact / Discipline Applications Pavement: While highway agencies have experimented with instrumented pavement sections at discrete locations for research purposes, this is just the beginning of a long-term trend toward interactive communication between pavements and other “things” through an IoT whose potential PMR benefits are very significant. The motivation for instrumented pavement sections is to measure real-time, structural responses, such as stresses and strains, in an effort to capture seasonal variations and explain long-term pavement performance. Though huge quantities of data have been collected to date, the research on fully utilizing this data is still a work in progress. In the future, these instrumented sections will evolve into an IoT that seamlessly collects a wide spectrum of data, including pavement structural responses, pavement condition, traffic and weather, and from a wide range of sources, including sensors embedded in the pavement structure, remote sensing and V2I applications. The IoT is likely to further evolve into real-time mechanistic analysis of these responses to provide automated notifications of PMR triggers.

B-22 Structures: Similarly for bridges and other structures, information collected through an IoT can feed into a virtually endless array of useful applications for individual structures and on a network and corridor basis, including real-time inspection and condition reporting, facilitated routings and tracking of special permit loads (for size and weight), and PMR activity decision making, among others. Drainage & Roadside: The IoT also finds applications in monitoring the condition of drainage networks and roadside facilities. Future stormwater treatment, storage, and reuse devices will be equipped with sensors, data communication capabilities, GPS, and automatic or remote- controllable/operable mechanic and electronic parts. The stormwater and drainage network forms a physical system that is designed to be capable of real-time data communication with the enterprise system and is linked with external weather forecasting systems and public alerting systems. Future stormwater systems will also be linked to regulatory enterprise data systems for real-time reporting and compliance tracking. Roadside developments such as bike lanes, bike share stations, pedestrian zones, streetscape and other features within right-of-way (ROW) will form as a connected, creative network of facilities and components, via embedded technology that can communicate with one another via the IoT to provide for better and more efficient functions. TSMO: The IoT will enable a seamless, interconnected network of TSMO devices and systems to provide real-time monitoring as an input into asset management systems. The IoT can also enable improved traveler information services, the ability to respond to emergency events virtually, from anywhere, and real-time traffic management through smart work zones. Maintenance & Construction Equipment: The IoT will enable a seamless, interconnected network of maintenance fleets (e.g. snowplows) and construction equipment to optimize jobsite management along with use of materials and labor, enhancing efficiency, safety, and quality. Plausibility Harnessing the full potential of the IoT will be a radical advancement. Widely touted as the next wave of digital revolution, the concept of IoT has been emerging organically in many areas, particularly given the growing maturity in the areas of data management and storage, nanotechnology, distributed sensors (fixed and vehicle-mounted probes), infrastructure embedded systems, roadway users’ personal devices, data mining and analytics, and wireless communication technologies. According to Gartner, Inc., there will be about 25 billion “things” in the IoT, including 250 million connected vehicles, by 2020. However, there are challenges that will need to be addressed for implementation in a highway agency from technical and business perspectives, including (i) the need to change legacy business processes, (ii) inadequacy of current data acquisition and management infrastructure, (iii) lack of standards, protocols and specifications for device installation, data acquisition, management and communication, data interoperability and interpretation, (iv) resilience against cybersecurity threats, and (v) organizational resource needs and competencies in advanced technologies.

B-23 SELF-DIAGNOSING/REPORTING AND WORK ORDERING Description A self-diagnosing, self-reporting and work ordering infrastructure is envisioned to function as an automatic system that continuously collects data on condition and performance, tracks usage of assets, monitors their structural and functional conditions using performance measures, recognizes if performance indicators move beyond their thresholds of acceptability, diagnoses causes for interventions, selects an appropriate treatment type and optimal timing of application, places a work order for carrying out the treatment or automatically self-performs the activity if self-correction systems are feasible and installed. Transformative Aspects and Benefits Typical PMR activities involve all the above-mentioned processes, from data collection to diagnosis to treatment selection to work ordering, but typically proceed in discrete steps, often falling under different organizational units responding to different priorities. The result is often excessive time periods elapsing between when a condition is diagnosed and reported to when work on it is finally completed. The self-diagnosing, self-reporting and work ordering innovation automates the process under the watchful eye of staff to ensure the system is functioning properly. The result is that problems identified will be acted upon with a greater sense of urgency, culminating in maintenance activities and fixes to problems much sooner than the current sequential process of handoffs between responsibility centers. Not only does this innovation automatically execute the proactive “preservation first” approach and streamline business processes, but it also results in lower life-cycle costs, increased production efficiencies, and customer satisfaction. Scale of Impact / Discipline Applications Pavement, Structures, Drainage & Roadside: The scale of this impact is potentially far-ranging. A self-diagnosing, self-reporting and work ordering infrastructure, whether pavements, structures, or ancillary assets, will be the culmination of a proactive preservation practice, incorporating and benefiting from other innovations such as robust machine-learning based performance prediction models and seamless collection of asset attributes through advancements in sensors such as structural health monitoring and connectivity through the IoT. This system, in conjunction with the IoT, can also be futuristically extended to full automation of maintenance and preservation activities using 3D printing and construction robotics. TSMO: A self-diagnosing/reporting and work ordering approach to TSMO device maintenance is covered under the innovation, Advanced TSMO Device and Communications Systems Maintenance. Plausibility A self-diagnosing, self-reporting and work ordering system is a radical innovation for a highway agency. Such systems are also envisioned for electric grids. There are many barriers and

B-24 intermediate steps to successfully implement this system, but the technical plausibility for this innovation exists. This system’s precursor technologies (real-time structural health and functional condition monitoring systems, smart materials (e.g. self-healing, auto-adaptive materials), the IoT, integrated BIM like systems, predictive-preventive maintenance, machine learning in decision support systems, enterprise information systems, and robotics applications) are within the reach of a highway agency, and therefore, a self-diagnosing, self-reporting and work ordering infrastructure is not beyond plausibility.

B-25 PERPETUAL/LONG-LIFE HIGHWAY INFRASTRUCTURE Description The perpetual/long-life concept is aimed at constructing highway assets whose underlying physical elements last for extremely long periods of time with proper, periodic PMR treatments. This can mean pavements whose wearing surface occasionally needs surface milling and resurfacing, and filling and sealing of cracks, but whose underlying structure (subgrade and base courses) virtually never require reconstruction. It can mean bridges whose foundation and superstructure are also well protected and preserved with only deck treatments required from time to time. Transformative Aspects and Benefits Roadway structures are typically designed to specific demand criteria: i.e. to withstand up to a specific load level and repetitions of traffic axles, and perform satisfactorily over a given period of time under certain environmental conditions. Highway agencies undertake a combination of maintenance, minor and major rehabilitation actions until the asset needs reconstruction. For long-life structures, no major structural rehabilitation or reconstruction will be required, while only periodic maintenance and preservation activities will be undertaken to address routine wear and tear. In comparison with traditional design philosophy, long-life structures may require higher initial investments during construction but resulting in lower life-cycle costs. With the elimination of expensive major structural rehabilitation or reconstruction needs, long-life structures provide sustainability benefits in terms of conservation of natural resources, reduced energy consumption and lower emissions as well as reductions in road user delays and vehicle operating costs. Scale of Impact / Discipline Applications Pavement: Interest in “perpetual,” “long-life” and “zero-maintenance” pavement design is gaining traction among researchers and practitioners, with the promise of significant advances in the years and decades ahead. Long-life designs would drastically reduce and perhaps eliminate the need for renewal activities, while requiring occasional interventions for preservation and maintenance. With some early generation pavement designs implemented mostly on a pilot basis, this concept has enormous potential for high volume roadways where lane closures are very disruptive to road users. By augmenting the structural adequacy of pavements, these long-life designs strive to minimize durability issues. The long-life designs, in conjunction with high performance materials and better construction quality, will fundamentally improve the longevity and resiliency of pavement assets. Structures: New structures built with high performance materials and service life design methods, as well as with consideration of environmental and material corrosion issues during design, result in only minor periodic preservation activities to address routine wear and tear. To have longer service lives, initial designs will need to use improved predictive models to account for increases in traffic loading and the effects of possible lanes exclusive to freight and transit

B-26 (e.g., designing for future heavier and longer freight loads or platoons). In some cases, doubling the design life to 150 years or more will be the norm. Decisions will need to be made on service life design in the areas of durable high-strength materials taking into consideration climate and traffic loading. Plausibility While there are some examples of long-life pavements and bridges in the U.S. and abroad, it is far from common practice. Perennial pressures to minimize initial costs invariably lead to “penny-wise, pound-foolish,” less durable designs that cost more over the long run. Invariably, the long run arrives, and agencies are saddled with reconstruction and major rehabilitation costs that might have been avoided if perpetual/long-life designs were employed. Highway agencies should explore opportunities for innovative procurement and financing alternatives to overcome year-one budgetary constraints, particularly for high-cost, heavily trafficked roadways. Fortunately, the concept of long-life designs will benefit from the evolution of improved structural design methodologies and advanced roadway materials.

B-27 ADVANCED TSMO DEVICE AND COMMUNICATIONS SYSTEMS MAINTENANCE Description TSMO covers all aspects of managing and operating the use of roadways. It encompasses both operational management strategies themselves and the technologies applied, including the complete range of Intelligent Transportation Systems (ITS), incorporating detection and communication technologies, static and dynamic signs, signals, pavement markings, roadside lighting, supporting ancillary structures such as gantries, advanced maintenance fleet technologies, emergency response resources, and other advanced operations equipment. Also included are certain supporting physical infrastructure located offsite (e.g., traffic management centers) or along the roadside (e.g., commercial vehicle inspection facilities and weigh stations). Not only do TSMO devices require their own set of preservation, maintenance, and renewal activities, but they are instrumental in the management of traffic during PMR of other assets. Advanced TSMO device and communications systems maintenance presents an opportunity to significantly improve an asset management approach planned maintenance and obsolescence. Transformative Aspects / Scale of Impact TSMO or ITS device maintenance approaches traditionally have been reactive (failure-based) or preventative (performed on a fixed cycle). Conventional tools include ITS inspection and maintenance manual procedures for testing and field inspections and computer-based programs with databases that support efficient and careful inspection and maintenance of ITS facilities. These procedures have been embodied in maintenance decision support systems for certain types of conditions and facilities. Some ITS asset management systems have been developed with GIS capabilities, data management, visualization and user interface abilities and remote access features. The application of advanced TSMO device and communications systems maintenance brings together several innovations to permit TSMO devices to become “advanced” with respect to how planned maintenance is conducted. This innovation will drive a move away from conventional reactive and preventive maintenance routines to predictive and proactive methods that can lead to more systematic and optimized maintenance strategies. Predictive maintenance methods benefit from real-time status monitoring to gauge the appropriate timing of maintenance interventions. Proactive methods take this a step further and apply asset management analytics and machine learning algorithms (Artificial Intelligence) to better discern optimized maintenance regimes. Both methods rely on using real-time data of sufficient coverage and robustness, gleaned from device-embedded and external sensors that communicate wirelessly. Devices and systems can communicate among one another and with central data aggregators and computational engines. The IoT enables this concept by providing a seamless, interconnected network of TSMO devices and systems across a unified platform. Monitoring solutions alert a maintenance system at the onset of a developing condition and prescribe an appropriate response. Device-specific experience and record databases can be mined and combined with algorithms that consider component conditions and failure modes to support advanced asset management strategies. The data and notifications can be assessed on a time and

B-28 frequency basis, features compared using various types of pattern recognition analytics, performance visualized and predicted, and appropriate corrective routines identified. The platform used to manage and analyze data can also direct the deployment of remote sensing equipment (drones) to capture additional data not acquired through embedded sensors. In all, these innovations provide an “intelligent maintenance system” for TSMO devices and systems that manages status monitoring, condition assessment, fault detection, prediction or prognostication, and response identification. This innovation also can supply real-time inputs to refine predictive methodologies and algorithms to adjust predicted life-cycle curves/trends and computation of obsolescence windows. Obsolescence analysis can be incorporated into enterprise information and asset management systems. Benefits Maintenance responses would no longer based simply on fault occurrence and diagnostics but fault avoidance and prognostics. This innovation (combine with others) would result in near-zero or zero downtime for TSMO systems and devices. With their availability and reliability approaching 100 percent, agencies would have greater confidence of consistent, complete system coverage and support, while reducing or eliminating a need for employing redundant devices or procedures in the event of critical failures—which are unlikely to occur. Further, resource planning and usage can be optimized since maintenance activities would be known with sufficient lead time to efficiently plan an appropriate response, whether that response is automated or involves agency or contracted staff. Lean supply chain management and inventory management approaches can be applied, since the need to source, stock, and maintain inventories of spare parts can be kept to a minimum and would never need to be accessed on a reactive basis, except in an emergency. Plausibility Tools and techniques for an “intelligent maintenance system” are available from other sectors. At the present time, there is limited demand within transportation, but the growth in the deployment inventory of TSMO devices will make these methods more attractive. As with the innovation of a self-diagnosing, self-reporting and work ordering system, advanced TSMO device and communications systems maintenance is a radical innovation for a highway agency requiring several precursor technologies to be implemented, such as machine learning, the IoT, and robust database management capabilities.

B-29 CONNECTED VEHICLE-TO-INFRASTRUCTURE (V2I) TECHNOLOGY PROVIDING COMMUNICATIONS BETWEEN PASSING VEHICLES AND ROADSIDE UNITS Description The connected vehicle concept provides connectivity both among vehicles (V2V) to enable crash prevention and between vehicles and the infrastructure (V2I) to enable safety, mobility and environmental applications. V2I connections provides a parallel and complementary path to the objectives of automated vehicle capabilities. Information can be collected by roadside infrastructure from individual vehicles or vehicles in a location (at an intersection, on a road segment), which then can be analyzed and communicated back to all vehicles and to system managers through the roadside infrastructure in the form of upstream conditions, traffic control, flow control and roadway physical conditions. The “I” component of V2I consists of a network or roadside radios, related communications, data analysis, and management on the part of infrastructure owner-operators. Transformative Aspects and Benefits The V2I functionalities of the connected vehicle concept are designed to supplement onboard and V2V systems to: • Capitalize on the opportunity to further reduce crashes using upstream and downstream data, and device-to-vehicle communication for collision avoidance • Assess network performance for real-time traffic management purposes (such as connected cruise control) • Provide travel information to drivers about system conditions and choices • Collect data regarding roadway physical conditions (discussed under the innovation, connected vehicle applications to supply real-time conditions information) These V2I applications can supplement onboard automation features to enhance intersection collision avoidance and prioritization, roadway departure prevention, speed management and dynamic speed harmonization, queue warning, and others. These functionalities can address human errors lying behind the vast majority of collisions (and their related social costs). But they also offer the potential for significant increases in effective capacity of available right-of-way and improvement in the comfort and convenience of vehicle use. These characteristics are judged to have potentially significant impacts (increases) in traffic flow, vehicle miles traveled, and trip length, all of which may impact asset deterioration cycles or suggest design modifications, such as restriping three lanes to four (narrower) lanes. In addition, the ITS infrastructure associated with V2I roadside infrastructure will introduce new issues associated with the burden of high-tech device maintenance. Scale of Impact / Discipline Applications Structures: V2I technology could communicate information on unsafe travel conditions (ice on bridge decks, flooding, major failure due to impact). On-bridge speed limits might be varied in

B-30 real-time with upstream warnings when infrastructure sensors detect heavily loaded vehicles approaching a structure or work zone. TSMO: Within work zones, connected vehicles can supply a continuous stream of V2I telematics for optimizing smart work zone operations and incident response. Condition data, such as speed and queue information, will have far greater fidelity and can be shared and analyzed instantaneously to make real-time adjustments to work zone operations. Connected vehicles as probes also have the potential to eliminate the need for certain TSMO devices, as they may substitute for data capture systems such as road weather information system sensors or vehicle detection equipment (e.g. loop detectors). Plausibility The deployment of V2I technologies and systems faces many challenges in common with other automation and connection functionalities. These include: • Technical standards and industry consensus regarding applications • V2V and V2I security • Spectrum availability and sharing • Driver responsiveness and uptake • Liability and privacy V2I in particular faces another set of challenges because it requires public sector owner- operators to be directly in the service provision loop. Currently, there are pilot programs deploying a limited footprint of certain V2I-based applications, and FHWA is developing guidance for mainstreaming V2I development. However, significant deployment is dependent on transportation agencies developing (or acquiring) the systems engineering staff capacities to support development, deployment and operations of V2I roadside infrastructure, backhaul, analytics and customer communications and facilities management. Cost of V2I infrastructure is certainly an issue in financially constrained settings. Furthermore, V2I technology presents a significant challenge to agency culture, organization and staffing, collaboration, funding and procurement.

B-31 AUTOMATED ENFORCEMENT FOR WORK ZONES Description Automated Enforcement for Work Zones promises to be a far-reaching innovation to improve safety in work zones. What began as an alternative to the expense and practicality of locating police officer vehicles or pull-off areas for manual speed enforcement, Automated Enforcement for Work Zones are evolving to include all aspects of smart work zones: • Speed enforcement – use of fixed or portable cameras that capture vehicle license plates and potentially driver images and issue citations through automated look-up of vehicle registration databases. • Speed management – application of variable speed limits adjusted appropriate to traffic or construction conditions. • Queue detection and management – queue length measurement and queue/speed advisory systems (visual, tactile) to warn of conditions ahead and merge tapers; also to provide an input into alternative routing advisories. • Merge management – techniques to dynamically optimize merge movements that reduce the speed difference between merging lanes, and thus vehicle conflicts and aggressive maneuvers. • Incident detection and response – methods employing TSMO devices and multiagency collaboration to detect and respond to work zone incidents more quickly. • Traveler information – real-time information on work zone related travel conditions and routing alternatives. Transformative Aspects, Scale of Impact and Benefits Most PMR activities involve the establishment and management of work zones. Smart work zone payoffs include, but are not limited to, speed enforcement with attendant work zone safety benefits (for workers, law enforcement and drivers). Of importance to PMR are reductions in cost of enforcement, increases in speed of construction given increased spatial margins of safety, systems relocation flexibility, and enhanced capabilities for nighttime construction. Given customer concerns about construction impacts, key benefits include reduced construction disruption to traffic flow and speeds. A number of innovations, singly and collectively, will greatly advance smart work zone strategies that provide automated enforcement. Several are characterized by expanded capacities to sense, analyze, and communicate real-time traffic conditions to optimize work zone management decisions and traveler information dissemination. Additional advanced TSMO application to work zones can include automated systems to install raised pavement markers, automated cone deployment systems, mobile barriers, remotely operated lane barriers, and work space intrusion warning and detection systems. Smart work zone strategies will integrate more effectively, become more reliable and more cost-effective.

B-32 Plausibility As in the case of red light cameras, automated speed enforcement has generated public concerns about privacy, reliability of technology, and requires state/local legislation, although the applications in work zones appear to be more publicly acceptable. Capturing the payoffs from smart work zones networking with traveler information and traffic management depends on institutionalizing a more integrated approach within transportation agencies, as distinct from ad hoc individual project approaches, as well as integration of smart work zones into the state/regional systems architecture. Advances in TSMO applications such as Integrated Corridor Management, linking facility operations on a network basis, will provide a context for improved work zones as well.

B-33 CONSTRUCTION ROBOTICS Description Construction Robotics is an advanced form of automation that focuses on mechanizing construction processes with no or little human intervention. These processes and the automated equipment vary in scale and application—from those worn or held by construction workers, to light- and heavy-duty equipment operated or supervised onsite or remotely, and used in inspection, testing, maintenance, repair, site preparation, fabrication, and assembly processes. Transformative Aspects and Benefits To date, there are limited applications for robotics in construction, primarily for single-purpose repetitive operations, such as bricklaying and machine controlled earth-moving. Applications generally have been limited to mining (automated haul trucks) and agriculture (tractors) where the work environment is highly controlled and consistent. Infrastructure construction sites are constantly changing in terms of layout and activity, and along with substantial investments in contemporary long-life equipment, automated worksite vehicles are not yet in use. As a first step, remotely operated heavy equipment is now becoming available (e.g. bulldozers and loaders) that will augment and evolve into autonomously operated equipment. In addition, construction robotics has evolved to deploy software programmable robots with geographic intelligence (i.e. positional capabilities) and sophisticated precision control for semi-autonomous maintenance operations, such as pothole patching, welding, crack detection and sealing, and asset inspections. With rapid advances in machine learning and artificial intelligence, robotics will evolve in their mobility as well as analytical and decision making capabilities, and in legged locomotion in humanoid robots that can traverse the uneven, unpredictable, and continuously changing terrains of construction worksites. These systems will possess a range of sophisticated safety and control systems to handle emergency scenarios and potentially unsafe operating conditions. Prior to substantial advances in robots with humanoid capabilities—and perhaps continuing to be used alongside them—wearable technology will vastly improve the capacity of worksite laborers. Situational awareness, visualization, and recording will be enhanced through virtual reality-enabled smart headgear and eyewear. Monitoring devices such as smart vests can track worker vitals and prevent injury, falls, or warn against work zone accidents from equipment operation or intrusion of nearby traffic. More comprehensive exoskeletons or bionic suits, currently seeing application in the military and healthcare industry, could be used by construction and maintenance workers to increase their strength, reduce fatigue, and avoid injury from heavy lifting and repetitive motion. Construction robotics are expected to significantly advance the application of prefabrication and modular assembly of construction elements, especially in conjunction with 3D printing. Fabrication can occur on or offsite, and the mechanical and often repetitive processes for assembly can be enabled at several scales including large (as with mobile robotic cranes), medium (humanoid robots), or small (ant-like robots that self-organize and collaborate).

B-34 In total, the benefits are substantial. They include increased productivity, enhanced safety and lower risk exposure, automatic detection and fixing, reduced materials and workmanship defects, reduced consumption of natural resources and energy, and reduced costs for labor, motor fuel, and vehicle maintenance. Scale of Impact / Discipline Applications Construction robotics are expected to significantly affect PMR activities at all scales, from ongoing monitoring and executing real-time PMR decisions, to routine maintenance activities, to large-scale renewal projects. Pavement, Structures, Drainage & Roadside: Robotic applications with spatial intelligence and decision making capabilities may reduce the need for traffic control to fast track PMR operations, such as for condition assessment, crack sealing or joint retrofits, with greater safety. Robotics can be used for hard-to-inspect structures or components, including high-tower suspension cables, interiors of box girders and areas that require dangerous rope climbs to reach. Both autonomous and semi-autonomous robotic devices can contribute to a variety of PMR activities relating to D&R, including stormwater treatment, mowing, litter control, subsurface drainage repairs, cleaning and sediment removal in ditches, storm sewers, flumes, and along curbs and gutters. Robotics can potentially enable large-scale automation of pavement PMR activities when integrated with connected V2I technology communications, 3D printing, and self-diagnosing/reporting and work ordering infrastructure. Enhancing construction quality with robotics would contribute toward improving pavement and structure performance in the long run and lower life-cycle costs. Plausibility Depending on the automation level and utilization of construction robotics, it can be construed as incremental (as in intelligent construction machines) or radical (as in humanoid robots) advancement for the transportation industry. There is huge potential for automation and robotics applications in prefabrication, modular assembly, condition assessment, construction, maintenance and repair of roadway infrastructure elements. The evolution of robotics applications depends on advancements in material technology, microelectronics and mechatronics, and robot learning. However, since the transportation industry has relied heavily on traditional labor-based solutions, any large-scale implementation of robotics may lead to societal and political resistance. The role of traditional heavy and specialized equipment operators could evolve to be more like commercial airline pilots—expected to intervene in an emergency or as required by regulation, and otherwise providing management and oversight. This evolution would have significant effects on workforce makeup, skills, and training, along with undetermined economic impacts, as traditional human labor-based activities are phased out.

B-35 REMOTE SENSING SYSTEMS – PMR APPLICATIONS Description Remote sensing system PMR applications fundamentally provide monitoring of: 1) conditions and performance that help in PMR activity decision making and 2) PMR activity progress and performance once underway. These systems to monitor the composition, condition and performance of highway assets are rapidly improving in reliability and accuracy, able to provide a spectrum of optical, spatial, spectral, and temporal measurement capabilities. New systems will include large use of smaller unmanned aircraft systems (drones) with miniature payloads of high resolution navigation and remote sensing devices with better real-time data transmission, ground control and battery fuel technologies using renewable energy. These remote sensing devices are expected to include infrared, thermal, multispectral, hyperspectral, and heat capacity mapping for optical imaging, and ultra-wideband synthetic aperture radar (2D and 3D radar with high resolution) for non-optical imaging. With increasing computing resources, there will be advancements in geospatial data processing methods. Transformative Aspects and Benefits Highway agencies typically use terrestrial video or lidar imagery for remote sensing. With technological advancements, future remote sensing systems will provide high resolution imagery gathered using a variety of payload sensors with benefits of less expensive, faster and large area coverage. Future remote sensing systems will find applications in real-time traffic monitoring and surveillance, roadside and roadway condition inventory and inspection, topographic surveying and mapping, inspection of structural condition, construction safety and security, construction monitoring including as an input into simulations that help review plan against progress, estimating earthwork volumes, real-time detection/monitoring of potential avalanches, landslides and unstable slopes, and crash reconstruction. Aerial non-optical imagery, for example microwave based, can be used to measure surface properties, including soil moisture, pavement smoothness, roadway texture and friction. Highway agencies will see benefits from the improved predictive, detection, and sensing capabilities of roadway conditions in real-time, which will permit them to make decisions on optimal PMR activity execution by augmenting other sources of activity data enabled through innovations like enhanced connectivity and the IoT with complete situational awareness. Reductions or elimination of field inspection and repair crews will enhance worker safety and reduce costs. Scale of Impact / Discipline Applications Pavement: Remote sensing applications can provide large-scale, real-time imagery of pavement surface conditions with greater geolocational accuracy and higher resolution. Such airborne data can identify roadway surface deficiencies, such as potholes and blow-ups, as well as discontinuities, such as cracking. When combined with new electromagnetic wave-based technologies, such as microwave or ultrasonic flaw detection, the remote sensing applications

B-36 can also provide information on pavement structural integrity to support PMR related decision making. Innovative remote sensing technologies indicate a significant breakthrough in the way highway agencies collects pavement condition data. These technologies facilitate a large-scale, faster, less resource-intensive and more accurate collection of pavement condition data in real-time. Not only do these technologies create efficiencies in pavement condition data collection, they also facilitate rapid response to urgent and emergency maintenance needs. Structures: Remote sensing technologies will facilitate condition inventories and assessments, and monitoring and inspection of structures (that which can’t be discerned with embedded sensors) to enhance predictive-proactive asset management strategies. Drainage & Roadside: Aerial mapping and sensing techniques utilizing drones or satellites provide an enhanced ability to inventory D&R assets over large swaths of land at relatively less cost and time. These include aboveground drainage and stormwater components as well as vegetation. Environmentally functional or sensitive resources within the ROW, such as stormwater ponds or protected wetlands can be efficiently monitored using remote sensing technologies. Discharge locations and stormwater facilities will use embedded sensors to remotely monitor discharge quality. Remote sensing systems can also monitor illicit discharge from construction sites. TSMO: Data collection capabilities will grow expansively using mobile or remote sensing devices equipped with GPS-enabled, laser-based, wireless, and networked technologies. These capabilities will be able to provide real-time condition inventory, monitoring, and inspection of TSMO devices (those without embedded sensors) to enhance predictive/proactive asset management strategies. These enhanced sensing technologies will also enable real-time traffic monitoring, surveillance, and data acquisition to optimize smart work zone operation. Smart work zones consist of TSMO-enabled strategies to optimally manage traffic within and around them (speeds, queues, traveler information on conditions and routing), as well as but also rely on coordination within a single roadway project or among several projects within a corridor, network, or region. Plausibility New remote sensing systems, particularly those using unmanned aircraft systems, will be an incremental advancement to highway agencies. Remote sensing for transportation applications will evolve with advancement in geomatics technologies, which are trending toward more powerful, lightweight, high resolution, less expensive and relatively easy to use. However, this trend opens up new regulatory issues relating to air space use. There are technical issues as well. With increasing reliance on aerial systems, there will be obstruction and radio disturbances in urban areas. Furthermore, there is a need for more sophisticated data processing to handle common issues relating to orientation and calibration in surface reconstruction using digital terrain models.

Next: Appendix C. Responsiveness of Emerging Long-Range PMR Practices to Scenario Elements »
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