Dense Road Network Research Articles

Spatial heterogeneity in health risks of illness-related absenteeism associated with PM2.5 exposure for elementary students

Little is known about the impact of socio-economic and environmental factors on the associations between PM2.5 exposure and health risk for elementary students. We estimated the space variability of effects of PM2.5 on daily illness-related absence rate for 2278 elementary schools from 97 counties across Jiangsu Province with data collected in the 2016-17 academic year. We evaluated the effects at school- and county-scales and examined the role of socio-economic and environmental factors with generalized addictive models (GAM). With an interquartile range (IQR, 32 μg/m3) increase in PM2.5 concentration, the relative risk of absence rate for a given school ranged between 1.00 and 2.81. Factors including high economic development level, low health expenditure, dense road network, dense population and low vegetation coverage drove strong effects for schools/counties. For the implementation of efficient clean air policies and public health interventions, we should concern about not only high-polluted areas but also areas under specific socio-economic and environmental conditions.

Comparing and Contrasting the Impacts of Macro-Level Factors on Crash Duration and Frequency.

Road traffic crashes cause social, economic, physical and emotional losses. They also reduce operating speed and road capacity and increase delays, unreliability, and productivity losses. Previous crash duration research has concentrated on individual crashes, with the contributing elements extracted directly from the incident description and records. As a result, the explanatory variables were more regional, and the effects of broader macro-level factors were not investigated. This is in contrast to crash frequency studies, which normally collect explanatory factors at a macro-level. This study explores the impact of various factors and the consistency of their effects on vehicle crash duration and frequency at a macro-level. Along with the demographic, vehicle utilisation, environmental, and responder variables, street network features such as connectedness, density, and hierarchy were added as covariates. The dataset contains over 95,000 vehicle crash records over 4.5 years in Greater Sydney, Australia. Following a dimension reduction of independent variables, a hazard-based model was estimated for crash duration, and a Negative Binomial model was estimated for frequency. Unobserved heterogeneity was accounted for by latent class models for both duration and frequency. Income, driver experience and exposure are considered to have both positive and negative impacts on duration. Crash duration is shorter in regions with a dense road network, but crash frequency is higher. Highly connected networks, on the other hand, are associated with longer length but lower frequency.

Hotspot identification on urban arterials at the meso level

Urban arterials form the main structure of city street networks, and typically have high traffic volume and high crash frequency. To reduce the number of crashes, hotspot identification (HSID) is the first step in the traffic safety management process, and often utilizes crash prediction models. Classical crash prediction models investigate the relationship between roadway characteristics and traffic safety at the micro level, that is, they treat road segments and intersections as isolated units. Micro-level analysis has limitations, however, when examining urban arterial crashes: 1) signal spacing is typically short for urban arterials in dense street networks, and there are interactions between intersections and road segments that classical models do not accommodate; and 2) for practical engineering, a hotspot to which countermeasures are applied generally consists of several adjacent intersections and road segments instead of a single intersection or road segment. To address these concerns, signalized intersections and their adjacent road segments were combined into meso-level units, which were adopted to investigate traffic safety data from 21 urban arterials in Shanghai, China. To determine if the meso-level unit is the most suitable research unit for identifying hotspots on urban arterials, and if so, which HSID method can most consistently identify them, this study identified micro-level (separate intersection and road segment) and meso-level (combined intersection and road segment) hotspots using crash frequency, empirical Bayesian (EB), potential for safety improvement (PSI), and full Bayesian (FB) methods. To evaluate the performance of the HSID methods, hotspot consistency over two years was tested. The results showed that 1) EB and PSI performed better than the other methods no matter which research unit was used; 2) substantial inconsistency between the identified micro- and meso-level hotspots. To identify both hazardous corridors and individual intersections and road segments on urban arterials, a combination of micro- and meso-level hotspots should be recommended to local transportation authorities.

Recent Advancements in Techniques used to Solve the RSU Deployment Problem in VANETs: A Comprehensive Survey

Abstract: Vehicular ad hoc network makes use of Roadside Unit to boost the communication ability and provide internet access to vehicles. Unfortunately, in big cities with dense road network topology, wireless propagation is disrupted due to several obstacles. Hence, a crowded RSU deployment is required for full coverage of the urban environment. However, the installation cost of RSU is immense; researchers thought of solving this issue as an optimization problem whose objective is to cover all parts of the roads with a minimum number of RSUs, in return reducing the cost of the entire VANET deployment. In this article, a brief account has been given on VANET and its challenges, followed by a survey of different methods introduced to solve the RSU deployment problem. It will help the reader to get a quick glance at the progress made in the field over the years.

Human ignitions on private lands drive USFS cross-boundary wildfire transmission and community impacts in the western US

Wildfires in the western United States (US) are increasingly expensive, destructive, and deadly. Reducing wildfire losses is particularly challenging when fires frequently start on one land tenure and damage natural or developed assets on other ownerships. Managing wildfire risk in multijurisdictional landscapes has recently become a centerpiece of wildfire strategic planning, legislation, and risk research. However, important empirical knowledge gaps remain regarding cross-boundary fire activity in the western US. Here, we use lands administered by the US Forest Service as a study system to assess the causes, ignition locations, structure loss, and social and biophysical factors associated with cross-boundary fire activity over the past three decades. Results show that cross-boundary fires were primarily caused by humans on private lands. Cross-boundary ignitions, area burned, and structure losses were concentrated in California. Public lands managed by the US Forest Service were not the primary source of fires that destroyed the most structures. Cross-boundary fire activity peaked in moderately populated landscapes with dense road and jurisdictional boundary networks. Fire transmission is increasing, and evidence suggests it will continue to do so in the future. Effective cross-boundary fire risk management will require cross-scale risk co-governance. Focusing on minimizing damages to high-value assets may be more effective than excluding fire from multijurisdictional landscapes.

'I HAD A DREAM...' - THE DEDICATION OF AN EQUES OF THE ALA I DARDANORUM IN THE SURROUNDINGS OF TROESMIS AND THE PROBLEM OF RURAL SETTLEMENTS IN NORTHERN MOESIA INFERIOR

Within the framework of multidisciplinary investigations, under the ongoing Troesmis-project the basic topographic features of the Roman, Late Roman and Byzantine settlement of Troesmis (Turcoaia, Tulcea County, RO) were clarified. The decisive result was the localisation of the approximately 16 ha legionary camp and the approximately 30-35 ha civilian settlement around it. It was possible not only to postulate the boundaries of the ancient settlement, but also to register for the first time numerous new sites in the surroundings. The central settlement of Troesmis is embedded in a dense network of roads and rural settlement sites of different types. Additionally, for older accidental finds of stone monuments (inscriptions, sculpture etc.) it was possible to recover, at least at hypothetical level, the initial context of display and to correlate the archaeological data with the epigraphical evidence. This paper presents the case of the votive altar ISM V 218, dedicated, following a dream, to the divine power of Liber Pater by M. Antistius Caecina, eques of ala I Dardanorum Antoniniana . Its provenance also provides the opportunity for short comments on the Roman rural estates and settlements in the Northern Moesia Inferior.

Identifying hotspots and priority areas for xenarthran research and conservation

AbstractAimLimited funds for conservation and research require the development of prioritization schemes. Traditionally, biodiversity metrics were used to delineate priority areas; however, a growing realization emphasizes that logistic factors should be also considered. Here, we combine species richness, past collection efforts, degree of habitat loss and accessibility to define priority areas and spatially orient fieldwork in a cost‐effective manner for xenarthrans.LocationNeotropics.MethodsWe assessed spatial patterns of species richness in Xenarthra and identified diversity hotspots based on species distribution models. Spatial patterns and biases in the Xenarthra past collection efforts were analysed using a comprehensive database including 33,464 individual records for 34 species of Xenarthra. Finally, we produced priority area indices relating species richness and collection efforts with levels of habitat loss and accessibility (roads and rivers network) to highlight important but neglected areas.ResultsCollection efforts were concentrated to a small portion of the Neotropics (central‐western Brazil, eastern Bolivia and north‐western Argentina) and were biased towards access routes. Only 4% of the Neotropics can be considered well sampled. Major xenarthran diversity hotspots include the Amazonian lowlands of Bolivia and the dry Chaco of Paraguay and Argentina. Priority areas for research varied depending on the metric analysed. Amazon holds a high diversity that remains poorly explored. Central Argentina and eastern Brazil are priority areas for research and conservation given the low sampling efforts, high diversity and endemic species, high levels of habitat loss and a dense road network.Main conclusionsMost areas of the Neotropics lack a proper assessment of the xenarthran assemblage, reflecting extensive knowledge shortfalls. Sites close to roads tend to be better sampled, but many areas with a dense road network are undersampled, being good candidates for low‐cost studies. Xenarthra diversity hotspots facing the highest levels of habitat alteration are the least protected and are priority for both research and conservation. Simultaneous efforts of data collection and conservation actions across most of the xenarthran range should be stimulated.

Anthropogenic and environmental determinants of alien plant species spatial distribution on an island scale

Mediterranean islands are considered especially vulnerable to biological invasions by alien plants. However, there is a lack of studies on island scale regarding the factors that determine alien plant's spatial distribution, and the way they affect invasion process. A roadside survey of alien plant species was conducted on Lesvos, the 8th largest island in Mediterranean basin. Data on species counts and explanatory variables were aggregated to a 1 sq. km vector grid and brought together into a single GIS layer. Alien species counts were modelled by using a Negative-binomial model while a Generalised Additive Model was used to examine possible non-linear relationships to the predictors by using splines. A subset of significant factors, related both to human activities and the environment, shaped the spatial distribution of aliens and influenced, in various ways, their future invasion outcome. Transformed areas with high levels of anthropogenic pressures and disturbances, including high population numbers, dense road network, ports, and intensive land use, as is the case for coastal zones, promoted the presence of alien species. Contrary, modified areas, such as grazed lands, seemed to restrict alien species occurrences, possibly due to the long grazing history these areas present, a regime in which aliens are not adapted. Alien plants presence was positively associated with high levels of NPP, diversity of geological substrates, and a west-facing aspect. Anthropogenic determinants of alien spatial patterns were primarily connected to increased propagule pressure, whereas environmental factors demonstrated the preference of alien plants for resource-rich environments.

A Research Paper on Convolutional Neural Network (CNN) Theory based Automated Traffic and Road Sign Detection and Recognition System

India is a country with a dense road network and has a complex system to maintain road safety. As we all know that we have a complex traffic system in which we have more than 100 types of traffic symbols in it. While driving, it is tough to take care of all the symbols placed at the road end. Sometimes the driver does not know what that symbol says. In this system sometimes the driver misses the road signs because the attention of the driver is overdriving the vehicle safe which leads to an accident or issuing Challan. Sometimes the traffic signs don't notice by the driver. So all the drivers or the vehicle need a system which is capable to read and recognize the traffic symbol placed at the road end and the system must be capable of giving simple instruction to the driver. So that system can automatically detect which type of symbol is this and can notify the driver. The system must have a good accuracy rate, as well as the system, must have a very good speed of working. This system can also be used in driverless cars to notify the system about the road signals and hence the system can tackle all the symbols carefully.

Vehicle travel path recognition in urban dense road network environments by using mobile phone data

ABSTRACT Vehicle travel paths provide basic information for improving traffic forecasting models, tracking epidemics transmission, and road construction. Nevertheless, the challenge of recognition and verification still exists, especially in urban dense road networks. This paper proposes a vehicle path recognition model combined with mobile phone data. In path fitting module, the spatio-temporal density-based clustering algorithm and Gaussian filter were combined to smooth the position fluctuations of mobile phone data; then non-uniform rational B-splines were used to fit travel paths. In path recognition module, the modified probabilistic map matching algorithm was used to match fitting knots to road networks; then matching results were repaired considering the road network topology and the direction angles. The results were verified from trip lengths, urban environments, and road categories. The recognition accuracy was around 90%, 24.22% higher than that of existing methods. The error rate was around 6%, 30.28% lower than that of existing methods.

The Effects of Residential Built Environment on Supporting Physical Activity Diversity in High-Density Cities: A Case Study in Shenzhen, China.

In high-density cities, physical activity (PA) diversity is an essential indicator of public health and urban vitality, and how to meet the demands of diverse PA in a limited residential built environment is critical for promoting public health. This study selected Shenzhen, China, as a representative case; combined the diversity of PA participants, types, and occurrence times to generate a comprehensive understanding of PA diversity; fully used data from multiple sources to measure and analyze PA diversity and residential built environment; analyzed the relationships between the built environment and PA diversity; and explored the different effects in clustered and sprawled high-density urban forms. PAs in clustered areas were two times more diverse than those in sprawled areas. Accessibility, inclusiveness, and landscape attractiveness of residential built environment jointly improved PA diversity. Clustered areas had significant advantages in supporting PA diversity since they could keep the balance between dense residence and landscape reservation with an accessible and inclusive public space system. The residential built environment with dense street networks, public traffic and service, multi-functional public space system, and attractive landscapes is crucial to improve the diverse PA to achieve more public health outputs in high-density cities. To promote health-oriented urban development, clustered urban form is advocated, and step-forward strategies should be carried out.

Continent-wide modelling of potential habitats and connectivity for the Eurasian lynx (Lynx lynx) in Europe

Eurasian lynx (Lynx lynx) once inhabited almost the whole of the European continent. After facing nearly complete extinction about a century ago, they now form a more or less connected metapopulation with eleven broad-scale populations and numerous isolated habitat patches throughout Europe. Increasing public interest and a favourable legislative status by the EU give rise to hope for the return of the felide to parts of Central and Western Europe, where it is currently absent. In this context, a solid understanding of the overall suitability of the European landscape for a well-connected lynx population is crucial. Therefore, this work is looking into the current situation as well as potential habitats, ease of movement between those habitats, and potential future distribution, with the main focus on broad-scale spatial patterns. In addition to existing populations, eight habitat clusters were identified across Europe, using a modelling approach, rooted in a rule-based assessment accompanied by least-cost path identification, generating possible movement corridors. Key findings include large areas of potential habitat, yet overall low connectivity, with lynx movement restricted by a lack of forest cover as well as human land use, including mainly linear barriers like highways and dense road networks. This situation provides a number of challenges and opportunities for national and international conservation: The results clearly indicate that for a recolonisation of many parts of the European landscape reintroduction measures are indispensable in many parts of Europe. They also offer a first step towards a general understanding of how large carnivores like the Eurasian lynx might exist within a landscape increasingly shaped by anthropogenic actions and land use.

A deep learning based approach for trajectory estimation using geographically clustered data

This study presents a novel approach to predict a complete source to destination trajectory of a vehicle using a partial trajectory query. The proposed architecture is scalable to extremely large-scale data with respect to the dense road network. A deep learning model Long Short Term Memory (LSTM) has been used for analyzing the temporal data and predicting the complete trajectory. To handle a large amount of data, clustering of similar trajectory data is used that helps in reducing the search space. The clusters based on geographical locations and temporal values are used for training different LSTM models. The proposed approach is compared with the other published work on the parameters as Average distance error and one step prediction accuracy The one-step prediction accuracy is as good as 81% and Distance error are .33 Km. Our proposed approach termed Clustered LSTM is outperforming in both the parameters when compared with other reported results. The proposed solution is a clustering-based predictive model that effectively contributes to accurately handle the large scale data. The outcome of this study leads to improvise the navigation systems, route prediction, traffic management, and location-based recommendation systems.

Life in the fast lane: roadkill risk along an urban–rural gradient

AbstractWildlife-vehicle collisions are a major cause of mortality in animal populations and can cause significant population-level effects. Urban areas are typically associated with higher road densities and unique wildlife communities in comparison to rural areas, and therefore have the potential to be associated with high numbers of collisions, and roadkill risk. Here, we use a citizen science database of wildlife roadkill and species distribution models to assess how roadkill risk (probability of roadkill observation per km2) varied along an urban–rural gradient for British wildlife. Roadkill risk was positively associated with road density, until around 5000 m/km2, a value representing villages or the outskirts of towns and cities. Beyond 5000 m/km2, risk remained high for some species (hedgehog, fox, pigeons and gulls) but reduced for other species (badger, rabbit, pheasant). Roadkill risk was a function of live species distribution for badger, hedgehog and rabbit, with significant overlap between spatial patterns of roadkill risk and the species’ live distribution. This was not the case for fox, pheasant, pigeons and gulls. Fox roadkill risk was underrepresented in rural areas, possibly due to low road density, while pheasant risk was overrepresented. For pigeons and gulls—well-known urban exploiters—roadkill risk was overrepresented in urban areas given their live distributions, possibly due to risks associated with foraging, particularly roadkill scavenging by gulls. Our results highlight the dangers of the UK’s dense road networks to wildlife, even to species considered adapted to urban environments and human disturbance.

Travel Behaviours of Sharing Bicycles in the Central Urban Area Based on Geographically Weighted Regression: The Case of Guangzhou, China

Mobile information and communication technologies (MICTs) have fully penetrated everyday life in smart societies; this has greatly compressed time, space, and distance, and consequently, reshaped residents’ travel behaviour patterns. As a new mode of shared mobility, the sharing bicycle offers a variety of options for the daily travel of urban residents. Extant studies have mainly examined the travel characteristics and influencing factors of public bicycles with piles, while the travel patterns for sharing bicycles and their driving mechanisms have been largely ignored. Using one week’s travel data for Mobike, this study investigated the spatial and temporal distribution patterns of sharing bicycle travel behaviours in the central urban area of Guangzhou, China; furthermore, it identified the influences of built environment density factors on sharing bicycle travel behaviours based on the geographically weighted regression method. Obvious morning and evening peaks were observed in the sharing bicycle travel patterns for both weekdays and weekends. The old urban area, which had a high degree of mixed function, dense road networks, and cycling-friendly built environments, was the main travel area that attracted sharing bicycles on both weekdays and weekends. Furthermore, factors including the point of interest (POI) for the density of public transport stations, the functional mixing degree, and the density of residential POIs significantly affected residents’ travel behaviours. These findings could enrich discourse regarding shared mobility with a Chinese case characterised by rapidly developing MICTs and also provide references to local authorities for improving slow traffic environments.

The identification of wildlife-vehicle collision hotspots: Citizen science reveals spatial and temporal patterns

Linear infrastructures (e.g., roads, railways, pipelines, and powerlines) pose a serious threat to wildlife, due to the risk of wildlife-vehicle collisions (roadkills). The placement of mitigation measures, such as crossing structures, should consider species’ life cycles and ecological requirements. Such an assessment would require data collection over large areas, which may be possible by employing citizen science. In this study, we aimed to identify spatio-temporal trends of roadkill occurrence using citizen science data from one of the most urbanized and biodiversity-rich regions of Italy. Temporal trends were analyzed using generalized additive models, while landscape patterns were assessed by identifying significant thresholds over land cover gradients, related to increases in relative roadkill abundance, by employing threshold indicator taxa analysis. Our approach recorded a total of 529 roadkills, including 33 different species, comprising 13 mammal, 10 bird, 6 reptile, and 2 amphibian species. Statistical analysis indicated significant temporal trends for the red fox, the European hedgehog, the stone marten and the European badger, with peaks in roadkill occurrence between the winter and spring months. Relative roadkill abundance increased mostly in landscapes with anthropogenic land cover classes, such as complex cultivations, orchards, or urban surfaces. Our results allowed us to develop a map of potential roadkill risk that could assist in planning the placement of mitigation measures. Citizen science contributions from highly populated areas allowed data collection over a large area and a dense road network, and also directly led to the evaluation of management decisional options.

Hidden Markov map matching based on trajectory segmentation with heading homogeneity

Map matching is to locate GPS trajectories onto the road networks, which is an important preprocessing step for many applications based on GPS trajectories. Currently, hidden Markov model is one of the most widely used methods for map matching. However, both effectiveness and efficiency of conventional map matching methods based on hidden Markov model will decline in the dense road network, as the number of candidate road segments enormously increases around GPS point. To overcome the deficiency, this paper proposes a segment-based hidden Markov model for map matching. The proposed method first partitions GPS trajectory into several GPS sub-trajectories based on the heading homogeneity and length constraint; next, the candidate road segment sequences are searched out for each GPS sub-trajectory; last, GPS sub-trajectories and road segment sequences are matched in hidden Markov model, and the road segment sequences with the maximum probability is identified. A case study is conducted on a real GPS trajectory dataset, and the experiment result shows that the proposed method improves the effectiveness and efficiency of the conventional HMM map matching method.

Landslide susceptibility mapping using GIS along the Niš-North Macedonia highway

Landslide events are a serious challenge worldwide, as well as in the Republic of Serbia. According to current estimates, 25-30% of the territory of Serbia is endangered by landslides. Due to intensive landslides, residential, infrastructural, energy, water management and industrial facilities, as well as natural and cultural goods are endangered. The subject of this paper is the use of GIS for the production of a landslide susceptibility map (LSM) in the area of the corridor of the highway E-75, section Niš - border of the Republic of Northern Macedonia. Emphasis is placed on the analysis of certain factors that influence the landslide events, as well as on the modifiers of the process itself. In this paper the following factors were selected for the assessment and susceptibility mapping: lithology, pedological factors, precipitation, aspect, slope, distance from watercourses, land use and distance from roads. The AHP method was then used to determine the relative significance and priority of predisposing factors. The layers were overlapped using ArcGISPro software. The results show that 17.2% of the surface is not susceptible (very low and low susceptibility), and 31.9% of the surface is prone to landslide events (high and very high susceptibility). The remaining 49.1% of the area belongs to the area of moderate susceptibility. Adding to this dense road network in the study area, as well as a large number of populated places, it can be concluded that the damage from the activation of the landslide would be significant. The results suggest that 60.7 km of the highway is in an area of high and greatest risk of landslides.

Przyczyny pożarów i skuteczność systemu ochrony przeciwpożarowej Lasów Państwowych w latach 2010–2019 na przykładzie Nadleśnictwa Kielce

Celem opracowania jest ustalenie przyczyn pożarów lasów i skuteczności systemu przeciwpożarowego Lasów Państwowych na terenie Nadleśnictwa Kielce. Dokonano analizy porównawczej przebiegu wybranych danych meteorologicznych sprzyjających powstawaniu pożarów z danymi dotyczącymi pożarów lasów (bazy TETRAX elektronik, SILP, Llas.2) w latach 2010–2019, ze szczególnym uwzględnieniem pożarów o nieustalonych przyczynach. Pozwoliło to ustalić dominację czynników antropogenicznych nad naturalnymi w wywoływaniu pożarów. Stwierdzono dużą skuteczność systemu przeciwpożarowego, co związane jest m.in. z gęstą siecią dróg i brakiem większych, zwartych kompleksów leśnych.

Developing an analytical framework for liveable streets in Shanghai

This research explores how to understand the concept of ‘liveable streets’ in the context of Shanghai. It follows the Chinese State Council’s call (2015) for ungated communities and dense street networks, meant to improve liveability in cities. Factors of liveability have been extracted from the international and Chinese literature and refined through locally administered online questionnaires (n = 95) and semi-structured interviews (n = 12) with a sample of urban studies professionals. The outcome of this research is an analytical framework consisting of qualities, factors and indicators to assess and distinguish what constitutes a liveable street in Shanghai. A set of 28 indicators is proposed to facilitate the straightforward application of the framework in fast-growing urban settings. This research reveals the importance of contextualising liveability factors and their relevance in informing policy-making and the practice of urban design and planning in Chinese cities.