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InSAR (Interferometric Synthetic Aperture Radar) is widely recognized as a crucial remote sensing tool for monitoring various geological disasters because it provides all-day and all-weather monitoring. Nevertheless, the current interpretation methods for InSAR heavily depend on the interpreter's experience, which hinders efficiency and fails to meet the requirements for the timely detection of geologic hazards. Furthermore, the results obtained through current InSAR processing carry inherent noise interference, further complicating the interpretation process. To address those issues, this paper proposes an approach that enables automatic and rapid identification of deformation zones. The proposed method leverages IPTA (Interferometric Point Target Analysis) technology for SAR data processing. It combines the power of HNSW (Hierarchical Navigable Small Word) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms to cluster deformation results. Compared with traditional methods, the computational efficiency of the proposed method is improved by 11.26 times, and spatial noise is suppressed. Additionally, the clustering results are fused with slope units determined using DEM (Digital Elevation Model), which facilitates the automatic identification of slopes experiencing deformation. The experimental verification in the western mountainous area of Beijing has identified 716 hidden danger areas, and this method is superior to the traditional technology in speed and automation. [ABSTRACT FROM AUTHOR]

The pedestrian detection model has a high requirement on the quality of the dataset. Concerning this problem, this paper uses data cleaning technology to improve the quality of the dataset, so as to improve the performance of the pedestrian detection model. The dataset used in this paper is obtained from subway stations in Beijing and Nanjing. The data images' quality is subject to motion blur, uneven illumination, and other noisy factors. Therefore, data cleaning is very important for this paper. The data cleaning process in this paper is divided into two parts: detection and correction. First, the whole dataset goes through blur detection, and the severely blurred images are filtered as the difficult samples. Then, the image is sent to DeblurGAN for deblur processing. 2D gamma function adaptive illumination correction algorithm is used to correct the subway pedestrian image. Then, the processed data is sent to the pedestrian detection model. Under different data cleaning datasets, through the analysis of the detection results, it is proved that the data cleaning process significantly improves the detection model's performance. [ABSTRACT FROM AUTHOR]

In the new era, the key measure to accelerate the construction of smart city, so as to promote the modernization of urban governance system and governance capacity, is to establish a good urban innovation ecosystem, and guide its continuous evolution to the direction of the highest efficiency and the best performance. Focusing on solving the practical problem of "how the urban innovation ecosystem evolves", this paper develops a NK algorithm using BP neural network and DEMATEL method. First, through literature research, constructing the urban innovation ecosystem including five subsystems of innovation talents, innovation subjects, innovation resources, innovation environment and innovation network. Then, taking Beijing as an example, the weights and the number of epistatic relationships of each subsystem in its innovation ecosystem are calculated by BP neural network and DEMATEL method, and the NK model is modified; on this basis, the fitness values corresponding to different states of the system are calculated using MATLAB software, and the optimal evolution path of Beijing innovation ecosystem is determined through the comparison of 100,000 simulation results. The results show that the optimal evolution path of Beijing's innovation ecosystem is to create a favorable environment and culture for innovation first; then increase the input of innovation resources; and then promote the development of innovation network assets; on this basis, cultivate, attract and retain innovative talents; and finally strengthen the construction of innovation subjects. [ABSTRACT FROM AUTHOR]

With the popularity of location-based services, the restricted relationship between the availability of big location data and user's privacy security has become a challenging issue. The spatial division is an effective measure for statistical location data characteristics. This paper proposes a grid adaptive bucketing algorithm based on differential privacy (GAB) to solve the problem that the existing differential privacy location data division method does not fully consider the distribution characteristics of spatial data points and noise superimposition. The algorithm first divides the mobile app location spatial data set into two layers of grids. It then performs adaptive binning judgment according to the square sum error value in the divided area. It puts areas with similar distributions into the same bucket, reducing the uniform assumption error and the noise error caused by many blank areas. Finally, a noise allocation strategy based on Hopkins statistic is adopted to achieve a reasonable noise allocation. The experimental results on two real large-scale location data sets, Checkin and Beijing, show that, compared with other classical algorithms based on differential privacy space partitioning, the GAB algorithm performs better in common evaluation indicators such as relative error and absolute error, which means GAB can obtain better range query effects and privacy protection effects. [ABSTRACT FROM AUTHOR]

Featured Application: This study purposes a synergy approach to generate the IFC model of very large quantities of data along with its graph database. Giving the identifier to each tile, following database syntax, the historic information and metadata are merged into the graph database. This synergy approach is developed on Grasshopper and Neo4j but is also applicable to other platforms. The investigation of cultural heritage is a continuous process, in which a large scale of metadata is gradually accumulated, i.e., condition data, intervention data, and the comprehension of building techniques. We take the roof tile of Yangxindian Palace as an example. The total number of roof tiles is 17,154. Their positions are various, as they are equally distributed on double-curved irregular surfaces. Their metadata are multisource and heterogeneous. Therefore, the integration between the model and the data can be complex. This paper aims to generate the M1 (metalevel) model with its graph database based on the M3 (metalevel) model through algorithm modeling. This synergy approach consists of three parts. First is the data collection. The unique identifier is used as the index while investigating onsite, which eventually forms the metadata repository. Second is the algorithm modeling. All the roof tiles are rebuilt in Grasshopper with identifiers. Their metadata are merged through Cypher syntax, which can generate the graph database in Neo4j. Third is the delivery. The IFC model and knowledge graph are connected in real time during the whole process. The significance of this paper is to provide a synergy approach and discuss its efficiency for the integration between a model and data of very large quantities. The results show that it is efficient to express the semantic relationships of both conditions and interventions. It is practicable to lightweight the IFC model and to associate metadata into the graph database, enabling online browsing and querying. [ABSTRACT FROM AUTHOR]

With the development of global economy, the evaluation of industrial clusters has become an important method to scientifically analyze the advantages and disadvantages of industrial development. The current research model uses the traditional evaluation method, which leads to the problem of unsatisfactory evaluation results and low effect. This paper proposes an evaluation model based on the BP neural network combined with the LM algorithm, which has the advantages of fast convergence speed and strong application ability. The comprehensive evaluation model of industrial clusters is put forward from the comprehensive application of the scale, benefit, and 27 related evaluation indexes of industrial clusters. This paper takes Beijing, Tianjin, and Hebei industrial clusters for BP-LM evaluation and analysis, which fully illustrates the advantages of this method. The evaluation results show that the evaluation target value and the average error of the overall parity have obvious advantages compared with those of other models, which provides application guidance for local economic development and policy formulation. [ABSTRACT FROM AUTHOR]

The construction layout of charging piles restricts the rapidly development of EVs. To address this area, this paper first simulates the current layout of charging piles in Beijing and finds that there is an unbalanced distribution problem of oversupply and idle resources. Then, based on the current situation, this study takes the total charge station construction cost as the objective function and unfolds in three levels, using MIP for conditional constraints. Finally, an improved particle swarm algorithm is employed to simulate electric vehicles by generating demand points in the region to obtain the optimum location of charging stations. This method overcomes the limitation of using static data in traditional research, and the simulation can reflect the dynamic law of electric vehicle operation, which is more consistent with the real situation. The calculation results show that the method adopted in this study can reasonably plan the layout of charging stations, relieve the charging pressure of some charging stations, and minimize the overall service cost of new charging stations based on the continuation of the existing layout. [ABSTRACT FROM AUTHOR]

The security travel of freight vehicles is of high societal concern and is the key issue for urban managers to effectively supervise and assess the possible social security risks. With continuous improvements in motion-based technology, the trajectories of freight vehicles are readily available, whose unusual changes may indicate hidden urban risks. Moreover, the increasing high spatial and temporal resolution of trajectories provides the opportunity for the real-time recognition of the abnormal or risky vehicle motion. However, the existing researches mainly focus on the spatial anomaly detection, and there are few researches on the real-time temporal anomaly detection. In this paper, a grid-based algorithm, which combines the spatial and temporal anomaly detection, is proposed for tracing the risk of urban freight vehicles trajectory by considering local temporal window. The travel time probability distribution of vehicle historical trajectory is analyzed to meet the time complexity requirements of real-time anomaly calculation. The developed methodology is applied to a case study in Beijing to demonstrate its accuracy and effectiveness. [ABSTRACT FROM AUTHOR]

With the traffic congestion problem deteriorating, people increasingly choose urban rail transit (URT) to travel. Although URT alleviates traffic congestion, the long-term operation of a large number of trains leads to huge energy consumption. In order to adapt the major social development concept of "Low carbon", a multi-train energy-saving control collaborative optimization method is proposed in this paper. First, the composition of single train operating conditions is determined by the conversion of operating conditions between stations and the force changes under the premise of ensuring safe and on-time train operation. A single-train energy consumption calculation combinatorial optimization model with the dual control objectives of reducing passengers' average waiting time as well as train traction energy consumption is established. The energy saving control strategy of a single train is investigated by ARMA-Radial Basis Function Neural Network (ARMA-RBFNN) and Genetic Algorithm (GA). Next, the queuing theory is introduced to analyze the variation in passenger waiting time for multiple trains at different arrival intervals. A Deep Reinforcement Learning (DRL) algorithm is designed to obtain the correlation among passenger waiting time, arrival interval and train stopping time. The optimization objective is to minimize the multi-train traction energy consumption and the average passenger waiting time while considering conditions such as train operating safety interval, speed limit, multiple operating state and single train energy-saving models, etc. Then, a multi-train cooperative energy-saving control model is proposed based on the Dragonfly Algorithm (DA). Finally, a case study of Beijing Metro Line 4 is conducted to illustrate the effectiveness of the proposed method. The results demonstrate that the total traction energy consumption and passenger waiting time are reduced by 3.1% and 5 s, respectively, compared with the method of independently optimizing the single-train control strategy. The findings can aid in the development of energy-saving strategies and also provide a basis for energy-saving operation control of multiple trains. [ABSTRACT FROM AUTHOR]

With the development of economy, energy dilemma and environment protection give urgent pressure on automobile industry. More and more countries are making new energy vehicle policy. How to choose a proper routing when driving an electric vehicle? Does charging stations layout in a city meet the demand of electric vehicles? This paper gives out the electric vehicles' routing optimizing algorithm. The algorithm focus on decision policy before an electric vehicle starts. There are 3 kinds of situations: the situation moving to destination, moving to a charging station and staying origin. Each situation has its judgment and it is discussed in detail. Except that, the electric vehicles' routing optimization is solved in this paper. By applying routing optimizing algorithm, a multi-agent simulation model is build and the model is running with real data of charging stations in Beijing. The simulation results show that this model is valid. Through analysis of simulation data, this paper concludes that the layout of charging stations in Beijing is not reasonable, because charging stations are not visited by electric vehicles equally. 4 charging stations have higher visiting frequency. More charging stations should be built near these areas. [ABSTRACT FROM AUTHOR]

In order to implement real-time detection of passengers in subway stations, this paper proposes the SPDNet based on YOLOv4. Aiming at the low detection accuracy of passengers in the subway station due to uneven light conditions, we introduce the attention mechanism CBAM to recalibrate the extracted features and improve the robustness of the network. For the crowded areas in the subway station, we use the K-means++ algorithm to generate anchors that are more consistent with the passenger aspect ratio based on the dataset KITTI, which mitigates the missing caused by the incorrect suppression of true positive boxes by the Nonmaximum Suppression algorithm. We train and test our SPDNet on the KITTI dataset and prove the superiority of our method. Then, we carry out transfer learning based on the subway surveillance video dataset collected by ourselves to make it conform to the distorted passenger targets under the angle of the surveillance camera. Finally, we apply our network in a Beijing subway station and achieve satisfactory results. [ABSTRACT FROM AUTHOR]

Railway transport becomes a more popular transportation in many countries due to its large transport capacity, low energy consumption, and benign environment. The passenger train service planning is the key of the rail operations system to balance the transport service and the passenger demand. In this paper, we propose a mixed binary linear programming formulation for the passenger train service planning to optimize the train route, frequency, stop schedule, and passenger assignment simultaneously. In addition, we analyze the computational complexities of the model and develop a Benders decomposition algorithm with valid inequalities to solve this problem. Finally, our model and algorithm are tested on a real-world instance of the Beijing-Shanghai high-speed railway line. The computational results show that our approach can solve these problems within reasonable solution time and small optimality gaps (less than 2.5%). [ABSTRACT FROM AUTHOR]

This paper studies a location-allocation problem to determine the selection of emergency shelters, medical centers, and distribution centers after the disaster. The evacuation of refugees and allocation of relief resources are also considered. A mixed-integer nonlinear multiobjective programming model is proposed to characterize the problem. The hierarchical demand of different refugees and the limitations of relief resources are considered in the model. We employ a combination of the simulated annealing (SA) algorithm and the particle swarm optimization (PSO) algorithm method to solve the complex model. To optimize the result of our proposed algorithm, we absorb the group search, crossover, and mutation operator of GA into SA. We conduct a case study in a district of Beijing in China to validate the proposed methodology. Some computational experiments are conducted to analyze the impact of different factors, such as the target weight setting, selection of candidate shelters, and quantity of relief resources. [ABSTRACT FROM AUTHOR]

While facing the needs for Vehicle Infrastructure Integration (VII) applications in traffic management, the paper deals with the problem of locating Road Side Units (RSU) for VII deployment. After analyzing the difference between traditional problems of locating traffic information detector and the problem of RSU location, a significance ranking model for RSU localization and three kinds of Significance Degree (SD) computing strategies are put forward. A VII simulation environment for the purpose of RSU localization optimization within VISSIM microscopic traffic simulation software is established developing add-on functions using VISSIM's Component Object Model (COM). A VII test bed of the Olympic Park network in Beijing is taken as an example to evaluate the performance of RSU localization model. The results of simulation experiments indicate that the mixed SD strategy considering both speed and route monitoring is superior to the other two SD strategies. Then, the impact of RSU number and OBE market penetration rate on the evaluation measures of traffic monitoring are studied with reference to the proposed mixed SD strategy. In this case, the evaluation measures of optimized RSU configurations generated by the ranking algorithm are always better than those of random RSU configurations. In addition, the benefits of optimized RSU configurations increase along with RSU number and market penetration rate while the benefits of random RSU configurations are more fluctuant. [ABSTRACT FROM PUBLISHER]

The accurate and rapid prediction of ticket prices for a public-private partnership (PPP) subway system, which is an important research topic in the field of civil engineering management, is of critical importance to ensure its smooth operation. To effectively cope with the effects of multiple influencing factors and strong nonlinearity among them, the mean impact value (MIV) method and the back-propagation (BP) feed-forward neural network improved by the sparrow search algorithm (SSA) are used in this study to develop an intelligent prediction model. First, we considered the relationship of the supply and the subway system service, which is a typical quasi-public product, and analyzed the relevant factors affecting its price adjustment. Then, we developed an intelligent method for the prediction of ticket prices based on the SSA-BP. This model not only makes full use of the powerful nonlinear modeling ability of the BP algorithm, but also takes advantage of the strong optimization ability and fast convergence speed of the SSA. Finally, this study screened out the key input factors by adopting the MIV method to simplify the structure of the BP algorithm and achieve a high prediction accuracy. In this study, Beijing Subway Line 4, Wuhan Metro Line 2, and Chengdu Metro Line 1 were selected as case study sites. The results showed that the linear correlations between influencing factors and ticket price for the PPP subway system service were weak, which indicated the need for using nonlinear analysis methods such as the BP algorithm. Compared with other prediction methods (the price adjustment method based on PPP contract, the traditional BP algorithm, the BP neural network improved by the genetic algorithm, the BP algorithm improved by the particle swarm optimization, and the support vector machine), the model proposed in this paper showed better prediction accuracy and calculation stability. [ABSTRACT FROM AUTHOR]

The adverse effects caused by PM2.5 have drawn extensive concern and it is of great significance to identify its spatial distribution. Satellite-derived aerosol optical depth (AOD) has been widely used for PM2.5 estimation. However, the coarse spatial resolution and the gaps caused by data deficiency impede its better application at the urban scale. Additionally, obtaining accurate results in unsampled spatial areas when PM2.5 ground sites are insufficient and distribute sparsely is also a challenging issue for PM2.5 spatial distribution estimation. This paper aimed to develop a model, i.e., spatially local extreme gradient boosting (SL-XGB), combining the powerful fitting ability of machine learning and optimal bandwidths of local models, to better estimate PM2.5 concentration at the urban scale by using Beijing as the study area. This paper adopted simplified high-resolution MODIS aerosol retrieval algorithm (SARA) AOD at 500 m resolution as the major independent variable, hence, ensuring the estimation can be operated at a fine scale. Moreover, the extreme gradient boosting (XGBoost) model was adopted to fill the gaps in SARA AOD, thus improving its availability. Then, based on full-covered SARA AOD and other multisource data, the SL-XGB model, integrating multiple local XGBoost models and particular optimal bandwidths, was trained to estimate PM2.5 concentration. For comparison, SL-XGB and two other models, XGBoost and geographically weighted regression (GWR), were evaluated by 10-fold cross validation (CV). The sample-based CV results reveal that the SL-XGB performed the best as assessed through R2 (0.88), root mean square error (RMSE = 24.08 μg/m3) and mean prediction error (MPE = 16.90 μg/m3). Additionally, SL-XGB also performed the best in the site-based CV with a R2 of 0.86, a RMSE of 26.15 μg/m3 and a MPE of 17.97 μg/m3, which shows its good spatial generalization ability. These results demonstrate that SL-XGB can better simultaneously handle non-linear and spatial heterogeneity issues despite spatially limited data at the urban scale. As far as the PM2.5 concentration distribution was concerned, it presented a gradient increase in PM2.5 concentrations from the northwest to the southeast in Beijing, with abundant spatial details. Overall, the proposed approach for PM2.5 estimation showed outstanding performance and can support preventive pollution control and mitigation at the urban scale. [ABSTRACT FROM AUTHOR]

This paper introduces a new algorithm (BUNTUS—Built-up, Nighttime Light, and Travel time for Urban Size) using remote sensing techniques to delineate urban boundaries. The paper is part of a larger study of the role of urbanisation in changing fossil fuel emissions. The method combines estimates of land cover, nighttime lights, and travel times to classify contiguous urban areas. The method is automatic, global and uses data sets with enough duration to establish trends. Validation using ground truth from Landsat-8 OLI images revealed an overall accuracy ranging from 60% to 95%. Thus, this approach is capable of describing spatial distributions and giving detailed information of urban extents. We demonstrate the method with examples from Brisbane, Australia, Melbourne, Australia, and Beijing, China. The new method meets the criteria for studying overall trends in urban emissions. [ABSTRACT FROM AUTHOR]

Intelligent vehicles face a more complex driving environment, which requires their path planning algorithms to have the ability to respond quickly to complex environmental changes. Therefore, it is very necessary to plan the optimal path for smart vehicles in real-time. Recently, the convergence of graph dynamics and its equivalence with the shortest path solution have been proved, and the biased minimum consensus algorithm has been successfully applied to the shortest path planning problem. In this paper, considering the road conditions (degree of congestion, whether an accident occurred, etc.) and the Euclidean distance of the route, we improve biased minimum consensus algorithm to achieve optimal path planning in the complex situation of real-time updating of road conditions. In the simulation, we used the algorithm for path planning in the road topological maps of Beijing City, and compared with the RRT (Rapidly-exploring Random Trees Algorithm) algorithm and the BRRT (Bidirectional Rapidly-exploring Random Trees Algorithm) algorithm. The improved biased minimum consensus algorithm has better performance in real-time path planning problems. The research results shed new light on the real-time dynamic path planning in real traffic, which has practical significance. [ABSTRACT FROM AUTHOR]

Determining accurate PM2.5 pollution concentrations and understanding their dynamic patterns are crucial for scientifically informed air pollution control strategies. Traditional reliance on linear correlation coefficients for ascertaining PM2.5-related factors only uncovers superficial relationships. Moreover, the invariance of conventional prediction models restricts their accuracy. To enhance the precision of PM2.5 concentration prediction, this study introduces a novel integrated model that leverages feature selection and a clustering algorithm. Comprising three components—feature selection, clustering, and integrated prediction—the model first employs the non-dominated sorting genetic algorithm (NSGA-III) to identify the most impactful features affecting PM2.5 concentration within air pollutants and meteorological factors. This step offers more valuable feature data for subsequent modules. The model then adopts a two-layer clustering method (SOM+K-means) to analyze the multifaceted irregularity within the dataset. Finally, the model establishes the Extreme Learning Machine (ELM) weak learner for each classification, integrating multiple weak learners using the AdaBoost algorithm to obtain a comprehensive prediction model. Through feature correlation enhancement, data irregularity exploration, and model adaptability improvement, the proposed model significantly enhances the overall prediction performance. Data sourced from 12 Beijing-based monitoring sites in 2016 were utilized for an empirical study, and the model's results were compared with five other predictive models. The outcomes demonstrate that the proposed model significantly heightens prediction accuracy, offering useful insights and potential for broadened application to multifactor correlation concentration prediction methodologies for other pollutants. [ABSTRACT FROM AUTHOR]

The steady increase in data generation by GPS systems poses storage challenges. Previous studies show the need to address trajectory compression. The demand for accuracy and the magnitude of data require effective compression strategies to reduce storage. It is posited that the combination of TD-TR simplification, Kalman noise reduction, and analysis of road network information will improve the compression ratio and margin of error. The GR algorithm is developed, integrating noise reduction and path compression techniques. Experiments are applied with trajectory data sets collected in the cities of California and Beijing. The GR algorithm outperforms similar algorithms in compression ratio and margin of error, improving storage efficiency by up to 89.090%. The combination of proposed techniques presents an efficient solution for GPS trajectory compression, allowing to improve storage in trajectory analysis applications. [ABSTRACT FROM AUTHOR]

A new harmonic power standard has been developed at the National Institute of Metrology (NIM), Beijing, China, for the calibration of harmonic power analyzers under nonsinusoidal conditions at fundamental frequencies of 50 and 60 Hz. The standard is based on digital sampling techniques that do not require synchronization. A compensation algorithm is presented in this paper. A new definition of the uncertainty for harmonic measurement is proposed that is referred to the fundamental. A characterization signal and its application are introduced. Results have shown that over its operating range of up to 50 A, 500 V, and the sixtieth-order harmonic, the harmonic power standard has uncertainties (k = 2) of less than 30 μV/V, 36 μA/A, and 42 μW/VA for voltage, current, and power measurements, respectively. [ABSTRACT FROM AUTHOR]

Foreign Object Debris (FOD) refers to any foreign material on the airfield that may injure and threaten the aircraft and airport system. Due to the complex background on the airfield pavement and weak target echoes in long-distance monitoring, it is not easy to detect objects of various types and sizes. The existing FOD radar system's detection method has a short effective range, and the detectable objects' radar cross-section intensity is no less than −20 dBsm. In this paper, we propose an integrated FOD automatic target detection algorithm for millimeter-wave (MMW) surveillance radar to improve small target detection under long-range conditions of over 660 m. The signal form of FOD and a clutter model of ground clutter received by millimeter-wave radar are primarily utilized and established theoretically. The runway edge detection means that it is employed based on the in-continuity features as the runway region of interest during the automatic extraction step. Following the clutter map constant false alarm detection algorithm, we utilize a time-domain algorithm that functions as the vital detection processor. Moreover, an explicit definition of the FOD detection performance is developed in a characteristic quantitative way. This criterion involves an absolute reference value for all FOD radar systems. The well-designed FOD frequency-modulated continuous-wave MMW surveillance radar is utilized, and actual experiments are carried out in a real airport in Beijing, China. The results validate the proposed method's effectiveness and the superior performance of FOD target detection in long-range situations. [ABSTRACT FROM AUTHOR]

The satellite-based Aerosol Optical Depth (AOD) retrieval algorithms are generally needed to construct Land Surface Reflectance (LSR) database. However, errors are unavoidable due to the surface complexity, especially for the short observation period and high-resolution images, such as Sentinel-2 Multi-Spectral Instrument (MSI) data. To address this, reference day images are used instead of the LSR database. The surface is assumed to be Lambertian; however, the fact is that not all pixels meet it well. Therefore, we proposed a window-based AOD retrieval algorithm, which can ignore the unreliable/non-Lambertian pixels in a retrieval window based on two main filtering processes. Finally, using Sentinel-2 Band 1 (60 m), the AODs (120 m) of 134 reference images to 43 reference images were retrieved by this algorithm from 2017 to 2021 in Beijing region, China. The results show that the retrieved AOD with the proposed algorithm exhibits good agreement with the ground-based measured AOD (R > 0.97). The high-resolution AOD presents comparable spatial distributions to the Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm AOD (1 km) products. Moreover, the very little noise and very high spatial continuity of retrieval AOD imply that this algorithm could be ported to other algorithms as part of improving AOD quality. [ABSTRACT FROM AUTHOR]

This paper focuses on the optimization problem of a signal timing design based on the concept of bus priority. This optimization problem is formulated in the form of a bi-level programming model that minimizes average passenger delay at intersections and vehicle delay in lanes simultaneously. A solution framework that implements the differential evolution (DE) algorithm is developed to efficiently solve the model. A case study based on a real-world intersection in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods. The experiment's result shows that the optimization model can not only significantly improve the priority capacity of the buses at the intersection but also reduce the adverse impact of bus-priority approaches on the private vehicles for the intersections. [ABSTRACT FROM AUTHOR]

The values of ratio a of the linear relationship between specific attenuation and specific differential phase vary significantly in convective storms as a result of resonance scattering. The best-linear-fit ratio a at X band is determined using the modified attenuation correction algorithm based on differential phase and attenuation, as well as the premise that reflectivity is unattenuated in S band radar detection. Meanwhile, the systemic reflectivity bias between the X band radar and S band radar and water layer attenuation (ZW) on the wet antenna cover of the X band radar are also considered. The good performance of the modified correction algorithm is demonstrated in a moderate rainfall event. The data were collected by four X band dual-polarization (X-POL) radar sites, namely, BJXCP, BJXFS, BJXSY, and BJXTZ, and a China's New Generation Weather Radar (CINRAD/SA radar) site, BJSDX, in Beijing on 20 July 2016. Ratio a is calculated for each volume scan of the X band radar, with a mean value of 0.26 dB deg−1 varying from 0.20 to 0.31 dB deg−1. The average values of systemic reflectivity bias between the X band radar (at BJXCP, BJXFS, BJXSY, and BJXTZ) and S band radar (at BJSDX) are 0, −3, 2, and 0 dB, respectively. The experimentally determined ZW is in substantial agreement with the theoretically calculated ones, and their values are an order of magnitude smaller than rain attenuation. The comparison of the modified attenuation correction algorithm and the empirical-fixed-ratio correction algorithm is further evaluated at the X-POL radar. It is shown that the modified attenuation correction algorithm in the present paper provides higher correction accuracy for rain attenuation than the empirical-fixed-ratio correction algorithm. [ABSTRACT FROM AUTHOR]

Onboard the Chinese GaoFen-5 (GF5) satellite, the Environmental trace gases Monitoring Instrument (EMI) is a nadir-viewing wide-field spectrometer that was launched on May 9, 2018. EMI measures the back-scattered earthshine solar radiance in the ultraviolet and visible spectral range. By using the differential optical absorption spectrometry (DOAS) method and the EMI measurements in the VIS1 band (405–465 nm), we performed retrievals of NO2. Some first retrieval results of NO2 from EMI and a comparison with OMI and TROPOMI products are presented in this paper. The monthly mean total vertical column densities (VCD) of NO2 show similar spatial distributions to OMI and TROPOMI (r > 0.88) and their difference is less than 27%. A comparison of the daily total VCD shows that EMI could detect the NO2 patterns in good agreement with OMI (r = 0.93) and TROPOMI (r = 0.95). However, the slant column density (SCD) uncertainty (0.79 × 1015 molec cm−2) of the current EMI algorithm is relatively larger than OMI. The daily variation pattern of NO2 from EMI in Beijing in January 2019 is consistent with TROPOMI (r = 0.96). The spatial distribution correlation of the tropospheric NO2 VCD of EMI with OMI and TROPOMI is 0.88 and 0.89, respectively, but shows an overestimate compared to OMI (15%) and TROPOMI (23%), respectively. This study demonstrates the capability of using EMI for global NO2 monitoring. [ABSTRACT FROM AUTHOR]

With the boom of big data, the Internet contains more and more personal behavior information, but it is difficult to extract effectively. A model involving multivariate processing capability must be constructed to deal with these time series with complex characteristics. In this paper, a novel hybrid model embedding Baidu Search Index is therefore proposed to implement multi-step ahead subway passenger flow forecasting. Firstly, we collect data from informative Baidu Search Index, reduce dimensionality, and screen out the powerful predictors by statistical analysis. Secondly, we extract matching common modes at similar time scales between the subway passenger flow and screened Baidu Search Index via multivariate mode decomposition being optimized by multi-objective algorithm. Furthermore, to eliminate pseudo statistical causality, we select the optimal combination of modal components between subway passenger flow and its corresponding Baidu Search Index at each time scale by an innovative multi–modal analysis strategy. Thirdly, we reconstruct the forecasting values of each selected optimal combination as the final results. The empirical results of Beijing, Shanghai and Guangzhou show that the proposed model can significantly outperform six benchmark models in both the level and directional accuracy. So introducing Baidu Search Index creates a sound opportunity to enhance the subway passenger flow forecasting ability. [ABSTRACT FROM AUTHOR]

Typical urban and rural temperature records are essential for the estimation and comparison of urban heat island effects in different regions, and one of the key issues is how to identify the typical urban and rural stations. This study tried to analyze the similarity of air temperature sequences by using dynamic time warping algorithm (DTW) to improve the selection of typical stations. We examined the similarity of temperature sequences of 20 stations in Beijing, and the results indicated that DTW algorithm could identify the difference of temperature sequence, and clearly divide them into different groups according to their probability distributions. DTW algorithm could create more rigorous and reasonable classification for typical urban and rural stations in Beijing by comparing with remote sensing and cluster analysis method. We also found that some traditional rural stations did not represent temperature variation in rural areas because their surrounding environments have been highly modified by urbanization process in last decades, and this might lead to an underestimate of the urban climate effect by 1.24℃. The study suggests that DTW algorithm is a simple and efficient method for selecting appropriate temperature records and analyzing temperature sequences with a good potential in improving urban heat island estimation. [ABSTRACT FROM AUTHOR]

The interactions between group members often have a significant impact on the results of group recommendations. The traditional group recommendation algorithm does not consider the trust and social influence among users. It involves a low utilization rate of social relationship information, which leads to a low accuracy and satisfaction of group recommendations. Considering these issues, in this study, we propose a travel group recommendation model based on user trust and social influence. Based on the user trust relationship, this model defines the user direct and indirect trust and calculates the user global trust by combining the two trusts. Subsequently, the PageRank algorithm is used to calculate the social influence of users based on their interaction relationship history. Thereafter, a consensus model integrating the intra- and intergroup prediction scores is designed by integrating users' global trust and social influence to realize group recommendations for tourist attractions. Comparison experiments with several well-known group recommendation models for datasets of different scenic spots in Beijing demonstrate that the proposed model provides a better recommendation performance. [ABSTRACT FROM AUTHOR]

As a critical foundation for train traffic management, a train stop plan is associated with several other plans in high-speed railway train operation strategies. The current approach to train stop planning in China is based primarily on passenger demand volume information and the preset high-speed railway station level. With the goal of efficiently optimising the stop plan, this study proposes a novel method that uses machine learning techniques without a predetermined hypothesis and a complex solution algorithm. Clustering techniques are applied to assess the features of the service nodes (e.g., the station level). A modified Markov decision process (MDP) is conducted to express the entire stop plan optimisation process considering several constraints (service frequency at stations and number of train stops). A restrained MDP-based stop plan model is formulated, and a numerical experiment is conducted to demonstrate the performance of the proposed approach with real-world train operation data collected from the Beijing-Shanghai high-speed railway. [ABSTRACT FROM AUTHOR]

In China, developing intelligent cities has become an irresistible trend of development in cities at present. Intelligent cities have been constructed in various regions. With the continuous implementation of intelligent cities, the evaluation index system has been gradually improved. Based on this, intelligent city evaluation algorithm based on analytic hierarchy process is studied. The evaluation model was firstly constructed, followed by the analysis of the analytic hierarchy process, and the analytic hierarchy process was applied to the evaluation of intelligent cities. Then, an empirical study of the algorithm was conducted in Fangshan District of Beijing as an example to explain the application of the algorithm. [ABSTRACT FROM AUTHOR]

The article presents a study on the recovery of aerosol optical attributes in several wavelengths of ground-based multi-axis differential absorption spectroscopy (MAXDOAS) in Beijing, China. The study involves the application of inversion algorithm at multiple wavelengths and calculation of aerosol optical depths (AODs). It assumes the correction factor in the measured differential slant column densities (DSCDs). Results of the study reveal the high quality of tetraoxygen (4) DSCDs.

Background: The Beijing Declaration on food safety and security was signed by over fifty countries with the aim of developing comprehensive programs for monitoring food safety and security on behalf of their citizens. Currently, comprehensive systems for food safety and security are absent in many countries, and the systems that are in place have been developed on different principles allowing poor opportunities for integration. Methodology/Principal Findings: We have developed a user-friendly analytical tool based on network approaches for instant customized analysis of food alert patterns in the European dataset from the Rapid Alert System for Food and Feed. Data taken from alert logs between January 2003 - August 2008 were processed using network analysis to i) capture complexity, ii) analyze trends, and iii) predict possible effects of interventions by identifying patterns of reporting activities between countries. The detector and transgressor relationships are readily identifiable between countries which are ranked using i) Google's PageRank algorithm and ii) the HITS algorithm of Kleinberg. The program identifies Iran, China and Turkey as the transgressors with the largest number of alerts. However, when characterized by impact, counting the transgressor index and the number of countries involved, China predominates as a transgressor country. Conclusions/Significance: This study reports the first development of a network analysis approach to inform countries on their transgressor and detector profiles as a user-friendly aid for the adoption of the Beijing Declaration. The ability to instantly access the country-specific components of the several thousand annual reports will enable each country to identify the major transgressors and detectors within its trading network. Moreover, the tool can be used to monitor trading countries for improved detector/transgressor ratios. [ABSTRACT FROM AUTHOR]

Recent studies have shown that the high-resolution satellite Landsat-8 has the capability to retrieve aerosol optical depth (AOD) over urban areas at a 30 m spatial resolution. However, its long revisiting time and narrow swath limit the coverage and frequency of the high resolution AOD observations. With the increasing number of Earth observation satellites launched in recent years, combining the observations of multiple satellites can provide higher temporal-spatial coverage. In this study, a fusing retrieval algorithm is developed to retrieve high-resolution (30 m) aerosols over urban areas from Landsat-8 and Sentinel-2 A/B satellite measurements. The new fusing algorithm was tested and evaluated over Beijing city and its surrounding area in China. The validation results show that the retrieved AODs show a high level of agreement with the local urban ground-based Aerosol Robotic Network (AERONET) AOD measurements, with an overall high coefficient of determination (R2) of 0.905 and small root mean square error (RMSE) of 0.119. Compared with the operational AOD products processed by the Landsat-8 Surface Reflectance Code (LaSRC-AOD), Sentinel Radiative Transfer Atmospheric Correction code (SEN2COR-AOD), and MODIS Collection 6 AOD (MOD04) products, the AOD retrieved from the new fusing algorithm based on the Landsat-8 and Sentinel-2 A/B observations exhibits an overall higher accuracy and better performance in spatial continuity over the complex urban area. Moreover, the temporal resolution of the high spatial resolution AOD observations was greatly improved (from 16/10/10 days to about two to four days over globe land in theory under cloud-free conditions) and the daily spatial coverage was increased by two to three times compared to the coverage gained using a single sensor. [ABSTRACT FROM AUTHOR]

Under the "Internet+" environment, the R&D intensity of products and services has increased; hence, organizations need to improve their ability to integrate knowledge and technology resources. Knowledge gaps will arise when an organization's knowledge reserves fail to meet the needs of innovation activities. This research established a network of complete knowledge topics under the "Internet+" environment based on the Word2Vec model. The word vectors and word frequencies of organizational reserve knowledge texts were analyzed to establish an organizational reserve knowledge topic network. The Term Frequency-Inverse Document Frequency algorithm was used to identify the demanded knowledge topic. The satisfaction capability of demanded knowledge in the reserve knowledge topic network was calculated via the eigenvector centrality and the fuzzy evaluation method. The corresponding strategies were then put forward to make up the knowledge gap. Finally, a case study was conducted and compared with SWOT (Strengths, Weaknesses, Opportunities, and Threats) and Venn diagram analysis on the economic and management college of a university in Beijing to verify the effectiveness of this method. [ABSTRACT FROM AUTHOR]

In this study we present the retrieval of the column-averaged dry air mole fraction of carbon dioxide (XCO2) from the TanSat observations using the ACOS (Atmospheric CO2 Observations from Space) algorithm. The XCO2 product has been validated with collocated ground-based measurements from the Total Carbon Column Observing Network (TCCON) for 2 years of TanSat data from 2017 to 2018. Based on the correlation of the XCO2 error over land with goodness of fit in three spectral bands at 0.76, 1.61 and 2.06 μm, we applied an a posteriori bias correction to TanSat retrievals. For overpass averaged results, XCO2 retrievals show a standard deviation (SD) of ~2.45 ppm and a positive bias of ~0.27 ppm compared to collocated TCCON sites. The validation also shows a relatively higher positive bias and variance against TCCON over high-latitude regions. Three cases to evaluate TanSat target mode retrievals are investigated, including one field campaign at Dunhuang with measurements by a greenhouse gas analyzer deployed on an unmanned aerial vehicle and two cases with measurements by a ground-based Fourier-transform spectrometer in Beijing. The results show the retrievals of all footprints, except footprint-6, have relatively low bias (within ~2 ppm). In addition, the orbital XCO2 distributions over Australia and Northeast China between TanSat and the second Orbiting Carbon Observatory (OCO-2) on 20 April 2017 are compared. It shows that the mean XCO2 from TanSat is slightly lower than that of OCO-2 with an average difference of ~0.85 ppm. A reasonable agreement in XCO2 distribution is found over Australia and Northeast China between TanSat and OCO-2. [ABSTRACT FROM AUTHOR]