Automatic video analysis from urban surveillance cameras is a fast-emerging field based on computer vision techniques. We present here a comprehensive review of the state-of-the-art computer vision ...for traffic video with a critical analysis and an outlook to future research directions. This field is of increasing relevance for intelligent transport systems (ITSs). The decreasing hardware cost and, therefore, the increasing deployment of cameras have opened a wide application field for video analytics. Several monitoring objectives such as congestion, traffic rule violation, and vehicle interaction can be targeted using cameras that were typically originally installed for human operators. Systems for the detection and classification of vehicles on highways have successfully been using classical visual surveillance techniques such as background estimation and motion tracking for some time. The urban domain is more challenging with respect to traffic density, lower camera angles that lead to a high degree of occlusion, and the variety of road users. Methods from object categorization and 3-D modeling have inspired more advanced techniques to tackle these challenges. There is no commonly used data set or benchmark challenge, which makes the direct comparison of the proposed algorithms difficult. In addition, evaluation under challenging weather conditions (e.g., rain, fog, and darkness) would be desirable but is rarely performed. Future work should be directed toward robust combined detectors and classifiers for all road users, with a focus on realistic conditions during evaluation.
Performance-based contracts (PBC) have been adopted increasingly in various countries. However, establishing an objective and convincing benefit evaluation framework to facilitate promotion remains a ...significant challenge for those who wish to use PBCs. A recent study introduced PBC in the context of daily inspection and repair of urban roads. The pilot program in that study successfully addressed the needs of road users, road maintenance authorities, and contractors. Consequently, this study develops the benefit evaluation framework for this type of PBC for broader applications. The proposed benefit evaluation framework, consisting of one qualitative and eight quantitative performanceevaluation items, calculates the effectiveness of introducing PBC to verify its practicality. The validation results show that qualitative benefit evaluation using the Pavement Condition Index achieves a “satisfactory” rating. For the quantitative benefit evaluation, an overall benefit-cost ratio (BCR) of 5.35 suggests that it is economically viable. The developed benefit evaluation framework can be used as a simple and easy-to-use evaluation tool for other organizations to assess whether to introduce PBC. This study fills this research gap by addressing the use of PBCs in the maintenance of all categories of toll-free roads, and contributes to expanding the application of PBCs in road maintenance services.
Road vector data are an important part of geographic information databases and play a leading role in social and economic development. Among them, the accuracy and current situation of vector data ...are the key values of their application. In recent years, with the development of remote sensing technology, remote sensing images contain increasingly more road information, which provides a large number of available features for the establishment of vector correction models. Therefore, the vector-to-image vector correction method based on remote sensing images has become an important means to ensure the accuracy and current situation of vector data. However, in actual scenes, there are great differences in the design structure and design index between urban roads and rural roads, which is reflected in the serious spatial heterogeneity between them in the remote sensing images. Therefore, when the existing methods use remote sensing images for vector correction, the universality is limited by the change of regional scenes, so it is difficult to realize the simultaneous correction of urban road and rural road vectors, with poor applicability and low compatibility. To solve this problem, this paper proposes a vector data partition correction method supported by deep learning. First, the U
2
-net model and line segment sequence method are used to generate the feature set. Second, according to the characteristics of the poor quality of urban road extraction results, the regular geometric form of vector data and the poor structural information of road images, the methods of vector line decomposition, subvector line correction, subvector line centralization and vector line synthesis are proposed to correct the vector lines of urban roads. Finally, according to the characteristics of high-quality rural road extraction, the irregular geometric form of vector data and the strong information of road image structures, this paper proposes road edge extraction, a road centreline reasoning model and a vector line connection method to complete the correction of rural vector road data. The quantitative analysis of the experimental results shows that compared with other methods, the urban road vector correction method in this paper is much better than the comparison methods and has achieved better correction results in rural areas. This method has the advantages of compatibility and better accuracy for roads in different areas.
•Damage in bananas was influenced by package position\ height in the road train.•Rear position of the rear trailer exhibited the highest mechanical damage levels.•Top-tier cartons in each ...pallet-position revealed the worst damage in bananas.•Vibration transmissibility in the top-tiers amplified between 3–20 Hz.
Mechanical damage induced by vibration is a known cause of quality deterioration and wastage of fresh produce in post-harvest supply chains. The need to minimize visual defects in fruits, such as bananas, is being driven by the growing consumer preference for high quality produce. Transport of produce interstate and internationally from the growing regions to major retail markets, however, increases the risk of exposure of fruits to injurious vibration excitation. This study measured the vibration, and consequential mechanical damage, to bananas stacked at different stack heights and positions in a multi-trailer road train during an interstate road transport of over 3000 km in distance. Significantly different damage levels in bananas were observed in different pallet positions of the road train with the highest damage propensity revealed at the most rear pallet position. The damage levels in each pallet position were found to closely correspond with the Root-Mean-Square (RMS) acceleration of the vibration excitation on the trailer floor. The highest energy Power-Spectral Density (PSD) peaks were revealed to be concentrated in the lower frequency range (0.1–5 Hz). The cartons stacked in the top tiers of each pallet showed significantly increased mechanical damage followed by the bottom tiers with the middle-tiers exhibiting minimal damage. Palletized banana cartons subjected to simulated vibration, on a laboratory vibration simulator, demonstrated that vibration in the high frequency range (>30 Hz) was attenuated with the height of the carton in the pallet. However, the transmissibility of vibration energy in the range of 3–20 Hz was the greatest in the top-tier cartons, resulting in excessive mechanical damage to the bananas. The characterization of damage to bananas at different stack positions\heights of multi-trailer road trains is an integral step for the development of damage reduction mechanisms. These would require the design, optimization, and simulation testing of better packaging alternatives targeted at minimizing the occurrence of mechanical injury in-transit.
•We apply loss aversion to renegotiation in BOT road projects.•We present conditions under which renegotiation takes place.•We examine the initial contract in anticipation of ex post ...renegotiation.•We reexamine the model results under some relaxed assumptions.•Some managerial implications are derived from model results.
In BOT road project, the government offers a firm an ex ante contract, which specifies toll price and concession period based on the forecasted demand. When the demand states are observed in the operation period, the government may request renegotiation to adapt the initial contract to the realized demand state. By considering the loss aversion behavior of the private firm, this paper shows that renegotiation takes place only if the private firm’s extent of loss aversion is sufficiently small. However, in what direction the government adjusts toll price and concession period depends on the combined effects of initial price, demand level, and demand uncertainty in each demand state. This paper has further investigated the optimal initial contract. We find that if one demand state realizes with a sufficiently large probability, then the optimal initial contract is renegotiation-proof in this demand state while inducing renegotiation in other demand states; if all demand states realize with almost equal probabilities, whether the optimal initial contract prevents or induces renegotiations in all demand states depends on the private firm’s extent of loss aversion. This paper makes two major contributions to the literature. First, we apply loss aversion to the context of renegotiation in BOT road projects and show that renegotiation is costly. Second, we consider the optimal initial contract in anticipation of ex post renegotiation and show that the government should trade off between ex ante social welfare and ex post psychological loss. To obtain more insights and to strengthen our model results, we have reexamined the optimal renegotiation and initial contracts under some relaxed assumptions.
•Transport networks underpin economic cities competiveness and society functioning.•During flooding transport infrastructure can be directly or indirectly damaged.•This paper reviewed modelling ...studies of the impacts of weather on transport.•The paper derived a new empirical function to relate flood depth and vehicle speed.•The function move forwards from the binary consideration of flood roads.•The function can be incorporated into flood risk analysis and transport appraisal.
Transport networks underpin economic activity by enabling the movement of goods and people. During extreme weather events transport infrastructure can be directly or indirectly damaged, posing a threat to human safety, and causing significant disruption and associated economic and social impacts. Flooding, especially as a result of intense precipitation, is the predominant cause of weather-related disruption to the transport sector. Existing approaches to assess the disruptive impact of flooding on road transport fail to capture the interactions between floodwater and the transport system, typically assuming a road is fully operational or fully blocked, which is not supported by observations. In this paper we develop a relationship between depth of standing water and vehicle speed. The function that describes this relationship has been constructed by fitting a curve to video analysis supplemented by a range of quantitative data that has be extracted from existing studies and other safety literature. The proposed relationship is a good fit to the observed data, with an R-squared of 0.95. The significance of this work is that it is simple to incorporate our function into existing transport models to produce better estimates of flood induced delays and we demonstrate this with an example from the 28th June 2012 flood in Newcastle upon Tyne, UK.
Biodiversity in Latin America is at risk today due to habitat loss, land conversion to agriculture and urbanization. To grow their economies the developing countries of Latin America have begun to ...invest heavily in new road construction. An assessment of research on the impacts of roads on wildlife in Latin America will help define science-based conservation strategies aimed at mitigating road expansion. The aim of this review was to qualitatively and quantitatively assess scientific research papers addressing road impacts on vertebrate species in Latin America. We searched for scientific papers published as early as 1990 to 2017. We reviewed a total of 197 papers. Published research showed an increasing trend in the last decade with a strong geographic bias with a majority of papers from Brazil. Mammals were the most studied taxa followed by birds, reptiles and amphibians. The majority of studies focused on road mortality and at the individual species level. Studies documented an increase in deforestation rates, in land conversion to agriculture, illegal activities (hunting, logging) and the establishment of human settlements. The effects of roads on species richness and populations abundance varied among taxa with no apparent pattern within taxa. Forest-dependent species tended to avoid crossing roads. Amphibians had the highest median road-kill rate, followed by reptiles, mammals and birds. Our results suggest that there is an urgent need for more research, particularly in Central America and to employ predictive tools for difficult-to-sample or understudied species and critical conservation areas. We recommend a two-speed approach to guide future research: one focusing on quantifying individual species responses towards roads and their implications on population viability; a second consisting of regional or continental-scale analyses and modelling of road risks to species and populations to inform road planning immediately.
•Planned roads in Latin America are a threat to biodiversity conservation.•Published research has grown in the last decade with a geographic bias for Brazil.•Studies are focused on road mortality, especially on medium-large mammals.•There is a need for more research, particularly on the impacts of roads at level of genes and populations.•Quantifying individual species responses towards roads and predictive analyses are needed to inform road mitigation
BRAZIL ROAD-KILL Grilo, Clara; Coimbra, Michely R.; Cerqueira, Rafaela C. ...
Ecology (Durham),
11/2018, Volume:
99, Issue:
11
Journal Article
Peer reviewed
Open access
Mortality from collision with vehicles is the most visible impact of road traffic on wildlife. Mortality due to roads (hereafter road-kill) can affect the dynamic of populations of many species and ...can, therefore, increase the risk of local decline or extinction. This is especially true in Brazil, where plans for road network upgrading and expansion overlaps biodiversity hotspot areas, which are of high importance for global conservation. Researchers, conservationists and road planners face the challenge to define a national strategy for road mitigation and wildlife conservation. The main goal of this dataset is a compilation of geo-referenced road-kill data from published and unpublished road surveys. This is the first Data Paper in the BRAZIL series (see ATLANTIC, NEOTROPICAL, and BRAZIL collections of Data Papers published in Ecology), which aims make public road-kill data for species in the Brazilian Regions. The dataset encompasses road-kill records from 45 personal communications and 26 studies published in peer-reviewed journals, theses and reports. The road-kill dataset comprises 21,512 records, 83% of which are identified to the species level (n = 450 species). The dataset includes records of 31 amphibian species, 90 reptile species, 229 bird species, and 99 mammal species. One species is classified as Endangered, eight as Vulnerable and twelve as Near Threatened. The species with the highest number of records are: Didelphis albiventris (n = 1,549), Volatinia jacarina (n = 1,238), Cerdocyon thous (n = 1,135), Helicops infrataeniatus (n = 802), and Rhinella icterica (n = 692). Most of the records came from southern Brazil. However, observations of the road-kill incidence for non-Least Concern species are more spread across the country. This dataset can be used to identify which taxa seems to be vulnerable to traffic, analyze temporal and spatial patterns of road-kill at local, regional and national scales and also used to understand the effects of road-kill on population persistence. It may also contribute to studies that aims to understand the influence of landscape and environmental influences on road-kills, improve our knowledge on road-related strategies on biodiversity conservation and be used as complementary information on large-scale and macroecological studies. No copyright or proprietary restrictions are associated with the use of this data set other than citation of this Data Paper.
Road extraction from high-resolution remote sensing images has been an important research problem for decades. Despite the breakthrough progress of road extraction studies in recent years due to the ...rapid advancement of deep learning techniques in a remote sensing domain, the vast majority of methods pay little attention to the basic structure of roads. Especially in complex scenes such as a tree or shadow occlusion and stacking of multiple roads, existing road extraction methods still suffer from generating broken road surfaces, inaccurate topology, and connections. In this work, we propose a novel structure-aware road extraction method, named RoadCorrector, which solves the above limitations of existing methods by adding structure-related assistance branches and two correction modules. Specifically, RoadCorrector encompasses three main stages: road segmentation, connectivity refinement, and topology correction. First, we design a multibranch road extraction network (MBRE-Net) that combines intersection and frequency domain information to efficiently extract road masks. Subsequently, a connectivity refinement strategy based on energy function is introduced to deal with the discontinuity of roads in the occluded and intersection regions. Finally, the topology correction module aims at constructing vectorized road networks with more accurate connection relations. Experimental results on several public datasets show that our RoadCorrector achieves remarkable improvements compared with state-of-the-art methods, with the <inline-formula> <tex-math notation="LaTeX">F1 </tex-math></inline-formula>-score and intersection over union (IoU) improved by 3.3%-4.5% and 2.0%-5.1%, respectively. Moreover, the road network extraction results of RoadCorrector have more accurate topological properties, demonstrating its great potential in actual application scenes. The code and dataset of RoadCorrector will be released at https://github.com/Lijp411/RoadCorrector .
Ethnic favoritism is seen as antithetical to development. This paper provides credible quantification of the extent of ethnic favoritism using data on road building in Kenyan districts across the ...1963-2011 period. Guided by a model, it then examines whether the transition in and out of democracy under the same president constrains or exacerbates ethnic favoritism. Across the post-independence period, we find strong evidence of ethnic favoritism: districts that share the ethnicity of the president receive twice as much expenditure on roads and have five times the length of paved roads built. This favoritism disappears during periods of democracy.
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