More Than Smart Pavements: Connected Infrastructure Paves the Way for Enhanced Winter Safety and Mobility on Highways

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To address this pressing issue, this commentary proposes that the physical and digital infrastructures should be upgraded to take advantage of emerging technologies and facilitate the vehicle-infrastructure integration (VII), to better inform decision-makers at various levels. Driven by the paradigm shift towards more automation and more intelligent transportation, it is time to reimagine the vehicle-infrastructure ecosystem with the cold-climate issues in mind, and to enhance communications and coordination among various highway users and stakeholders. This commentary envisages the deployment of vehicle-to-everything (V2X) technologies to bring about transformative changes and substantial benefits in terms of enhanced winter safety and mobility on highways. At the center of the commentary is a conceptualized design of next-generation highways in cold climates, including the existing infrastructure entities that are appropriate for possible upgrade to connected infrastructure (CI) applications, to leverage the immensely expanded data availability fueled by better spatial and temporal coverage. The commentary also advances the idea that CI solutions can augment the sensing capabilities and confidence level of connected or autonomous vehicles. The application scenarios of VII system is then briefly explored, followed by some discussion of the paradigm shift towards V2X applications and a look to the future including some identified research needs in the arena of CI. This work aims to inspire dialogues and synergistic collaborations among various stakeholders of the VII revolution, because the specific challenges call for systematic, holistic, and multidisciplinary approaches accompanied by concerted efforts in the research, development, pilot testing, and deployment of CI technologies. Environmental Engineering Connected infrastructure Intelligent infrastructure Winter weather Resilience Road maintenance operations Sensing Energy harvesting Figures Figure 1 Figure 2 Figure 3 Introduction Winter weather presents a host of profound challenges to the safety, productivity, reliability, and user experience of roadways in cold climates, through reduced visibility and pavement friction, compromised vehicle maneuverability, and decreased traffic speed and volume [Strong et al. 2010]. In the U.S. alone, traffic crashes on snowy, slushy, or icy pavements have been responsible for more than 1,300 human fatalities and more than 116,800 human injuries per year [FHWA 2011]. Fu and Kwon [2018] reviewed six case studies and found the reductions in traffic volume due to winter weather ranged widely, between 1.8% for light snowfall and 53% for heavy snowfall. Kwon et al. [2013] collected data for an urban freeway in Canada and their analysis suggested that the free-flow speed and capacity reductions were 17.0% and 44.2%, respectively, given a snow precipitation rate of 5 mm/hr and a Road Surface Index (RSI, a friction-like measure) of 0.2 (snow covered). Climate change can introduce such winter challenges to areas unfamiliar with snow and ice conditions or extreme cold-weather events. Improving winter road maintenance (WRM) operations and traveler information services could result in fewer crashes, enhanced mobility, fewer emergency service disruptions, reduced travel costs, better fuel economy, and sustained economic productivity. To this end, it is desirable to use the most recent technological advances; current practices [Shi and Lu 2018] include many intelligent transportation system (ITS) solutions such as smart snowplows equipped with automatic vehicle location (AVL), road weather information systems (RWIS), fixed automated spray technology (FAST), maintenance decision support system (MDSS), dynamic message signs (DMS), and traveler information systems. For traffic management, it is noteworthy that current ITS technologies (loop detectors, video/camera detectors, and radar sensors) are generally limited to obtaining traffic information at the macroscopic level [Wu 2018]. The advent of technologies in connected and autonomous vehicles (CAVs), Internet of Things (IoT), and advanced driver assistance (ADAS) systems, along with advances in information and communications technology (ICT), is envisaged to bring fundamental changes to the current practices. The physical and digital infrastructures should be upgraded to take advantage of CVs/AVs and facilitate the cooperation between roadway infrastructure and CVs/AVs, i.e., vehicle-infrastructure integration (VII). In the near future, one expects to see mixed traffic flows of connected vehicles (CVs), autonomous vehicles (AVs), and conventional vehicles on highways. This presents new opportunities for better system performance and higher level of service, along with new infrastructure requirements. For instance, connected infrastructure (CI) solutions are desirable for bridging the communication gap between unconnected vehicles and CVs/AVs. CVs equipped with sensors could enhance mobile road weather data collection [Dey et al., 2015] and supplement or compliment current roadway sensing entities, raising the effectiveness of the system operations to react to changing conditions. The 360° awareness by vehicle operators and increased system reliability will help reduce the risk of vehicle crashes and enhance the efficiency of system operations. The Architecture Reference for Cooperative and Intelligent Transportation (ARC-IT) developed by the U.S. Federal Highway Administration (FHWA) has defined how CVs will contribute to various service packages including those for weather, vehicle safety, sustainable travel, traffic management, and traveler information [Iteris, 2020]. Earlier work also envisioned that CV technologies would enable traffic managers to deliver real-time, localized road weather advisories and forecasts directly to the vehicle onboard unit as a visual display to drivers [FHWA, 2014]. Moreover, collected real-time raw data can be shared with commercial application developers to build value-added services [Chapman and Drobot 2012]. AVs tend to be CVs at the same time and can offer a multitude of benefits including alleviated congestion, reduced energy use and emissions, and improved traffic safety [Shladover 2013, Sobanjo 2019]. Recent years have seen substantial progress in the development and pilot testing of technologies that enable CVs to transmit data to and receive data from other CVs (vehicle-to-vehicle, V2V), to and from infrastructure (vehicle-to-infrastructure, V2I), and to and from bicyclists or pedestrians. These can be collectively termed as vehicle-to-everything (V2X) communication, for which use cases, requirements, and design considerations of roadways have been discussed by Boban et al. [2018]. In the context of upcoming VII revolution and increased road user expectations, this commentary aims to inspire dialogues and synergistic collaborations among various stakeholders and across various disciplines. The commentary is organized in five main sections. Following this introduction is a discussion of conceptual design of next-generation highways in cold climates, including the existing infrastructure entities that are appropriate for possible upgrade to CI applications. Subsequently, the application scenarios of VII system are briefly discussed, with a focus on coordinated truck platooning, safety applications, mobility applications, and road weather applications. The final sections provide concluding remarks regarding the paradigm shift towards V2X applications and a look to the future. Fig. 1 presents a conceptual design of next-generation highways in cold climates, where a number of CI entities are envisioned. These, along with V2X and ICT capabilities, are expected to greatly enhance the spatial and temporal resolutions of road weather data and thus better inform stakeholders such as highway operators, emergency responders, truck drivers and other highway users. They can also augment the sensing capabilities and confidence level of CVs/AVs, reduce the cost and uncertainties of sensing, enrich the sources of information for both CVs/AVs and unconnected vehicles, and improve the response time. The following sections will further discuss some entities shown in Fig. 1, including: Connected Environmental Sensing Stations (ESS); Connected FAST system; ICT capabilities, including but are not limited to: a) roadside units, b) mobile connectivity, and global positioning system (GPS); Energy-harvesting, connected roadways and roadside infrastructure assets (RIAs); and Self-sensing or anti-icing pavement. Note that the 4 th and 5 th components are secondary to the implementation of VII ecosystem but would greatly enhance the viability and value of VII. Other CI entities will not be discussed in detail, and those may include: weather-responsive traffic signals, smart work zone equipment, and non-contact static charging for vehicles. This work does not cover any discussion of AV-enabling infrastructure components, such as smart signage for automatic driving, standardized pavement markings for machine vision, and magnetic nails and reflective striping for lane-keeping. Connected ESS RWIS-ESS could be further enhanced so as to serve as integral part of the CI solution and of the larger vehicle-infrastructure ecosystem. Currently, this technology is mainly deployed at fixed locations to better inform WRM operations and travelers, and the deployment has been hindered by high cost and maintenance needs. RWIS refers to “networks of ESS that observe the near-surface atmosphere and pavement surface and subsurface” [Albrecht et al. 2018]. Each ESS consist of various sensors that provide site-specific, real-time data on the meteorological conditions, road surface condition (RSC), and subsurface temperature, which altogether enables pro-active WRM practices such as anti-icing, improves the roadway level of service and resource allocation, and enhances traveler information, traffic management and emergency response [Strong and Fay 2007, Kwon et al. 2017]. Note that the mobile data collection by CVs/AVs will likely bolster the accuracy of road weather forecasting models, which in turn induces advances in the RWIS-ESS technology. This technology is going portable as well [Tessier, 2016], and its limitations will likely be overcome by design improvements and cost reductions. Connected FAST system FAST system is a system of fixed assets designed to spray anti-icing liquid on targeted ice-prone or safety-critical areas once the sensors (and associated algorithm) detect the likely risk of ice formation. Many roadway agencies have employed the FAST technology to prevent or pro-actively mitigate black ice or bonding of compacted snow and ice to pavement (or bridge deck). Conceptually, the design of FAST system could be further enhanced to improve its connectedness and thus contribute to the vehicle-infrastructure ecosystem. Ye et al. [2013] surveyed the state of the art of FAST systems and confirmed their potential in delivering substantial benefits, such as less need for mobile operations and WRM materials and reduced crash frequency and traffic delay. This technology “works best for frost and light snow events”, but its application has been hindered by challenges in sensor malfunctioning, system maintenance, and training. A study by Veneziano et al. [2015] examined the safety effects of FAST systems operated by the Colorado Department of Transportation and recommended the deployment of such system at “high-traffic, high-crash severity locations”. For instance, FAST systems were able to contribute to “an annual reduction of 16% to 70% on urban Interstates, 31% to 57% on rural Interstates, and 19% to 40% on interchange ramps between Interstates”, when sited and operated properly. In the context of VII revolution, no single communication technology “(in the near future) can support such a variety of expected V2X applications for a large number of vehicles”, at least not efficiently [Abboud et al. 2016]. Currently, dedicated short-range communications (DSRC) and cellular networks are the two main technologies for V2X communications. Abboud et al. [2016] reviewed various DSRC-cellular hybrid architectures and discussed the interworking challenges between the two technologies, with DSRC transceiver embedded in RSUs interacting with in-vehicle onboard units (OBUs) as well as the backhaul network (via cellular or wired Internet connections). Dey et al. [2016] developed a method to optimize available communication options for V2V and V2I applications in a heterogeneous wireless network consisting of DSRC and “other wireless technologies (e.g., Wi-Fi, LTE, and WiMAX)”. Nonetheless, Jenkins et al. [2017] concluded that “DSRC for wireless access in vehicular environments (WAVE) (protocol) is (currently) the only way to provide V2X communications with high reliability and low latency” and thus suitable for dissemination of basic safety messages. Now is the dawn of the 5G era, which stands for the 5 th generation mobile network and features communication capabilities better meeting the needs of IoT and V2X applications, particularly safety-critical use cases [Boban et al. 2016]), i.e., higher data rates, ultralow latency, ultra-reliability, and increased availability. Boban et al. [2016] proposed an architecture of the 5G V2X network, embodied in a heterogeneous multi-radio V2X network designed to leverage the strengths of “cellular systems in centimeter (cmWave) and millimeter (mmWave) frequency bands, vehicular visible light communication (VVLC)” and DSRC/WAVE. The computing infrastructure, either distributed or centralized, also plays a crucial role in the TCPS to enable smart and seamless mobility, and should be planned, designed, deployed, operated and maintained properly [Khan et al. 2019]. To attain outstanding performance and reliability of V2X applications, this Commentary sees that innovations are needed in the field of information technology in terms of system design, hardware and software. Naturally, one needs to tap into recent advances in edge computing, big data analytics, machine learning, artificial intelligence, and so on. For instance, Raza et al. [2018] described an advanced ITS based on ultrahigh speed, ultralow latency 5G scenario, where multiple networking technologies were integrated to provide ubiquitous network connectivity and efficient computing that supports V2X communications. Among them, edge computing (a.k.a., fog computing) refers to “the enabling technologies allowing computation to be performance at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IoT services” [Shi et al. 2016], and it brings many advantages in “addressing the concerns of response time requirement, battery life constraint, bandwidth cost savings, and data safety and privacy”. Arguably, there are a number of roadside or CV solutions other than embedded pavement sensors that can augment the FAST’s detection capability and improve its reliability by providing additional information on the RSC. Promising technologies include non-invasive pavement friction sensors [Ewan et al. 2013], vehicle-mounted sensors for thermal mapping [Todeschini et al. 2016, Hu et al. 2019], slippery surface detection [Padarthy and Heyns, 2019], or RSC monitoring [Chen et al. 2011, Pu et al. 2019], and the combined use of mobile camera and smart phone [Linton and Fu 2016] or mobile RWIS technologies [Ye et al. 2012] for RSC monitoring. Most of them could be readily integrated with the AVL technology [Santiago-Chaparro et al. 2012] to unlock their potential in enabling more proactive, efficient and resilient WRM operations. ICT capabilities This commentary agrees that as part of the transportation cyber-physical system (TCPS), the physical infrastructure of roadways should be “upgraded with digital (ICT) infrastructure that evolves with increasing CV penetration levels (so as to) create an environment suitable for fostering beneficial V2I innovations” [Khan et al. 2019]. This is critical for enabling the timely and reliable sensing, processing and communication of the unprecedented amount of data available in the VII environment. Khan et al. [2019] summarized the typical roadway digital infrastructure components (as shown in Fig. 2), including roadside units (RSUs), traffic signals, loop detectors, traffic cameras, and DMS communicating with both CVs/AVs and the backend infrastructure (servers) in a real-time or near-real-time fashion. Energy-harvesting, connected roadways and RIAs Connected roadways need reliable supply of energy. The massive number of roadway mileages and vast amount of lands in the right-of-way present a great opportunity to capture and utilize the energy dissipated from the ambient roadway system, such as mechanical energy from vehicle or wind loadings and thermal energy from the sun or earth. Such energy harvesting is particularly beneficial for roadways in remote, off-grid areas where the lifeline of CI applications is endangered by the lack of access to power. In the U.S. alone, there are 2.6 million miles of paved roads and highways, of which approximately 93 percent has an asphalt surface [Mohamed Jaafar 2019] and 3,000 (linear) miles are equipped with noise barrier [Poe et al. 2017]. The “(unpaved) land cover in close proximity to the National Highway System” in the U.S. has been estimated to be “roughly 68 percent, or 3.4 acres” [Earsom et al. 2010]. A suite of on-road energy-harvesting technologies are available, as illustrated in Fig. 3 [Wang et al. 2018]. These technologies entail the use of piezoelectric materials, micro wind turbines, photovoltaic panels or solar cell roads, geothermal heat pumps or pipe-pavement thermoelectric generator (PP-TEG) system. They could be potentially incorporated into either the connected roadways such as pavements and traffic signals, or RIAs such as noise barriers and structural snow fences, to meet the energy needs of a multitude of sensing and communication needs. They also feature a wide variety of cost, energy output and efficiency, service life, dimensions, maintenance requirements, recyclability, and other characteristics. Gholikhani et al. (2020) concluded that “thermoelectric and piezoelectric technologies are the most readily available methods” and Wang et al. (2018) discussed that technologies other than piezoelectric energy harvesters (PEHs) have their own strengths and limitations. For instance, photovoltaic systems can produce high energy output but the use of solar panels in roadways may complicate vehicle operations or pose a risk to traffic safety, and more research is needed to address such concerns. One promising niche application is solar-powered roadway lighting by light emitting diode (LED) (Yoomak et al. 2018). Geothermal heat pumps are considered a mature technology, which is “geologically and geographically limited” and most appropriate for safety-critical areas, and they have shown a desirable benefit/cost ratio for bridge deck de-icing (Habibzadeh-Bigdarvish et al. 2019). While PP-TEG produces a low energy output at high cost and more research is needed to improve the system efficiency (Zhu et al. 2019), a PP-TEG system can be potentially more cost-effective than a piezoelectric system in harvesting renewable energy from pavements (Guo and Lu 2017). The last four years have seen increased interest and promising progress in demonstrating the use of piezoelectric materials to harvest deformation energy from asphalt pavement. Yet, the amount of energy harvested is limited but suitable for applications such as “powering wireless sensors embedded into pavement structure” [Roshani et al. 2017] and other microelectronics, “heating road surface on bridge deck for anti-icing, lighting, or powering traffic devices” [Wang et al. 2018]. A few representative advances in the development of piezoelectric energy harvester (PEH) technology is summarized in Table 1. Table 1. Recent advances in PEH technology for roadways Configuration Optimal energy performance Other performance considerations Reference A stacked configuration of transducers Not reported Able to support loadings up to 150 kN and remained effective after 100,000 cyclic loadings Yang et al. [2017] Stacked units including U-shaped interlayer copper foil electrode structure Output power: 22.8 mW at a resistive load of 20 kΩ, under 0.7 MPa (compression loading) and 10 Hz (vibration frequency); output voltage: 28.0 V Stable performance after 50,000 simulated cyclic loading, with attenuations of: open-circuit voltage by 4.4 V, output power by 3.3 mW Wang et al. [2019] Bridge transducer with layered poling and electrode design Output power: 2.1 mW at a resistive load of 400 k W , under 0.07MPa, 5 Hz; output voltage: 556 V Balanced the desire for improved energy output and the need for less risk of stress concentration Jasim et al. [2017] Several PEH prototypes to be embedded into asphalt pavement Output power: > 25 mW under 15 MPa (for each piezoelectric element) and 10 Hz; output voltage (nearly 20 V) and output current (> 100 µA) at a load of 3 kN Higher frequency “represents higher traffic speed and greater traffic volume” (e.g., on Interstate highways), and leads to better output power. Roshani et al. [2017] One layer of “piezoelectric elements with a higher piezoelectric stress constant” and two layers of “more flexible conductive asphalt mixtures” Output power: ranging from 1.2 mW to 300 mW at 30 Hz The cost of electricity produced by this PEH can be as low as “$19.15/kWh at a high-volume roadway within a 15-year service life”. Guo and Lu [2017] A 100-m piezoelectric pavement with packaged multi-layer transducer Output power: up to 231 mW (0.58 J), under 0.7 MPa Under the daily traffic volume of 15100 vehicles, this 100-m pavement can produce up to 1.93 MJ (i.e., the energy needs of 35 mobile phones). The additional construction cost: approximately $57/m. Cao et al. [2020] In addition to smart pavements, there are a number of conceptual scenarios that energy-harvesting technologies may be incorporated into the roadway infrastructure. For instance, portable micro wind turbines could be mounted on structural snow fences, traffic signals, and so on. Being a cost-effective technology to prevent blowing and drifting snow, snow fences can improve road safety and provide additional benefits, if designed and sited properly [Du et al. 2017]. Nabavi and Zhang [2016] reported three groups of portable wind energy harvesters, i.e., piezoelectric-, electromagnetic-, and electrostatic-based generators, with different wind-flow-trapping mechanisms and varying dimensions and energy conversion efficiencies. Photovoltaic panels can be strategically installed either in the right-of-way providing sufficient space for such a “distributed solar power plant” [Asanov et al. 2019], or integrated with noise barriers to produce a considerable amount of renewable energy [Poe et al. 2017]. Qiao et al. [2011] proposed the concept of a “smart microgrid that optimally utilizes the public right-of-way and roadway infrastructure to provide cost-effective, highly efficient, and reliable wind/solar electric power production, distribution, storage, and utilization”. The design entails a “grid-connected wind/solar hybrid generation system installed on the pole of a roadway/traffic signal light”. Considering the great temperature difference between the air and the relatively warm soil beneath pavement, another potential technology to explore is thermoelectric generators. With the thermal gradient in the opposite direction, this concept was demonstrated in south Texas [Datta et al. 2017] where a TEG prototype produced “an average of 10 mW of electric power continuously over a period of 8 h”. Self-sensing or anti-icing pavement Self-sensing pavements can serve as an integral part of connected roadway infrastructure or VII system, and the related research is still in the burgeoning stage and the cost-effectiveness of this technology over the long term remains unknown. Han et al. [2013] reported the traffic detection performance of a self-sensing pavement being tested at the Minnesota Road Research Facility, USA, both in winter and in summer. This smart pavement was enabled by the admixed carbon nanotubes in concrete, and was able to “accurately detect the passing of different vehicles under different vehicular speeds and test environments”. Relative to conventional strain gauges, this self-sensing concrete exhibited advantages in its ease of installation and maintenance, compatibility with pavement structures, and durability. Liu et al. [2014] reported an exploratory study that suggests the potential use of conductive asphalt materials for self-sensing applications, because the different stages of damage evolution corresponded to certain change patterns in their electrical resistivity. Xiang et al. [2020] reported the use of piezoelectric sensors to detect moving traffic loads on pavement, i.e., for the weigh-in-motion application. Anti-icing pavements are often not designed for CI applications, but they can contribute to the mobility of surface transportation system during snowy weather. A variety of technologies have been explored to enable anti-icing pavements, ranging from “anti-freezing pavements that rely on physical action, to high-friction in situ anti-icing polymer overlays, to asphalt pavements containing anti-icing additives, to heated pavements using energy transfer systems” [Shi et al. 2018]. All of them aim to “prevent or reduce the bond of ice or compacted snow to pavement or to prevent or treat winter precipitation”. Pan et al. [2015] and Zhang et al. [2020] presented a comprehensive review on the use of conductive and salt-releasing asphalt mixtures as anti-icing pavements, respectively. Saleh et al. (2020) discussed the design, construction and evaluation of hydronic asphalt pavement (HAP), which is another energy-harvesting technology explored for anti-icing (and deicing) applications. Johnsson (2017) found that HAP could be engineered as part of a system to harvest sufficient geothermal energy for areas with mild winters and greatly “reduce the annual number of hours with risk for ice formation”. Note that none of these technologies have been widely adopted by transportation agencies, and this is mainly due to concerns over their long-term performance and cost. Nonetheless, this Commentary proposes that there is great potential in overcoming such technological barrier in the near future. Application Scenarios Of Vii System This section identifies some possible application scenarios of VII system, which are grouped into safety, mobility, road weather, agency data, and other categories, as detailed in Table 2. One can expect CI-enabled improvements to be materialized in these applications, given the notable advances in spatial and temporal coverage offered by CI technologies. The key to success is the timely and reliable sensing, processing and communication of the big data collected by the VII system. Table 2 is not intended to be comprehensive, as innovations may lead to new possibilities and new application scenarios. It is noteworthy that the enhanced spatial and temporal resolutions of road weather data can better inform a multitude of stakeholders and produce benefits across the spectrum of many applications. Both road weather and agency data applications are intertwined with highway safety and mobility objectives, because they aim to “provide a safe and efficient transportation system to move people and goods” [Sobanjo 2019]. The USDOT V2I research program has developed a computing platform known as V2I hub [Chang 2017], which “interfaces with a variety of ITS equipment such as RSU, traffic signal controllers, and DMS” and the GPS and transportation management center (TMC). For instance, an RSU can broadcast intersection geometry data (a.k.a., MAP message), signal phase and timing (SPaT) message, GPS correction data, and curve speed warning to incoming vehicles, or receive them from nearby RSUs [Change 2017, NOCoE 2020]. Table 2. Some identified application scenarios of VII system Application Type Application Scenario Reference Safety Cooperative-ITS platform: advance warning of hazardous road situations (to vehicle drivers) Padarthy and Heyns [2019] V2X applications for roadway safety and vehicle safety Abboud et al. [2016]; FHWA [2020] Intersection collision warning, emergency vehicle pre-emption, work zone alerts, curve speed warning, railroad crossing violation warning Barbaresso and Johnson [2014] In-vehicle signage, oversize vehicle warning, red-light or stop-sign violation warning, reduced speed zone warning/lane closure, restricted lane warning, spot weather impact warning Iteris [2019] Cooperative collision avoidance Chowdhury et al. [2018]; Wang and Li [2019] Blind spot warning and lane change warning Theriot et al. [2017]; Howe et al. [2016] ADAS functionalities Liu et al. [2017] Pedestrian detection and warning He and Zeng [2017] Deer crossing road detection: with multiple roadside LiDAR (Light Detection and Ranging) sensors deployed to enable real-time, micro-level and high-resolution sensing of road users Wu [2018] Mobility Coordinated truck platooning Gungor and Al-Qadi [2020]; Gungor and Al-Qadi [2020] Traffic management and sustainable travel Li et al. [2020]; Gopalakrishna, et al. [2016]; Iteris [2020] Advanced traveler information system, corridor management, transit vehicle priority, and multimodal intelligent traffic signal system Barbaresso and Johnson [2014] V2X applications for vehicle traffic optimization Abboud et al. [2016] Incident detection and response; traffic queue or bottleneck detection; traffic network flow optimization Khazraeian [2017]; Chowdhury et al. [2018] Emergency vehicle priority Head [2016] Adaptive signal control Yao et al. [2020] Cooperative adaptive cruise control Huang et al. [2020] Dynamic routing support Genders and Razavi [2016] Smart DMS, routing support and data-driven apps for freight carriers, transit vehicles and emergency responders Iteris [2019]; Akin et al. [2018] Road weather V2X applications for motorist advisories and warnings, information for maintenance and fleet management systems, and MDSS Barbaresso and Johnson [2014]; Young et al. [2019]; FHWA [2020] Agency Data V2I applications for probe-based traffic monitoring, probe-based pavement condition monitoring, and performance measures Barbaresso and Johnson [2014]; FHWA [2014]; Li et al. [2019] Other V2X applications for passenger infotainment (via in-vehicle Internet access) and car manufacturer services (e.g., point-of-interest notification and remote vehicle diagnostics). Abboud et al. [2016] Value-added services by commercial application developers Chapman and Drobot [2012] The USDOT has estimated that combined V2V and V2I systems may potentially address about 81% of all-vehicle target crashes, 83% of all light-vehicle target crashes, and 72% of all heavy-truck target crashes [Najafi et al. 2016]. Such safety benefits of CV are likely to be more significant during adverse weather conditions, by enhancing all levels of decision-making by stakeholders. For example, the enriched road weather condition information can be communicated to the general public in a timely fashion, such that they can slow down, choose a different route, or stay home in light of inclement weather. In addition to safety benefits, the deployment of V2X technologies and advances in VII are likely to produce mobility and resilience benefits on winter highways, by better informing all the stakeholders and enabling location-specific and timelier detection of and response to disruptions. By definition, resilience is “the ability of a system to resume normal function at a performance level equal that which existed before a disruptive event”, and can be characterized by metrics such as robustness, adaptability, agility, redundancy, response time, recovery time, level of recovery, and performance loss [Muller 2012]. The transmission of microlevel road weather data by vehicles through V2X communication has been demonstrated in the European WiSafeCar project [Sukuvaara and Nurmi, 2012], which is anticipated to benefit the efficiency of road weather management [Ma et al., 2012]. One major mobility application scenario is coordinated truck platooning. Connected and autonomous trucks (CATs) are increasingly introduced into the market, which enables the grouping of densely-spaced trucks to travel together on highways to save on the cost and fuel consumption and improve the efficiency of freight operations. This practice, known as truck platooning, has unintended negative consequences on how the trucks could induce damage in the pavement infrastructure, due to the “channelized truck loading application” [Gungor and Al-Qadi 2020]. To mitigate this detrimental effect, Gungor and Al-Qadi [2020] proposed a framework to optimally randomize the pattern of axel loadings of truck platoons using V2I communication. Such a coordinated truck platooning approach entails cooperative automation among trucks and roadway infrastructure, which uniformly distributes the loadings of truck platoons over the pavement lanes, leading to an overall reduction in damage accumulation in the pavement and a longer service life of the pavement. It is interesting to note that winter weather may induce vulnerabilities in vehicles on highways as well as in physical and digital infrastructures. For instance, the performance and reliability of CVs/AVs (and other sensors) and communications could be destabilized by extremely cold temperatures and heavy snowfall conditions, and such risks should be considered in the design of the VII system to operate in cold climates. Conclusion And Outlook Currently, there is an urgent demand for more cost-effective, resource-efficient and reliable solutions to address safety, mobility, and resilience challenges on highways enduring snowy winter weather. One can envisage a fundamentally changed landscape for WRM operations, traveler information, and traffic management on winter highways, amid the increasing introduction of innovative concepts (e.g., Smart Cities, Crowdsourcing, and V2X) and more penetration of emerging technologies (e.g., CVs/AVs/CATs, IoT, 5G, Cloud Computing, and Edge Computing) into the transportation sector. These concepts and technologies will catalyze the increasing momentum of VII to enable a significantly higher level of service. As such, there is the need to conceptualize and strategically plan for a more connected roadway infrastructure for VII system, even though many of the CI solutions are still in the nascent stages of development. Driven by the paradigm shift towards more automation and more intelligent transportation, it is time to reimagine the vehicle-infrastructure ecosystem with the cold-climate issues in mind, and to enhance communications and coordination among various highway users and stakeholders. A next step is to promote the synergies among the enabling technologies and unlock their potential for the specific needs of highway agencies. For instance, CI technologies are currently less mature than CV technologies. Great benefits can be achieved by integrating the array of both CI and CV technologies. Because such integration would provide better, more accurate and timely knowledge of conditions throughout the roadway network (albeit with a focus on critical locations and road segments), in terms of traffic characteristics, RSCs, and environmental conditions. This big data could be processed, archived, and communicated to highway operators and traveling public in a timely fashion. In addition to informing travelers, such information can be fed into agency decision support tools such as the Maintenance Decision Support System (Ye et al. 2009, Rennie and Groeneweg 2017) to greatly benefit the roadway level of service and the overall safety, mobility, and productivity of the surface transportation system. To inspire dialogues and synergistic collaborations among various stakeholders of the VII revolution, this commentary has underscored the need to plan, design, and engineer a multifunctional, next-generation highway infrastructure that is more intelligent, safer and more resilient and adaptive than the conventional highway infrastructure, fueled by more reliable and rapid collection, processing and communication of big data. The challenges in achieving better safety and mobility on winter highways may continue to evolve and they must be addressed with systematic, holistic, and multidisciplinary approaches. A next step is to carry out concerted efforts in the research, development, pilot testing, and deployment of CI technologies, which needs to bring together expertise from different disciplines to transform the built highway environment to one that better facilitates the real-time detection of localized conditions and the flow of high-quality road weather data (e.g., V2X). The ultimate goal is to improve the safety and mobility of highways in cold climates, which in turn would translate to a broad array of social, economic and environmental benefits. The VII revolution is still in its infancy and there are many unexplored territories and dynamics, unanswered questions, and open challenges. Innovations are much needed to overcome the various technological and institutional barriers to the successful implementation of VII system for highways in cold climates, and to maximize the synergies between the physical and digital infrastructures. It is imperative to note, however, that innovations should be anchored in answering the user requirements, i.e., following a needs-pull (vs. technology-push) approach. Recommendations The following presents some research needs identified in the arena of CI: Tap into recent advances in various V2X and ICT technologies and investigate better compatibility, automation, and integration among them, to provide the best possible road weather information, in terms of spatial and temporal resolutions, reliability, and so on. The efforts may be in the aspects of system design [Li et al. 2019, Muller 2012], hardware, and software and should take into account the given constraints of cost, communications speed and reliability, etc. as well as the specific performance requirements and functionalities needed by the VII system. The objective is to have a cohesive and affordable VII system that operates efficiently and reliably during disruptive weather of snow and ice. Address the technical and non-technical challenges vis-à-vis scalability, interoperability, privacy and security, in anticipation of increased number and heterogeneity of smart devices and immense amount of sensor data available, similar to those hindering the implementation of Smart City services and applications [Balakrishna 2012]. Investigate the transformation of physical roadway infrastructure for the needs of VII applications, build resilience into highways, and tap into the opportunities of improving the design, health monitoring and diagnosis, preservation, and utility of roads, bridges, tunnels, culverts, and RIAs, while reducing their life-cycle cost and environmental footprints. Develop and demonstrate technologies that can reliably supply cost-effective energy for roadways in remote, off-grid areas. A promising approach could be the hybrid use of PEHs and pyroelectric materials. Future research should focus on achieving balanced performances in energy output and cost-efficiency, reliability and resilience, durability, recyclability, and sustainability over the life cycle of the energy harvesting system. Areas of improvement may include: the selection and design of materials, PEH design (packaging, composite configuration of elements, etc.) and power electronics, optimized as a function of given traffic patterns, in-service environmental conditions, and specific energy requirements of the CI application. Develop and demonstrate a set of cost-effective, durable asphalt pavement mixtures or surface layers that enable reliable real-time detection of key vehicle flow parameters and other functionalities such as in situ anti-icing, sensing of the surface condition (dry, wet, snowy, icy, etc.) and/or sensing of the overall health condition of the pavement itself. Abbreviations ADAS: advanced driver assistance; ARC-IT: Architecture Reference for Cooperative and Intelligent Transportation; AVL: automatic vehicle location; CATs: connected and autonomous trucks; CAVs: connected and autonomous vehicles; CI: connected infrastructure; CV: connected vehicle; DMS: dynamic message sign; DSRC: dedicated short-range communications; ESS: environmental sensing station; FAST: fixed automated spray technology; FHWA: Federal Highway Administration; GPS: global positioning system; HAP: hydronic asphalt pavement; ICT: information and communications technology; IoT: Internet of Things; ITS: intelligent transportation system; PEH: piezoelectric energy harvester; LiDAR: Light Detection and Ranging; LTE: Long-Term Evolution; OBU: onboard unit; PP-TEG: pipe-pavement thermoelectric generator; RIA: roadside infrastructure asset; RSC: road surface condition; RSU: roadside unit; RWIS: road weather information system; TCPS: transportation cyber-physical system; TMC: transportation management center; V2I: vehicle-to-infrastructure; V2X: vehicle-to-anything; V2V: vehicle-to-vehicle; VII: vehicle-infrastructure integration; VVLC: vehicular visible light communication; USDOT: U.S. Department of Transportation; WAVE: wireless access in vehicular environments; WiMAX: Worldwide Interoperability for Microwave Access; WRM: winter road maintenance. Declarations Availability of data and materials All data have been presented in the Commentary. Acknowledgments The author acknowledges the editorial assistance from Ms. Cheryl Reed and the constructive criticism by the anonymous reviewers. Funding The author would like to acknowledge funding support from the Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE), a Tier 1 UTC (University Transportation Center) sponsored by the US Department of Transportation. Contributions XS contributed solely to this commentary. Corresponding author Correspondence to Xianming Shi. Ethics declarations Competing interests The authors declare that they have no competing interests. Other Declarations Ethics approval and consent to participate: not applicable Consent for publication: not applicable References Strong, C. K., Ye, Z., & Shi, X. (2010). Safety effects of winter weather: the state of knowledge and remaining challenges. Transport reviews , 30 (6), 677-699. FHWA (2011). “ Snow and Ice” . Road Weather Management, Federal Highway Administration, U.S. Department of Transportation, Washington, D.C. Accessed August 10, 2011. [Online]. http://ops.fhwa.dot.gov/Weather/weather_events/snow_ice.htm . Fu, L., Kwon, & T.J. (2018). Chapter 8: Mobility effects of winter weather and road maintenance operations. In X. Shi, L. Fu (Eds.), Sustainable Winter Road Operations, Wiley-Blackwell, pp. 131-155. Kwon, T. J., Fu, L., & Jiang, C. (2013). “Effect of winter weather and road surface conditions on macroscopic traffic parameters.” Transportation research record, Vol. 2329, 54-62. Shi, X., & Fu, L. (Eds.). (2018). Sustainable winter road operations . Wiley Blackwell. Wu, J. (2018). Data processing algorithms and applications of LiDAR-enhanced connected infrastructure sensing (Doctoral dissertation). Dey, K.C., Mishra, A., Chowdhury, M. (2015). “Potential of intelligent transportation systems in mitigating adverse weather impacts on road mobility: A review.” IEEE Transactions on Intelligent Transportation Systems , 16(3), 1107-1119. Iteris (2020). “ARC-IT version 8.3, Service Packages.” Accessed on Sept. 18, 2020. [Online]. Available: https://local.iteris.com/arc-it/html/servicepackages/servicepackages-areaspsort.html . FHWA (2014). “Connected Vehicle Reference Implementation Architecture 2014.” Accessed on Nov. 12, 2014. [Online]. Available: http://www.iteris.com/cvria/ . Chapman, M.B., and Drobot, S. (2012). “The use of connected vehicle observations in weather applications for various highly impacted users of the roads.” Proc. 16th Int. Road Weather Conf., Helsinki, Finland, 1–7. Shladover, S. (2013). Why automated vehicles need to be connected vehicles. In IEEE Vehicular Networking Conference. Accessed October 1, 2013. [Online]. http://www. ieeevnc.org/2013/media/IEEE_VNC_BostonKeynote_Shladover.pdf . Sobanjo, J. O. (2019). Civil Infrastructure Management Models for the Connected and Automated Vehicles Technology. Infrastructures , 4 (3), 49. Boban, M., Kousaridas, A., Manolakis, K., Eichinger, J., & Xu, W. (2018). Connected roads of the future: Use cases, requirements, and design considerations for vehicle-to-everything communications. IEEE vehicular technology magazine , 13 (3), 110-123. Sukuvaara, T., & Nurmi, P. (2012). Connected vehicle safety network and road weather forecasting–The WiSafeCar project. In Proceedings of the 16 th International Road Weather Conference, SIRWEC 2012 (pp. 23-25). Ma, Y., Chowdhury, M., Sadek, A., and Jeihani, M. (2012). “Integrated traffic and communication performance evaluation of an intelligent Vehicle Infrastructure Integration (VII) system for online travel-time prediction.” IEEE Trans. Intell. Transp. Syst., vol. 13, no. 3, pp. 1369-1382. Najafi, S., Flintsch, G. W., & Khaleghian, S. (2016). Fuzzy logic inference-based Pavement Friction Management and real-time slippery warning systems: a proof of concept study. Accident Analysis & Prevention , 90 , 41-49. Muller, G. (2012). Fuzzy architecture assessment for critical infrastructure resilience. Procedia Computer Science , 12 , 367-372. Mohamed Jaafar, Z.F.B. (2019). Computational Modeling and Simulations of Condition Deterioration to Enhance Asphalt Highway Pavement Design and Asset Management. Dissertation for Ph.D. in Engineering Science. University of Mississippi, Electronic Theses and Dissertations, 1643. https://egrove.olemiss.edu/etd/1643 . Earsom, S., Hallett, R., Perrone, T., Poe, C., & Greenfield, M. (2010). Carbon Sequestration Pilot Program Results: Estimated Land Available for Carbon Sequestration in the National Highway System. Report for the Federal Highway Administration, Washington, D.C. Poe, C., Plovnick, A., Hodges, T., Hastings, A., & Dresley, S. (2017). Highway renewable energy: photovoltaic noise barriers (No. DOT-VNTSC-FHWA-17-20; FHWA-HEP-17-088). United States. Federal Highway Administration. Office of Natural Environment. Wang, H., Jasim, A., & Chen, X. (2018). Energy harvesting technologies in roadway and bridge for different applications–A comprehensive review. Applied energy , 212 , 1083-1094. Gholikhani, M., Roshani, H., Dessouky, S., & Papagiannakis, A. T. (2020). A critical review of roadway energy harvesting technologies. Applied Energy , 261 , 114388. Yoomak, S., Jettanasen, C., Ngaopitakkul, A., Bunjongjit, S., & Leelajindakrairerk, M. (2018). Comparative study of lighting quality and power quality for LED and HPS luminaires in a roadway lighting system. Energy and Buildings , 159 , 542-557. Habibzadeh-Bigdarvish, O., Yu, X., Lei, G., Li, T., & Puppala, A. J. (2019). Life-Cycle cost-benefit analysis of Bridge deck de-icing using geothermal heat pump system: A case study of North Texas. Sustainable cities and society , 47 , 101492. Zhu, X., Yu, Y., & Li, F. (2019). A review on thermoelectric energy harvesting from asphalt pavement: Configuration, performance and future. Construction and Building Materials , 228 , 116818. Guo, L., & Lu, Q. (2017). Potentials of piezoelectric and thermoelectric technologies for harvesting energy from pavements. Renewable and Sustainable Energy Reviews , 72 , 761-773. Yang, H., Wang, L., Hou, Y., Guo, M., Ye, Z., Tong, X., & Wang, D. (2017). Development in stacked-array-type piezoelectric energy harvester in asphalt pavement. Journal of Materials in Civil Engineering , 29 (11), 04017224. Wang, C., Song, Z., Gao, Z., Yu, G., & Wang, S. (2019). Preparation and performance research of stacked piezoelectric energy-harvesting units for pavements. Energy and Buildings , 183 , 581-591. Jasim, A., Wang, H., Yesner, G., Safari, A., & Maher, A. (2017). Optimized design of layered bridge transducer for piezoelectric energy harvesting from roadway. Energy , 141 , 1133-1145. Roshani, H., Dessouky, S., Papagiannakis, A. T., & Montoya, A. (2017). Experimental and finite element assessment of three energy harvesting prototypes for roadways. Innovative Infrastructure Solutions , 2 (1), 7. Guo, L., & Lu, Q. (2017). Modeling a new energy harvesting pavement system with experimental verification. Applied energy , 208 , 1071-1082. Cao, Y., Sha, A., Liu, Z., Li, J., & Jiang, W. (2020). Energy output of piezoelectric transducers and pavements under simulated traffic load. Journal of Cleaner Production , 123508. Du, S., Petrie, J., & Shi, X. (2017). Use of Snow Fences to Reduce the Impacts of Snowdrifts on Highways: Renewed Perspective. Transportation research record , 2613 (1), 45-51. Nabavi, S., & Zhang, L. (2016). Portable wind energy harvesters for low-power applications: A survey. Sensors , 16 (7), 1101. Asanov, I., Sharaborova, E., & Loktionov, E. (2019). Evaluation of technical and economic effect from setup of distributed photovoltaic generation on the Right-of-Way land of the North Caucasus Railway. In E3S Web of Conferences (Vol. 114, p. 05004). EDP Sciences. Qiao, W., Sharma, A., Hudgins, J. L., Jones, E. G., & Rilett, L. (2011, July). Wind/solar hybrid generation-based roadway microgrids. In 2011 IEEE power and energy society general meeting (pp. 1-7). IEEE. Datta, U., Dessouky, S., & Papagiannakis, A. T. (2017). Harvesting thermoelectric energy from asphalt pavements. Transportation Research Record , 2628 (1), 12-22. Han, B., Zhang, K., Burnham, T., Kwon, E., & Yu, X. (2012). Integration and road tests of a self-sensing CNT concrete pavement system for traffic detection. Smart Materials and Structures , 22 (1), 015020. Liu, X., Liu, W., Wu, S., & Wang, C. (2014). Effect of carbon fillers on electrical and road properties of conductive asphalt materials. Construction and Building Materials , 68 , 301-306. Xiang, T., Huang, K., Zhang, H., Zhang, Y., Zhang, Y., & Zhou, Y. (2020). Detection of moving load on pavement using piezoelectric sensors. Sensors , 20 (8), 2366. Shi, X., Huang, J., Yang, Z. (2018). Chapter 18: Pavement Treatments for Sustainable Winter Road Maintenance. In X. Shi, L. Fu (Eds.), Sustainable Winter Road Operations, Wiley-Blackwell, pp. 402-422. Pan, P., Wu, S., Xiao, F., Pang, L., & Xiao, Y. (2015). Conductive asphalt concrete: A review on structure design, performance, and practical applications. Journal of Intelligent Material Systems and Structures , 26 (7), 755-769. Zhang, Y, Liu, Z., Shi, X. A Review on Anti-Icing Asphalt Pavement with Salt-Storage Additive. ASCE Journal of Transportation Engineering, Part B: Pavements , 2020, in press. Saleh, N. F., Zalghout, A. A., Sari Ad Din, S. A., Chehab, G. R., & Saad, G. A. (2020). Design, construction, and evaluation of energy-harvesting asphalt pavement systems. Road Materials and Pavement Design , 21 (6), 1647-1674. Johnsson, J. (2017). Winter Road Maintenance using Renewable Thermal Energy (Doctoral dissertation, Chalmers Tekniska Hogskola (Sweden)). Ye, Z., Wu, J., Ferradi, N. E., & Shi, X. (2013). Anti-icing for key highway locations: fixed automated spray technology. Canadian Journal of Civil Engineering , 40 (1), 11-18. Veneziano, D., Muthumani, A., & Shi, X. (2015). Safety effects of fixed automated spray technology systems. Transportation Research Record , 2482 (1), 102-109. Todeschini, I., Di Napoli, C., Pretto, I., Merler, G., Cavaliere, R., Apolloni, R., ... & Benedetti, G. (2016, August). Thermal mapping as a valuable tool for road weather forecast and winter road maintenance: an example from the Italian Alps. In Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016) (Vol. 9688, p. 96880H). International Society for Optics and Photonics. Hu, Y., Almkvist, E., Gustavsson, T., & Bogren, J. (2019). Modeling Road Surface Temperature from Air Temperature and Geographical Parameters—Implication for the Application of Floating Car Data in a Road Weather Forecast Model. Journal of applied meteorology and climatology , 58 (5), 1023-1038. Ewan, L., Al-Kaisy, A., & Veneziano, D. (2013, January). Remote Sensing of Weather and Road Surface Conditions: Is Technology Mature for Reliable ITS Applications. In TRB 2013 Annnual Meeting, pp: I-18 . Padarthy, M., & Heyns, E. (2019). Identification and classification of slippery winter road conditions using commonly available vehicle variables. Transportation research record , 2673 (2), 60-70. Chen, K., Lu, M., Fan, X., Wei, M., & Wu, J. (2011, August). Road condition monitoring using on-board three-axis accelerometer and GPS sensor. In 2011 6th International ICST conference on communications and networking in China (CHINACOM) (pp. 1032-1037). IEEE. Pu, Z., Liu, C., Wang, Y., Shi, X., & Zhang, C. (2019). Road Surface Condition Prediction using Long Short-Term Memory Neural Network based on Historical Data (No. 19-03118). Linton, M. A., & Fu, L. (2016). Connected vehicle solution for winter road surface condition monitoring. Transportation Research Record , 2551 (1), 62-72. Ye, Z., Shi, X., Strong, C. K., & Larson, R. E. (2012). Vehicle-based sensor technologies for winter highway operations. IET Intelligent Transport Systems , 6 (3), 336-345. Santiago-Chaparro, K. R., Chitturi, M., Szymkowski, T., & Noyce, D. A. (2012). Evaluation of performance of automatic vehicle location and TowPlow for winter maintenance operations in Wisconsin. Transportation research record , 2272 (1), 136-143. Albrecht, C., Patterson, R., & Sturges, L. (2018). Weather Services for Sustainable Winter Road Operations. Sustainable Winter Road Operations , 2318 , 58. Tessier, R. (2016). Evaluation of Portable Road Weather Information Systems (No. UMTC-16.03). Massachusetts. Dept. of Transportation. Office of Transportation Planning. Strong, C., & Fay, L. (2007). RWIS usage report. Alaska DOT and Public Facilities, Final Report. Division of Program Development, Juneau, AK . Kwon, T. J., Fu, L., & Melles, S. J. (2017). Location optimization of road weather information system (RWIS) network considering the needs of winter road maintenance and the traveling public. Computer‐Aided Civil and Infrastructure Engineering , 32 (1), 57-71. Boban, M., Manolakis, K., Ibrahim, M., Bazzi, S., & Xu, W. (2016, October). Design aspects for 5G V2X physical layer. In 2016 IEEE Conference on Standards for Communications and Networking (CSCN) (pp. 1-7). IEEE. Raza, N., Jabbar, S., Han, J., & Han, K. (2018, June). Social vehicle-to-everything (V2X) communication model for intelligent transportation systems based on 5G scenario. In Proceedings of the 2nd International Conference on Future Networks and Distributed Systems (pp. 1-8). Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE internet of things journal , 3 (5), 637-646. Khan, S. M., Chowdhury, M., Morris, E. A., & Deka, L. (2019). Synergizing Roadway Infrastructure Investment with Digital Infrastructure for Infrastructure-Based Connected Vehicle Applications: Review of Current Status and Future Directions. Journal of Infrastructure Systems , 25 (4), 03119001. Chowdhury, M., Rahman, M., Rayamajhi, A., Khan, S. M., Islam, M., Khan, Z., & Martin, J. (2018). Lessons learned from the real-world deployment of a connected vehicle testbed. Transportation Research Record , 2672 (22), 10-23. Wang, S., & Li, Z. (2019). Roadside sensing information enabled horizontal curve crash avoidance system based on connected and autonomous vehicle technology. Transportation research record , 2673 (5), 49-60. Theriot, M., Osman, O. A., Ishak, S., Alecsandru, C., Mousa, S. R., & Bakhit, P. R. (2017). Impact of Connected-Vehicle Market Penetration on the Effectiveness of Blind Spot Warning Applications: A Driving Simulation Study (No. 17-04930). Howe, G., Xu, G., Hoover, D., Elsasser, D., & Barickman, F. (2016). Commercial connected vehicle test procedure development and test results–blind spot warning/lane change warning (No. DOT HS 812 317). Liu, H., Wei, H., Zuo, T., Li, Z., & Yang, Y. J. (2017). Fine-tuning ADAS algorithm parameters for optimizing traffic safety and mobility in connected vehicle environment. Transportation research part C: emerging technologies , 76 , 132-149. He, X., & Zeng, D. (2017, November). Real-time pedestrian warning system on highway using deep learning methods. In 2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) (pp. 701-706). IEEE. Abboud, K., Omar, H. A., & Zhuang, W. (2016). Interworking of DSRC and cellular network technologies for V2X communications: A survey. IEEE transactions on vehicular technology , 65 (12), 9457-9470. Dey, K. C., Rayamajhi, A., Chowdhury, M., Bhavsar, P., & Martin, J. (2016). Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication in a heterogeneous wireless network–Performance evaluation. Transportation Research Part C: Emerging Technologies , 68 , 168-184. Jenkins, M., Duggan, D., & Negri, A. (2017, March). Towards a connected bicycle to communicate with vehicles and infrastructure: Multimodel alerting interface with Networked Short-Range Transmissions (MAIN-ST). In 2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA) (pp. 1-3). IEEE. Gungor, O. E., & Al-Qadi, I. L. (2020). All for one: Centralized optimization of truck platoons to improve roadway infrastructure sustainability. Transportation Research Part C: Emerging Technologies , 114 , 84-98. Chang, J. (2017). An overview of USDOT connected vehicle roadside unit research activities (No. FHWA-JPO-17-433). United States. Dept. of Transportation. ITS Joint Program Office. NOCoE (2020). “Implementation Guide: SPAT Challenge.” National Operations Center of Excellence. Accessed on May 20, 2020. [Online]. Available: https://www.transportationops.org/spatchallenge/resources/Implementation-Guide . Iteris (2019). “Applications”. Connected Vehicle Reference Implementation Architecture. Accessed on May 20, 2019. [Online]. Available: https://local.iteris.com/cvria/html/applications/applications.html . Akin, M., He, Y., & Shi, X. (2018). The Use of Connected Vehicle Technology to Facilitate Multimodal Winter Travel (Phase I) (No. Project ID: 2017 Project 09). Final Report for the Center for Advanced Multimodal Mobility Solutions and Education, Charlotte, NC. Li, H., Wolf, J. C., Mathew, J. K., Navali, N., Zehr, S. D., Hardin, B. L., & Bullock, D. M. (2020). Leveraging Connected Vehicles to Provide Enhanced Roadway Condition Information. Journal of Transportation Engineering, Part A: Systems , 146 (8), 04020073. Gopalakrishna, D., Serulle, N. U., Kitchener, F., Garrett, K., & Newton, D. (2016). Guidelines for deploying connected vehicle-enabled weather responsive traffic management strategies (No. FHWA-JPO-17-478). United States. Federal Highway Administration. Barbaresso, J.C.; Johnson, P. (2014). Connected Vehicle Infrastructure Deployment Considerations: Lessons Learned from the Safety Pilot Model Deployment; Report Contract No. DTFH61-11-C-00040; HNTB Corporation, for University of Michigan Transportation Research Institute: Ann Arbor, MI, USA, 30 May 2014; 49p. Young, R. K., Welch, B. M., & Siems-Anderson, A. R. (2019). Generating Weather Alerts Including High Wind Blowover Hazards Using Pikalert® for the Wyoming Connected Vehicle Pilot Project (No. 19-02065). Khazraeian, S. (2017). "Methods for Utilizing Connected Vehicle Data in Support of Traffic Bottleneck Management". FIU Electronic Theses and Dissertations . 3496. Dissertation for Civil Engineering, Florida International University. https://digitalcommons.fiu.edu/etd/3496 . Head, L. (2016, June). The Multi Modal Intelligent Traffic Signal System (MMITSS): A Connected Vehicle Dynamic Mobility Application. In Mid Year Meeting, Traffic Signal Systems Committee, Transportation Research Board https://i95coalition. org/wp-content/uploads/2016/03/Head. MMITSS. I-95.06 (Vol. 20). Yao, Z., Jiang, Y., Zhao, B., Luo, X., & Peng, B. (2020). A dynamic optimization method for adaptive signal control in a connected vehicle environment. Journal of Intelligent Transportation Systems , 24 (2), 184-200. Huang, L., Zhai, C., Wang, H., Zhang, R., Qiu, Z., & Wu, J. (2020). Cooperative Adaptive Cruise Control and exhaust emission evaluation under heterogeneous connected vehicle network environment in urban city. Journal of Environmental Management , 256 , 109975. Genders, W., & Razavi, S. N. (2016). Impact of connected vehicle on work zone network safety through dynamic route guidance. Journal of Computing in Civil Engineering, 30(2), 04015020. FHWA (2020). "ITS ePrimer", developed for the ITS Professional Capacity Building Program. [Online]. Accessed on Sept. 1, 2020. https://www.pcb.its.dot.gov/eprimer/module13.aspx . Li, H., Peters, L., Banuelos, C., Zaugg, J., Sharma, A., & Bullock, D. M. (2019). Leveraging Snow Plow Dashboards Cams and Connected Vehicle Speed Data to Improve Winter Operations Performance Measures. Li, X., Medal, H., & Qu, X. (2019). Connected infrastructure location design under additive service utilities. Transportation Research Part B: Methodological , 120 , 99-124. Balakrishna, C. (2012, September). Enabling technologies for smart city services and applications. In 2012 sixth international conference on next generation mobile applications, services and technologies (pp. 223-227). IEEE. Cite Share Download PDF Status: Published Journal Publication published 19 Nov, 2020 Read the published version in Journal of Infrastructure Preservation and Resilience → Version 2 posted Editorial decision: Accept 04 Nov, 2020 Editor assigned by journal 07 Oct, 2020 Submission checks completed at journal 06 Oct, 2020 Editor invited by journal 06 Oct, 2020 You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-31618","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Commentary","associatedPublications":[],"authors":[{"id":3295652,"identity":"f4dc82b9-0a4a-4e66-ae26-2c3d80c6696b","order_by":0,"name":"Xianming Shi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYDACZhBhACJ4GA58gIgZEK/l4AyitCAADwMzDzFaDI4zP3t0o+COXQP/2YOHbdvqEhvYm7dJ4NMi2cxmbpxj8Cy5QSIv4XBu2+HEBp5jZXi18DMzmEnnGBxOZpDgMTicu+1AYoNEjhleLWzM7N8gWvjPGBy23AZ0mPwb/Fr4mXnAttgxMABJxm3MQFt48GuRbOYpA2lJYJPIMTjY+++wcRtPWrEFPi0G549vk875c9ien/+M8YcfZ+pk+9kPb7yBTwsMJLbBfUeMchCwJ1bhKBgFo2AUjEAAAKpwQ3Z8HHGZAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xianming","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2020-05-26 03:30:12","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-31618/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-31618/v2","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s43065-020-00014-x","type":"published","date":"2020-11-19T15:01:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":3019218,"identity":"e3df1342-cefd-4121-a07b-21f8b83a72b8","added_by":"auto","created_at":"2020-10-16 09:50:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75990,"visible":true,"origin":"","legend":"Conceptualized design of next-generation highways in cold climates. Application scenarios include safety applications (e.g., blind spot warning and collision warning) and mobility applications (e.g., traveler information, routing support, and coordinated truck platooning [not shown]). ","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-31618/v2/367b90b018f7509b87f48bf1.jpg"},{"id":3019219,"identity":"e68d4659-5350-4f26-a2a7-1f8e4e8df425","added_by":"auto","created_at":"2020-10-16 09:50:38","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70285,"visible":true,"origin":"","legend":"Roadway digital infrastructure components [courtesy of Khan et al. 2019]","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-31618/v2/faa0f5771acab0f1cfad390d.jpg"},{"id":3019220,"identity":"03f2b512-4f36-4ca0-aa65-34424756927b","added_by":"auto","created_at":"2020-10-16 09:50:38","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":69088,"visible":true,"origin":"","legend":"Available on-road energy-harvesting technologies [modified from Wang et al. 2018]","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-31618/v2/7faf3e0211f2104a47f7766a.jpg"},{"id":13603952,"identity":"39891daf-a30e-40fb-a543-e8db0140bd26","added_by":"auto","created_at":"2021-09-17 05:57:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":477681,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-31618/v2/f682c11b-71b1-4fd6-b58b-0cfd79947a98.pdf"}],"financialInterests":"","formattedTitle":"More Than Smart Pavements: Connected Infrastructure Paves the Way for Enhanced Winter Safety and Mobility on Highways","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWinter weather presents a host of profound challenges to the safety, productivity, reliability, and user experience of roadways in cold climates, through reduced visibility and pavement friction, compromised vehicle maneuverability, and decreased traffic speed and volume [Strong et al. 2010]. In the U.S. alone, traffic crashes on snowy, slushy, or icy pavements have been responsible for more than 1,300 human fatalities and more than 116,800 human injuries per year [FHWA 2011]. Fu and Kwon [2018] reviewed six case studies and found the reductions in traffic volume due to winter weather ranged widely, between 1.8% for light snowfall and 53% for heavy snowfall. Kwon et al. [2013] collected data for an urban freeway in Canada and their analysis suggested that the free-flow speed and capacity reductions were 17.0% and 44.2%, respectively, given a snow precipitation rate of 5 mm/hr and a Road Surface Index (RSI, a friction-like measure) of 0.2 (snow covered). Climate change can introduce such winter challenges to areas unfamiliar with snow and ice conditions or extreme cold-weather events.\u003c/p\u003e\n\u003cp\u003eImproving winter road maintenance (WRM) operations and traveler information services could result in fewer crashes, enhanced mobility, fewer emergency service disruptions, reduced travel costs, better fuel economy, and sustained economic productivity. To this end, it is desirable to use the most recent technological advances; current practices [Shi and Lu 2018] include many intelligent transportation system (ITS) solutions such as smart snowplows equipped with automatic vehicle location (AVL), road weather information systems (RWIS), fixed automated spray technology (FAST), maintenance decision support system (MDSS), dynamic message signs (DMS), and traveler information systems. For traffic management, it is noteworthy that current ITS technologies (loop detectors, video/camera detectors, and radar sensors) are generally limited to obtaining traffic information at the macroscopic level [Wu 2018]. The advent of technologies in connected and autonomous vehicles (CAVs), Internet of Things (IoT), and advanced driver assistance (ADAS) systems, along with advances in information and communications technology (ICT), is envisaged to bring fundamental changes to the current practices.\u003c/p\u003e\n\u003cp\u003eThe physical and digital infrastructures should be upgraded to take advantage of CVs/AVs and facilitate the cooperation between roadway infrastructure and CVs/AVs, i.e., vehicle-infrastructure integration (VII). In the near future, one expects to see mixed traffic flows of connected vehicles (CVs), autonomous vehicles (AVs), and conventional vehicles on highways. This presents new opportunities for better system performance and higher level of service, along with new infrastructure requirements. For instance, connected infrastructure (CI) solutions are desirable for bridging the communication gap between unconnected vehicles and CVs/AVs. CVs equipped with sensors could enhance mobile road weather data collection [Dey et al., 2015] and supplement or compliment current roadway sensing entities, raising the effectiveness of the system operations to react to changing conditions. The 360\u0026deg; awareness by vehicle operators and increased system reliability will help reduce the risk of vehicle crashes and enhance the efficiency of system operations. The Architecture Reference for Cooperative and Intelligent Transportation (ARC-IT) developed by the U.S. Federal Highway Administration (FHWA) has defined how CVs will contribute to various\u0026nbsp; service packages including those for weather, vehicle safety, sustainable travel, traffic management, and traveler information [Iteris, 2020]. Earlier work also envisioned that CV technologies would enable traffic managers to deliver real-time, localized road weather advisories and forecasts directly to the vehicle onboard unit as a visual display to drivers [FHWA, 2014]. Moreover, collected real-time raw data can be shared with commercial application developers to build value-added services [Chapman and Drobot 2012]. AVs tend to be CVs at the same time and can offer a multitude of benefits including alleviated congestion, reduced energy use and emissions, and improved traffic safety [Shladover 2013, Sobanjo 2019].\u003c/p\u003e\n\u003cp\u003eRecent years have seen substantial progress in the development and pilot testing of technologies that enable CVs to transmit data to and receive data from other CVs (vehicle-to-vehicle, V2V), to and from infrastructure (vehicle-to-infrastructure, V2I), and to and from bicyclists or pedestrians. These can be collectively termed as vehicle-to-everything (V2X) communication, for which use cases, requirements, and design considerations of roadways have been discussed by Boban et al. [2018].\u003c/p\u003e\n\u003cp\u003eIn the context of upcoming VII revolution and increased road user expectations, this commentary aims to inspire dialogues and synergistic collaborations among various stakeholders and across various disciplines. The commentary is organized in five main sections. Following this introduction is a discussion of conceptual design of next-generation highways in cold climates, including the existing infrastructure entities that are appropriate for possible upgrade to CI applications. Subsequently, the application scenarios of VII system are briefly discussed, with a focus on coordinated truck platooning, safety applications, mobility applications, and road weather applications. The final sections provide concluding remarks regarding the paradigm shift towards V2X applications and a look to the future.\u003c/p\u003e\u003cp\u003eFig. 1 presents a conceptual design of next-generation highways in cold climates, where a number of CI entities are envisioned. These, along with V2X and ICT capabilities, are expected to greatly enhance the spatial and temporal resolutions of road weather data and thus better inform stakeholders such as highway operators, emergency responders, truck drivers and other highway users. They can also augment the sensing capabilities and confidence level of CVs/AVs, reduce the cost and uncertainties of sensing, enrich the sources of information for both CVs/AVs and unconnected vehicles, and improve the response time.\u003c/p\u003e\n\u003cp\u003eThe following sections will further discuss some entities shown in Fig. 1, including:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eConnected Environmental Sensing Stations (ESS);\u003c/li\u003e\n\u003cli\u003eConnected FAST system;\u003c/li\u003e\n\u003cli\u003eICT capabilities, including but are not limited to: a) roadside units, b) mobile connectivity, and global positioning system (GPS);\u003c/li\u003e\n\u003cli\u003eEnergy-harvesting, connected roadways and roadside infrastructure assets (RIAs); and\u003c/li\u003e\n\u003cli\u003eSelf-sensing or anti-icing pavement.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eNote that the 4\u003csup\u003eth\u003c/sup\u003e and 5\u003csup\u003eth\u003c/sup\u003e components are secondary to the implementation of VII ecosystem but would greatly enhance the viability and value of VII. Other CI entities will not be discussed in detail, and those may include: weather-responsive traffic signals, smart work zone equipment, and non-contact static charging for vehicles. This work does not cover any discussion of AV-enabling infrastructure components, such as smart signage for automatic driving, standardized pavement markings for machine vision, and magnetic nails and reflective striping for lane-keeping.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConnected ESS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRWIS-ESS could be further enhanced so as to serve as integral part of the CI solution and of the larger vehicle-infrastructure ecosystem. Currently, this technology is mainly deployed at fixed locations to better inform WRM operations and travelers, and the deployment has been hindered by high cost and maintenance needs. RWIS refers to \u0026ldquo;networks of ESS that observe the near-surface atmosphere and pavement surface and subsurface\u0026rdquo; [Albrecht et al. 2018]. Each ESS consist of various sensors that provide site-specific, real-time data on the meteorological conditions, road surface condition (RSC), and subsurface temperature, which altogether enables pro-active WRM practices such as anti-icing, improves the roadway level of service and resource allocation, and enhances traveler information, traffic management and emergency response [Strong and Fay 2007, Kwon et al. 2017]. Note that the mobile data collection by CVs/AVs will likely bolster the accuracy of road weather forecasting models, which in turn induces advances in the RWIS-ESS technology. This technology is going portable as well [Tessier, 2016], and its limitations will likely be overcome by design improvements and cost reductions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConnected FAST system\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFAST system is a system of fixed assets designed to spray anti-icing liquid on targeted ice-prone or safety-critical areas once the sensors (and associated algorithm) detect the likely risk of ice formation. Many roadway agencies have employed the FAST technology to prevent or pro-actively mitigate black ice or bonding of compacted snow and ice to pavement (or bridge deck). Conceptually, the design of FAST system could be further enhanced to improve its connectedness and thus contribute to the vehicle-infrastructure ecosystem. Ye et al. [2013] surveyed the state of the art of FAST systems and confirmed their potential in delivering substantial benefits, such as less need for mobile operations and WRM materials and reduced crash frequency and traffic delay. This technology \u0026ldquo;works best for frost and light snow events\u0026rdquo;, but its application has been hindered by challenges in sensor malfunctioning, system maintenance, and training. A study by Veneziano et al. [2015] examined the safety effects of FAST systems operated by the Colorado Department of Transportation and recommended the deployment of such system at \u0026ldquo;high-traffic, high-crash severity locations\u0026rdquo;. For instance, FAST systems were able to contribute to \u0026ldquo;an annual reduction of 16% to 70% on urban Interstates, 31% to 57% on rural Interstates, and 19% to 40% on interchange ramps between Interstates\u0026rdquo;, when sited and operated properly.\u003c/p\u003e\n\u003cp\u003eIn the context of VII revolution, no single communication technology \u0026ldquo;(in the near future) can support such a variety of expected V2X applications for a large number of vehicles\u0026rdquo;, at least not efficiently [Abboud et al. 2016]. Currently, dedicated short-range communications (DSRC) and cellular networks are the two main technologies for V2X communications. Abboud et al. [2016] reviewed various DSRC-cellular hybrid architectures and discussed the interworking challenges between the two technologies, with DSRC transceiver embedded in RSUs interacting with in-vehicle onboard units (OBUs) as well as the backhaul network (via cellular or wired Internet connections). Dey et al. [2016] developed a method to optimize available communication options for V2V and V2I applications in a heterogeneous wireless network consisting of DSRC and \u0026ldquo;other wireless technologies (e.g., Wi-Fi, LTE, and WiMAX)\u0026rdquo;. Nonetheless, Jenkins et al. [2017] concluded that \u0026ldquo;DSRC for wireless access in vehicular environments (WAVE) (protocol) is (currently) the only way to provide V2X communications with high reliability and low latency\u0026rdquo; and thus suitable for dissemination of basic safety messages.\u003c/p\u003e\n\u003cp\u003eNow is the dawn of the 5G era, which stands for the 5\u003csup\u003eth\u003c/sup\u003e generation mobile network and features communication capabilities better meeting the needs of IoT and V2X applications, particularly safety-critical use cases [Boban et al. 2016]), i.e., higher data rates, ultralow latency, ultra-reliability, and increased availability. Boban et al. [2016] proposed an architecture of the 5G V2X network, embodied in a heterogeneous multi-radio V2X network designed to leverage the strengths of \u0026ldquo;cellular systems in centimeter (cmWave) and millimeter (mmWave) frequency bands, vehicular visible light communication (VVLC)\u0026rdquo; and DSRC/WAVE.\u003c/p\u003e\n\u003cp\u003eThe computing infrastructure, either distributed or centralized, also plays a crucial role in the TCPS to enable smart and seamless mobility, and should be planned, designed, deployed, operated and maintained properly [Khan et al. 2019]. To attain outstanding performance and reliability of V2X applications, this Commentary sees that innovations are needed in the field of information technology in terms of system design, hardware and software. Naturally, one needs to tap into recent advances in edge computing, big data analytics, machine learning, artificial intelligence, and so on. For instance, Raza et al. [2018] described an advanced ITS based on ultrahigh speed, ultralow latency 5G scenario, where multiple networking technologies were integrated to provide ubiquitous network connectivity and efficient computing that supports V2X communications. Among them, edge computing (a.k.a., fog computing) refers to \u0026ldquo;the enabling technologies allowing computation to be performance at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IoT services\u0026rdquo; [Shi et al. 2016], and it brings many advantages in \u0026ldquo;addressing the concerns of response time requirement, battery life constraint, bandwidth cost savings, and data safety and privacy\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eArguably, there are a number of roadside or CV solutions other than embedded pavement sensors that can augment the FAST\u0026rsquo;s detection capability and improve its reliability by providing additional information on the RSC. Promising technologies include non-invasive pavement friction sensors [Ewan et al. 2013], vehicle-mounted sensors for thermal mapping [Todeschini et al. 2016, Hu et al. 2019], slippery surface detection [Padarthy and Heyns, 2019], or RSC monitoring [Chen et al. 2011, Pu et al. 2019], and the combined use of mobile camera and smart phone [Linton and Fu 2016] or mobile RWIS technologies [Ye et al. 2012] for RSC monitoring. Most of them could be readily integrated with the AVL technology [Santiago-Chaparro et al. 2012] to unlock their potential in enabling more proactive, efficient and resilient WRM operations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eICT capabilities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis commentary agrees that as part of the transportation cyber-physical system (TCPS), the physical infrastructure of roadways should be \u0026ldquo;upgraded with digital (ICT) infrastructure that evolves with increasing CV penetration levels (so as to) create an environment suitable for fostering beneficial V2I innovations\u0026rdquo; [Khan et al. 2019]. This is critical for enabling the timely and reliable sensing, processing and communication of the unprecedented amount of data available in the VII environment. Khan et al. [2019] summarized the typical roadway digital infrastructure components (as shown in Fig. 2), including roadside units (RSUs), traffic signals, loop detectors, traffic cameras, and DMS communicating with both CVs/AVs and the backend infrastructure (servers) in a real-time or near-real-time fashion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnergy-harvesting, connected roadways and RIAs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConnected roadways need reliable supply of energy. The massive number of roadway mileages and vast amount of lands in the right-of-way present a great opportunity to capture and utilize the energy dissipated from the ambient roadway system, such as mechanical energy from vehicle or wind loadings and thermal energy from the sun or earth. Such energy harvesting is particularly beneficial for roadways in remote, off-grid areas where the lifeline of CI applications is endangered by the lack of access to power. In the U.S. alone, there are 2.6 million miles of paved roads and highways, of which approximately 93 percent has an asphalt surface [Mohamed Jaafar 2019] and 3,000 (linear) miles are equipped with noise barrier [Poe et al. 2017]. The \u0026ldquo;(unpaved) land cover in close proximity to the National Highway System\u0026rdquo; in the U.S. has been estimated to be \u0026ldquo;roughly 68 percent, or 3.4 acres\u0026rdquo; [Earsom et al. 2010].\u003c/p\u003e\n\u003cp\u003eA suite of on-road energy-harvesting technologies are available, as illustrated in Fig. 3 [Wang et al. 2018]. These technologies entail the use of piezoelectric materials, micro wind turbines, photovoltaic panels or solar cell roads, geothermal heat pumps or pipe-pavement thermoelectric generator (PP-TEG) system. They could be potentially incorporated into either the connected roadways such as pavements and traffic signals, or RIAs such as noise barriers and structural snow fences, to meet the energy needs of a multitude of sensing and communication needs. They also feature a wide variety of cost, energy output and efficiency, service life, dimensions, maintenance requirements, recyclability, and other characteristics. Gholikhani et al. (2020) concluded that \u0026ldquo;thermoelectric and piezoelectric technologies are the most readily available methods\u0026rdquo; and Wang et al. (2018) discussed that technologies other than piezoelectric energy harvesters (PEHs) have their own strengths and limitations. For instance, photovoltaic systems can produce high energy output but the use of solar panels in roadways may complicate vehicle operations or pose a risk to traffic safety, and more research is needed to address such concerns. One promising niche application is solar-powered roadway lighting by \u003ca href=\"https://www.sciencedirect.com/topics/engineering/light-emitting-diodes\"\u003elight emitting diode\u003c/a\u003e\u0026nbsp;(LED)\u0026nbsp;(Yoomak et al. 2018). Geothermal heat pumps are considered a mature technology, which is \u0026ldquo;geologically and geographically limited\u0026rdquo; and most appropriate for safety-critical areas, and they have shown a desirable benefit/cost ratio for bridge deck de-icing (Habibzadeh-Bigdarvish et al. 2019). While PP-TEG produces a low energy output at high cost and more research is needed to improve the system efficiency (Zhu et al. 2019), a PP-TEG system can be potentially more cost-effective than a piezoelectric system in harvesting renewable energy from pavements (Guo and Lu 2017).\u003c/p\u003e\n\u003cp\u003eThe last four years have seen increased interest and promising progress in demonstrating the use of piezoelectric materials to harvest deformation energy from asphalt pavement. Yet, the amount of energy harvested is limited but suitable for applications such as \u0026ldquo;powering wireless sensors embedded into pavement structure\u0026rdquo; [Roshani et al. 2017] and other microelectronics, \u0026ldquo;heating road surface on bridge deck for anti-icing, lighting, or powering traffic devices\u0026rdquo; [Wang et al. 2018]. A few representative advances in the development of piezoelectric energy harvester (PEH) technology is summarized in Table 1.\u003c/p\u003e\u003cp style='margin-top:12.0pt;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eTable 1. Recent advances in PEH technology for roadways\u003c/p\u003e\n\u003ctable style=\"border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112.25pt;border: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cem\u003eConfiguration\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.75in;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cem\u003eOptimal energy performance\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.25in;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cem\u003eOther performance considerations\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112.25pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eA stacked configuration of transducers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.75in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eNot reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.25in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eAble to support loadings up to 150 kN and remained effective after 100,000 cyclic loadings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eYang et al. [2017]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112.25pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eStacked units including U-shaped interlayer copper foil electrode structure\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.75in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eOutput power: 22.8 mW at a resistive load of 20 k\u0026Omega;, under 0.7 MPa (compression loading) and 10 Hz (vibration frequency); output voltage: 28.0 V\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.25in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eStable performance after 50,000 simulated cyclic loading, with attenuations of: open-circuit voltage by 4.4 V, output power by 3.3 mW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eWang et al. [2019]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112.25pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eBridge transducer with layered poling and electrode design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.75in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eOutput power: 2.1 mW at a resistive load of 400 k\u003cspan style=\"font-family:Symbol;\"\u003eW\u003c/span\u003e, under 0.07MPa, 5 Hz; output voltage: 556 V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.25in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eBalanced the desire for improved energy output and the need for less risk of stress concentration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eJasim et al. [2017]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112.25pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eSeveral PEH prototypes to be embedded into asphalt pavement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.75in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eOutput power: \u0026gt; 25 mW under 15 MPa (for each piezoelectric element) and 10 Hz; output voltage (nearly 20 V) and output current (\u0026gt; 100 \u0026micro;A) at a load of 3 kN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.25in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eHigher frequency \u0026ldquo;represents higher traffic speed and greater traffic volume\u0026rdquo; (e.g., on Interstate highways), and leads to better output power.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eRoshani et al. [2017]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112.25pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eOne layer of \u0026ldquo;piezoelectric elements with a higher piezoelectric stress constant\u0026rdquo; and two layers of \u0026ldquo;more flexible conductive asphalt mixtures\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.75in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eOutput power: ranging from 1.2 mW to 300 mW at 30 Hz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.25in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eThe cost of electricity produced by this PEH can be as low as \u0026ldquo;$19.15/kWh at a high-volume roadway within a 15-year service life\u0026rdquo;.\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eGuo and Lu [2017]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112.25pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eA 100-m piezoelectric pavement with packaged multi-layer transducer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1.75in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eOutput power: up to 231 mW (0.58 J), under 0.7 MPa\u0026nbsp;\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.25in;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style=\"color:#2E2E2E;\"\u003eUnder the daily traffic volume of 15100 vehicles, this 100-m\u0026nbsp;\u003c/span\u003epavement can produce up to 1.93 MJ (i.e., the energy needs of 35 mobile phones). The additional construction cost: approximately $57/m.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eCao et al. [2020]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cbr\u003e\u003cp\u003eIn addition to smart pavements, there are a number of conceptual scenarios that energy-harvesting technologies may be incorporated into the roadway infrastructure. For instance, portable micro wind turbines could be mounted on structural snow fences, traffic signals, and so on. Being a cost-effective technology to prevent blowing and drifting snow, snow fences can improve road safety and provide additional benefits, if designed and sited properly [Du et al. 2017]. Nabavi and Zhang [2016] reported three groups of portable wind energy harvesters, i.e., piezoelectric-, electromagnetic-, and electrostatic-based generators, with different wind-flow-trapping mechanisms and varying dimensions and energy conversion efficiencies. Photovoltaic panels can be strategically installed either in the right-of-way providing sufficient space for such a \u0026ldquo;distributed solar power plant\u0026rdquo; [Asanov et al. 2019], or integrated with noise barriers to produce a considerable amount of renewable energy [Poe et al. 2017]. Qiao et al. [2011] proposed the concept of a \u0026ldquo;smart microgrid that optimally utilizes the public right-of-way and roadway infrastructure to provide cost-effective, highly efficient, and reliable wind/solar electric power production, distribution, storage, and utilization\u0026rdquo;. The design entails a \u0026ldquo;grid-connected wind/solar hybrid generation system installed on the pole of a roadway/traffic signal light\u0026rdquo;. Considering the great temperature difference between the air and the relatively warm soil beneath pavement, another potential technology to explore is thermoelectric generators. With the thermal gradient in the opposite direction, this concept was demonstrated in south Texas [Datta et al. 2017] where a TEG prototype produced \u0026ldquo;an average of 10 mW of electric power continuously over a period of 8 h\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelf-sensing or anti-icing pavement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSelf-sensing pavements can serve as an integral part of connected roadway infrastructure or VII system, and the related research is still in the burgeoning stage and the cost-effectiveness of this technology over the long term remains unknown. Han et al. [2013] reported the traffic detection performance of a self-sensing pavement being tested at the Minnesota Road Research Facility, USA, both in winter and in summer. This smart pavement was enabled by the admixed carbon nanotubes in concrete, and was able to \u0026ldquo;accurately detect the passing of different vehicles under different vehicular speeds and test environments\u0026rdquo;. Relative to conventional strain gauges, this self-sensing concrete exhibited advantages in its ease of installation and maintenance, compatibility with pavement structures, and durability. Liu et al. [2014] reported an exploratory study that suggests the potential use of conductive asphalt materials for self-sensing applications, because the different stages of damage evolution corresponded to certain change patterns in their electrical resistivity. Xiang et al. [2020] reported the use of piezoelectric sensors to detect moving traffic loads on pavement, i.e., for the weigh-in-motion application.\u003c/p\u003e\n\u003cp\u003eAnti-icing pavements are often not designed for CI applications, but they can contribute to the mobility of surface transportation system during snowy weather. A variety of technologies have been explored to enable anti-icing pavements, ranging from \u0026ldquo;anti-freezing pavements that rely on physical action, to high-friction\u003cem\u003e in situ\u003c/em\u003e anti-icing polymer overlays, to asphalt pavements containing anti-icing additives, to heated pavements using energy transfer systems\u0026rdquo; [Shi et al. 2018]. All of them aim to \u0026ldquo;prevent or reduce the bond of ice or compacted snow to pavement or to prevent or treat winter precipitation\u0026rdquo;. Pan et al. [2015] and Zhang et al. [2020] presented a comprehensive review on the use of conductive and salt-releasing asphalt mixtures as anti-icing pavements, respectively. Saleh et al. (2020) discussed the design, construction and evaluation of hydronic asphalt pavement (HAP), which is another energy-harvesting technology explored for anti-icing (and deicing) applications. Johnsson (2017) found that HAP could be engineered as part of a system to harvest sufficient geothermal energy for areas with mild winters and greatly \u0026ldquo;reduce the annual number of hours with risk for ice formation\u0026rdquo;. Note that none of these technologies have been widely adopted by transportation agencies, and this is mainly due to concerns over their long-term performance and cost. Nonetheless, this Commentary proposes that there is great potential in overcoming such technological barrier in the near future.\u003c/p\u003e"},{"header":"Application Scenarios Of Vii System","content":"\u003cp\u003eThis section identifies some possible application scenarios of VII system, which are grouped into safety, mobility, road weather, agency data, and other categories, as detailed in Table 2. One can expect CI-enabled improvements to be materialized in these applications, given the notable advances in spatial and temporal coverage offered by CI technologies. The key to success is the timely and reliable sensing, processing and communication of the big data collected by the VII system. Table 2 is not intended to be comprehensive, as innovations may lead to new possibilities and new application scenarios.\u003c/p\u003e\n\u003cp\u003eIt is noteworthy that the enhanced spatial and temporal resolutions of road weather data can better inform a multitude of stakeholders and produce benefits across the spectrum of many applications. Both road weather and agency data applications are intertwined with highway safety and mobility objectives, because they aim to \u0026ldquo;provide a safe and efficient transportation system to move people and goods\u0026rdquo; [Sobanjo 2019]. The USDOT V2I research program has developed a computing platform known as V2I hub [Chang 2017], which \u0026ldquo;interfaces with a variety of ITS equipment such as RSU, traffic signal controllers, and DMS\u0026rdquo; and the GPS and transportation management center (TMC). For instance, an RSU can broadcast intersection geometry data (a.k.a., MAP message), signal phase and timing (SPaT) message, GPS correction data, and curve speed warning to incoming vehicles, or receive them from nearby RSUs [Change 2017, NOCoE 2020].\u003c/p\u003e\u003cp style='margin-top:12.0pt;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eTable 2. Some identified application scenarios of VII system\u003c/p\u003e\n\u003ctable style=\"width: 4.8e+2pt;border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112.2pt;border: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cem\u003eApplication Type\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 279.05pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cem\u003eApplication Scenario\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cstrong\u003e\u003cem\u003eReference\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"9\" style=\"width: 112.2pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eSafety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eCooperative-ITS platform: advance warning of hazardous road situations (to vehicle drivers)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003ePadarthy and Heyns [2019]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eV2X applications for roadway safety and vehicle safety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eAbboud et al. [2016]; FHWA [2020]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;height: 53.9pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eIntersection collision warning, emergency vehicle pre-emption, work zone alerts, curve speed warning, railroad crossing violation warning\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;height: 53.9pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eBarbaresso and Johnson [2014]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eIn-vehicle signage, oversize vehicle warning, red-light or stop-sign violation warning, reduced speed zone warning/lane closure, restricted lane warning, spot weather impact warning\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eIteris [2019]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eCooperative collision avoidance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eChowdhury et al. [2018]; Wang and Li [2019]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eBlind spot warning and lane change warning\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eTheriot et al. [2017]; Howe et al. [2016]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eADAS functionalities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eLiu et al. [2017]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003ePedestrian detection and warning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eHe and Zeng [2017]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eDeer crossing road detection: with multiple roadside LiDAR (Light Detection and Ranging) sensors deployed to enable real-time, micro-level and high-resolution sensing of road users\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eWu [2018]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" style=\"width: 112.2pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eMobility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eCoordinated truck platooning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eGungor and Al-Qadi [2020]; Gungor and Al-Qadi [2020]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eTraffic management and sustainable travel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eLi et al. [2020]; Gopalakrishna, et al. [2016]; Iteris [2020]\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eAdvanced traveler information system, corridor management, transit vehicle priority, and multimodal intelligent traffic signal system\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eBarbaresso and Johnson [2014]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eV2X applications for vehicle traffic optimization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eAbboud et al. [2016]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eIncident detection and response; traffic queue or bottleneck detection; traffic network flow optimization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eKhazraeian [2017];\u003cspan style=\"color:#222222;background:white;\"\u003e\u0026nbsp;\u003c/span\u003eChowdhury et al. [2018]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eEmergency vehicle priority\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eHead [2016]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eAdaptive signal control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eYao et al. [2020]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eCooperative adaptive cruise control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eHuang et al. [2020]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eDynamic routing support\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eGenders and Razavi [2016]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eSmart DMS, routing support and data-driven apps for freight carriers, transit vehicles and emergency responders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eIteris [2019]; Akin et al. [2018]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112.2pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0in 5.4pt;height: 87.5pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eRoad weather\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;height: 87.5pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eV2X applications for motorist advisories and warnings, information for maintenance and fleet management systems, and MDSS\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;height: 87.5pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eBarbaresso and Johnson [2014]; Young et al. [2019]; FHWA [2020]\u0026nbsp;\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003e\u003cspan style=\"color:#222222;background:white;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112.2pt;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-left: 1pt solid windowtext;border-image: initial;border-top: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eAgency Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eV2I applications for probe-based traffic monitoring, probe-based pavement condition monitoring, and performance measures\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eBarbaresso and Johnson [2014]; FHWA [2014]; Li et al. [2019]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 112.2pt;border-top: none;border-left: 1pt solid windowtext;border-bottom: 1.5pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eV2X applications for passenger infotainment (via in-vehicle Internet access) and car manufacturer services (e.g., point-of-interest notification and remote vehicle diagnostics).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eAbboud et al. [2016]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 279.05pt;border-top: none;border-left: none;border-bottom: 1.5pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eValue-added services by commercial application developers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85.5pt;border-top: none;border-left: none;border-bottom: 1.5pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:6.0pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\"Calibri\",sans-serif;'\u003eChapman and Drobot [2012]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cbr\u003e\u003cp\u003eThe USDOT has estimated that combined V2V and V2I systems may potentially address about 81% of all-vehicle target crashes, 83% of all light-vehicle target crashes, and 72% of all heavy-truck target crashes [Najafi et al. 2016]. Such safety benefits of CV are likely to be more significant during adverse weather conditions, by enhancing all levels of decision-making by stakeholders. For example, the enriched road weather condition information can be communicated to the general public in a timely fashion, such that they can slow down, choose a different route, or stay home in light of inclement weather.\u003c/p\u003e\n\u003cp\u003eIn addition to safety benefits, the deployment of V2X technologies and advances in VII are likely to produce mobility and resilience benefits on winter highways, by better informing all the stakeholders and enabling location-specific and timelier detection of and response to disruptions. By definition, resilience is \u0026ldquo;the ability of a system to resume normal function at a performance level equal that which existed before a disruptive event\u0026rdquo;, and can be characterized by metrics such as robustness, adaptability, agility, redundancy, response time, recovery time, level of recovery, and performance loss [Muller 2012]. The transmission of microlevel road weather data by vehicles through V2X communication has been demonstrated in the European WiSafeCar project [Sukuvaara and Nurmi, 2012], which is anticipated to benefit the efficiency of road weather management [Ma et al., 2012].\u003c/p\u003e\n\u003cp\u003eOne major mobility application scenario is coordinated truck platooning. Connected and autonomous trucks (CATs) are increasingly introduced into the market, which enables the grouping of densely-spaced trucks to travel together on highways to save on the cost and fuel consumption and improve the efficiency of freight operations. This practice, known as truck platooning, has unintended negative consequences on how the trucks could induce damage in the pavement infrastructure, due to the \u0026ldquo;channelized truck loading application\u0026rdquo; [Gungor and Al-Qadi 2020]. To mitigate this detrimental effect, Gungor and Al-Qadi [2020] proposed a framework to optimally randomize the pattern of axel loadings of truck platoons using V2I communication. Such a coordinated truck platooning approach entails cooperative automation among trucks and roadway infrastructure, which uniformly distributes the loadings of truck platoons over the pavement lanes, leading to an overall reduction in damage accumulation in the pavement and a longer service life of the pavement.\u003c/p\u003e\n\u003cp\u003eIt is interesting to note that winter weather may induce vulnerabilities in vehicles on highways as well as in physical and digital infrastructures. For instance, the performance and reliability of CVs/AVs (and other sensors) and communications could be destabilized by extremely cold temperatures and heavy snowfall conditions, and such risks should be considered in the design of the VII system to operate in cold climates.\u003c/p\u003e"},{"header":"Conclusion And Outlook","content":"\u003cp\u003eCurrently, there is an urgent demand for more cost-effective, resource-efficient and reliable solutions to address safety, mobility, and resilience challenges on highways enduring snowy winter weather. One can envisage a fundamentally changed landscape for WRM operations, traveler information, and traffic management on winter highways, amid the increasing introduction of innovative concepts (e.g., Smart Cities, Crowdsourcing, and V2X) and more penetration of emerging technologies (e.g., CVs/AVs/CATs, IoT, 5G, Cloud Computing, and Edge Computing) into the transportation sector. \u0026nbsp;These concepts and technologies will catalyze the increasing momentum of VII to enable a significantly higher level of service. As such, there is the need to conceptualize and strategically plan for a more connected roadway infrastructure for VII system, even though many of the CI solutions are still in the nascent stages of development.\u003c/p\u003e\n\u003cp\u003eDriven by the paradigm shift towards more automation and more intelligent transportation, it is time to reimagine the vehicle-infrastructure ecosystem with the cold-climate issues in mind, and to enhance communications and coordination among various highway users and stakeholders. A next step is to promote the synergies among the enabling technologies and unlock their potential for the specific needs of highway agencies. For instance, CI technologies are currently less mature than CV technologies. Great benefits can be achieved by integrating the array of both CI and CV technologies. Because such integration would provide better, more accurate and timely knowledge of conditions throughout the roadway network (albeit with a focus on critical locations and road segments), in terms of traffic characteristics, RSCs, and environmental conditions. This big data could be processed, archived, and communicated to highway operators and traveling public in a timely fashion. In addition to informing travelers, such information can be fed into agency decision support tools such as the Maintenance Decision Support System (Ye et al. 2009, Rennie and Groeneweg 2017) to greatly benefit the roadway level of service and the overall safety, mobility, and productivity of the surface transportation system.\u003c/p\u003e\n\u003cp\u003eTo inspire dialogues and synergistic collaborations among various stakeholders of the VII revolution, this commentary has underscored the need to plan, design, and engineer a multifunctional, next-generation highway infrastructure that is more intelligent, safer and more resilient and adaptive than the conventional highway infrastructure, fueled by more reliable and rapid collection, processing and communication of big data.\u003c/p\u003e\n\u003cp\u003eThe challenges in achieving better safety and mobility on winter highways may continue to evolve and they must be addressed with systematic, holistic, and multidisciplinary approaches. A next step is to carry out concerted efforts in the research, development, pilot testing, and deployment of CI technologies, which needs to bring together expertise from different disciplines to transform the built highway environment to one that better facilitates the real-time detection of localized conditions and the flow of high-quality road weather data (e.g., V2X). The ultimate goal is to improve the safety and mobility of highways in cold climates, which in turn would translate to a broad array of social, economic and environmental benefits.\u003c/p\u003e\n\u003cp\u003eThe VII revolution is still in its infancy and there are many unexplored territories and dynamics, unanswered questions, and open challenges. Innovations are much needed to overcome the various technological and institutional barriers to the successful implementation of VII system for highways in cold climates, and to maximize the synergies between the physical and digital infrastructures. It is imperative to note, however, that innovations should be anchored in answering the user requirements, i.e., following a needs-pull (vs. technology-push) approach.\u003c/p\u003e"},{"header":"Recommendations","content":"\u003cp\u003eThe following presents some research needs identified in the arena of CI:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTap into recent advances in various V2X and ICT technologies and investigate better compatibility, automation, and integration among them, to provide the best possible road weather information, in terms of spatial and temporal resolutions, reliability, and so on. The efforts may be in the aspects of system design [Li et al. 2019, Muller 2012], hardware, and software and should take into account the given constraints of cost, communications speed and reliability, etc. as well as the specific performance requirements and functionalities needed by the VII system. The objective is to have a cohesive and affordable VII system that operates efficiently and reliably during disruptive weather of snow and ice.\u003c/li\u003e\n\u003cli\u003eAddress the technical and non-technical challenges vis-\u0026agrave;-vis scalability, interoperability, privacy and security, in anticipation of increased number and heterogeneity of smart devices and immense amount of sensor data available, similar to those hindering the implementation of Smart City services and applications [Balakrishna 2012].\u003c/li\u003e\n\u003cli\u003eInvestigate the transformation of physical roadway infrastructure for the needs of VII applications, build resilience into highways, and tap into the opportunities of improving the design, health monitoring and diagnosis, preservation, and utility of roads, bridges, tunnels, culverts, and RIAs, while reducing their life-cycle cost and environmental footprints.\u003c/li\u003e\n\u003cli\u003eDevelop and demonstrate technologies that can reliably supply cost-effective energy for roadways in remote, off-grid areas. A promising approach could be the hybrid use of PEHs and pyroelectric materials. Future research should focus on achieving balanced performances in energy output and cost-efficiency, reliability and resilience, durability, recyclability, and sustainability over the life cycle of the energy harvesting system. Areas of improvement may include: the selection and design of materials, PEH design (packaging, composite configuration of elements, etc.) and power electronics, optimized as a function of given traffic patterns, in-service environmental conditions, and specific energy requirements of the CI application.\u003c/li\u003e\n\u003cli\u003eDevelop and demonstrate a set of cost-effective, durable asphalt pavement mixtures or surface layers that enable reliable real-time detection of key vehicle flow parameters and other functionalities such as \u003cem\u003ein situ \u003c/em\u003eanti-icing, sensing of the surface condition (dry, wet, snowy, icy, etc.) and/or sensing of the overall health condition of the pavement itself.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADAS: advanced driver assistance; ARC-IT: Architecture Reference for Cooperative and Intelligent Transportation; AVL: automatic vehicle location; CATs: connected and autonomous trucks; CAVs: connected and autonomous vehicles; CI: connected infrastructure; CV: connected vehicle; DMS: dynamic message sign; DSRC: dedicated short-range communications; ESS: environmental sensing station; FAST: fixed automated spray technology; FHWA: Federal Highway Administration; GPS: global positioning system; HAP: hydronic asphalt pavement; ICT: information and communications technology; IoT: Internet of Things; ITS: intelligent transportation system; PEH: piezoelectric energy harvester; LiDAR: Light Detection and Ranging; LTE: Long-Term Evolution; OBU: onboard unit; PP-TEG: pipe-pavement thermoelectric generator; RIA: roadside infrastructure asset; RSC: road surface condition; RSU: roadside unit; RWIS: road weather information system; TCPS: transportation cyber-physical system; TMC: transportation management center; V2I: vehicle-to-infrastructure; V2X: vehicle-to-anything; V2V: vehicle-to-vehicle; VII: vehicle-infrastructure integration; VVLC: vehicular visible light communication; USDOT: U.S. Department of Transportation; WAVE: wireless access in vehicular environments; WiMAX: Worldwide Interoperability for Microwave Access; WRM: winter road maintenance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data have been presented in the Commentary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author acknowledges the editorial assistance from Ms. Cheryl Reed and the constructive criticism by the anonymous reviewers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author would like to acknowledge funding support from the Center for Advanced Multimodal Mobility Solutions and Education (CAMMSE), a Tier 1 \u003cem\u003eUTC\u003c/em\u003e (University Transportation Center) sponsored by the US Department of Transportation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXS contributed solely to this commentary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Xianming Shi.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOther Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEthics approval and consent to participate: not applicable\u003c/li\u003e\n\u003cli\u003eConsent for publication: not applicable\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"References","content":"\u003cp\u003eStrong, C. K., Ye, Z., \u0026amp; Shi, X. (2010). Safety effects of winter weather: the state of knowledge and remaining challenges. \u003cem\u003eTransport reviews\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(6), 677-699.\u003c/p\u003e\n\u003cp\u003eFHWA (2011). \u0026ldquo;\u003cem\u003eSnow and Ice\u0026rdquo;\u003c/em\u003e. Road Weather Management, Federal Highway Administration, U.S. Department of Transportation, Washington, D.C. Accessed August 10, 2011. [Online]. \u003ca href=\"http://ops.fhwa.dot.gov/Weather/weather_events/snow_ice.htm\"\u003ehttp://ops.fhwa.dot.gov/Weather/weather_events/snow_ice.htm\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFu, L., Kwon, \u0026amp; T.J. (2018). Chapter 8: Mobility effects of winter weather and road maintenance operations. In X. Shi, L. Fu (Eds.), Sustainable Winter Road Operations, Wiley-Blackwell, pp. 131-155.\u003c/p\u003e\n\u003cp\u003eKwon, T. J., Fu, L., \u0026amp; Jiang, C. (2013). \u0026ldquo;Effect of winter weather and road surface conditions on macroscopic traffic parameters.\u0026rdquo; Transportation research record, Vol. 2329, 54-62.\u003c/p\u003e\n\u003cp\u003eShi, X., \u0026amp; Fu, L. (Eds.). (2018). \u003cem\u003eSustainable winter road operations\u003c/em\u003e. Wiley Blackwell.\u003c/p\u003e\n\u003cp\u003eWu, J. (2018). \u003cem\u003eData processing algorithms and applications of LiDAR-enhanced connected infrastructure sensing\u003c/em\u003e (Doctoral dissertation).\u003c/p\u003e\n\u003cp\u003eDey, K.C., Mishra, A., Chowdhury, M. (2015). \u0026ldquo;Potential of intelligent transportation systems in mitigating adverse weather impacts on road mobility: A review.\u0026rdquo; \u003cem\u003eIEEE Transactions on Intelligent Transportation Systems\u003c/em\u003e, 16(3), 1107-1119.\u003c/p\u003e\n\u003cp\u003eIteris (2020). \u0026ldquo;ARC-IT version 8.3, Service Packages.\u0026rdquo; Accessed on Sept. 18, 2020. [Online]. Available: \u003ca href=\"https://local.iteris.com/arc-it/html/servicepackages/servicepackages-areaspsort.html\"\u003ehttps://local.iteris.com/arc-it/html/servicepackages/servicepackages-areaspsort.html\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eFHWA (2014). \u0026ldquo;Connected Vehicle Reference Implementation Architecture 2014.\u0026rdquo; Accessed on Nov. 12, 2014. [Online]. Available: \u003ca href=\"http://www.iteris.com/cvria/\"\u003ehttp://www.iteris.com/cvria/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eChapman, M.B., and Drobot, S. (2012). \u0026ldquo;The use of connected vehicle observations in weather applications for various highly impacted users of the roads.\u0026rdquo; Proc. 16th Int. Road Weather Conf., Helsinki, Finland, 1\u0026ndash;7.\u003c/p\u003e\n\u003cp\u003eShladover, S. (2013). Why automated vehicles need to be connected vehicles. In \u003cem\u003eIEEE Vehicular Networking Conference. \u003c/em\u003eAccessed October 1, 2013. [Online]. \u003cem\u003ehttp://www. ieeevnc.org/2013/media/IEEE_VNC_BostonKeynote_Shladover.pdf\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eSobanjo, J. O. (2019). Civil Infrastructure Management Models for the Connected and Automated Vehicles Technology. \u003cem\u003eInfrastructures\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(3), 49.\u003c/p\u003e\n\u003cp\u003eBoban, M., Kousaridas, A., Manolakis, K., Eichinger, J., \u0026amp; Xu, W. (2018). Connected roads of the future: Use cases, requirements, and design considerations for vehicle-to-everything communications. \u003cem\u003eIEEE vehicular technology magazine\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(3), 110-123.\u003c/p\u003e\n\u003cp\u003eSukuvaara, T., \u0026amp; Nurmi, P. (2012). Connected vehicle safety network and road weather forecasting\u0026ndash;The WiSafeCar project. In\u0026nbsp;Proceedings of the 16 th International Road Weather Conference, SIRWEC 2012\u0026nbsp;(pp. 23-25).\u003c/p\u003e\n\u003cp\u003eMa, Y., Chowdhury, M., Sadek, A., and Jeihani, M. (2012). \u0026ldquo;Integrated traffic and communication performance evaluation of an intelligent Vehicle Infrastructure Integration (VII) system for online travel-time prediction.\u0026rdquo; IEEE Trans. Intell. Transp. Syst., vol. 13, no. 3, pp. 1369-1382.\u003c/p\u003e\n\u003cp\u003eNajafi, S., Flintsch, G. W., \u0026amp; Khaleghian, S. (2016). Fuzzy logic inference-based Pavement Friction Management and real-time slippery warning systems: a proof of concept study.\u0026nbsp;\u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e,\u0026nbsp;\u003cem\u003e90\u003c/em\u003e, 41-49.\u003c/p\u003e\n\u003cp\u003eMuller, G. (2012). Fuzzy architecture assessment for critical infrastructure resilience. \u003cem\u003eProcedia Computer Science\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 367-372.\u003c/p\u003e\n\u003cp\u003eMohamed Jaafar, Z.F.B. (2019). Computational Modeling and Simulations of Condition Deterioration to Enhance Asphalt Highway Pavement Design and Asset Management. Dissertation for Ph.D. in Engineering Science. University of Mississippi, Electronic Theses and Dissertations, 1643. \u003ca href=\"https://egrove.olemiss.edu/etd/1643\"\u003ehttps://egrove.olemiss.edu/etd/1643\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eEarsom, S., Hallett, R., Perrone, T., Poe, C., \u0026amp; Greenfield, M. (2010). Carbon Sequestration Pilot Program Results: Estimated Land Available for Carbon Sequestration in the National Highway System. Report for the Federal Highway Administration, Washington, D.C.\u003c/p\u003e\n\u003cp\u003ePoe, C., Plovnick, A., Hodges, T., Hastings, A., \u0026amp; Dresley, S. (2017). \u003cem\u003eHighway renewable energy: photovoltaic noise barriers\u003c/em\u003e (No. DOT-VNTSC-FHWA-17-20; FHWA-HEP-17-088). United States. Federal Highway Administration. Office of Natural Environment.\u003c/p\u003e\n\u003cp\u003eWang, H., Jasim, A., \u0026amp; Chen, X. (2018). Energy harvesting technologies in roadway and bridge for different applications\u0026ndash;A comprehensive review. \u003cem\u003eApplied energy\u003c/em\u003e, \u003cem\u003e212\u003c/em\u003e, 1083-1094.\u003c/p\u003e\n\u003cp\u003eGholikhani, M., Roshani, H., Dessouky, S., \u0026amp; Papagiannakis, A. T. (2020). A critical review of roadway energy harvesting technologies.\u0026nbsp;\u003cem\u003eApplied Energy\u003c/em\u003e,\u0026nbsp;\u003cem\u003e261\u003c/em\u003e, 114388.\u003c/p\u003e\n\u003cp\u003eYoomak, S., Jettanasen, C., Ngaopitakkul, A., Bunjongjit, S., \u0026amp; Leelajindakrairerk, M. (2018). Comparative study of lighting quality and power quality for LED and HPS luminaires in a roadway lighting system.\u0026nbsp;\u003cem\u003eEnergy and Buildings\u003c/em\u003e,\u0026nbsp;\u003cem\u003e159\u003c/em\u003e, 542-557.\u003c/p\u003e\n\u003cp\u003eHabibzadeh-Bigdarvish, O., Yu, X., Lei, G., Li, T., \u0026amp; Puppala, A. J. (2019). Life-Cycle cost-benefit analysis of Bridge deck de-icing using geothermal heat pump system: A case study of North Texas.\u0026nbsp;\u003cem\u003eSustainable cities and society\u003c/em\u003e,\u0026nbsp;\u003cem\u003e47\u003c/em\u003e, 101492.\u003c/p\u003e\n\u003cp\u003eZhu, X., Yu, Y., \u0026amp; Li, F. (2019). A review on thermoelectric energy harvesting from asphalt pavement: Configuration, performance and future.\u0026nbsp;\u003cem\u003eConstruction and Building Materials\u003c/em\u003e,\u0026nbsp;\u003cem\u003e228\u003c/em\u003e, 116818.\u003c/p\u003e\n\u003cp\u003eGuo, L., \u0026amp; Lu, Q. (2017). Potentials of piezoelectric and thermoelectric technologies for harvesting energy from pavements.\u0026nbsp;\u003cem\u003eRenewable and Sustainable Energy Reviews\u003c/em\u003e,\u0026nbsp;\u003cem\u003e72\u003c/em\u003e, 761-773.\u003c/p\u003e\n\u003cp\u003eYang, H., Wang, L., Hou, Y., Guo, M., Ye, Z., Tong, X., \u0026amp; Wang, D. (2017). Development in stacked-array-type piezoelectric energy harvester in asphalt pavement. \u003cem\u003eJournal of Materials in Civil Engineering\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(11), 04017224.\u003c/p\u003e\n\u003cp\u003eWang, C., Song, Z., Gao, Z., Yu, G., \u0026amp; Wang, S. (2019). Preparation and performance research of stacked piezoelectric energy-harvesting units for pavements.\u0026nbsp;\u003cem\u003eEnergy and Buildings\u003c/em\u003e,\u0026nbsp;\u003cem\u003e183\u003c/em\u003e, 581-591.\u003c/p\u003e\n\u003cp\u003eJasim, A., Wang, H., Yesner, G., Safari, A., \u0026amp; Maher, A. (2017). Optimized design of layered bridge transducer for piezoelectric energy harvesting from roadway. \u003cem\u003eEnergy\u003c/em\u003e, \u003cem\u003e141\u003c/em\u003e, 1133-1145.\u003c/p\u003e\n\u003cp\u003eRoshani, H., Dessouky, S., Papagiannakis, A. T., \u0026amp; Montoya, A. (2017). Experimental and finite element assessment of three energy harvesting prototypes for roadways. \u003cem\u003eInnovative Infrastructure Solutions\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1), 7.\u003c/p\u003e\n\u003cp\u003eGuo, L., \u0026amp; Lu, Q. (2017). Modeling a new energy harvesting pavement system with experimental verification. \u003cem\u003eApplied energy\u003c/em\u003e, \u003cem\u003e208\u003c/em\u003e, 1071-1082.\u003c/p\u003e\n\u003cp\u003eCao, Y., Sha, A., Liu, Z., Li, J., \u0026amp; Jiang, W. (2020). Energy output of piezoelectric transducers and pavements under simulated traffic load.\u0026nbsp;\u003cem\u003eJournal of Cleaner Production\u003c/em\u003e, 123508.\u003c/p\u003e\n\u003cp\u003eDu, S., Petrie, J., \u0026amp; Shi, X. (2017). Use of Snow Fences to Reduce the Impacts of Snowdrifts on Highways: Renewed Perspective. \u003cem\u003eTransportation research record\u003c/em\u003e, \u003cem\u003e2613\u003c/em\u003e(1), 45-51.\u003c/p\u003e\n\u003cp\u003eNabavi, S., \u0026amp; Zhang, L. (2016). Portable wind energy harvesters for low-power applications: A survey. \u003cem\u003eSensors\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(7), 1101.\u003c/p\u003e\n\u003cp\u003eAsanov, I., Sharaborova, E., \u0026amp; Loktionov, E. (2019). Evaluation of technical and economic effect from setup of distributed photovoltaic generation on the Right-of-Way land of the North Caucasus Railway. In \u003cem\u003eE3S Web of Conferences\u003c/em\u003e (Vol. 114, p. 05004). EDP Sciences.\u003c/p\u003e\n\u003cp\u003eQiao, W., Sharma, A., Hudgins, J. L., Jones, E. G., \u0026amp; Rilett, L. (2011, July). Wind/solar hybrid generation-based roadway microgrids. In \u003cem\u003e2011 IEEE power and energy society general meeting\u003c/em\u003e (pp. 1-7). IEEE.\u003c/p\u003e\n\u003cp\u003eDatta, U., Dessouky, S., \u0026amp; Papagiannakis, A. T. (2017). Harvesting thermoelectric energy from asphalt pavements. \u003cem\u003eTransportation Research Record\u003c/em\u003e, \u003cem\u003e2628\u003c/em\u003e(1), 12-22.\u003c/p\u003e\n\u003cp\u003eHan, B., Zhang, K., Burnham, T., Kwon, E., \u0026amp; Yu, X. (2012). Integration and road tests of a self-sensing CNT concrete pavement system for traffic detection. \u003cem\u003eSmart Materials and Structures\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(1), 015020.\u003c/p\u003e\n\u003cp\u003eLiu, X., Liu, W., Wu, S., \u0026amp; Wang, C. (2014). Effect of carbon fillers on electrical and road properties of conductive asphalt materials. \u003cem\u003eConstruction and Building Materials\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e, 301-306.\u003c/p\u003e\n\u003cp\u003eXiang, T., Huang, K., Zhang, H., Zhang, Y., Zhang, Y., \u0026amp; Zhou, Y. (2020). Detection of moving load on pavement using piezoelectric sensors.\u0026nbsp;\u003cem\u003eSensors\u003c/em\u003e,\u0026nbsp;\u003cem\u003e20\u003c/em\u003e(8), 2366.\u003c/p\u003e\n\u003cp\u003eShi, X., Huang, J., Yang, Z. (2018). Chapter 18: Pavement Treatments for Sustainable Winter Road Maintenance. In X. Shi, L. Fu (Eds.), Sustainable Winter Road Operations, Wiley-Blackwell, pp. 402-422.\u003c/p\u003e\n\u003cp\u003ePan, P., Wu, S., Xiao, F., Pang, L., \u0026amp; Xiao, Y. (2015). Conductive asphalt concrete: A review on structure design, performance, and practical applications. \u003cem\u003eJournal of Intelligent Material Systems and Structures\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(7), 755-769.\u003c/p\u003e\n\u003cp\u003eZhang, Y, Liu, Z., Shi, X. A Review on Anti-Icing Asphalt Pavement with Salt-Storage Additive. \u003ca href=\"http://www.editorialmanager.com/jrnmteng/\"\u003e\u003cem\u003eASCE Journal of Transportation Engineering, Part B: Pavements\u003c/em\u003e\u003c/a\u003e, 2020, in press.\u003c/p\u003e\n\u003cp\u003eSaleh, N. F., Zalghout, A. A., Sari Ad Din, S. A., Chehab, G. R., \u0026amp; Saad, G. A. (2020). Design, construction, and evaluation of energy-harvesting asphalt pavement systems.\u0026nbsp;\u003cem\u003eRoad Materials and Pavement Design\u003c/em\u003e,\u0026nbsp;\u003cem\u003e21\u003c/em\u003e(6), 1647-1674.\u003c/p\u003e\n\u003cp\u003eJohnsson, J. (2017).\u0026nbsp;\u003cem\u003eWinter Road Maintenance using Renewable Thermal Energy\u003c/em\u003e\u0026nbsp;(Doctoral dissertation, Chalmers Tekniska Hogskola (Sweden)).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYe, Z., Wu, J., Ferradi, N. E., \u0026amp; Shi, X. (2013). Anti-icing for key highway locations: fixed automated spray technology. \u003cem\u003eCanadian Journal of Civil Engineering\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(1), 11-18.\u003c/p\u003e\n\u003cp\u003eVeneziano, D., Muthumani, A., \u0026amp; Shi, X. (2015). Safety effects of fixed automated spray technology systems. \u003cem\u003eTransportation Research Record\u003c/em\u003e, \u003cem\u003e2482\u003c/em\u003e(1), 102-109.\u003c/p\u003e\n\u003cp\u003eTodeschini, I., Di Napoli, C., Pretto, I., Merler, G., Cavaliere, R., Apolloni, R., ... \u0026amp; Benedetti, G. (2016, August). Thermal mapping as a valuable tool for road weather forecast and winter road maintenance: an example from the Italian Alps. In \u003cem\u003eFourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016)\u003c/em\u003e (Vol. 9688, p. 96880H). International Society for Optics and Photonics.\u003c/p\u003e\n\u003cp\u003eHu, Y., Almkvist, E., Gustavsson, T., \u0026amp; Bogren, J. (2019). Modeling Road Surface Temperature from Air Temperature and Geographical Parameters\u0026mdash;Implication for the Application of Floating Car Data in a Road Weather Forecast Model. \u003cem\u003eJournal of applied meteorology and climatology\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(5), 1023-1038.\u003c/p\u003e\n\u003cp\u003eEwan, L., Al-Kaisy, A., \u0026amp; Veneziano, D. (2013, January). Remote Sensing of Weather and Road Surface Conditions: Is Technology Mature for Reliable ITS Applications. In \u003cem\u003eTRB 2013 Annnual Meeting, pp: I-18\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003ePadarthy, M., \u0026amp; Heyns, E. (2019). Identification and classification of slippery winter road conditions using commonly available vehicle variables.\u0026nbsp;\u003cem\u003eTransportation research record\u003c/em\u003e,\u0026nbsp;\u003cem\u003e2673\u003c/em\u003e(2), 60-70.\u003c/p\u003e\n\u003cp\u003eChen, K., Lu, M., Fan, X., Wei, M., \u0026amp; Wu, J. (2011, August). Road condition monitoring using on-board three-axis accelerometer and GPS sensor. In \u003cem\u003e2011 6th International ICST conference on communications and networking in China (CHINACOM)\u003c/em\u003e (pp. 1032-1037). IEEE.\u003c/p\u003e\n\u003cp\u003ePu, Z., Liu, C., Wang, Y., Shi, X., \u0026amp; Zhang, C. (2019). \u003cem\u003eRoad Surface Condition Prediction using Long Short-Term Memory Neural Network based on Historical Data\u003c/em\u003e (No. 19-03118).\u003c/p\u003e\n\u003cp\u003eLinton, M. A., \u0026amp; Fu, L. (2016). Connected vehicle solution for winter road surface condition monitoring. \u003cem\u003eTransportation Research Record\u003c/em\u003e, \u003cem\u003e2551\u003c/em\u003e(1), 62-72.\u003c/p\u003e\n\u003cp\u003eYe, Z., Shi, X., Strong, C. K., \u0026amp; Larson, R. E. (2012). Vehicle-based sensor technologies for winter highway operations. \u003cem\u003eIET Intelligent Transport Systems\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(3), 336-345.\u003c/p\u003e\n\u003cp\u003eSantiago-Chaparro, K. R., Chitturi, M., Szymkowski, T., \u0026amp; Noyce, D. A. (2012). Evaluation of performance of automatic vehicle location and TowPlow for winter maintenance operations in Wisconsin. \u003cem\u003eTransportation research record\u003c/em\u003e, \u003cem\u003e2272\u003c/em\u003e(1), 136-143.\u003c/p\u003e\n\u003cp\u003eAlbrecht, C., Patterson, R., \u0026amp; Sturges, L. (2018). Weather Services for Sustainable Winter Road Operations. \u003cem\u003eSustainable Winter Road Operations\u003c/em\u003e, \u003cem\u003e2318\u003c/em\u003e, 58.\u003c/p\u003e\n\u003cp\u003eTessier, R. (2016).\u0026nbsp;\u003cem\u003eEvaluation of Portable Road Weather Information Systems\u003c/em\u003e\u0026nbsp;(No. UMTC-16.03). Massachusetts. Dept. of Transportation. Office of Transportation Planning.\u003c/p\u003e\n\u003cp\u003eStrong, C., \u0026amp; Fay, L. (2007). RWIS usage report. \u003cem\u003eAlaska DOT and Public Facilities, Final Report. Division of Program Development, Juneau, AK\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eKwon, T. J., Fu, L., \u0026amp; Melles, S. J. (2017). Location optimization of road weather information system (RWIS) network considering the needs of winter road maintenance and the traveling public. \u003cem\u003eComputer‐Aided Civil and Infrastructure Engineering\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(1), 57-71.\u003c/p\u003e\n\u003cp\u003eBoban, M., Manolakis, K., Ibrahim, M., Bazzi, S., \u0026amp; Xu, W. (2016, October). Design aspects for 5G V2X physical layer. In \u003cem\u003e2016 IEEE Conference on Standards for Communications and Networking (CSCN)\u003c/em\u003e (pp. 1-7). IEEE.\u003c/p\u003e\n\u003cp\u003eRaza, N., Jabbar, S., Han, J., \u0026amp; Han, K. (2018, June). Social vehicle-to-everything (V2X) communication model for intelligent transportation systems based on 5G scenario. In \u003cem\u003eProceedings of the 2nd International Conference on Future Networks and Distributed Systems\u003c/em\u003e (pp. 1-8).\u003c/p\u003e\n\u003cp\u003eShi, W., Cao, J., Zhang, Q., Li, Y., \u0026amp; Xu, L. (2016). Edge computing: Vision and challenges. \u003cem\u003eIEEE internet of things journal\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(5), 637-646.\u003c/p\u003e\n\u003cp\u003eKhan, S. M., Chowdhury, M., Morris, E. A., \u0026amp; Deka, L. (2019). Synergizing Roadway Infrastructure Investment with Digital Infrastructure for Infrastructure-Based Connected Vehicle Applications: Review of Current Status and Future Directions. \u003cem\u003eJournal of Infrastructure Systems\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(4), 03119001.\u003c/p\u003e\n\u003cp\u003eChowdhury, M., Rahman, M., Rayamajhi, A., Khan, S. M., Islam, M., Khan, Z., \u0026amp; Martin, J. (2018). Lessons learned from the real-world deployment of a connected vehicle testbed. \u003cem\u003eTransportation Research Record\u003c/em\u003e, \u003cem\u003e2672\u003c/em\u003e(22), 10-23.\u003c/p\u003e\n\u003cp\u003eWang, S., \u0026amp; Li, Z. (2019). Roadside sensing information enabled horizontal curve crash avoidance system based on connected and autonomous vehicle technology.\u0026nbsp;\u003cem\u003eTransportation research record\u003c/em\u003e,\u0026nbsp;\u003cem\u003e2673\u003c/em\u003e(5), 49-60.\u003c/p\u003e\n\u003cp\u003eTheriot, M., Osman, O. A., Ishak, S., Alecsandru, C., Mousa, S. R., \u0026amp; Bakhit, P. R. (2017).\u0026nbsp;\u003cem\u003eImpact of Connected-Vehicle Market Penetration on the Effectiveness of Blind Spot Warning Applications: A Driving Simulation Study\u003c/em\u003e\u0026nbsp;(No. 17-04930).\u003c/p\u003e\n\u003cp\u003eHowe, G., Xu, G., Hoover, D., Elsasser, D., \u0026amp; Barickman, F. (2016).\u0026nbsp;\u003cem\u003eCommercial connected vehicle test procedure development and test results\u0026ndash;blind spot warning/lane change warning\u003c/em\u003e\u0026nbsp;(No. DOT HS 812 317).\u003c/p\u003e\n\u003cp\u003eLiu, H., Wei, H., Zuo, T., Li, Z., \u0026amp; Yang, Y. J. (2017). Fine-tuning ADAS algorithm parameters for optimizing traffic safety and mobility in connected vehicle environment.\u0026nbsp;\u003cem\u003eTransportation research part C: emerging technologies\u003c/em\u003e,\u0026nbsp;\u003cem\u003e76\u003c/em\u003e, 132-149.\u003c/p\u003e\n\u003cp\u003eHe, X., \u0026amp; Zeng, D. (2017, November). Real-time pedestrian warning system on highway using deep learning methods. In\u0026nbsp;\u003cem\u003e2017 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)\u003c/em\u003e\u0026nbsp;(pp. 701-706). IEEE.\u003c/p\u003e\n\u003cp\u003eAbboud, K., Omar, H. A., \u0026amp; Zhuang, W. (2016). Interworking of DSRC and cellular network technologies for V2X communications: A survey. \u003cem\u003eIEEE transactions on vehicular technology\u003c/em\u003e, \u003cem\u003e65\u003c/em\u003e(12), 9457-9470.\u003c/p\u003e\n\u003cp\u003eDey, K. C., Rayamajhi, A., Chowdhury, M., Bhavsar, P., \u0026amp; Martin, J. (2016). Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication in a heterogeneous wireless network\u0026ndash;Performance evaluation. \u003cem\u003eTransportation Research Part C: Emerging Technologies\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e, 168-184.\u003c/p\u003e\n\u003cp\u003eJenkins, M., Duggan, D., \u0026amp; Negri, A. (2017, March). Towards a connected bicycle to communicate with vehicles and infrastructure: Multimodel alerting interface with Networked Short-Range Transmissions (MAIN-ST). In \u003cem\u003e2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)\u003c/em\u003e (pp. 1-3). IEEE.\u003c/p\u003e\n\u003cp\u003eGungor, O. E., \u0026amp; Al-Qadi, I. L. (2020). All for one: Centralized optimization of truck platoons to improve roadway infrastructure sustainability. \u003cem\u003eTransportation Research Part C: Emerging Technologies\u003c/em\u003e, \u003cem\u003e114\u003c/em\u003e, 84-98.\u003c/p\u003e\n\u003cp\u003eChang, J. (2017). \u003cem\u003eAn overview of USDOT connected vehicle roadside unit research activities\u003c/em\u003e (No. FHWA-JPO-17-433). United States. Dept. of Transportation. ITS Joint Program Office.\u003c/p\u003e\n\u003cp\u003eNOCoE (2020). \u0026ldquo;Implementation Guide: SPAT Challenge.\u0026rdquo; National Operations Center of Excellence. Accessed on May 20, 2020. [Online]. Available: \u003ca href=\"https://www.transportationops.org/spatchallenge/resources/Implementation-Guide\"\u003ehttps://www.transportationops.org/spatchallenge/resources/Implementation-Guide\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eIteris (2019). \u0026ldquo;Applications\u0026rdquo;. Connected Vehicle Reference Implementation Architecture. Accessed on May 20, 2019. [Online]. Available: \u003ca href=\"https://local.iteris.com/cvria/html/applications/applications.html\"\u003ehttps://local.iteris.com/cvria/html/applications/applications.html\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eAkin, M., He, Y., \u0026amp; Shi, X. (2018).\u0026nbsp;The Use of Connected Vehicle Technology to Facilitate Multimodal Winter Travel (Phase I)\u0026nbsp;(No. Project ID: 2017 Project 09). Final Report for the Center for Advanced Multimodal Mobility Solutions and Education, Charlotte, NC.\u003c/p\u003e\n\u003cp\u003eLi, H., Wolf, J. C., Mathew, J. K., Navali, N., Zehr, S. D., Hardin, B. L., \u0026amp; Bullock, D. M. (2020). Leveraging Connected Vehicles to Provide Enhanced Roadway Condition Information.\u0026nbsp;\u003cem\u003eJournal of Transportation Engineering, Part A: Systems\u003c/em\u003e,\u0026nbsp;\u003cem\u003e146\u003c/em\u003e(8), 04020073.\u003c/p\u003e\n\u003cp\u003eGopalakrishna, D., Serulle, N. U., Kitchener, F., Garrett, K., \u0026amp; Newton, D. (2016).\u0026nbsp;Guidelines for deploying connected vehicle-enabled weather responsive traffic management strategies\u0026nbsp;(No. FHWA-JPO-17-478). United States. Federal Highway Administration.\u003c/p\u003e\n\u003cp\u003eBarbaresso, J.C.; Johnson, P. (2014). Connected Vehicle Infrastructure Deployment Considerations: Lessons Learned from the Safety Pilot Model Deployment; Report Contract No. DTFH61-11-C-00040; HNTB Corporation, for University of Michigan Transportation Research Institute: Ann Arbor, MI, USA, 30 May 2014; 49p.\u003c/p\u003e\n\u003cp\u003eYoung, R. K., Welch, B. M., \u0026amp; Siems-Anderson, A. R. (2019).\u0026nbsp;\u003cem\u003eGenerating Weather Alerts Including High Wind Blowover Hazards Using Pikalert\u0026reg; for the Wyoming Connected Vehicle Pilot Project\u003c/em\u003e\u0026nbsp;(No. 19-02065).\u003c/p\u003e\n\u003cp\u003eKhazraeian, S. (2017). \"Methods for Utilizing Connected Vehicle Data in Support of Traffic Bottleneck Management\".\u0026nbsp;\u003cem\u003eFIU Electronic Theses and Dissertations\u003c/em\u003e. 3496. Dissertation for Civil Engineering, Florida International University. \u003ca href=\"https://digitalcommons.fiu.edu/etd/3496\"\u003ehttps://digitalcommons.fiu.edu/etd/3496\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eHead, L. (2016, June). The Multi Modal Intelligent Traffic Signal System (MMITSS): A Connected Vehicle Dynamic Mobility Application. In\u0026nbsp;\u003cem\u003eMid Year Meeting, Traffic Signal Systems Committee, Transportation Research Board https://i95coalition. org/wp-content/uploads/2016/03/Head. MMITSS. I-95.06\u003c/em\u003e\u0026nbsp;(Vol. 20).\u003c/p\u003e\n\u003cp\u003eYao, Z., Jiang, Y., Zhao, B., Luo, X., \u0026amp; Peng, B. (2020). A dynamic optimization method for adaptive signal control in a connected vehicle environment.\u0026nbsp;\u003cem\u003eJournal of Intelligent Transportation Systems\u003c/em\u003e,\u0026nbsp;\u003cem\u003e24\u003c/em\u003e(2), 184-200.\u003c/p\u003e\n\u003cp\u003eHuang, L., Zhai, C., Wang, H., Zhang, R., Qiu, Z., \u0026amp; Wu, J. (2020). Cooperative Adaptive Cruise Control and exhaust emission evaluation under heterogeneous connected vehicle network environment in urban city.\u0026nbsp;\u003cem\u003eJournal of Environmental Management\u003c/em\u003e,\u0026nbsp;\u003cem\u003e256\u003c/em\u003e, 109975.\u003c/p\u003e\n\u003cp\u003eGenders, W., \u0026amp; Razavi, S. N. (2016). Impact of connected vehicle on work zone network safety through dynamic route guidance.\u0026nbsp;Journal of Computing in Civil Engineering,\u0026nbsp;30(2), 04015020.\u003c/p\u003e\n\u003cp\u003eFHWA (2020). \"ITS ePrimer\", developed for the ITS Professional Capacity Building Program. [Online]. Accessed on Sept. 1, 2020. \u003ca href=\"https://www.pcb.its.dot.gov/eprimer/module13.aspx\"\u003ehttps://www.pcb.its.dot.gov/eprimer/module13.aspx\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eLi, H., Peters, L., Banuelos, C., Zaugg, J., Sharma, A., \u0026amp; Bullock, D. M. (2019). Leveraging Snow Plow Dashboards Cams and Connected Vehicle Speed Data to Improve Winter Operations Performance Measures.\u003c/p\u003e\n\u003cp\u003eLi, X., Medal, H., \u0026amp; Qu, X. (2019). Connected infrastructure location design under additive service utilities. \u003cem\u003eTransportation Research Part B: Methodological\u003c/em\u003e, \u003cem\u003e120\u003c/em\u003e, 99-124.\u003c/p\u003e\n\u003cp\u003eBalakrishna, C. (2012, September). Enabling technologies for smart city services and applications. In \u003cem\u003e2012 sixth international conference on next generation mobile applications, services and technologies\u003c/em\u003e (pp. 223-227). IEEE.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-infrastructure-preservation-and-resilience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jipr","sideBox":"Learn more about [Journal of Infrastructure Preservation and Resilience](https://jipr.springeropen.com)","snPcode":"43065","submissionUrl":"https://submission.nature.com/new-submission/43065/3","title":"Journal of Infrastructure Preservation and Resilience","twitterHandle":"@SpringerEng","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Connected infrastructure, Intelligent infrastructure, Winter weather, Resilience, Road maintenance operations, Sensing, Energy harvesting","lastPublishedDoi":"10.21203/rs.3.rs-31618/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-31618/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Currently, there is an urgent demand for more cost-effective, resource-efficient and reliable solutions to address safety and mobility challenges on highways enduring snowy winter weather. To address this pressing issue, this commentary proposes that the physical and digital infrastructures should be upgraded to take advantage of emerging technologies and facilitate the vehicle-infrastructure integration (VII), to better inform decision-makers at various levels. Driven by the paradigm shift towards more automation and more intelligent transportation, it is time to reimagine the vehicle-infrastructure ecosystem with the cold-climate issues in mind, and to enhance communications and coordination among various highway users and stakeholders. This commentary envisages the deployment of vehicle-to-everything (V2X) technologies to bring about transformative changes and substantial benefits in terms of enhanced winter safety and mobility on highways. At the center of the commentary is a conceptualized design of next-generation highways in cold climates, including the existing infrastructure entities that are appropriate for possible upgrade to connected infrastructure (CI) applications, to leverage the immensely expanded data availability fueled by better spatial and temporal coverage. The commentary also advances the idea that CI solutions can augment the sensing capabilities and confidence level of connected or autonomous vehicles. The application scenarios of VII system is then briefly explored, followed by some discussion of the paradigm shift towards V2X applications and a look to the future including some identified research needs in the arena of CI. This work aims to inspire dialogues and synergistic collaborations among various stakeholders of the VII revolution, because the specific challenges call for systematic, holistic, and multidisciplinary approaches accompanied by concerted efforts in the research, development, pilot testing, and deployment of CI technologies.","manuscriptTitle":"More Than Smart Pavements: Connected Infrastructure Paves the Way for Enhanced Winter Safety and Mobility on Highways","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2020-10-16 09:50:36","doi":"10.21203/rs.3.rs-31618/v2","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accept","date":"2020-11-05T00:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2020-10-07T12:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-10-06T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-10-06T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-infrastructure-preservation-and-resilience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jipr","sideBox":"Learn more about [Journal of Infrastructure Preservation and Resilience](https://jipr.springeropen.com)","snPcode":"43065","submissionUrl":"https://submission.nature.com/new-submission/43065/3","title":"Journal of Infrastructure Preservation and Resilience","twitterHandle":"@SpringerEng","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2020-06-09 17:53:34","doi":"10.21203/rs.3.rs-31618/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2020-09-10T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-08-28T12:00:00+00:00","index":2,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"reviewerAgreed","content":"","date":"2020-08-12T12:00:00+00:00","index":2,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-08-06T12:00:00+00:00","index":1,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's policy.\n"},{"type":"reviewersInvited","content":"","date":"2020-07-15T12:00:00+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2020-07-15T12:00:00+00:00","index":1,"fulltext":""},{"type":"editorInvited","content":"","date":"2020-06-04T12:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2020-06-04T12:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-06-03T12:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"","date":"2020-05-27T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-infrastructure-preservation-and-resilience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jipr","sideBox":"Learn more about [Journal of Infrastructure Preservation and Resilience](https://jipr.springeropen.com)","snPcode":"43065","submissionUrl":"https://submission.nature.com/new-submission/43065/3","title":"Journal of Infrastructure Preservation and Resilience","twitterHandle":"@SpringerEng","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d8c10ff4-5e66-4c01-ae07-5a3681bd49a9","owner":[],"postedDate":"October 16th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":116551,"name":"Environmental Engineering"}],"tags":[],"updatedAt":"2020-11-22T15:02:10+00:00","versionOfRecord":{"articleIdentity":"rs-31618","link":"https://doi.org/10.1186/s43065-020-00014-x","journal":{"identity":"journal-of-infrastructure-preservation-and-resilience","isVorOnly":false,"title":"Journal of Infrastructure Preservation and Resilience"},"publishedOn":"2020-11-19 15:01:46","publishedOnDateReadable":"November 19th, 2020"},"versionCreatedAt":"2020-10-16 09:50:36","video":"","vorDoi":"10.1186/s43065-020-00014-x","vorDoiUrl":"https://doi.org/10.1186/s43065-020-00014-x","workflowStages":[]},"version":"v2","identity":"rs-31618","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-31618","identity":"rs-31618","version":["v2"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}