The canyon country of southern Utah and northern Arizona—a celebrated desert of rock and sand punctuated by gorges and mesas—is a region hotly contested among vying and disparate interests, from ...industrial developers to wilderness preservation advocates. Roads are central to the conflicts raging in an area perceived as one of the last large roadless places in the continental United States. The canyon country in fact contains an extensive network of dirt trails and roads, many originally constructed under the authority of a one-sentence statute in an 1866 mining law, later known as R.S. 2477. While well-groomed and paved roads came to signify the industrialization of the modern age, twentiethcentury conservationists have regarded roads as intrusive human imprints on the nation’s wild lands. Roads connect rural communities, spur economic growth, and in some cases blend harmoniously into the landscape, but they also fracture and divide, disturb wildlife and habitat, facilitate industrial development, and spoil wilderness.
Rogers reflects on the meaning of roads amid environmental conflicts that continue to grip the canyon country. Transporting readers from road controversies like the infamous Burr Trail battle to the contentious web of roads in Grand Staircase–Escalante National Monument to off-roading in Arch Canyon, Rogers demonstrates how the conflicts are deeply rooted in history and culture. The first permanent Anglo-American settlers in the region were Mormon pioneers and current views about land and resource use in southern Utah often derive from stories about how those pioneer ancestors defied wilderness to found their communities in the desert. Roads in the Wilderness will be of interest to environmentalists, historians, and those who live in the American West, challenging readers to think about the canyon country and the stories embedded in the land.
Forest road pavement needs an evaluation methodology based on a comprehensive assessment of road conditions. This research was conducted to evaluate the performance of a method for rating the surface ...condition of forest roads and eventually to adapt the method to the situation prevailing in a forest road network. The rating method selected as the basis for this experiment was the pavement condition index (PCI) developed by the U.S. Army Corps of Engineers for urban roads. In addition, unpaved road condition index (URCI) that has a good index for unpaved road evaluation used for comparison. A 53 km of forest roads were selected containing the most influential factors and variability of conditions. Eventually, 201 road segments were delineated between 150–300 m in length. Within the given segments, sample plots were set 20 m in length consecutively. It was concluded that the panel scores for distress and surface condition of sample unit and section differed from the forest road pavement condition index (FRPCI), URCI, and PCI. Linear regression was used to derive equations between distress and URCI and PCI scores to determine effective FRPCI parameters that provide a numerical rating for the condition of road segments within the road network, where 0 worlds are the worst possible condition, and 100 is the best possible condition best. In addition, regression analysis showed that the FRPCI model with a 0.77 correlation for the total of the road is a performance index used for the first time in forest roads. This study showed a range of FRPCI from 7.8 to 96.3, different from PCI and URCI ratings (0.85–45 and 1.2–53). The FRPCI index helps forest managers in road maintenance, harvesting, and planning to use road information.
Rural roads play a crucial role in fostering economic and social development in Africa. Local Road Authorities (LRAs) struggle to collect road condition data using conventional means due to ...logistical and resource issues. Poor road conditions and restricted mobility have severe economic consequences for the transport of goods and services. Lack of maintenance can increase costs three-fold. In this work, a novel framework is proposed in which earth observations using high-resolution optical satellite imagery are applied to measure the condition of unpaved roads, providing a vital input to maintenance planning and prioritisation. A trial was conducted using this method on 83 roads in Tanzania totalling 131.7 km. The experimental results demonstrate that, by analysing variations in pixel intensity of the road surface, the condition can be estimated with an accuracy of 71.9% when compared to ground truth information. Machine Learning techniques are applied to the same network to test the performance of the system in predicting road conditions. A blended classifier approach achieves an accuracy of 88%. The proposed framework enables LRAs to define the information they receive based on their specific priorities, offering a rapid, objective, consistent and potentially cost-effective system that overcomes the current challenges faced by LRAs.
Humans have long fantasized about self-driving vehicles for the sake of luxury, style, safety, and ease. Free road space detection for collision avoidance and path planning is a vital part of ...autonomous driving vehicles. Despite many researchers focusing on free road space detection, it remains an open and challenging problem for real-world applications. Many studies have attempted to fuse depth and LiDAR features with visual features to improve the overall performance of free road space detection. However, there is no guideline on how such features should be fused to complement the visual features. Additionally, most of the previously proposed methods are computationally expensive and not suitable for real-life applications. The main motivation of this study is to realize a lightweight model that addresses these problems without compromising performance. As the LiDAR and visual features exist in different spaces, the proposed method attempts to learn various transformation and fusion operations from LiDAR features to complement the visual features. To validate the performance of the proposed method, we conduct comprehensive experiments on prominent benchmark datasets. The results of the experiments reveal the superior performance of the proposed model while being lightweight. LRDNet ranks third overall (with a minor difference) and second among LiDAR-based methods on the KITTI road benchmark dataset. Furthermore, the proposed model is the least computationally expensive among state-of-the-art methods and can be considered an optimal trade-off between speed and accuracy.
•A material stock model based on remote sensing image was built for regional urban road system.•An integrated MFA-LCA method to quantify the material stocks and environmental impacts of regional ...urban road system.•Massive macadam flowed into urban road system and contributed to enormous environmental impacts.•The configuration of road network in Nanjing is generally reasonable, however the arterial road is over-emphasized.
Road construction area expands rapidly in China which challenges the conservations of natural resources. While available studies largely focused on consumptions at project level, this study integrated material flow analysis and life cycle assessment methods to quantify the material stocks and environmental impacts of urban road system. Remote sensing images were processed to extract road network skeletons. Taking Nanjing as an example, the results show that: 1) arterial road stores the largest amount of materials (42.9 million tons) of entire road system; 2) macadam occupies nearly 80% of total material stocks; 3) increase of road class coincides with the increase of material stock per unit area and enhances the material utilization efficiency; 4) arterial road is over-emphasized and expressway is still of a small scale in Nanjing. The methodology developed in this study provides a useful tool for road planning to effectively control resource depletion in the long term.
This study proposes an effective trajectory planning algorithm based on the quartic Bézier curve and dangerous potential field for automatic vehicles. To generate collision-free trajectories, ...potential field functions are introduced to evaluate the collision risk of path candidates. However, many studies on artificial potential field approaches primarily focus on static and straight roads, and attach less importance to more complex driving scenarios, such as curving roads. In this study, a novel method based on the Frenet coordinate system is proposed to address such limitations. Moreover, to balance the driving comfortability and the driving safety of the path candidate, the path-planning problem is converted to an optimisation problem, and sequential quadratic programming algorithm is employed to tackle this task. Another merit of this algorithm is the curvature of the generated path is continuous even at the joints of adjacent sub-trajectories by utilising several specific properties of the Bézier curve. Furthermore, to execute the generated trajectory, a framework of velocity generation is proposed while vehicle dynamic constraints are considered. Some typical traffic scenarios, including lane-changing, lane-keeping, and collision avoidance have been designed to verify the performance of the proposed algorithm, and simulations demonstrate the validity of this method.
•Distractions in the road environment put pedestrian at risk when crossing the road.•Pedestrian’s visual attention is affected by the façade of the street.•Younger children are at higher risk when ...distracted.•Visual distractions are more detrimental than auditory distractions.
Pedestrians are subject to an increasing number of stimuli and distractions derived from the roadside environment. Although the effect of distractions on child road crossing ability was recognized, there has been no systematic exploration of the effects of roadside distractions on child road crossing behavior. This work was aimed at studying the effect of roadside distractions on pedestrian road crossing behavior, focusing on elementary school-aged children, who are less capable of making a safe road crossing decision and are more vulnerable to the effect of distractions. Three types of audio distractions (a. sudden, momentary, and prominent noise, b. multiplicity of auditory elements, and c. continuous loud noise) and similar three types of visual distractions were pre-defined. Fifty-two children (aged 7–13) and adults arrived at the dome virtual reality laboratory and viewed 20 simulated crossing scenarios, embedded with visual and auditory distractions, and decided on the appropriate time to start crossing the virtual road. The results demonstrate that when exposed to environmental distractions, participants chose smaller crossing gaps, took more time to make crossing decisions, were slower to respond to the crossing opportunity, and allocated less visual attention to the peripheral regions of the road. Those effects were age related, and affected younger participants more significantly. Furthermore, visual distractions affected pedestrian behavior more than auditory type distractions. This study highlights an issue not yet adequately addressed, and the results should be considered by transportation professionals, and road safety educators, so better road safety programs to educate children can be created.
To assess spatial heterogeneity in geographic data, geographically weighted regression (GWR) has been widely used. This study used an advanced version of GWR, multiscale geographically weighted ...regression (MGWR), which provides a unique extension that allows each predictor to be associated with a distinct bandwidth in predicting traffic fatalities in Texas. Traffic data from fatality analysis reporting system (FARS) between 2010 and 2015, aggregated at the census tract level (N = 5265), were used to examine different scales at which selected economic variables explain the traffic road fatality rate per 100,000 population. Twelve economic variables were initially selected and reduced to four factors (ride-sharing to work, driving alone, mean travel time to work, and work commuting) using the varimax rotation technique. The spatial pattern of the four factors in the GWR model differs significantly from MGWR in spatial patterns, signs, and values relative to the traffic fatality rate. The diagnostic results showed that traditional GWR over fitted the predictors compared to MGWR (max. condition number in GWR = 28.3 versus MGWR = 9.6; adjusted R
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GWR = 61.8% versus MGWR = 44.5%). The application of the MGWR technique is a robust technique in ensuring the correct process scale or bandwidth in modeling spatial data such as road traffic fatality for place and scale-specific intervention purposes. We discussed the three levels of scale identified in the MGWR model for traffic planning intervention and policymaking. Lastly, we concluded with how MGWR mitigates the common problem of aggregated data such as MAUP.
•An on-road experiment involving 20 users was conducted on a low traffic rural road.•Six days of testing were used in order to investigate into speed changes over time.•Results show that route ...familiarity is related to increases in speed over time.•Driver factors highly influence speed increases, while visibility is less related.•Drivers’ familiarity should be considered in practical matters of road engineering.
Differences in driving behavior due to the presence of users familiar (or unfamiliar) with the road are considered in the road and traffic engineering. However, although considered, the matter is largely unexplored: there is a lack of theoretical foundations and data on determining the impact of route familiarity on accident rates, speed choice and risk perception. On the other hand, some literature studies confirm that route familiarity is influential on driving behavior, encouraging research in this sense.
This paper reports the results of an on-road test carried out on a two lane rural road in the District of Bari in the Puglia Region (Italy) over six days of testing by following this time schedule: first four tests in four consecutive days, the fifth test in the ninth day after the first test and the sixth test in the twenty-sixth day after the first test. The main aim of the experiment was to find relationships between route familiarity and speed choice. In particular, speed data were analyzed by considering the influence of road geometry and human factors.
The main finding is that speed choice seems to be affected by route familiarity: speed increases with the repetition of travels on the same route. The particular schedule used for the tests allows to consider the influence of memory on the speed behavior of the test drivers. Moreover, some relationships between changes in speed over days, road geometry and drivers’ attitudes were shown.
Safety issues for road users arising from road infrastructure - different treatment of rail and road - road users responsible for road safety - road authorities' control over roads - application of ...workplace health and safety legislation to protect third parties from work-related harm - definition of key terms in the Health and Safety at Work Act 2015 - suggested drafting changes to the Act.
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