Intercity travel congestion during the main national holidays takes place every year at different places around the world. Charge reduction measurements on existing toll roads have been implemented ...to promote an efficient use of the expressways and to reduce congestion on the public transit networks. However, some of these policies have had negative effects. A more comprehensive understanding of the determinants of holiday intercity travel patterns is critical for better policymaking. This paper aims to investigate the effectiveness of the road toll discount policy on mode choice behavior for intercity travel. A mixed logit model is developed to model the mode choices of intercity travelers, which is estimated based on survey data about intercity journeys from Beijing during the 2017 Chinese Spring Festival holiday. The policy impact is further discussed by elasticity and scenario simulations. The results indicate that the expressway toll discount does increase the car use and decrease the public transit usage. Given the decreased toll on expressways, the demand tends to shift from car to public transit, in an order of coach, high-speed rail, conventional rail, and airplane. When it comes to its effect on socio-demographic groups, men and lower-income travelers are identified to be more likely to change mode in response to variation of road toll. Finally, policy effectiveness is found to vary for travelers in different travel distance groups. Conclusions provide useful insights on road pricing management.
Road weather conditions such as ice, snow, or heavy rain can have a significant impact on driver safety. In this paper, we present an approach to continuously monitor the road conditions in real time ...by equipping a fleet of vehicles with sensors. Based on the observed conditions, a physical road weather model is used to forecast the conditions for the following hours. This can be used to deliver timely warnings to drivers about potentially dangerous road conditions. To optimally process the large data volumes, we show how artificial intelligence is used to (1) calibrate the sensor measurements and (2) to retrieve relevant weather information from camera images. The output of the road weather model is compared to forecasts at road weather station locations to validate the approach.
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 article, 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 multiscale 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.
Detecting the free road surface ahead of a moving vehicle is an important research topic in different areas of computer vision, such as autonomous driving or car collision warning. Current ...vision-based road detection methods are usually based solely on low-level features. Furthermore, they generally assume structured roads, road homogeneity, and uniform lighting conditions, constraining their applicability in real-world scenarios. In this paper, road priors and contextual information are introduced for road detection. First, we propose an algorithm to estimate road priors online using geographical information, providing relevant initial information about the road location. Then, contextual cues, including horizon lines, vanishing points, lane markings, 3-D scene layout, and road geometry, are used in addition to low-level cues derived from the appearance of roads. Finally, a generative model is used to combine these cues and priors, leading to a road detection method that is, to a large degree, robust to varying imaging conditions, road types, and scenarios.
The current review summarizes the knowledge generated by the recently published studies of the last twenty years, in the field of forest road networks, concerning the impact of forest road ...construction on hydrological processes. The currently applied methodology techniques/practices are discussed, the findings are highlighted and effective mitigation measures to mitigate the impact of forest roads are proposed. Critical for the minimization of the impact of forest roads on overland flow is the significant decrease in road surface runoff and overland flow velocity. The decrease in runoff energy reduces the detachment of soil particles and transportation in streams. The disturbances of forest roads in logging areas should be limited to decrease soil erosion. Additionally, aiming to minimize sediment transportation into the streams, it is very important to reduce the connectivity between the forest roads (or skid trails) and streams. The positive role of vegetation and organic matter on the road prism, naturally/technically established riparian buffers along the streams, and the use of appropriate bioengineering designs for each area significantly decrease the runoff generation and sedimentation. From a construction point of view, the decrease in short and long-term forest road-related impact could be achieved by reducing the depth of excavations and the use of soil compaction limiting technology during forest works. The road network design should be more efficient, avoiding hydrologically active zero-order basins. Techniques that minimize the length and connectivity among skid trails, unpaved roads and streams are highly crucial. Broad-based dips, immediate revegetation and outsloping of the road base are considered good road construction practices. Research should be focused on the hydrologic behavior of forest road networks and on the impact at the watershed scale, the degree of connectivity, utilizing plenty of qualitative field data, especially during intense rainfall events, which has been proven to exacerbate the runoff and sediment generation and transportation into the stream networks.
Estimating crash prediction models and applying the Empirical Bayesian approach in identifying hotspots for roads under municipal jurisdiction is often challenging due to the lack of traffic count ...data. This study presents five hotspot identification (HSID) methods in which annual average daily traffic (AADT) information is not required (i.e., crash frequency CF, equivalent property damage only, relative severity index, excess predicted average crash frequency using method of moments MOM, and cross sectional analysis CSA), to identify hotspots for road segments under municipal jurisdiction in Connecticut. The segments were categorized into 11 homogenous groups based on the roadway geometric characteristics. The five HSID methods were applied to all segments in each roadway group separately and across the entire State for a systemic analysis. Four quantitative tests (i.e., site consistency test, method consistency test, total rank difference test, and total score test) were used to compare the performance of the five HSID methods. The results indicate that the MOM outperforms others in identifying hotspots for urban one-way arterials, urban one-way local roads, urban two-lane two-way local roads, urban multilane two-way arterials, and urban multilane two-way collectors; the CF outperforms others for rural arterials and collectors, rural local roads, urban one-way collectors, urban two-lane two-way arterials, urban two-lane two-way collectors and urban multilane two-way local roads, and the CSA performs best in all of the five HSID methods in identifying and ranking the roadway hotspots for all roadway groups together.
Response of Moose to a High-Density Road Network WATTLES, DAVID W.; ZELLER, KATHERINE A.; DESTEFANO, STEPHEN
The Journal of wildlife management,
07/2018, Letnik:
82, Številka:
5
Journal Article
Recenzirano
Road networks and the disturbance associated with vehicle traffic alter animal behavior, movements, and habitat selection. The response of moose (Alces americanus) to roads has been documented in ...relatively rural areas, but less is known about moose response to roads in more highly roaded landscapes. We examined road-crossing frequencies and habitat use of global positioning system (GPS)-collared moose in Massachusetts, USA, where moose home ranges have road densities approximately twice that of previous studies. We compared seasonal road-crossing frequencies of moose with a null movement model. We estimated moose travel speeds during road-crossing events and compared them with speeds during other home range movements. To estimate the extent of the road effect zone and determine how roads influenced moose habitat use, we fit a third-order resource selection function. With the exception of the lowest use road class (<10 vehicles/day), we found moose crossed roads less than expected based on the null movement model and frequency decreased with increasing road size and traffic. Moose crossed roads faster than they traveled during other times. This effect increased with increasing road use intensity. Overall, roads were a major factor determining what portions of Massachusetts moose used and how they moved among habitat patches. Our results suggest that moose in Massachusetts can adapt to a high-density road network, but the road effect is still strongly negative and, in some cases, is more pronounced than in study areas with lower road densities. Future road construction and the expansion of road networks may have a large effect on moose and other wildlife.
•A road-capable piezoelectric harvester (RCPH) was demonstrated under an actual road.•Optimal power/depth characteristics were determined with finite element simulations.•The 1 cm landfilled module ...(1LFM) produces 1150 mWmax and 1.15 W/cm2.•The proposed system can generate electricity to drive road-emergency-lighting systems.
We demonstrate the use of a road-capable piezoelectric harvester (RCPH) with improved maintenance and power-generation characteristics. The RCPH obtained its ingress and moisture protection system rating (IP 66) through a new housing system used to evaluate its waterproof performance. Finite element simulations were performed to identify the proper depth (1, 3, or 5 cm) under an actual road for its placement to achieve increased output power. The highest von Mises stress value was measured by the 1 cm landfilled module (1LFM). The RCPH was installed under a test road and was tested with the use of the exposed and landfilled method to compare output power levels. Correspondingly, the output voltage and output power of the 1LFM were higher the exposed module. When a minivan drove over the 1LFM at 90 km/h, an output voltage of 18 Vmax and an output power of 1150 mWmax (power density: 1.15 mW/cm2) were measured at a load resistance level of 910 Ω. In a test road environment, the electrical energy generated by the 1LFM was sufficient to illuminate four delineators for 40 s. This system could be used on actual roads by connecting the piezoelectric modules to an emergency lighting that can be powered by the electricity generated by the module.
•Policy decision: Parliament approval can grant legitimacy to a Vision Zero policy.•Policy problem: All visions, except fire safety, state that failures in system design causes accidents.•Policy ...goal: All goal formulations are similar except suicide that stands out as more complex.•Policy measures: Physically coherent areas use engineering while care-related areas use soft measures.•Policy variation: The policy variation depends on context, such as resources, support and anchoring.
The Vision Zero policy was adopted by the Swedish parliament in 1997 as a new direction for road traffic safety. The aim of the policy is that no one should be killed or seriously injured due to traffic accidents and that the design of the road transport system should be adapted to those requirements. Vision Zero has been described as a policy innovation with a focus on the tolerance of the human body to kinetic energy and that the responsibility for road safety falls on the system designers. In Sweden, the Vision Zero terminology has spread to other safety-related areas, such as fire safety, patient safety, workplace safety and suicide. The purpose of this article is to analyze, through a comparative content analysis, each Vision Zero policy by identifying the policy decision, policy problem, policy goal, and policy measures. How a policy is designed and formulated has a direct effect on implementation and outcome. The similarities and differences between the policies give an indication of the transfer method in each case. The results show that the Vision Zero policies following the Vision Zero for road traffic contain more than merely a similar terminology, but also that the ideas incorporated in Vision Zero are not grounded within each policy area as one would expect. The study shows that it is easier to imitate formulations in a seemingly successful policy and harder to transform Vision Zero into a workable tool in each policy area.
Automatically extracting roads from very high-resolution (VHR) remote sensing images is of great importance in a wide range of remote sensing applications. However, complex shapes of roads (i.e., ...long, geometrically deformed, and thin) always affected the extraction accuracy, which is one of the challenges of road extraction. Based on the insight into road shape characteristics, we propose a novel road shape-aware network (RSANet) to achieve efficient and accurate road extraction. First, we introduce the efficient strip transformer module (ESTM) to efficiently capture the global context to model the long-distance dependence required by long roads. Second, we design a geometric deformation estimation module (GDEM) to adaptively extract the context from the shape deformation caused by shooting roads from different perspectives. Third, we provide a simple but effective road edge focal loss (REF loss) to make the network focus on optimizing the pixels around the road to alleviate the unbalanced distribution of foreground and background pixels caused by the roads being too thin. Finally, we conduct extensive evaluations on public datasets to verify the effectiveness of RSANet and each of the proposed components. Experiments validate that our RSANet outperforms state-of-the-art methods for road extraction in remote sensing images.
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