Complicated Urban Environment Research Articles

A reliable NLOS error identification method based on LightGBM driven by multiple features of GNSS signals

In complicated urban environments, Global Navigation Satellite System (GNSS) signals are frequently affected by building reflection or refraction, resulting in Non-Line-of-Sight (NLOS) errors. In severe cases, NLOS errors can cause a ranging error of hundreds of meters, which has a substantial impact on the precision and dependability of GNSS positioning. To address this problem, we propose a reliable NLOS error identification method based on the Light Gradient Boosting Machine (LightGBM), which is driven by multiple features of GNSS signals. The sample data are first labeled using a fisheye camera to classify the signals from visible satellites as Line-of-Sight (LOS) or NLOS signals. We then analyzed the sample data to determine the correlation among multiple features, such as the signal-to-noise ratio, elevation angle, pseudorange consistency, phase consistency, Code Minus Carrier, and Multi-Path combined observations. Finally, we introduce the LightGBM model to establish an effective correlation between signal features and satellite visibility and adopt a multifeature-driven scheme to achieve reliable identification of NLOSs. The test results show that the proposed method is superior to other methods such as Extreme Gradient Boosting (XGBoost), in terms of accuracy and usability. The model demonstrates a potential classification accuracy of approximately 90% with minimal time consumption. Furthermore, the Standard Point Positioning results after excluding NLOSs show the Root Mean Squares are improved by 47.82%, 56.68%, and 36.68% in the east, north, and up directions, respectively, and the overall positioning performance is significantly improved.

Unveiling 3-D evolution and mechanisms of ozone pollution in Changzhou, China: Insights from lidar observations and modelling

Ground ozone (O3) pollution has emerged as a prominent environmental concern in eastern cities of China, particularly during the summer and autumn seasons. However, a comprehensive investigation into the three-dimensional (3-D) evolution characteristics of O3 within complicated urban environments, especially in lake-land environment, is notably scarce. To enhance our understanding of the mechanisms underlying elevated O3 concentrations within a 3-D scale, this study employed an ozone lidar to delineate vertical ozone profiles in Changzhou, a typical city in China with complicated anthropogenic and biogenic emissions and complex land cover. The process analysis tool integrated into the Weather Research and Forecasting with Chemistry (WRF-Chem) model was further utilized to analyze the formation processes of O3. The results unveil a persistent O3 pollution episode lasting over 15 days in Changzhou during the study period, with multiple peaks exceeding 200 μg m⁻³. Notably, O3 predominantly accumulated within the boundary layer, confined below 1.2 km. Both ground and vertical contributions to this pollution were mainly due to local chemical reactions, with a maximum near-surface contribution reaching 19 ppb h−1 and a vertical contribution of 10 ppb h−1 at the height of 900 ± 200 m. Furthermore, episodes of the enhanced O3 concentrations on August 9 and August 26, 2021, were influenced by external advection process. Our study also found that local circulation plays an important role in the accumulation of surface O3 during certain periods. There was a temperature difference between the surface of Lake Tai and the adjacent land, resulting in the formation of lake-land breezes that facilitate the transport of O3 from the lake surface to the terrestrial environment during pollution events. Our study emphasizes the necessity of reducing local pollutant emissions and implementing joint emission controls as the primary strategies for mitigating O3 pollution in Changzhou and the surrounding region.

Effect of urban morphology on wind flow distribution in dense urban areas

Environmental flow modelling can be achieved either by using wind-tunnel studies or via the utilization of Computational Fluid Dynamics (CFD) techniques. Conducting wind-tunnel studies may be expensive and time consuming, particularly in the event of additional test requirements with modified building and/or environmental configurations. On the other hand, CFD can provide significant cost benefits for assessing and optimizing engineering design solutions related to environmental concerns and appear attractive as a potential alternative tool. By using CFD in this manner it is anticipated that these types of advanced performance-based studies will be a useful tool and essential aid for urban designers and environmental planners. This paper discusses ongoing development and application of CFD simulations through a case study using CFD software for simulating air flow around a specific vernacular urban fabric of Ghardaïa. The potential of CFD for prediction of wind speeds around a complicated urban environment and a complex fabric structures is investigated. As the 3D solid model of the geometry was not available a captured Google Earth image of the fabric structure was used as a backdrop in the AC3D software, supplied as a utility to Phoenics. The outlines of the buildings were then traced to create polygons, which were then extruded to produce the individual buildings. Finally, the entire scene was exported to the VR-Editor as a single Phoenics object.

Self-Adaptive Filtering for Ultra-Large-Scale Airborne LiDAR Data in Urban Environments Based on Object Primitive Global Energy Minimization

Filtering from airborne LiDAR datasets in urban area is one important process during the building of digital and smart cities. However, the existing filters encounter poor filtering performance and heavy computational burden when processing large-scale and complicated urban environments. To tackle this issue, a self-adaptive filtering method based on object primitive global energy minimization is proposed in this paper. In this paper, mode points were first acquired for generating the mode graph. The mode points were the cluster centers of the LiDAR data obtained in a mean shift algorithm. The graph constructed with mode points was named “mode graph” in this paper. By defining the energy function based on the mode graph, the filtering process is transformed to iterative global energy minimization. In each iteration, the graph cuts technique was adopted to achieve global energy minimization. Meanwhile, the probability of each point belonging to the ground was updated, which would lead to a new refined ground surface using the points whose probabilities were greater than 0.5. This process was iterated until two successive fitted ground surfaces were determined to be close enough. Four urban samples with different urban environments were adopted for verifying the effectiveness of the filter developed in this paper. Experimental results indicate that the developed filter obtained the best filtering performance. Both the total error and the Kappa coefficient are superior to those of the other three classical filtering methods.

Temporal dynamics clustering for analyzing cell behavior in mobile networks

In recent years, management and resource allocation of mobile networks have become crucial because of a dramatic growth of mobile traffic. For Internet service providers, resource allocation is a challenging problem due to the inhomogeneous distributed nature of user demand, which increases the difficulty of configuring base stations in complicated urban environments. To manage mobile networks in an efficient way, understanding the typical usage behavior is very valuable for network operators. This paper aims at providing tools to analyze the behavior of mobile network cells in an urban environment. To achieve this goal, we propose a new time series clustering algorithm denoted as temporal dynamics clustering. Unlike conventional clustering approaches, this algorithm generates a new representation of time series by summarizing the distribution of the sequence of differences; in this way, temporal dynamics clustering creates clusters based on the variability of time series. We apply the proposed algorithm on a real-world mobile network dataset, showing its superior performance and much faster running time with respect to conventional methods such as hierarchical clustering and K-medoids. We also train neural networks on the clusters generated by temporal dynamics clustering, and the results show that the forecasting difficulty of time series is closely related to its temporal dynamics. Using this approach, we have analyzed the cell behavior with two weeks observation collected in Turin, and obtained interpretable results, in that the behavior of each cell is strongly related to the characteristics of the area and the related human activity. We also study the cell behavior in different time slots by considering time-dependent characteristic of human activity. Through experiments, we show the effectiveness of the proposed algorithm at providing a deeper understanding of traffic usage patterns in intricate urban environments.

A Fast Accurate Attention-Enhanced ResNet Model for Fiber-Optic Distributed Acoustic Sensor (DAS) Signal Recognition in Complicated Urban Environments

The fiber-optic distributed acoustic sensor (DAS), which utilizes existing communication cables as its sensing media, plays an important role in urban infrastructure monitoring and natural disaster prediction. In the face of a wide, dynamic environment in urban areas, a fast, accurate DAS signal recognition method is proposed with an end-to-end attention-enhanced ResNet model. In preprocessing, an objective evaluation method is used to compare the distinguishability of different input features with the Euclidean distance between the posterior probabilities classified correctly and incorrectly; then, an end-to-end ResNet is optimized with the chosen time-frequency feature as input, and a convolutional block attention module (CBAM) is added, which can quickly focus on key information from different channels and specific signal structures and improves the system recognition performance further. The results show that the proposed ResNet+CBAM model has the best performance in recognition accuracy, convergence rate, generalization capability, and computational efficiency compared with 1-D CNN, 2-D CNN, ResNet, and 2-D CNN+CBAM. An average accuracy of above 99.014% can be achieved in field testing; while dealing with multi-scenario scenes and inconsistent laying or burying environments, it can still be kept above 91.08%. The time cost is only 3.3 ms for each signal sample, which is quite applicable in online long-distance distributed monitoring applications.

Automation and connectivity of electric vehicles: Energy boon or bane?

With the arrival of autonomous and connected vehicles, it remains unclear how automation and connectivity will impact the energy consumption of electric vehicles. Here, we report the energy impacts induced by these emerging technologies on electric vehicles in a realistic urban environment based on high-resolution empirical data and high-fidelity simulations. We find that, contrary to optimistic predictions, automated vehicles (AVs) alone in fact increase energy consumption compared with human-driven vehicles (HDVs) in a complicated urban environment (e.g., with distributed signalized intersections), while connected and automated vehicles outperform in energy, safety, and mobility. Also, based on our field experiments, scenario AV has a lower average speed compared with scenario HDV. These findings suggest that systematic efforts integrating AV technology innovations from the private sector and connected vehicle infrastructure investment from the public sector are essential to accomplish the ultimate vision of green urban transportation.

CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-Based Autonomous Urban Driving

Vision-based autonomous urban driving in dense traffic is quite challenging due to the complicated urban environment and the dynamics of the driving behaviors. Widely-applied methods either heavily rely on hand-crafted rules or learn from limited human experience, which makes them hard to generalize to rare but critical scenarios. In this paper, we present a novel CAscade Deep REinforcement learning framework, CADRE, to achieve model-free vision-based autonomous urban driving. In CADRE, to derive representative latent features from raw observations, we first offline train a Co-attention Perception Module (CoPM) that leverages the co-attention mechanism to learn the inter-relationships between the visual and control information from a pre-collected driving dataset. Cascaded by the frozen CoPM, we then present an efficient distributed proximal policy optimization framework to online learn the driving policy under the guidance of particularly designed reward functions. We perform a comprehensive empirical study with the CARLA NoCrash benchmark as well as specific obstacle avoidance scenarios in autonomous urban driving tasks. The experimental results well justify the effectiveness of CADRE and its superiority over the state-of-the-art by a wide margin.

No Perfect Outdoors: Towards a Deep Profiling of GNSS-Based Location Contexts

While both outdoor and indoor localization methods are flourishing, how to properly marry them to offer pervasive localizability in urban areas remains open. Recently, proposals on indoor–outdoor detection have made the first step towards such an integration, yet complicated urban environments render such a binary classification incompetent. Fortunately, the latest developments in Android have granted us access to raw GNSS measurements, which contain far more information than commonly derived GPS location indicators. In this paper, we explore these newly available measurements in order to better characterize diversified urban environments. Essentially, we tackle the challenges introduced by the complex GNSS data and apply a deep learning model to identify representations for respective location contexts. We further develop two preliminary applications of our deep profiling: one, we offer a more fine-grained semantic classification than binary indoor–outdoor detection; and two, we derive a GPS error indicator that is more meaningful than that provided by Google Maps. These results are all corroborated by our extensive data collection and trace-driven evaluations.

Trajectory planning for unmanned aerial vehicles in complicated urban environments: A control network approach

Trajectory planning is crucial for Unmanned Aerial Vehicles (UAVs) applications. It is still a challenge to plan UAV trajectories in large-scale complicated three-dimensional urban environments, mainly due to the complexities associated with handling a tremendous number of obstacles. To relieve this difficulty, this study proposes a two-stage control network approach. The first stage is an offline planning stage, aiming at constructing a discrete control network in the working environment achieving collision avoidance and meanwhile considering UAV kinetic properties. The second stage is an online planning stage, where a feasible corridor is generated based on the shortest path in the control network, and the obtained route is smoothed by a three-dimensional Dubins curve algorithm; then, a time-optimal trajectory generation algorithm is applied to trace the designed spatial path. Numerical studies based on a virtual city as well as a large-scale real-world urban area are conducted. The results show that the proposed approach is able to identify reasonable UAV trajectories within negligible computational time.

Disturbance Effect of Pipe Jacking Group Adjacent Excavation on Surrounding Soil

Under the modern and complicated urban environment, clarifying the interaction mechanism and mutual influence mechanism between the urban tunnel and the surrounding environment is of considerable significance to the construction and operation of the tunnel. To understand and grasp the excavation-induced disturbance range and mechanism of adjacent pipeline jacking group on surrounding soils, this paper carries out relevant researches. Based on a case history of a three-hole parallel adjacent jacking pipeline in shallow overburden, a field test and assessment are performed. By setting monitoring points around the monitoring section, the earth pressure and water pressure under different buried depths and different spacing are monitored. For further discussion, the general law of soil disturbance around single-hole, double-hole, and three-hole jacking is investigated, the calculation method of lateral disturbance range and overlapping area range of pipe jacking group is deduced, and the degree of soil disturbance is evaluated as well. The results show that there will be different degrees of disturbance superposition in parallel multihole jacking due to the influence of the spacing, and the disturbance intensity is greatly affected by the parallel distance and buried depth.

A 3-D Pareto-Based Shading Analysis on Solar Photovoltaic System Design Optimization

This paper utilizes a Pareto-based, three-dimensional (3-D) analysis to identify complete and partial shading of photovoltaic (PV) systems for a complicated urban environment, where unusual shape of PV and installation topology is studied. The Pareto optimization attempts to minimize losses in a certain area with an improved output energy and without compromising the overall efficiency of the system of which the nominal operating cell temperature (NOCT) for a glass/glass-module is considered as a significant parameter. The system is referenced to the environment based on IEC61215 via a closed-circuit and resistive load to ensure the module operates at the maximum power point. A maximum power point tracking controller is enhanced with an advanced perturb and observe algorithm to maintain the PV operating point at its maximum output under various working conditions. The most cost-effective design of the PV module is achieved via optimizing installation parameters such as tilt angle, pitch, and shading to improve the energy yield. The parameter settings and suitability of the design are also determined based on the reduced amount of CO2 emissions. An experimental investigation has been carried out to verify the 3-D shading analysis and NOCT technique for both open-circuit and grid-connected PV modules.

Automatic morphological filtering algorithm for airborne lidar data in urban areas.

Filtering is a key step for most airborne lidar post-applications in urban areas. To solve the problems of complex parameter settings and low filtering accuracy in complicated urban environments, an automatic morphological filter was proposed. In this paper, the optimal maximum filtering window can be determined automatically by applying a series of morphological top-hat operations. Meanwhile, the thresholds for filtering were calculated adaptively according to the gradient changes. Seven publicly available data sets provided by the International Society for Photogrammetry and Remote Sensing were used to evaluate the performance. Experimental results show that the proposed method achieved an average total error of 4.07% and an average kappa coefficient of 90.90%, which are the best performances when compared to some other filtering methods.

Spatiotemporal modelling of urban quality of life (UQoL) using satellite images and GIS

ABSTRACTIn modern cities, life is primarily formed in interaction with various environmental, social, economic, infrastructural, hygienic, safety, political and cultural conditions. Urban quality of life (UQoL) is the result of such interactions. In general, both objective and subjective approaches are used in the study and modelling of UQoL. So far the studies have been conducted in forms of social and large-scale geographical studies mainly ignoring spatial differences of quality of life (QoL) in complicated urban environments. Moreover, QoL as one of the attributes of geographical environment is a dynamic and variable concept which has received little attention. Spatiotemporal modelling of this concept can contribute to monitoring the UQoL and planning for its improvement. The current study aims to develop a method for spatiotemporal modelling of UQoL. For this purpose, multi criteria decision making (MCDM) method and fuzzy logic are used. Moreover, given the variability of some indicators, temporal modelling of UQoL was conducted based on snapshot method in seasonal scale. In order to evaluate the proposed procedure, modelling of QoL was done at urban blocks scale in regions 3, 6, and 11 of Tehran, Iran. The results show the existence of a fairly regular pattern as an increase in desirability of UQoL from south to north of the area. The seasonal changes of UQoL show the improvement of environmental condition in spring and autumn compared to winter and summer. The result of sensitivity analysis shows the reliability of the modelling results. Since the effect of inputs on the fuzzy gamma output (2.5%) are less than the corresponding amount in VIKOR-fuzzy method (48%), it can be concluded that the output of fuzzy-gamma is more reliable.

UAV Detection with S Transform of t-student Window

So far, the detection of helicopters, especially micro helicopters, has been widely studied, while few works are available on detection of rotor unmanned aerial vehicle (UAV). Due to its low flight height, small RCS and the complicated urban environment, the traditional methods are unable to achieve effective detection of rotor UAV. In this paper, we present a novel S transform with improved window based on t-student to deal with this problem. The simulation and field experiments all show that the proposed S transform can suppress Gaussian noise effectively, and obtain the clear time-frequency distribution of the micro-motion target. Thus, the proposed S transform provides some references for detecting the micro-motion target with small RCS and small rotation amplitude such as the rotor UAV.

Locating Automated External Defibrillators in a Complicated Urban Environment Considering a Pedestrian-Accessible Network that Focuses on Out-of-Hospital Cardiac Arrests

Automated external defibrillators (AEDs) are portable devices that defibrillate and diagnose sudden-cardiac-arrest patients. Therefore, AEDs are widely installed in public places such as airports, schools, sport complexes, etc., and the installation of AEDs is required by law in these places. However, despite their usefulness, AEDs are mostly installed indoors with limited coverage outdoors. Hence, this study conducts research in the placement of AEDs in outdoor locations. This study considers a complicated urban environment using a pedestrian network dataset and network barriers. We draw on the Teitz and Bart’s (1968) heuristic method that was built in the location-allocation solver in ArcMap. The results of this study found that a total of 455 AEDs, including 227 pre-installed AEDs, could be placed in the study area, thus providing an additional 228 devices. Compared with 10 different installation methods that were set as experimental groups, our test results found that additional installations were able to cover 10% to 30% more actual out-of-hospital cardiac-arrest cases. The main contribution of this study is the proposal of a new method in locating AEDs in optimal areas while considering complicated urban environments. We predict that the cardiac-arrest-related mortality rate would be reduced through implementing the findings of this study.

An Intersection-based QoS Routing in Vehicular Ad Hoc Networks

Vehicular Ad hoc Networks (VANET) face many challenging issues because of the rapid topology changes, frequent network partitions, and complicated urban environments. These special VANET characteri...

캠퍼스 지하공간 개발전략 연구 - 국내 주요대학 캠퍼스 분석을 중심으로 -

University campus has grown and developed along with history of human in connection with transition characteristics of city. In particular, reflecting the features in the times, the masterplan should explore its main roles today when international growth of university is pursued. In particular, center core and quad space, focusing on main components of campus such as main building, student union hall and main lecture facility, are the prototype of campus components and have been pursued to have importance in terms of shape and function as traditional campus space strategy. Recently, however, new strategy has been proposed to explore orders of campus utilizing the underground space focusing on main universities. Design of Hana Plaza of Korea University and Bekyangro of Yeonsei University, the targets of this study, actively explore this strategy. This study analyzed such strategies in terms of concept of space to obtain space, design trend of main universities at home and abroad, and viewpoint of integrated masterplan proposal. Conclusions from this study are as follows. First, concept of quad and center space found in the design of traditional campus is reinterpreted into new design aspects through development of underground space. Second, development of underground space is approached to overcome the limit of existing campus and to present the vision of universities 100 years ahead. Third, ever-changing complicated urban environment has been reflected in space configuration of campus and positioned as strategy to implement this as modern construction technique. Third, implementation of pedestrian-oriented campus has been pursued by reinforcing utilization of inclined topography of urban environment in Korea. This enables to obtain spaces for main activities of students and to implement the space where student support facilities are concentrated and activities are performed actively. Such features can be presented as conclusions of this study.

Self-Supervised Online Long-Range Road Estimation in Complicated Urban Environments

In this paper, we propose a long-range road estimation method for autonomousmobile robots in unstructured urban environments. Near-range road surface conditions are estimated by using remission value as reflectivity of a laser scanner. Graph cut algorithm is applied to estimate road region robustly also in complicated environments. Moreover, we propose a novel image segmentation method to estimate long-range road surface. A compact texture/color feature is integrated with level-set method to estimate precise road boundaries robustly. Our proposed image segmentation approach gives better performance compared with standard classification approach. Finally, we run our autonomous mobile robot in “Tsukuba Challenge 2009” and our university campus, and experimental results have shown a marked increase accuracy in road estimation over standard methods.

A Practical Radiosity Method for Predicting Transmission Loss in Urban Environments

The ability to predict transmission loss or field strength distribution is crucial for determining coverage in planning personal communication systems. This paper presents a practical method to accurately predict entire average transmission loss distribution in complicated urban environments. The method uses a 3D propagation model based on radiosity and a simplified city information database including surfaces of roads and building groups. Narrowband validation measurements with line-of-sight (LOS) and non-line-of-sight (NLOS) cases at 1800 MHz give excellent agreement in urban environments.