The Global Navigation Satellite System—Real-Time Network (GNSS-RTN) is a satellite-based positioning system using a network of ground receivers (also called continuously operating reference stations (CORSs)) and a central processing center that provides highly accurate location services to the users in real-time over a broader geographic region. Such systems can provide geospatial location data with centimeter-level accuracy anywhere within the network. Geospatial location services are not only used in measuring ground distances and mapping topography; they have also become vital in many other fields such as aerospace, aviation, natural disaster management, and agriculture, to name but a few. The innovative and multi-disciplinary applications of geospatial data drive technological advancement towards precise and accurate location services available in real-time. Although GNSS-RTN technology is currently utilized in a few industries such as precision farming, construction industry, and land surveying, the implications of precise real-time location services would be far-reaching and more critical to many advanced transportation applications. The GNSS-RTN technology is promising in meeting the needs of automation in most advanced transportation applications. This article presents an overview of the GNSS-RTN technology, its current applications in transportation-related fields, and a perspective on the future use of this technology in advanced transportation applications.

Two-lane highways refer to roadways consisting of two lanes in the cross section, one for each direction of travel. Occasionally, passing lanes may be added to one or two sides of the roadway extending the cross section to three or four lanes at those locations. In this entry, two-lane highways strictly refer to roads in rural areas meeting the previous definition and do not include urban and suburban streets.

Road safety is considered one of the fundamental factors for sustainable mobility, which requires establishing effective highway safety management programs and processes. Identifying safety improvement sites (network screening) is a critical step in the state roadway safety programs. The overall effectiveness of these programs largely relies on the robustness of the network screening method in identifying sites with high potential for safety improvements. This study investigates the network screening performance of a new proposed methodology that employs heuristic scoring schemes to account for roadway and roadside characteristics, crash history, and traffic exposure. The new method, originally proposed for use on rural low-volume roads, is simplistic in that it does not require exact and detailed information on traffic and geometric characteristics and can still be applied in the absence of crash data. The performance evaluation is conducted using a sample of 1495 miles of rural two-lane highway segments in Oregon. The effectiveness of the proposed method is assessed using observed crash history and compared to the well-established Empirical Bayes method as a reference. The effect of traffic level on the performance of the proposed method was evaluated using separate analyses for lower and higher traffic volume segments. The study findings indicate that the effectiveness of the proposed method in identifying sites with potential safety improvements is overall high and slightly exceeded that of the more sophisticated and data-intensive Empirical Bayes method. When data were stratified by traffic volume, the proposed method was found to be more effective for lower volume roads; however, the Empirical Bayes method outperformed the proposed method for higher volume roads.

Understanding the influence of intersections on operating conditions along connecting roadway segments is important for the analysis of highway facilities and corridors. This study aims at assessing the influence area at signalized and stop-control intersections along rural corridors. The study used speed as a performance measure in examining the spatial extent of operational effects at intersections. High-fidelity connected vehicle (CV) trajectory data, collected at 11 different sites in Florida, was used in this study. The CV trajectory data consists of individual waypoints that included timestamps and location coordinates along with other attributes. Drivers’ speed profiles while driving through the intersection were established and analyzed to determine the length of upstream and downstream influence areas. Quantile regression models were developed to estimate the 50th, 70th, and 85th percentiles of upstream and downstream influence areas separately for signalized and stop-control intersections. Study results indicate that the upstream influence area is longer for a signalized intersection than for a stop-control intersection for comparable segment running speeds. Further, the downstream influence area is significantly longer than the upstream influence area at signalized intersections, and this was consistent at all study sites. Traffic flow level did not have a significant effect on the upstream or downstream influence area; however, midblock running speed, percent heavy vehicles, and facility type (multilane versus two-lane) were found to significantly affect the upstream and downstream influence areas at signalized intersections.

Weather presents considerable challenges to highway agencies both in terms of safety and operations. From a safety standpoint, snow, ice and other forms of precipitation may reduce pavement friction, increasing the potential for crashes when vehicles are traveling too fast for the conditions. From an operations standpoint, heavy snow storms may affect the connectivity of the highway network due to closures that need to be cleared in an efficient and timely fashion. Further, travelers should be informed about unusual pavement conditions and road closures on time to minimize the effect of adverse weather on the safety and mobility of the traveling public. For the aforementioned reasons, road weather information has become increasingly important for highway agencies particularly in regions that experience harsh winter weather conditions. Road Weather Information System (RWIS) consists of the hardware, software, and communications interfaces to collect and transfer road weather observations from or near the roadway to a display device at the user’s location (1). Today, most environmental sensor stations (ESS) for RWIS include various atmospheric sensors, some form of pavement sensor, and camera imaging. Additional sensors are also being added to some ESS locations to measure traffic volumes, traffic speeds, and vehicle classifications and weights (2). Road weather information has been used by highway agencies in many applications such as winter maintenance, traveler information, and other weather-related intelligent transportation systems (ITS) applications. Data adequacy, reliability, and geographic coverage are critical attributes to consider for these transportation applications. Highway agencies are often faced with the challenge of selecting a limited number of ESS sites from a larger pool of proposed sites given the limited budgets available. This process has been largely subjective in practice, relying on the expertise and judgement of the agency staff involved. Therefore, there is a need for an objective prioritization scheme for proposed ESS stations, which should guide future RWIS expansion and ensure maximum utility (benefits) from new ESS installations.