•Digitalization of highways using Internet of Things.•Smart highway lighting, smart traffic and emergency management for user safety.•Real-time implementation of renewable energy sources like wind, ...solar and piezoelectric on the highways.•Smart display board, and AI on highways for smart Vulnerable Road User model.
According to United Nations (UN) 2030 agenda, the transportation system needs to be enhanced for the establishment of access to safe, affordable, accessible, and sustainable transport systems along with enhanced road safety. The highway road transport system is one of the transport systems that enables to transits goods and humans from one location to another location. The agenda of UN 2030 for the transport system will be accomplished with the assistance of digital technologies like the internet of things (IoT) and artificial intelligence (AI). The implementation of these digital technologies on highways empowers to provide reliable, smarter, intelligent, and renewable energy sources experience to the users travelling along the highways. This study discusses the significance of the digitalization of highways that supporting and realizing a sustainable environment on the highways. To discuss the significance of digitalization, the study has categorized digitalization into five subcomponents namely smart highway lighting system, smart traffic and emergency management system, renewable energy sources on highways, smart display and AI in highways. An architecture-for smart highway lighting, smart traffic, and emergency management are proposed and discussed in the study. The significance of implementing smart display boards and renewable sources with real-time applications is also addressed in this study. Moreover, the integration of AI in highways is addressed with the perspective of enhancing road safety. The integration of deep learning (DL) in the edge-based vision node for predicting the patterns of traffic flow, highway road safety, and maintenance of quality roads have been addressed in the discussion section. Embedding the deep learning techniques in the vison node at the traffic junction and the highway lighting controller is able to deliver an intelligent system that provides sustained experience and management of the highways. Smart reflectors, adoption of renewable energy, developing vehicle-to-vehicle communication in vehicles, and smart lamppost are the few recommendations for the implementation of digitalizing highways.
Maintenance is a key component of managing a forest road network. Forest road networks in North America are managed to provide economic access to forest resources while minimizing the environmental ...impacts of those roads. While managers understand the importantance of road maintenance, there is a considerable backlog in the maintenance required on most forest road networks. This article reviews challenges across North America in forest road maintenance. Challenges reviewed include those associated with climate change, changing land use and intermingled ownerships, legacy roads, decision support, and financial barriers.
Georeferenced information on road infrastructure is essential for spatial planning, socio-economic assessments and environmental impact analyses. Yet current global road maps are typically outdated ...or characterized by spatial bias in coverage. In the Global Roads Inventory Project we gathered, harmonized and integrated nearly 60 geospatial datasets on road infrastructure into a global roads dataset. The resulting dataset covers 222 countries and includes over 21 million km of roads, which is two to three times the total length in the currently best available country-based global roads datasets. We then related total road length per country to country area, population density, GDP and OECD membership, resulting in a regression model with adjusted R2 of 0.90, and found that that the highest road densities are associated with densely populated and wealthier countries. Applying our regression model to future population densities and GDP estimates from the Shared Socioeconomic Pathway (SSP) scenarios, we obtained a tentative estimate of 3.0-4.7 million km additional road length for the year 2050. Large increases in road length were projected for developing nations in some of the world's last remaining wilderness areas, such as the Amazon, the Congo basin and New Guinea. This highlights the need for accurate spatial road datasets to underpin strategic spatial planning in order to reduce the impacts of roads in remaining pristine ecosystems.
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.
Road traffic accidents are believed to be associated with not only road geometric feature and traffic characteristic, but also weather condition. To address these safety issues, it is of paramount ...importance to understand how these factors affect the occurrences of the crashes. Existing studies have suggested that the mechanisms of single-vehicle (SV) accidents and multivehicle (MV) accidents can be very different. Few studies were conducted to examine the difference of SV and MV accident probability by addressing unobserved heterogeneity at the same time. To investigate the different contributing factors on SV and MV, a mixed logit model is employed using disaggregated data with the response variable categorized as no accidents, SV accidents, and MV accidents. The results indicate that, in addition to speed gap, length of segment, and wet road surfaces which are significant for both SV and MV accidents, most of other variables are significant only for MV accidents. Traffic, road, and surface characteristics are main influence factors of SV and MV accident possibility. Hourly traffic volume, inside shoulder width, and wet road surface are found to produce statistically significant random parameters. Their effects on the possibility of SV and MV accident vary across different road segments.
We attempted a complete review of the empirical literature on effects of roads and traffic on animal abundance and distribution. We found 79 studies, with results for 131 species and 30 species ...groups. Overall, the number of documented negative effects of roads on animal abundance outnumbered the number of positive effects by a factor of 5; 114 responses were negative, 22 were positive, and 56 showed no effect. Amphibians and reptiles tended to show negative effects. Birds showed mainly negative or no effects, with a few positive effects for some small birds and for vultures. Small mammals generally showed either positive effects or no effect, mid-sized mammals showed either negative effects or no effect, and large mammals showed predominantly negative effects. We synthesized this information, along with information on species attributes, to develop a set of predictions of the conditions that lead to negative or positive effects or no effect of roads on animal abundance. Four species types are predicted to respond negatively to roads: (i) species that are attracted to roads and are unable to avoid individual cars; (ii) species with large movement ranges, low reproductive rates, and low natural densities; and (iii and iv) small animals whose populations are not limited by road-affected predators and either (a) avoid habitat near roads due to traffic disturbance or (b) show no avoidance of roads or traffic disturbance and are unable to avoid oncoming cars. Two species types are predicted to respond positively to roads: (i) species that are attracted to roads for an important resource (e.g., food) and are able to avoid oncoming cars, and (ii) species that do not avoid traffic disturbance but do avoid roads, and whose main predators show negative population-level responses to roads. Other conditions lead to weak or non-existent effects of roads and traffic on animal abundance. We identify areas where further research is needed, but we also argue that the evidence for population-level effects of roads and traffic is already strong enough to merit routine consideration of mitigation of these effects in all road construction and maintenance projects.
Carrying out repair works, reconstruction, and construction of new road surfaces is a permanent element of urban space. The quality of the new pavement for the adopted traffic category directly ...impacts the road infrastructure's durability. The choice of road surface structure depends on the adopted traffic category. The aim of the article is to assess the works carried out on selected road surfaces within the city of Płock (Poland) in terms of the technical specification requirements and the durability of road infrastructure. The paper presents the tests of three road layers: base layer, binding layer and wearing course. The tests were carried out on 11 streets, and 29 samples were collected.
This paper argues that the Chinese government's 'belt and road' initiative - the Silk Roads vision of land and maritime logistics and communications networks connecting Asia, Europe and Africa - has ...its roots in sub-national ideas and practices, and that it reflects their elevation to the national level more than the creation of substantially new policy content. Further, the spatial paradigms inherent in the Silk Roads vision reveal the reproduction of capitalist developmental ideas expressed particularly in the form of networks, which themselves have become a feature of contemporary global political economy. In other words, the Silk Roads vision is more of a 'spatial fix' than a geopolitical manoeuvre.
1. The effectiveness of measures installed to mitigate wildlife road-kill depends on their placement along the road. Road-kill hotspots are frequently used to identify priority locations for ...mitigation measures. However, in situations where previous road mortality has reduced population size, road-kill hotspots may not indicate the best sites for mitigation. 2. The purpose of this study was to identify circumstances in which road-kill hotspots are not appropriate indicators for the selection of the best road-kill mitigation sites. We predicted that: (i) road-kill hotspots can move in time from high-traffic road segments to low-traffic segments, due to population depression near the high-traffic segment caused by road mortality; (ii) this shift will occur earlier for more mobile species because they should interact more often with the road; (iii) this shift can occur even if the low-traffic segment runs through lower quality habitat than the high-traffic segment. To test these predictions, we simulated population size and road-kill over time for two populations, one exposed to a road segment with high traffic and the other to a road segment with low traffic. 3. Our simulation results supported Predictions 1 and 3, while Prediction 2 was not supported. 4. Synthesis and applications. Our results indicate that, for new roads, road-kill hotspots can be useful to indicate appropriate sites for mitigation. On older roads, road-kill hotspots may not indicate the best sites for road mitigation due to population depression caused by road mortality. Direct measures of the road impact on the population, such as per capita mortality, are better indicators of appropriate mitigation sites than road-kill hotspots.
Road information extraction based on aerial images is a critical task for many applications, and it has attracted considerable attention from researchers in the field of remote sensing. The problem ...is mainly composed of two subtasks, namely, road detection and centerline extraction. Most of the previous studies rely on multistage-based learning methods to solve the problem. However, these approaches may suffer from the well-known problem of propagation errors. In this paper, we propose a novel deep learning model, recurrent convolution neural network U-Net (RCNN-UNet), to tackle the aforementioned problem. Our proposed RCNN-UNet has three distinct advantages. First, the end-to-end deep learning scheme eliminates the propagation errors. Second, a carefully designed RCNN unit is leveraged to build our deep learning architecture, which can better exploit the spatial context and the rich low-level visual features. Thereby, it alleviates the detection problems caused by noises, occlusions, and complex backgrounds of roads. Third, as the tasks of road detection and centerline extraction are strongly correlated, a multitask learning scheme is designed so that two predictors can be simultaneously trained to improve both effectiveness and efficiency. Extensive experiments were carried out based on two publicly available benchmark data sets, and nine state-of-the-art baselines were used in a comparative evaluation. Our experimental results demonstrate the superiority of the proposed RCNN-UNet model for both the road detection and the centerline extraction tasks.
PDF
