A Clouded Leopard in the Middle of the Road is an eye-opening introduction to the ecological impacts of roads. Drawing on over ten years of active engagement in the field of road ecology, Darryl ...Jones sheds light on the challenges roads pose to wildlife—and the solutions taken to address them. One of the most ubiquitous indicators of human activity, roads typically promise development and prosperity. Yet they carry with them the threat of disruption to both human and animal lives. Jones surveys the myriad, innovative ways stakeholders across the world have sought to reduce animal- vehicle collisions and minimize road-crossing risks for wildlife, including efforts undertaken at the famed fauna overpasses of Banff National Park, the Singapore Eco-Link, "tunnels of love" in the Australian Alps, and others. Along the way, he acquaints readers with concepts and research in road ecology, describing the field's origins and future directions. Engaging and accessible, A Clouded Leopard in the Middle of the Road brings to the foreground an often-overlooked facet of humanity's footprint on earth.
El presente escrito presenta una reflexión sobre el ciclismo de ruta como un fenómeno sociocultural de la realidad colombiana, a partir de tres interrogantes: ¿Es el ciclismo de ruta una práctica ...corporal?, ¿Es el ciclismo de ruta una práctica social?, y ¿Es el ciclismo de ruta una expresión?, los cuales se resuelven procesualmente para dar respuesta al interrogante central: ¿El ciclismo de ruta es un constitutivo de la realidad sociocultural colombiana? Para su desarrollo, se toma como base la discusión en torno al ciclismo como práctica corporal, práctica social y expresión, aspectos que hacen de este deporte un constitutivo de la realidad sociocultural colombiana. Se concluye que el ciclismo de ruta en Colombia es una construcción de la realidad de quienes se involucran con él, y a su vez, es constitutivo de la historia reciente de su sociedad, condición que hace posible su posicionamiento en la conciencia de los colombianos.
A challenge still to be overcome in the field of visual perception for vehicle and robotic navigation on heavily damaged and unpaved roads is the task of reliable path and obstacle detection. The ...vast majority of the researches have scenario roads in good condition, from developed countries. These works cope with few situations of variation on the road surface and even fewer situations presenting surface damages. In this paper we present an approach for road detection considering variation in surface types, identifying paved and unpaved surfaces and also detecting damage and other information on other road surfaces that may be relevant to driving safety. Our approach makes use of Convolutional Neural Networks (CNN) to perform semantic segmentation, we use the U-NET architecture with ResNet34, in addition we use the technique known as Transfer Learning, where we first train a CNN model without using weights in the classes as a basis for a second CNN model where we use weights for each class. We also present a new Ground Truth with image segmentation, used in our approach and that allowed us to evaluate our results. Our results show that it is possible to use passive vision for these purposes, even using images captured with low cost cameras.
Barrier effect is a road‐related impact affecting several animal populations. It can be caused by behavioural responses towards roads (surface and/or gap avoidance), associated emissions ...(traffic‐emissions avoidance) and/or circulating vehicles (vehicle avoidance). Most studies so far have described road‐effect zones along major roads, without determining the actual factor inducing the behavioural response. The purpose of the present study was to assess the factors potentially causing road‐effect zones in a heterogeneous road network (with variations in road width, road surface and traffic volume) and eventually to estimate the reduction of habitat quality imposed by roads within a protected area (Doñana Biosphere Reserve, Spain). As model species, we used two ungulates, red deer Cervus elaphus and wild boar Sus scrofa. We surveyed the presence of both species along 200‐m transects. All transects started and were perpendicular to reference roads (those with a traffic volume above 10 cars per day), often intersecting unpaved minor roads with virtually no traffic. The presence probability of both species was mainly affected by the distance to the nearest road (in most cases unpaved roads without traffic), but also by the proximity to reference roads. Red deer presence was also affected by the traffic volume of the nearest reference road. At a regional scale, the overall road network within the protected area imposes a reduction in presence probability of 40% for red deer and 55% for wild boar. A road network optimization, decommissioning unused and unpaved roads, would re‐establish almost entirely the potential habitat quality (91% for both species). Synthesis and applications. We found that both study species avoided roads regardless of their surface or traffic volume, suggesting a response due to gap avoidance which may be based on the association between linear infrastructures and the possibility of vehicles occurring along them. The overall behavioural response can substantially decrease habitat quality over large scales, including the conservation value of protected areas. For this reason, we recommend road network optimization by road decommissioning to mitigate the impact of roads at a regional scale, with potential positive effects at ecosystem level.
•A novel road description using stochastic models in a hierarchical structure.•A method for how to generate data possible to simulate in a dynamic model.•Hilliness and stop intensity are the most ...prominent properties in the chosen range.•The statistical method is compared with real log data and the results match well.•Ideas discussed on how to make realistic predictions of CO2 emissions from vehicles.
We propose a novel statistical description of the physical properties of road transport operations by using stochastic models arranged in a hierarchical structure. The description includes speed signs, stops, speed bumps, curvature, topography, road roughness and ground type, with a road type introduced at the top of the hierarchy to group characteristics that are often connected. Methods are described how to generate data on a form (the operating cycle format) that can be used in dynamic simulations to estimate energy usage and CO2 emissions. To showcase the behaviour of the description, two examples are presented using a modular vehicle model for a heavy-duty truck: a sensitivity study on impacts from changes in the environment, and a comparison study on a real goods transport operation with respect to energy usage. It is found that the stop intensity and topography amplitude have the greatest impact in the sensitivity study (8.3% and 9.5% respectively), and the comparison study implies that the statistical description is capable of capturing properties of the road that are significant for vehicular energy usage. Moreover, it is discussed how the statistical description can be used in a vehicle design process, and how the mean CO2 emissions and its variation can be estimated for a vehicle specification.
The development of the modern urban economy is closely tied to road construction, with roads being one of the most crucial components in urban development. The use of high-resolution remote sensing ...images to monitor the road conditions in cities and surrounding towns has become a highly emphasized research focus in recent years. However, due to the complexity of the environment, this task still faces significant challenges. For instance, urban road structures are intricate, which involve multiple lanes, intersections, building obstructions, etc. Rural roads may consist of narrow paths or irregular dirt roads. In addressing these issues, this paper proposes a road extraction algorithm based on a Dual-Encoder-Decoder U- Net (DEDU-Net). The algorithm leverages a dual encoder-decoder network to extract multi-scale information from the image. It then employs a dual decoder network to restore the feature map to the original image, achieving precise road extraction. Additionally, a new module, the Global Fusion Module (GFM), is introduced. This module achieves global context information fusion by weighting features. In the experimental section, two publicly available datasets were used for testing: the CHN6-CUG dataset and the Gansu Mountain Road dataset. For example, for the Gansu Mountain Road and CHN6-CUG mixed dataset, the model achieved an Overall Accuracy (OA) of 94.947% and a mean Intersection over Union (mIoU) of 70.971%. The results indicate that compared to traditional methods, this proposed method exhibits higher accuracy and robustness. It can adapt to both urban and rural roads, delivering outstanding performance even in complex scenarios.
Introduction: Little is known about how characteristics of the environment affect pedestrians’ road crossing behavior. Method: In this work, the effect of typical urban visual clutter created by ...objects and elements in the road proximity (e.g., billboards) on adults and children (aged 9–13) road crossing behavior was examined in a controlled laboratory environment, utilizing virtual reality scenarios projected on a large dome screen. Results: Divided into three levels of visual load, results showed that high visual load affected children’s and adults’ road crossing behavior and visual attention. The main effect on participants’ crossing decisions was seen in missed crossing opportunities. Children and adults missed more opportunities to cross the road when exposed to more cluttered road environments. An interaction with age was found in the dispersion of the visual attention measure. Children, 9–10 and 11–13 years old, had a wider spread of gazes across the scene when the environment was highly loaded—an effect not seen with adults. However, unexpectedly, no other indication of the deterring effect was found in the current study. Still, according to the results, it is reasonable to assume that busier road environments can be more hazardous to adult and child pedestrians. Practical Applications: In that context, it is important to further investigate the possible distracting effect of causal objects in the road environment on pedestrians, and especially children. This knowledge can help to create better safety guideline for children and assist urban planners in creating safer urban environments.
► We study Macroscopic Fundamental Diagrams in networks with traffic signals. ► We simulate these networks using an efficient cellular automata model. ► MFDs depend strongly on the specific control ...strategy of the traffic signals. ► MFDs do exist for biased demand, but their shapes depend on the bias. ► Hysteresis can be clockwise or anticlockwise, depending on heterogeneity.
Using a stochastic cellular automaton model for urban traffic flow, we study and compare Macroscopic Fundamental Diagrams (MFDs) of arterial road networks governed by different types of adaptive traffic signal systems, under various boundary conditions. In particular, we simulate realistic signal systems that include signal linking and adaptive cycle times, and compare their performance against a highly adaptive system of self-organizing traffic signals which is designed to uniformly distribute the network density. We find that for networks with time-independent boundary conditions, well-defined stationary MFDs are observed, whose shape depends on the particular signal system used, and also on the level of heterogeneity in the system. We find that the spatial heterogeneity of both density and flow provide important indicators of network performance. We also study networks with time-dependent boundary conditions, containing morning and afternoon peaks. In this case, intricate hysteresis loops are observed in the MFDs which are strongly correlated with the density heterogeneity. Our results show that the MFD of the self-organizing traffic signals lies above the MFD for the realistic systems, suggesting that by adaptively homogenizing the network density, overall better performance and higher capacity can be achieved.
Secondary roads often suffer from diverted traffic trying to avoid congestion on major motorways. In this paper we study the traffic problems of a small town that is located parallel to a congested ...motorway and suffers from such diverted traffic (high local accident risks, local congestion and other nuisances). We assume that the motorway and the secondary road through the local town are under the jurisdiction of a different authority. A local government controls local accident risks and congestion using non-price measures such as speed bumps, traffic lights and explicit access restrictions for through traffic. A ‘federal’ government can control traffic levels on the motorway using tolls. We show the following results. First, competition between the federal and local authority leads to a Nash equilibrium where the toll is too high and there is too much traffic calming compared to the second-best social optimum. Second, if the local government uses traffic calming measures, imposing a federal toll on the main road is welfare-reducing, unless congestion on the main road is severe and accident risks and other traffic nuisances in the small town are unimportant. Third, traffic diverted from the main road to the local community gives the latter strong incentives to close the local road for through traffic, even when it is socially undesirable to do so. Fourth, if the access restriction only applies to through traffic by trucks, the conflict between federal and local authorities disappears: both will agree on restricting truck access. A numerical application using a two-link network between Leuven and Brussels (the highway and an alternative road passing through local communities) illustrates the theoretical results.
Extracting roads from complex high-resolution remote sensing images to update road networks has become a recent research focus. How to apply the contextual spatial correlation and topological ...structure of the roads properly to improve the extraction accuracy becomes a challenge in the increasingly complex road environment. In this paper, inspired by the prior knowledge of the road shape and the progress in deformable convolution, we proposed a road augmented deformable attention network (RADANet) to learn the long-range dependencies for specific road pixels. We developed a road augmentation module (RAM) to capture the semantic shape information of the road from four strip convolutions. Deformable attention module (DAM) combines the sparse sampling capability of deformable convolution with the spatial self-attention mechanism. The integration of RAM enables DAM to extract road features more specifically. Furthermore, RAM is placed behind the fourth stage of encoder, and DAM is placed between last four stages of encoder and decoder in RADANet to extract multi-scale road semantic information. Comprehensive experiments on representative public datasets (DeepGlobe and CHN6-CUG road datasets) demonstrate that our RADANet achieves advanced results compared with the state-of-the-art methods.
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