Forestry best management practices (BMPs) reduce sedimentation by minimizing soil erosion and trapping sediment. These practices are particularly important in relation to road construction and use ...due to the heightened potential for sediment delivery at stream crossings. This study quantifies the implementation and effectiveness of BMPs at 75 randomly selected forest road stream crossings on recent timber harvests in the Mountains, Piedmont, and Coastal Plain regions of Virginia. Road characteristics at stream crossings were used to estimate erosion using the Universal Soil Loss Equation for Forests and the Water Erosion Prediction Project for Roads. Stream crossings were evaluated based on the Virginia Department of Forestry (VDOF) BMP manual guidelines and categorized as BMP−, BMP‐standard, or BMP+ based on the quality of road template, drainage, ground cover, and stream crossing structure. BMP implementation scores were calculated for each stream crossing using VDOF audit questions. Potential erosion effects due to upgrading crossings were estimated by adjusting ground cover percentage and approach length parameters in the erosion models. Results indicate that erosion rates decrease as BMP implementation scores increase (p < 0.05). BMP‐standard and BMP+ ratings made up 83% of crossings sampled, with an average erosion rate of 6.8 Mg/ha/yr. Hypothetical improvements beyond standard BMP recommendations provided minimal additional erosion prevention.
Research Impact Statement: Erosion rates at haul road stream crossings decreased as the implementation of water quality best management practices increased. Most crossings were found to be at recommended BMP levels or above.
There are hundreds of millions of kilometers of paved roads and many people live in proximity. Pollution from road transportation is a well-documented problem potentially leading to chronic health ...impacts. However, research on the raw material production, construction, operation, maintenance, and end-of-life phases of paved roads, and corresponding supply chains, is generally limited to energy consumption and greenhouse gas emissions. No previous research efforts on the life-cycle stages of pavements and road operation connect pollutant emission inventories to intake of inhaled pollutants and resulting damages to exposed populations. We have developed a first-of-its-kind model quantifying human exposure to fine particulate matter (PM2.5) due to emissions from routine pavement resurfacing and vehicle operation. We utilize the Intervention Model Pollution Source-Receptor Matrix to calculate marginal changes in ground-level PM2.5 concentrations and resulting exposure intake from a spatially resolved primary and secondary PM2.5 emission precursors inventory. Under a scenario of annual road-resurfacing practices within the San Francisco Bay Area in California (population: 7.5 million), resurfacing activities, material production and delivery (i.e. cement, concrete, aggregate, asphalt, bitumen), and fuel (i.e. gasoline, diesel) supply chains contribute almost 65% to the annual PM2.5 intake from all the sources included in the study domain (the remaining 35% being due to on-road tailpipe emissions). Exposure damages range from $170 to $190 million (2019 USD). Complete electrification of on-road mobile sources would reduce annual intake by 64%, but a sizable portion would remain from material supply chains, construction activities, and brake and tire wear. Future mitigation policies should be enacted equitably. Results show that people of color experience higher-than-average PM2.5 exposure disparities from the emission sources included in the study, particularly from material production.
Adverse weather conditions for roads, which cause transportation systems to perform far below capacity, can severely affect society's economic output. As elimination of road weather events is not ...possible, transportation agencies perform proactive and reactive maintenance activities to minimize adverse impacts to keep roadways in optimum condition. While reactive maintenance activities are conducted to clear roadways after the occurrence of extreme weather events, proactive activities minimize these impacts beforehand. The success of proactive activities solely depends on the availability of accurate road weather information, however. Traditional road weather forecasting techniques rely on governmental weather services, which are not appropriate to predict route-specific road weather conditions. In this paper, the authors reviewed current intelligent transportation systems (ITS)-based solutions for minimizing road weather impacts and possible ITS innovations to incorporate diverse data sources to improve road weather management activities. ITS-based initiatives, such as road weather information system (RWIS), allow transportation agencies obtain accurate road weather assessments. Location-specific infrastructures such as RWIS are cost prohibitive for system-wide deployments. Connected vehicles equipped with weather sensors could enhance mobile road weather data collection. This strategy could improve proactive maintenance programs and reduce adverse effects of weather to the surface transportation system.
Understanding climate change impacts on winter road systems in Ontario’s Far North is critical due to the high dependence on such seasonal corridors by local residences, particularly among remote ...First Nations communities. In recent years, a warmer climate has resulted in a shorter winter road season and an increase in unreliable road conditions, thus limiting access among remote communities. This study focused on examining the future freezing degree day (FDD) accumulations during the preconditioning period of the winter roads at five locations using the multi-model ensembles of general circulation models (GCMs) and regional climate models (RCMs), under the representative concentration pathway (RCP) scenarios. The Statistical DownScaling Model Decision Centric Version 5 (SDSM-DC) was applied to validate the baseline climate. The results from the CMIP5 showed that by mid-century, the trends of FDDs under RCP4.5 for Moosonee and Kapuskasing were projected to decrease below the lowest threshold with the mean FDDs at 376 and 363, respectively. Under RCP8.5, the mean FDDs for Lansdowne House and Red Lake were projected to be below the lowest threshold, at 356 and 305, respectively, by the end of the century. Results of the FDD threshold measure indicated that climate conditions would possibly be unfavorable during the winter road construction period by mid-century for Moosonee and Kapuskasing and for Lansdowne House and Red Lake by the end of the century. For Big Trout Lake, on the other hand, climate conditions are expected to remain favorable for the winter road construction through the end of 2100.
The timely and proper rehabilitation of damaged roads is essential for road maintenance, and an effective method to detect road surface distress with high efficiency and low cost is urgently needed. ...Meanwhile, unmanned aerial vehicles (UAVs), with the advantages of high flexibility, low cost, and easy maneuverability, are a new fascinating choice for road condition monitoring. In this paper, road images from UAV oblique photogrammetry are used to reconstruct road three-dimensional (3D) models, from which road pavement distress is automatically detected and the corresponding dimensions are extracted using the developed algorithm. Compared with a field survey, the detection result presents a high precision with an error of around 1 cm in the height dimension for most cases, demonstrating the potential of the proposed method for future engineering practice.
Many studies have examined different factors contributing to the injury severity of crashes; however, relatively few studies have focused on the crashes by considering the specific effects of ...lighting conditions. This research investigates lighting condition differences in the injury severity of crashes using 3-year (2009–2011) crash data of two-lane rural roads of the state of Washington.
Separate ordered-probit models were developed to predict the effects of a set of factors expected to influence injury severity in three lighting conditions; daylight, dark, and dark with street lights. A series of likelihood ratio tests were conducted to determine if these lighting condition models were justified.
The modeling results suggest that injury severity in specific lighting conditions are associated with contributing factors in different ways, and that such differences cannot be uncovered by focusing merely on one aggregate model. Key differences include crash location, speed limit, shoulder width, driver action, and three collision types (head-on, rear-end, and right-side impact collisions).
This paper highlights the importance of deploying street lights at and near intersections (or access points) on two-lane rural roads because injury severity highly increases when crashes occur at these points in dark conditions.
•We have modeled injury severity based on different lighting conditions.•Injury severity in specific lighting conditions is associated with contributing factors in different ways which such differences cannot be uncovered by focusing merely on one aggregate model.•Speed limit is significantly associated with increased levels of injury severity in daylight conditions, but not in darkness.•Injury severity highly increases when crashes occur at intersections or access points in darkness, but not in a sufficient condition.
Roads have numerous negative impacts for mammals, but may also serve as attractants due to altered vegetation or provisioning of resources.
We reviewed the use of roads and their associated features ...by mammals, in order to understand the ecological factors contributing to road use.
We documented 129 studies that recorded road use by 116 mammalian species, spanning 15 orders and 35 families, in six continents. Carnivora was the most common order (40 species, 34% of all 116 species), followed by Artiodactyla (24 species, 20%) and Rodentia (21 species, 18%). The species were placed in the IUCN categories Least Concern (88 species, 76% of all 116 species), Vulnerable (11 species, 9%), Near Threatened (9 species, 8%), Endangered (6 species, 5%), and Critically Endangered (2 species, 2%).
We assigned road use to five ecological categories, reflecting the reason for it, with subcategories where appropriate: (1) communication; (2) foraging (subcategories: anthropogenic food, herbivory, predation, salt, scavenging, water); (3) movement (subcategories: bridges, environmental alterations, habitat connectivity); (4) refuge (subcategories: avoidance, burrowing and denning, cover, roosting); and (5) thermoregulation. Foraging, movement, and refuge were the most common uses.
Roads provide a variety of resources to mammals, but road use is highly dynamic in time and space. We suggest that the use of roads by mammals is extensive, both geographically and taxonomically. Road use is likely to influence mammalian ecology while contributing to the risk of collisions with vehicles.
We reviewed the ecological factors that contribute to use of roads by mammals. We documented 129 studies that recorded road use by 116 mammalian species, spanning 15 orders and 35 families, in six continents. Reasons for road use included communication, foraging, movement, refuge and thermoregulation. We suggest that use of roads by mammals is geographically and taxonomically widespread, and likely influences mammalian ecology while contributing to risk of vehicle collisions.
Traffic forecasts are employed in the toll road sector, inter alia, by private sector investors to gauge the bankability of candidate investment projects. Although much is written in the literature ...about the theory and practice of traffic forecasting, surprisingly little attention has been paid to the predictive accuracy of traffic forecasting models. This paper addresses that shortcoming by reporting the results from the largest study of toll road forecasting performance ever conducted. The author had access to commercial-in-confidence documentation released to project financiers and, over a 4-year period, compiled a database of predicted and actual traffic usage for over 100 international, privately financed toll road projects. The findings suggest that toll road traffic forecasts are characterised by large errors and considerable optimism bias. As a result, financial engineers need to ensure that transaction structuring remains flexible and retains liquidity such that material departures from traffic expectations can be accommodated.
This publication presents a three-part road classification system that utilises the vehicle's onboard signals of two-wheeled vehicles. First, a curve estimator was developed to identify and classify ...road curves. In addition, the curve estimator continuously classifies the road curviness. Second, the road slope was evaluated to determine the hilliness of a given road. Third, a modular road profile estimator has been developed to classify the road profile according to ISO 8608, which utilises the vehicle's transfer functions. The road profile estimator continuously classifies the driven road. The proposed methods for the classification of curviness, hilliness, and road roughness have been validated with measurements. The road classification system enables the collection of vehicle-independent field data of two-wheeled vehicles. The road properties are part of the customer usage profiles which are essential to define vehicle design targets.
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