Dense Urban Areas Research Articles

Reexamination of the association between development patterns and truck crashes: A case study in Dallas-Fort Worth, TX

Over the last decade, urban logistics operations have changed significantly due to globalized production and distribution systems and expanding online shopping sales. On the one hand, goods are distributed on a greater scale through large-scale transportation infrastructure. On the other, exploding online shopping shipment has added another layer of complexity to urban logistics operations. Nowadays, instant home delivery has become prevalent. Provided that the geography, extent, and frequency of freight trip generation have completely changed, it can be assumed that the relationship between the development pattern characteristics and road safety outcomes has also changed, accordingly. Then, it is imperative that the spatial distribution of truck crashes, in conjunction with development pattern characteristics, is reexamined. As a Dallas-Fort Worth, TX metro area case study, this research examines whether the spatial distribution of truck crashes on city streets is different from that of other vehicle crashes and tests whether truck crashes have a unique association with development patterns. Results show that truck and passenger car crashes are distinguished in terms of how they are associated with urban density and employment sector compositions. The explanatory variables with significant and expected signs of relationship are VMT per network mile (exposure), intersection density, household income, % non-white, and % no high school diploma. Results indicate that the spatial heterogeneity in goods shipment intensity has strong implications for the variation in truck crash patterns. Results also call for a comprehensive reexamination of trucking activity in dense urban areas.

Development of SDR-based Network Positioning Technology. LTE Reference Signals Reception and Processing Procedures

Introduction. The search for technical solutions to improve the positioning accuracy of devices using the infrastructure of base stations of existing and planned cellular radio networks is an important area of research and development in the last decade. Interest in network positioning is due to the unreliable reception of signals from global navigation satellite systems in dense urban areas. The instrument for experimental approbation of technical solutions is the software and hardware of the SDR software defined radio technology. This work is a continuation of the research on the use of SDR technology for network positioning tasks in terms of issues related to the reception and processing of reference signals of the LTE standard. Materials and methods. The paper presents an experimentally tested sequence of procedures for receiving and processing LTE reference signals in order to collect primary differential-range measurements for software implementation on a mock-up of a user device built using SDR software defined radio technology. Results. The software implementation of the presented procedures underlies the SDR layout of the user device for receiving and processing reference signals of the LTE standard. Discussion and conclusions. Approbation of the operation of the SDR testbed of the user device in laboratory conditions with SDR testbeds of base stations, as well as in the field with base stations of functioning LTE networks, makes it possible to search for technical solutions to improve the accuracy of positioning.

Ranging Code Design for UAV Swarm Self-Positioning in Green Aerial IoT

Utilizing the unmanned aerial vehicle (UAV) swarm to realize location awareness of ground users (GUs) is a promising technology in green aerial Internet of Things (IoT) systems. However, a typical UAV-based positioning system is frequently applied in dense urban areas, where the traditional satellite positioning systems are severely impaired. Due to the co-frequency interference and the inevitable defect of limited resources of the positioning anchors, the UAVs positioning ability and system resource utilization suffer serious challenges. In this article, we propose a novel ranging code design for UAV swarm self-positioning, which consists of the code truncation algorithm and Greedy-based code group optimization algorithm, aiming to obtain the code group with the optimal correlation characteristics and shorter code length based on the pseudo noise (PN) code. In particular, the design can flexibly change the truncated length, so as to ensure the self-positioning accuracy with less positioning resource. To evaluate the influence of the proposed design for the self-positioning of UAVs on the aerial IoT, the root-mean-square error (RMSE) of UAVs self-positioning is provided, and based on this, the Cramer–Rao lower bound (CRLB) of GUs location estimate is derived. Numerical results demonstrate that the ranging code design is a superior way for future green aerial IoT in both positioning and resource utilization, as compared with a state-of-the-art approach.

A Novel GNSS Fault Detection and Exclusion Method for Cooperative Positioning System

Global Navigation Satellite System (GNSS)-based cooperative positioning is highly degraded in dense urban areas due to non-line-of-sight (NLOS) and multipath effects. Multiple simultaneous faulty GNSS measurements can result in traditional fault detection and exclusion (FDE) methods finding a consistent but erroneous group of satellites. This paper describes a novel distributed FDE method based on cooperative reliability message (CRM), which is suitable for cooperative positioning system in connected vehicles. The idea is to take into account not only the consistency check on raw GNSS measurements, but also the prior knowledge about the quality of GNSS measurements offered by nearby cooperators so as to improve the ability to exclude multiple GNSS faults. The line-of-sight (LOS) satellites identified by cooperators are stored in CRMs, which will be shared to the host vehicle to generate the candidates for the set of LOS satellites observed locally. Based on the candidates, a bottom-up searching strategy is designed to find the healthy satellites and isolate the faulty ones precisely. Several experiments involving four moving vehicles were conducted in dense urban areas. Results indicate the superiority of CRM-based method in excluding multiple faults over existing methods.

Consensus-Based Autonomous Navigation of a Team of RIS-Equipped UAVs for LoS Wireless Communication With Mobile Nodes in High-Density Areas

The reconfigurable intelligent surface (RIS) technology has gained increased attention for improving the performance and efficiency of the fifth-generation (5G) millimeter-wave (mmWave) wireless communication by obviating the propagation and blockage issues. On the other hand, the great flexibility of unmanned aerial vehicles (UAVs) has made them effective gadgets to enhance the coverage of wireless communication networks. Combining these two emerging technologies, the RIS-outfitted UAV (RISoUAV) is a promising solution for providing line-of-sight (LoS) wireless links for mobile targets (MTs) in obstructed high-dense urban areas. In this light, high-speed real-time communication is essential for some vital municipal services like ambulances, fire engines, security guards, police, etc. This important goal is achievable thanks to the RISoUAV-assisted 5G/quasi-optic wireless communication. This paper develops a framework for optimal navigation of a team of RISoUAVs for maintaining LoS links with a team of ground vehicles in a dense urban area. The trajectories of the RISoUAVs are optimized considering the energy efficiency, communication channel gains, and constraints associated with RISoUAVs motion and LoS service. A consensus-based coordinating approach is adopted to coordinate the RISoUAVs navigation to cover all MTs under a good quality of service. Simulation results show the effectiveness of the method. Note to Practitioners—In this paper, we consider a scenario where vehicles need to have high-speed, uninterrupted data links in obstructed, highly dense urban environments. Due to spectrum crunch, the data links are increasingly likely to rely on a high-frequency spectrum, including mmWave with quasi-optic nature, visible light communications, or even laser. However, the obstructed LoS and propagation are critical issues with the 5G and beyond as they rely on the availability of an unobstructed path (e.g., the LoS or a quality reflective path). On the other hand, the RIS performs as a passive reflective element that provides an indirect LoS link, a one-bounce channel, to improve the performance and efficiency of mmWave, and beyond, wireless communication networks. UAVs equipped with RISs are suggested in this paper to be adopted as aerial transponders to reflect signals and facilitate communication in high-density environments. Therefore, the problem of UAV navigation and 3D trajectory planning should be addressed regarding the application, particularly, for providing LoS service for mobile vehicles with arbitrary directions. Autonomous navigation of RISoUAVs for LoS wireless communication is a natural multi-dimensional extension of autonomous navigation with obstacle avoidance where regions in which LoS communication is lost are viewed as obstacles to avoid. However, the environment and valid LoS links can change dynamically due to moving vehicles in the obstructed environment. This makes the navigation design an NP-hard problem that is tackled in this paper by developing an effective navigation program.

Coronavirus Disease of 2019 (COVID-19) and Household Food Security in High-Density Residential Areas of Lusaka: Experiences From Ng’ombe Settlement

Comparable to the rest of the world, African countries are orbiting from the health, economic and social effects of COVID-19. The continent’s administrations responded to COVID-19 guidelines by imposing restrictions to limit the spread of the virus. COVID-19 and measures responses to the pandemic undermine food production, processing and marketing but the most concerning experiences are on the demand-side, economic and physical access to food. Food and Agriculture Organization’s ( FAO) ‘four pillars’ availability, accessibility, stability and utilization provide a complete framework for analysing food security. However, the paper was looking at how people in high density residential areas are experiencing food security during COVID-19 period. The main aim was to assess the experiences of residents of high density urban residential areas of Lusaka in terms of household food security before and during COVID-19 pandemic era. The study used the embedded research design. Structured interviews were used to collect data from the respondents using questionnaires administered on a sample of 235 Ng’ombe residents. Key informants were selected purposively from Ng’ombe urban health Centre, Ng’ombe Catholic church, Food and Agriculture Organization (FAO), Ministry of Agriculture and World Food Programme (WFP) officials. Data was collected using semi-structured interviews with a tool called interview guide. The data was analyzed using descriptive statistic, paired t-test and chi-square using R statistical computing software and excel. The results were presented using tables, pie charts and graphs. The study revealed that Ng’ombe residents had a devastating experience during the COVID 19 era among which include reduction in income, hunger, job losses, skipped or reduced meal sizes and compromised diet. Also pinpointing areas for governments and other actors to intervene in the food system to protect the food security of households left vulnerable by COVID-19 for example empowering the affected households with money for businesses and jobs creation. Keywords: COVID-19, Food security, High density areas, dietary diversity

Learning-Based Traffic Scheduling in Non-Stationary Multipath 5G Non-Terrestrial Networks

In non-terrestrial networks, where low Earth orbit satellites and user equipment move relative to each other, line-of-sight tracking and adapting to channel state variations due to endpoint movements are a major challenge. Therefore, continuous line-of-sight estimation and channel impairment compensation are crucial for user equipment to access a satellite and maintain connectivity. In this paper, we propose a framework based on actor-critic reinforcement learning for traffic scheduling in non-terrestrial networks scenario where the channel state is non-stationary due to the variability of the line of sight, which depends on the current satellite elevation. We deploy the framework as an agent in a multipath routing scheme where the user equipment can access more than one satellite simultaneously to improve link reliability and throughput. We investigate how the agent schedules traffic in multiple satellite links by adopting policies that are evaluated by an actor-critic reinforcement learning approach. The agent continuously trains its model based on variations in satellite elevation angles, handovers, and relative line-of-sight probabilities. We compare the agent’s retraining time with the satellite visibility intervals to investigate the effectiveness of the agent’s learning rate. We carry out performance analysis while considering the dense urban area of Paris, where high-rise buildings significantly affect the line of sight. The simulation results show how the learning agent selects the scheduling policy when it is connected to a pair of satellites. The results also show that the retraining time of the learning agent is up to 0.1times the satellite visibility time at given elevations, which guarantees efficient use of satellite visibility.

Fabrication and Performance Analysis of the Aero-Leaf Savonius Wind Turbine Tree

Large wind turbines of the horizontal axis are commonly used to gather wind energy; however, their performance is found to be constrained in conditions of erratic and low-speed wind flow. In contrast, low wind conditions—which are typically present in dense urban areas—are found to favour vertical axis wind turbines (VAWT). These turbines have a simple design, are inexpensive and quiet, and are discovered to be better in low wind situations. In this research, we have chosen wind tree applications to absorb the most available wind energy. The new Aeroleaf Savonius Wind Turbine was developed numerically and a computational fluid dynamics simulation was performed on this new type of Savonius tree to predict its performance. The results indicated that the system could accept wind from any direction and could start rotating as soon as the site had a cut in wind speed of 3.3 m/s. The rotor speed increased by 10.4% from 5.5 to 6.3 m/s wind speed at 0.45 tip speed ratio. The tip speed ratio is 0.52 at the site’s high wind speed, and under these circumstances, the maximum Cp is 12.9%. The turbine was able to produce superior performance coefficients, according to the results.

The role of risk measures in relating earthquake risks at building and portfolio levels

AbstractThe potentially large spatial footprint of earthquake disasters and the increased concentration of population and values in dense urban areas call for an explicit consideration of seismic risk at a regional, building portfolio level. The relation between the building‐level seismic risk and the portfolio‐level seismic risk is helpful if one wants to meet a specific regional seismic risk tolerance level by specifying seismic risk targets for individual buildings. We examine four types of common risk measures and point to the importance of subadditivity, a risk measure mathematical property, for deriving a conservative upper‐bound relation between the building‐specific and the building portfolio seismic risks. Subadditive risk measures, such as the Expected Shortfall, allow estimates of conservative upper bounds of the portfolio‐level seismic risk. In a case study, we show that nonsubadditive risk measures commonly used in earthquake engineering can lead to counter‐intuitive and nonconservative perceptions of the regional seismic risk, especially when one extrapolates from individual buildings to the entire building portfolio. We also illustrate the advantages of subadditive risk measures for regional seismic risk assessment.

Unevenly spatiotemporal distribution of urban excess warming in coastal Shanghai megacity, China: Roles of geophysical environment, ventilation and sea breezes

The complexity and uncertainty of urban excess warming are modulated by the morphology of urban areas, canopy urban heat island (CUHI)–heat wave interactions, the geographical–climatological background and the local circulation. Focusing on the coastal Shanghai megacity, we analyzed the synergistic effects of local climate zones (LCZs), urban ventilation, and sea breezes on the urban excess warming related to the CUHI and heat waves in summer from 2013 to 2018. Over the whole urban areas, the average CUHI intensity (CUHII) increased by 128.91% during heat waves periods relative to non-heat wave periods. Moreover, the spatial-temporal distributions of both CUHII and heat waves exhibited heterogeneously. In dense urban areas (e.g., LCZ 1 and 2), the blocking effect of dense high-rise buildings, and poor ventilation with low wind speeds would exacerbate urban excess warming, which exhibited relatively higher occurrence of heat waves and an increase in the CUHII. By contrast, over open areas of the urban periphery (eg., LCZ5), more heat wave events and stronger CUHII were mainly affected by the horizontal advective transport of urban heat at medium wind speeds. Particularly, the sea breezes could weaken the CUHII and decrease the occurrence frequency of heat wave events during daytime over coastal areas. In addition, the shading effects of high-rise buildings and the evaporative cooling effects of water and vegetation would also mitigate urban warming at the local scale, which would reduce the CUHII as well as the frequency of heat waves.

Investigating the impact of urban microclimate on building thermal performance: A case study of dense urban areas in Hong Kong

Urban microclimate conditions could be an important factor influencing building thermal performance. However, most studies on urban building energy modeling (UBEM) use the typical meteorological year (TMY), often developed from observations of exposed/ rural sites, as input weather data. This study aims to assess the impacts of urban heat on building thermal performance by coupling a high-resolution urban climate simulation with UBEM. The simulated building thermal performance was compared with and without urban microclimate considerations, using weather data from the TMY, weather observations from an urban oasis, and outputs from the urban climate model. The physical parameters of typical building archetypes used in the UBEM were calibrated against indoor-measured data during summer days. Without considering urban microclimate, there was an underestimation of up to 140% of the average overheating risks of urban buildings. Furthermore, neighboring urban contexts and building ventilation rates considerably affected the thermal performance of individual buildings within high-density urban areas. The study reveals that neglecting the influence of the urban microclimate can result in a notable error in the building thermal performance assessment even at the urban level.

Barometer assisted smartphone localization for vehicle navigation in multilayer road networks

Multilayer transport structures, such as elevated roads and tunnels, are increasingly used to solve the growing traffic congestion problems in dense urban areas. It presents a challenge for navigation systems to determine which layer the vehicle is running on when elevated roads or tunnels are parallel to and close to surface roads. In this paper, we propose a smartphone-based positioning method to provide accurate position and road layer for vehicle navigation in multilayer road networks. Systemic errors of barometers are calibrated with GNSS measurements, local Digital Surface Model (DSM), and Mean Sea Level pressure. Then, barometer output absolute altitudes are integrated with GNSS and IMU to achieve 3D vehicle positioning. Using absolute altitudes, DSM, and GNSS information, the road layer can be identified without initial layer information in multilayer road networks. Experiment results demonstrate that the proposed method can provide accurate location and road layer in multilayer road networks.

Exploring Indoor and Outdoor Residential Factors of High-Density Communities for Promoting the Housing Development

Effective residence planning is crucial to encourage sustainable housing development. Residents in densely populated cities inevitably have negative residential experiences caused by compact land use. Still, this situation is improvable through optimizing the physical environment or increasing service facilities that cater to dwellers’ residential preferences. Therefore, understanding the factors impacting residential satisfaction in high-population metropolitan areas is essential mainly. This study surveyed the citizens’ residential environment and satisfaction in dense urban residential areas. Results of this survey indicate that 13 environmental factors significantly impact residential evaluation. Among them, improving ventilation, privacy, and property maintenance can effectively address the adverse effects of densely populated communities. Additionally, neighborhoods or amenities could further impact younger respondents’ residential evaluation. Elderly citizens place high importance on the quality of their indoor living environment. Green space could promote the residential satisfaction of young and more senior citizens. Environmental factors can significantly affect the residential satisfaction of citizens across all age groups with their homes. As a result, real estate planners should provide a range of dwelling unit designs to support housing development. Real estate planners can better understand the needs of potential clients by considering the housing preferences of individuals across different age groups and the surrounding neighborhood. The indoor or outdoor environment might be adjusted to meet households’ demands, while non-essential factors could be omitted to cut expenses. This study might aid in the sound development of dwellings.

Reliable and cost-efficient protection scheme for 5G fronthaul/backhaul network

One of the challenges for Mobile Network Operators (MNO) in 5G deployment is to ensure reliability in the various sections of the network as the new services and applications, for instance, video on demand, telemedicine, online learning, smart transportation and augmented reality require not only high bandwidth but also demand uninterrupted service. However, this reliability requires substantial investment. Therefore, MNO only deploys protection or backup resources in the network unless it is cost-effective. The study aims to present a reliable and low-cost protection scheme based on an Ultra-Dense Wavelength Division Multiplexing Passive Optical Network (UDWDM PON) for the transport layer of the 5G network, i.e., for the fronthaul/backhaul section. We have evaluated the Capital Expenditure (CAPEX) cost of UDWDM PON with and without protection in a dense urban area. We also measure the figure of merit between the cost and reliability of the system and, subsequently, confirm that the proposed protection scheme can achieve system reliability up to four nines with very low additional CAPEX investment. Finally, the efficacy of the proposed protection scheme is also demonstrated through simulation experiments.

Correspondence between spectral reflectance and features of the built environment for community resilience

As humans increasingly settle in dense urban areas, localized natural and anthropogenic shocks become more likely to impact larger numbers of individuals. Research suggests that resilience to shocks is a function of physical fortifications and social processes including critical infrastructure, social networks, and trust. Although physical fortifications are relatively easy to identify and catalog, social processes elude simple measurement due to data limitations and geographic constraints. Recent work has shown that certain types of infrastructure may correlate with social processes that enhance community resilience; however, the ability to assess where and to what extent that infrastructure exists depends on a complete representation of the built environment. OpenStreetMap (OSM) and Google Places are two sources of data commonly used to locate and characterize infrastructure, but they are often incomplete. We address this limitation by applying a convolutional neural network (CNN) to remote sensing data from Sentinel-2 to estimate the density and type of infrastructure. We compare the classification results to known infrastructure locations from OSM data. Our results show that the CNN classifier performs well and may be used to augment incomplete datasets for a deeper understanding of the prevalence of infrastructure associated with social processes that enhance community resilience.

Machine Learning Applied to LoRaWAN Network for Improving Fingerprint Localization Accuracy in Dense Urban Areas

In the area of low-power wireless networks, one technology that many researchers are focusing on relates to positioning methods such as fingerprinting in densely populated urban areas. This work presents an experimental study aimed at quantifying mean location estimation error in populated areas. Using a dataset provided by the University of Antwerp, a neural network was implemented with the aim of providing end-device location. In this way, we were able to measure the mean localization error in areas of high urban density. The results obtained show a deviation of less than 150 m in locating the end device. This offset can be decreased up to a few meters, provided that there is a greater density of nodes per square meter. This result could enable Internet of Things (IoT) applications to use fingerprinting in place of energy-consuming alternatives.

Experiments and numerical simulation of the reinforcement effect of channel-steel-reinforced shield tunnel segments under unloading conditions

The increasing emergence of foundation pit excavations in dense urban areas has caused a series of adverse impacts on operating tunnels. Thus, the investigation of tunnel segment reinforcement is crucial and essential for geotechnical engineers. This article intends to investigate the reinforcement effect of the channel steel reinforcement method through a three-ring full-scale test and finite element simulation. The three-ring full-scale test is carried out by considering different confining pressures under the influence of different tunnel burial depths and different distances between the foundation pit and tunnel (clearance distances). In addition, a three-dimensional refined FEM numerical simulation verified by the full-scale model test is conducted to study the influence of foundation pit retaining structure deformation. The research results show that channel steel reinforcement can significantly improve the stiffness of the segment and its bearing capacity. The effect of the channel steel is more prominent in tunnels with a large burial depth. The stress and deformation of the segment are relatively uniform under the conditions of different clearance distances after the reinforcement of the channel steel. The top and bottom convergence of the segment and the bolt stress increase as the deformation parameters of the envelope structure increase; furthermore, the top and bottom convergence of the segment can be reduced up to 46.88% after the channel steel reinforcement.

A Decision Support Tool for Green Infrastructure Planning in the Face of Rapid Urbanization

Multifunctional green infrastructure, a key component of compact sustainable cities, is challenged by the pressures associated with rapid urbanization. In this paper, we present a method that uses remote sensing, GIS modeling and stakeholder engagement to produce a decision support tool that communicates the availability and need for green infrastructure benefits. The case study presented is the City of Tshwane, South Africa, a Global South city facing rapid urbanization. We found that this method of mapping green infrastructure benefits can provide simultaneous oversight on multiple objectives for green infrastructure, including climate change adaptation, biodiversity, and equitable distribution of urban green space. We found that low-scoring benefit areas occur in dense urban areas where small-scale nature-based solutions or rehabilitation activities are required. Moderate benefit scores occurred in parts of the city that are vulnerable to urban expansion and densification activities, warranting the careful planning of green infrastructure provision, and that moderate-to-high-scoring areas can be protected as conservation areas. The results are discussed in terms of the role of decision support tools for urban planning practice. Composite indexes can provide important guidance to decision-makers involved in spatial planning and urban upgrading and expansion activities.

접지 전위 간섭의 특성을 반영한 접지저항 측정 방법

Domestic low-voltage electrical equipments have an environment prone to ground potential interference, due to the characteristics of grounding system. This ground potential interference affects electrical equipments and communication facilities, and amplify the error of ground resistance measurement. It is desirable to find and eliminate the root cause of ground potential interference, however it is difficult to remove that in an environment such as in a dense urban area or an area adjacent to the transmission line. In this paper, the pattern of the potential interference voltage and the effect on the conventional ground resistance measurement are analyzed based on the actual field measurement cases of the ground potential interference, and propose a ground resistance measurement method that reflects the characteristics of ground potential interference.

Low-latency GNSS multipath simulation and building wall detection in urban environments

Precise navigation for fully autonomous driving—especially in dense urban areas—requires periodic precise position estimates. Global Navigation Satellite System (GNSS) technology has the potential to provide absolute positioning accuracy at a centimeter level. However, buildings in urban environments cause signal distortions and signal reflections—the so-called multipath—which are the most challenging parts in the GNSS error budget. Hence, we developed a scalable real-time multipath simulator for mitigating potential multipath receptions. The simulator uses three-dimensional (3D) building information, satellite, and user positions. The key drivers of latency are the calculation of reflection, diffraction, and line-of-sight, as well as the response time of the 3D building model database. The memory manager of the graphic processing units (GPUs) in combination with a dedicated load balancer enables fast and efficient multipath analysis. Selected case studies demonstrate the simulator’s potential to significantly improve the position accuracy of the processing engine. The use of the multipath simulator reduces the error in 61% of the error measurements in a stress test scenario to less than half of the non-multipath processing. The scalability of the simulator is demonstrated by combining the multipath simulator with a traffic simulator. Furthermore, we present a novel methodology for the detection of walls using GNSS signals to better account for incomplete or erroneous 3D building information in GNSS signal processing.