Search

The rapid expansion of metro networks, e.g., in many cities of China, continuously introduces the operation of new stations every year. Due to the lack of historical data and complicate variations of short-term passenger flow in the early stage of operation, it is difficult to accurately predict inbound and outbound passenger flows of new metro stations in the short term, which would be the database for train scheduling for new stations before operation, dynamic capacity optimization for new stations under operation, short-term prediction of cycle sharing demands near new stations, and so on. Traditional methods usually failed to exactly reflect the complicate rules or were unusable without the new station's historical data. In order to solve the above problems, this paper proposes a short-term inbound and outbound passenger flow prediction model for new metro stations at the early stage of operation by combining the K-means clustering algorithm, an improved spatiotemporal long short-term memory model (Sp-LSTM), and a real-time feedback error model (mean absolute error, MAE), where passenger flows' spatial-temporal characteristics and land-use relevance are considered. The application in Guangzhou Metro, China, where Line 21 is regarded as a new line, shows that the proposed K-Sp-LSTM model has the best prediction accuracy compared with traditional methods. [ABSTRACT FROM AUTHOR]

This paper summarizes the results of an effort aimed at improving train operation schedules on Wuhan-Guangzhou high-speed railway (WG-HSR). The real-record train operation and passenger tickets-booking records of WG-HSR are used for statistical analysis on the train service quality and passenger distribution. More specifically, the train service frequency and interval time at each station are analyzed. Based on this, the temporal and spatial distribution of capacity utilization in each section are investigated. In order to get a holistic view of passenger flow characteristics, the passenger volume during different time periods and between several origin and destination (OD) pairs are investigated to characterize travellers' spatial-temporal preferences. The passenger distributions on some long-distance trains are shown to get the number and proportion of cross-line passengers travelling on the WG-HSR. Moreover, for a better understanding of the seat capacity utilization of trains, the load rates of trains in various sections and time periods are investigated. Specifically, the relationship between the average load rate of trains and trains' running distance is explored, finding that the longer the non-cross-line train travels is, the higher the average load rate is. This study provides insightful findings that help understanding HSR operation and conducting further research. [ABSTRACT FROM AUTHOR]

Historical and cultural blocks contribute to urban cultural construction in a very significant way. Regardless of their specific types, historical and cultural blocks not only add brilliance to the cultural heritage of cities but also take on more urban responsibilities through actively expanding their use values. This perfectly embodies the diversified protection and development of traditional blocks. In discussing the diversified development achievements of a block, its users usually have the most since their behaviors are closely related and mutually interactive with block environments. In this sense, collecting user evaluations helps to identify the diversified development status of a block from the inside and explore more values. Considering that the residential-type historical and cultural blocks in Guangzhou have realized diversified development in terms of location values, function expansion, and user integration, this paper takes two typical blocks in the city for a case study and performs user evaluation analysis. Evaluation data are collected through traditional questionnaire surveys and text mining to expand the scale of access to information and enhance data flexibility. An evaluation index set is established for residential-type blocks by discussing the achievements of the diversified block developments and extracting the key factors of block environments based on data analysis. The research results of this paper provide a basic framework for the in-depth analysis of blocks and evaluation models building and offer suggestions of protection and design for other blocks. [ABSTRACT FROM AUTHOR]

Passenger travel flows of urban rail transit during holidays usually show distinct characteristics different from normal days. To ensure efficient operation management, it is essential to accurately predict the distribution of holiday passenger flow. Based on Automatic Fare Collection (AFC) data, this paper explores the passengers' destination choice differences between normal days and holidays, as well as one-way tickets and public transportation cards, which provides support for variable selection in modeling. Then, a forecasting model of holiday travel distribution is proposed, in which the destination choice model is established for representing local and nonlocal passengers. Meanwhile, explanatory variables such as land matching degree, scenic spot dummy, and level of service variables are introduced to deal with the particularity of holiday passengers' travel behavior. The parameters calibrated by the improved weighted exogenous sampling maximum likelihood (WESML) method are applied to predict passenger flow distribution in different holiday cases with annual changes in the metro network, using the data collected from Guangzhou Metro, China. The results show that the proposed model is valid and performs better than the other comparable models in terms of forecasting accuracy. The proposed model has the capability to provide a more universal and accurate passenger flow distribution prediction method for urban rail transit in different holiday scenarios with network changes. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to deal with the perception variance problem in regret model by scaling perception variance and relaxing the distribution assumption of the random error term, which are corresponding to the attribute and alternative differences, respectively. Specifically, the power coefficient in regret function can capture individual perception variance of specific attributes and latent class structure is used to analyze individual heterogeneity of alternative differences by defining weight function. Accordingly, perceiving behaviors of individuals are analyzed in depth and generating absolute and relative behavior interpretation and asymmetric property. The proposed models are estimated and analyzed using GPS data of taxis in Guangzhou, and bicycle household surveys are collected in Tel Aviv metropolitan area. The results show both significant effects of attributes on drivers' and cyclists' route choice behavior and identification of different types (absolute or relative) of perceiving attribute and alternative regret differences of the two travelers by incorporating the ratio of length between chosen route and the shortest route. [ABSTRACT FROM AUTHOR]

Historical and cultural blocks not only carry numerous cultural heritages but always undertake urban functions. As a major driver of urban context construction, historical and cultural blocks have never been abandoned by the times. Regardless of the purposes for which they were built in the first place, all historical and cultural blocks should be extended functionally to rejuvenate them in current and future developments. In particular, in single-functional residential-type historical and cultural blocks, the large numbers of traditional architectures left vacant by out-migrating residents can be redeveloped for other functions. The effectiveness of block conservation and development affects the cultural construction of a city. More importantly, it is closely related to all kinds of users in blocks, as users engage in in-depth interactions with environments and are best able to discover the merits of block conservation and development. Moreover, the conservation and optimal design of historical and cultural blocks must, ultimately, be based on the users' needs. By collecting users' subjective evaluation opinions about two typical residential-type historical and cultural blocks based on the existing evaluation index and putting them through quantitative analysis, this paper analyzes block status in many aspects. It identifies focuses on optimization, offering references and suggestions for future development. [ABSTRACT FROM AUTHOR]

Summary: A higher accurate load forecasts can significantly reduce the potential cost for power system operation and power market decision. There have been extensive load forecasting methods proposed by now. Ensemble learning is an effective way to integrate various forecasts and further improve the forecasting accuracy. Traditional ensemble learning algorithms static. However, how to select a suitable forecasting method from a large number of load forecasting methods under the complex and varying environment conditions is still challenging. In this context, this paper proposes a dynamic integration approach for multiple load forecasts based on reinforcement learning. Compared with the traditional integration methods, this method adopts Q‐learning algorithm to realize the adaptive selection of the forecasting methods and dynamic weight calculation for different and varying environment. The basic principles and implementation processes of Q‐learning algorithm is firstly introduced. Then, the implementation methodology of dynamic integration approach of multiple load forecasts is proposed. On this basis, the environment state, action space and return function are designed according to the requirements of Q‐learning algorithm. Finally, a case study based on the real‐world data in Guangzhou shows that the average forecasting accuracy could be improved by almost 3% by adopting the dynamic integration approach with the traditional ones. [ABSTRACT FROM AUTHOR]

This paper focuses on the dynamic characteristics of the metro train's bogie frames based on the field test data. The acceleration signals of both motor bogie frame and trailer bogie frame of a standard B-type metro train were measured. Running tests on the Metro line 21 of Guangzhou (China) were carried out. The acquired acceleration signals of bogie frames were analyzed through several methods to identify the dynamic characteristics of the motor and trailer bogies in the time-frequency domain. The spectral analysis and time-frequency representations show that noise components exist in the high-frequency domain of the original signal, especially for the acceleration signal of the motor bogie frame. Then, the soft thresholding process and discrete wavelet transform decomposition process are conducted to obtain a denoised version of the original signals in the time-frequency domain. The vibration frequency domain and energy distribution of bogie frames under different train speeds are analyzed. The track irregularity wavelength of the metro line is calculated and analyzed based on the measured bogie frames' acceleration signals. The dynamic characteristics of the metro train's bogie frames in this paper can be adopted as a reference in the track diagnosis of the elevated metro line. [ABSTRACT FROM AUTHOR]

Pedestrians do not always comply with the crossing rules of when and/or where to cross the road at signalized intersections. This risky behavior tends to undermine greatly the effectiveness of safety countermeasures at such locations. Thus, it is very important to understand illegal behavior to develop more effective and targeting measures. In order to address the problem, this paper aimed to analyze characteristics of illegal crossings and their impact on behavior choice. Firstly, illegal crossing behaviors at signalized intersections were classified into two categories, including "crossing at a red light" and "crossing outside of a crosswalk." Secondly, two sets of data were collected to understand the behaviors. One set of data was collected from video-based observation conducted at 3 signalized intersections in Guangzhou, China, capturing 3334 valid illegal crossing cases in total. Another set of data, from a questionnaire survey conducted online, resulted in 275 valid responses. Finally, presentational characteristics of illegal crossings at signalized intersection were analyzed and two Bayesian network-based behavior models were developed to investigate the characteristics and their impacts on the two types of illegal crossing behaviors, "crossing at a red light" and "crossing outside of a crosswalk," respectively. Findings reveal that, (i) illegal crossings occur at various types of signalized intersections, with a higher probability for "crossing outside of a crosswalk" compared to "crossing at a red light;" (ii) Arc routing crossing has the highest probability to occur at signalized intersections compared to other types of out-side-crosswalk crossings. (iii) The location of origin and destination of a pedestrian has a significant effect on crossing outside of a crosswalk, the location of origin and destination of "one is inside of a crosswalk and another is outside of a crosswalk" has a highest proportion. These findings provide better understanding of illegal crossings and their impact factors so that the effectiveness of management and control of pedestrians at signalized intersections can be improved. [ABSTRACT FROM AUTHOR]

Real-time and accurate travel time information between bus stations is critical for passengers to make suitable travel plans to reduce waiting time at the stops. By mining and analyzing bus operational data, it can be obtained that factors such as the variation of vehicle speed in adjacent sections and the proportion of bus lanes between stations have affected the travel time between bus stations. Therefore, considering the temporal feature, spatial feature, and weather feature as the prediction model's input, travel time between bus stations prediction model based on eXtreme Gradient Boosting (XGBoost) was trained and established. The 28-day bus operation data of a certain bus line in Guangzhou was used for training and verification, and they were compared with the prediction models based on K -Nearest Neighbors (KNN), BP neural network, and Light Gradient Boosting Machine (LightGBM). In comparison with other models, the lowest MAPE of 11.96% was found for the XGBoost prediction model, which is 9.30% lower than other models on average. The sensitivity analysis of the proposed prediction model was further conducted: temporally, the accuracy of the prediction model was best during the flat peak hours; spatially, the MAPE of the model gradually decreased as the number of line units increased, and when the number of line units exceeded 18, the accuracy of the prediction model stabilized and was lower than 7%. The results confirm that the XGBoost model outperforms the KNN, BP, and LightGBM in terms of fitting, accuracy, and stability. [ABSTRACT FROM AUTHOR]

The construction of the Guangdong-Hong Kong-Macao Greater Bay Area requires the establishment of a comprehensive port cooperation mechanism. Coordination is key to port integration. This paper establishes a multi-group Lotka-Volterra model based on the theory of competition and cooperation to study the evolution of the relationships among Hong Kong Port, Shenzhen Port, and Guangzhou Port. The results show that the three major ports have predator-prey roles and form a symbiotic competition and cooperation ecological pattern. The extended Hotelling model is established to study the central ports' spatial differences; the results show that whether it is the central port cooperation body or the entire central port group, the gains from Shenzhen Port-Guangzhou Port cooperation are greater than between either and Hong Kong Port for Guangdong-Hong Kong-Macao Greater Bay Area's container business. The biggest competitor to Hong Kong Port in the container business is Guangzhou Port. Hong Kong Port will gain more from cooperation with Shenzhen Port and should strengthen such cooperation. The study provides quantitative support and reference for positioning the strategic development of the high-end shipping business in Hong Kong Port, the pilot free-trade port in Shenzhen Port, and the exploration of the Shenzhen-Hong Kong composite port, Guangzhou Port's comprehensive shipping hub. [ABSTRACT FROM AUTHOR]

The deep mining of passengers' travel data can identify competitive segments and gain insights into passengers' characteristics and differentiated demands. This can not only effectively support precise marketing strategy adjustment of railway transport but also improve its competitiveness in the passenger transportation market. In this paper, hidden railway travel behaviour is introduced and integrated with railway travel behaviour to create a complete passenger travel chain, based on existing distance-based competitive segment recognition methods. The loyalty index values of passengers are calculated using this travel chain to identify competitive segments. Furthermore, passenger classification and grouping currently ignore social relationships as well as personal travel characteristics. Therefore, a novel passenger grouping method is proposed; it integrates individuals' travel characteristics and social relations. Individual travel labels are created for travellers based on their travel data. Social relation topologies, such as ticketing relation, the relation of travelling together, and benefit relation via point redemption, can be extracted using these labels. Social relation traits can be retrieved using graph attention networks and multigraph fusion. Finally, travellers are categorised based on their individual travel characteristics. As an example, and a case study, the grouping of Guangzhou–Shanghai passengers in 2020 is taken which shows that the suggested method has the potential to improve both the precision and the feasibility of grouping railway passengers. As a result, new ideas for passenger grouping in railway marketing might be offered. [ABSTRACT FROM AUTHOR]

Super high-rise building is a typical architectural form of urban modernization, and it is also the trend of urban architectural development. This paper reviews the development history of super high-rise buildings in China combined with the engineering practice of representative super high-rise buildings since the 1980s, such as the Shanghai Center, Shanghai Oriental Pearl Tower, Jinmao Tower, Shanghai World Financial Center, Canton Tower, and Shanghai Tower. The development process of the key technologies of super high-rise projects from the aspects of several technologies, such as soft soil pile foundation and foundation pit technology, concrete structure construction technology, integral formwork equipment technology, and steel structure comprehensive installation technology, is also elaborated. At the same time, the Shanghai Tower is selected as a typical case to share the innovative technology of China's tallest building. Finally, prospects for development of super high-rise construction technology are presented. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we propose a robust sensor placement methodology, which takes into consideration of possible sensor failure encountered in sensory systems. Although the optimal sensor placement problem has been studied for decades, previous works assumed a fully functioned sensory network. In real practice, sensor failure is commonly encountered, and this may seriously degrade the performance of the sensor network. The proposed method considers the impact of sensor failure on the performance of the sensor configurations so that the designed configuration is robust against sensor failure. For this purpose, we define the robust information entropy to quantify the uncertainty of the identified model parameters with consideration of sensor failure. Furthermore, we propose a tailor‐made heuristic sequential search algorithm to enhance the computational efficiency. The proposed methodology is illustrated by designing the robust sensor configuration for a 20‐story shear building and a space truss. Moreover, a case study using the in‐field measurements of the Canton Tower is presented to demonstrate the performance of the proposal methodology. [ABSTRACT FROM AUTHOR]

Summary: Stochastic subspace identification (SSI) and Bayesian methods are now the main representative approaches for high‐performance system identification and uncertainty quantification. Both methods have been extensively used in engineering applications for the last 10 years despite them having quite different views in principle. However, no comparison has been carried out to inform which method is actually better for engineering applications in terms of modal identification accuracy, uncertainty quantification, and so forth. This paper therefore investigates the differences between these two quantifications of parameter uncertainty in system identification. Synthetic data for a six‐degree‐of‐freedom spring–mass numerical system are first studied to compare their identification accuracy and applicable conditions. The investigation is then extended to field data from actual structures, which involves ambient modal testing of Heritage Court Tower in Canada, Canton Tower in China, and Ting Kau Bridge in Hong Kong. Results from the synthetic data show that, under white‐noise excitation, identified modal parameters and quantified uncertainty for both methods are highly consistent with the values of frequentist statistics. However, when the excitation is not white noise, there may be some spurious modes identified via SSI, and the uncertainty quantified under colored‐noise excitation via SSI is almost always larger than those under white noise, while the Bayesian method is not disturbed. Results for the structural field data indicate that in general applications under environmental excitation the identified modal parameters from both methods are almost identical; the quantified uncertainty of the SSI method is slightly larger than that of the Bayesian method, but they are of the same order of magnitude and can meet engineering requirements. [ABSTRACT FROM AUTHOR]

With the development of society and the improvement of urban economic level, people are no longer satisfied with the simple material and functional requirements of the city; thus, the spiritual requirements of city beauty, environmental quality, place atmosphere, and so on need to be improved. Based on the above background, the purpose of this paper is to analyze the landscape planning of Guangzhou's digital city based on spatial information from the perspective of smart cities. Based on the relevant theoretical research, this paper combs the ideas of intelligent urban road landscape design. This paper analyzes the concept of urban road and smart road and puts forward the definition and characteristics of intelligent urban road landscape; according to the research on the development status of urban road and the connotation of smart city, combined with the concept and principle of urban road landscape design, it summarizes the design method of smart city road landscape. This paper, taking the innovative design of urban landscape as the research object and using the research methods of literature analysis and field investigation, innovatively combines the urban landscape design with digital information technology, changes the traditional landscape design ideas, constructs the urban landscape innovative design model, realizes the personalization of the urban landscape design, as well as the intelligent, digital, diversified, and humanized service and function. The experimental results show that nearly 60% of people are satisfied with the Guangzhou digital urban landscape planning based on spatial information in the smart city perspective. [ABSTRACT FROM AUTHOR]

Due to the complexity of the operation control of urban rail transit and diversity requirements for section running time standards, based on actual train operation data, this paper proposes a curve fitting method to find the interrelation between running time and energy consumption. According to features of the energy consumption-running time curve, the discriminant criterion of outliers is constructed to select the candidate fitting data set from the original data set. To fit the energy consumption-running time curve from two-dimensional scatter points, we propose a B-spline curve fitting method based on a genetic algorithm and the fitting method is proven to have high fitting accuracy and convergence speed. Furthermore, we propose an optimization method for the fitting curve based on dynamic adjustment of the fitting data set which is selected from the candidate fitting data set to obtain the optimal energy-running time curve. The validation of Guangzhou Metro's actual operation data shows that the energy-running time curve fitted and optimized by our method has lower energy and better continuity and smoothness and could be used for evaluation of train drivers' performance and energy consumption of train operation diagram. [ABSTRACT FROM AUTHOR]

A sector is a basic unit of airspace whose operation is managed by air traffic controllers. The operation complexity of a sector plays an important role in air traffic management system, such as airspace reconfiguration, air traffic flow management, and allocation of air traffic controller resources. Therefore, accurate evaluation of the sector operation complexity (SOC) is crucial. Considering there are numerous factors that can influence SOC, researchers have proposed several machine learning methods recently to evaluate SOC by mining the relationship between factors and complexity. However, existing studies rely on hand-crafted factors, which are computationally difficult, specialized background required, and may limit the evaluation performance of the model. To overcome these problems, this paper for the first time proposes an end-to-end SOC learning framework based on deep convolutional neural network (CNN) specifically for free of hand-crafted factors environment. A new data representation, i.e., multichannel traffic scenario image (MTSI), is proposed to represent the overall air traffic scenario. A MTSI is generated by splitting the airspace into a two-dimension grid map and filled with navigation information. Motivated by the applications of deep learning network, the specific CNN model is introduced to automatically extract high-level traffic features from MTSIs and learn the SOC pattern. Thus, the model input is determined by combining multiple image channels composed of air traffic information, which are used to describe the traffic scenario. The model output is SOC levels for the target sector. The experimental results using a real dataset from the Guangzhou airspace sector in China show that our model can effectively extract traffic complexity information from MTSIs and achieve promising performance than traditional machine learning methods. In practice, our work can be flexibly and conveniently applied to SOC evaluation without the additional calculation of hand-crafted factors. [ABSTRACT FROM AUTHOR]

With the development of industrialization and urbanization, cities have become the main carriers of economic activities. However, the long-term development of cities has also caused damage to resources and the environment. Hence, objective and scientific evaluation of urban low-carbon sustainable development capacity is very important. An index system of urban low-carbon sustainable development capability is constructed in this paper, and a TOPSIS-BP neural network model is established to evaluate the low-carbon sustainable development capability of Beijing, Shanghai, Shenzhen, and Guangzhou in China. At the same time, the difference degree of low-carbon sustainable development level in these four cities is analyzed by standard deviation and coefficient of variation, and the influencing factors of urban low-carbon sustainable development ability are extracted by grey correlation analysis. The results show that (1) the capability of low-carbon sustainable development in four cities is rising and the difference of low-carbon sustainable development capability is decreasing; (2) the general view that the higher the general investment in low-carbon sustainable development, the higher the level of low-carbon sustainable development in cities has not been verified; (3) with the change of time series, the factors affecting the capability of low-carbon sustainable development in the same city are different and the influence of the same factor on the capability of low-carbon sustainable development in different cities is different. [ABSTRACT FROM AUTHOR]

Ground deformation and additional stress on the segments of the firstly constructed tunnel may change significantly due to the construction of another tunnel, which is closely spaced with the existing one. A poor geotechnical condition possibly leads to the interaction between such closely spaced tunnels even making it harder to build such tunnels. A practical project located in Guangzhou City, China, consists of such two parallel tunnels with the smallest distance of 2.6 m, which sat on an upper-soft and lower-hard stratum. A 3D finite element model has been proposed to numerically investigate the behavior of these two parallel tunnels. The numerical results predicted by the FE model are in close agreement with that obtained from the field monitoring system, indicating the accuracy of the proposed FE model. The FE model was then used to further analyze the effect of reinforcing piles in eliminating the detrimental effect on both ground deformation and additional stress of the segments of the existing tunnel as a result of the construction of the new parallel tunnel. The research results obtained from the present paper can provide technical support and guidance for urban subway construction. [ABSTRACT FROM AUTHOR]

Taking Liangxiang Pagoda built in Liao Dynasty in Beijing as the research object, the vibration responses of the pagoda in the east-west direction and south-north direction under the action of trains on the Beijing–Guangzhou Railway Line and microseism were tested. On this basis, a numerical model was established by using ANSYS to further calculate the dynamic response of the pagoda, and the safety and integrity of the pagoda were evaluated based on existing standards. Cumulative fatigue damage theory was introduced to predict the remaining fatigue life of Liangxiang Pagoda. The following conclusions have been drawn: in the two directions mentioned above, the natural vibration frequencies of the pagoda of the first three orders are similar; the 1st-order vibration modes in the plane are bending, and the vibration modes of the 2nd and 3rd orders are shear-bending; under the action of trains, the peak vibration value of Liangxiang Pagoda at the position that bears the maximum load is 0.053 mm/s, which has a little impact on the safety and integrity of the pagoda; under the combined action of gravity and trains, the remaining fatigue life of Liangxiang Pagoda is 5.26 × 10 7 times. The research method used in this paper can provide data and scientific support for the protection of historical buildings, as well as the basis for the follow-up research of the studied ancient pagoda. [ABSTRACT FROM AUTHOR]

One of the major contradictions in China's healthcare service is the serious imbalance between the supply and demand of medical resources and the uneven distribution of medical resources. Although the government intended to provide equal access for all, in the final evaluation, fairness of the distribution of services was usually dictated by location. Measuring and predicting access to social services can help to adjust and better accommodate underserved regions. 3SFCA modes based on the Gaussian function are proposed to delineate the service area of providers delivering social services and produce a probability metric that maps the equity of the medical services in multiple traffic modes. We identified the types of medical shortage areas and analyzed the distribution characteristics of medical accessibility in Guangzhou. The results showed that the accessibility of medical facilities in the multiple traffic modes varied greatly. The accessibility level of medical service resources was the highest in the driving mode, followed by the public traffic mode and bike-riding mode. The walking mode had the lowest accessibility. Finally, the paper provided advice for further improvement of the equal distribution of medical service resources in Guangzhou. [ABSTRACT FROM AUTHOR]

A crucial task in traffic data analysis is similarity pattern discovery, which is of great importance to urban mobility understanding and traffic management. Recently, a wide range of methods for similarities discovery have been proposed and the basic assumption of them is that traffic data is complete. However, missing data problem is inevitable in traffic data collection process due to a variety of reasons. In this paper, we propose the Bayesian nonparametric tensor decomposition (BNPTD) to achieve incomplete traffic data imputation and similarity pattern discovery simultaneously. BNPTD is a hierarchical probabilistic model, which is comprised of Bayesian tensor decomposition and Dirichlet process mixture model. Furthermore, we develop an efficient variational inference algorithm to learn the model. Extensive experiments were conducted on a smart card dataset collected in Guangzhou, China, demonstrating the effectiveness of our methods. It should be noted that the proposed BNPTD is universal and can also be applied to other spatiotemporal traffic data. [ABSTRACT FROM AUTHOR]

This paper presents a case history of the developmental effect of a large-area excavation, 8 high-rise main buildings, a series of annex constructions, and ground overloaded building demolition on the deformation characteristics of an existing shield tunnel within Guangzhou Metro Line No. 1 in close proximity to the development. The shield tunnel lies in a sandy layer of the typical upper-soft and lower-hard strata in Guangzhou district, and the deformation of the tunnel has been monitored since the tunnel was put into operation. The monitoring results reveal that the adjacent construction induces an excessive tunnel settlement with a maximum of 14.4 mm and an excess tunnel displacement with a maximum of 5.2 mm, which are within the corresponding limitations of the codes for the safe operation of urban rail transit tunnels. While the station expansion project is being conducted beside the tunnels, a series of tunnel distresses, including large-area water seepage, spalling concrete blocks, and segmental cracks, are recorded. Our field monitoring data indicate that the tunnel is subjected to further vertical contraction and horizontal expansion due to the station expansion project, and a maximum tunnel flattening rate of 36.78% is detected. Furthermore, the tunnel linings are studied numerically and theoretically to obtain the limitations of tunnel deformation and discuss why tunnel distresses of water seepage, concrete spalling, and segmental cracking occur. Finally, on the basis of the analyses and discussions above, counteracting corrective measures, including compensation grouting soil strengthening and bonded steel plates, are adopted as exterior and interior strengthening methods, respectively, to eliminate further tunnel distresses and ensure safe operation. The lessons learned and summarized in this study may help prevent similar tunnel distresses from reoccurring in the future. [ABSTRACT FROM AUTHOR]

Complex nonlinear optimization problems are involved in optimal spatial search, such as location allocation problems that occur in multidimensional geographic space. Such search problems are generally difficult to solve by using traditional methods. The bat algorithm (BA) is an effective method for solving optimization problems. However, the solution of the standard BA is easily trapped at one of its local optimum values. The main cause of premature convergence is the loss of diversity in the population. The niche technique is an effective method to maintain the population diversity, to enhance the exploration of the new search domains, and to avoid premature convergence. In this paper, a geographic information system- (GIS-) based niche hybrid bat algorithm (NHBA) is proposed for solving the optimal spatial search. The NHBA is able to avoid the premature convergence and obtain the global optimal values. The GIS technique provides robust support for processing a substantial amount of geographical data. A case in Fangcun District, Guangzhou City, China, is used to test the NHBA. The comparative experiments illustrate that the BA, GA, FA, PSO, and NHBA algorithms outperform the brute-force algorithm in terms of computational efficiency, and the optimal solutions are more easily obtained with NHBA than with BA, GA, FA, and PSO. Moreover, the precision of NHBA is higher and the convergence of NHBA is faster than those of the other algorithms under the same conditions. [ABSTRACT FROM AUTHOR]

In the past decades, the booming growth of housing markets in China triggers the urgent need to explore how the rapid urban spatial expansion, large-scale urban infrastructural development, and fast-changing urban planning determine the housing price changes and spatial differentiation. It is of great significance to promote the existing governing policy and mechanism of housing market and the reform of real-estate system. At the level of city, an empirical analysis is implemented with the traditional econometric models of regressive analysis and GIS-based spatial autocorrelation models, focusing in examining and characterizing the spatial homogeneity and nonstationarity of housing prices in Guangzhou, China. There are 141 neigborhoods in Guangzhou identified as the independent individuals (named as area units), and their values of the average annual housing prices (AAHP) in (2009–2015) are clarified as the dependent variables in regressing analysis models used in this paper. Simultaneously, the factors including geographical location, transportation accessibility, commercial service intensity, and public service intensity are identified as independent variables in the context of urban development and planning. The integration and comparative analysis of multiple linear regression models, spatial autocorrelation models, and geographically weighted regressing (GWR) models are implemented, focusing on exploring the influencing factors of house prices, especially characterizing the spatial heterogeneity and nonstationarity of housing prices oriented towards the spatial differences of urban spatial development, infrastructure layout, land use, and planning. This has the potential to enrich the current approaches to the complex quantitative analysis modelling of housing prices. Particularly, it is favorable to examine and characterize what and how to determine the spatial homogeneity and nonstationarity of housing prices oriented towards a microscale geospatial perspective. Therefore, this study should be significant to drive essential changes to develop a more efficient, sustainable, and competitive real-estate system at the level of city, especially for the emerging and dynamic housing markets in the megacities in China. [ABSTRACT FROM AUTHOR]

In the peak period of a railway system, operators typically add additional trains to provide increased capacity to satisfy the increasing passenger demand. The paper proposes a new optimization framework for designing the operation plan, which includes the number of additional trains, train type, stop plan, and timetable, for additional trains in a peak period. A space-time network representation is used to obtain a feasible primary operation plan by finding a set of feasible space-time paths in the space-time network. Considering simultaneously the passenger demand and the trains' total travel times, we formulate a biobjective integer programming model for generating a cost and passenger responsible primary operation plan. A set of loading capacity constraints are formulated in the model to guarantee a suitable loading capacity for each station's passenger demand and better service for passengers. The CPLEX solver is used to solve the proposed model and to generate the optimal operation plan. Two sets of numerical experiments are conducted on a small-scale rail corridor and on the Wuhan-Guangzhou rail corridor to evaluate the performance of the proposed method. The results of the experiments show that the primary operation plan can be obtained within an acceptable computation time. [ABSTRACT FROM AUTHOR]

The application range of the earth pressure balance (EPB) shield has been expanded due to advances in optimization methods, one of which is the application of foam conditioning. This method is widely used in EPB tunnelling owing to its strong applicability in different hydrological and geological conditions. When applying the foam conditioning method under different circumstances, it is necessary to optimize the conditioning parameters. Therefore, it is necessary to propose a test procedure for evaluating foam properties and the conditioning effect. This paper proposes a procedure to assess foaming agents by the mixing test and microscopic observation of the foam and a procedure that combines the slump test, compression test, and shear test to assess the foam-conditioned soil and determine the optimal parameters of conditioning. The test method is introduced and performed on fully weathered granite from Guangzhou Metro Line 21. The test results demonstrate that the foam injection ratio and pressure and type of the foaming agent all influence the performance of the conditioned soil. Moreover, the suggested conditioning scheme is proposed, and the application of the scheme can improve the tunnelling efficiency. [ABSTRACT FROM AUTHOR]

Contextual location prediction is an important topic in the field of personalized location recommendation in LBS (location-based services). With the advancement of mobile positioning techniques and various sensors embedded in smartphones, it is convenient to obtain massive human mobile trajectories and to derive a large amount of valuable information from geospatial big data. Extracting and recognizing personally interesting places and predicting next semantic location become a research hot spot in LBS. In this paper, we proposed an approach to predict next personally semantic place with historical visiting patterns derived from mobile device logs. To address the problems of location imprecision and lack of semantic information, a modified trip-identify method is employed to extract key visit points from GPS trajectories to a more accurate extent while semantic information are added through stay point detection and semantic places recognition. At last, a decision tree model is adopted to explore the spatial, temporal, and sequential features in contextual location prediction. To validate the effectiveness of our approach, experiments were conducted based on a trajectory collection in Guangzhou downtown area. The results verified the feasibility of our approach on contextual location prediction from continuous mobile devices logs. [ABSTRACT FROM AUTHOR]

This paper studies the multistage pricing and seat allocation problems for multiple train services in a high-speed railway (HSR) with multiple origins and destinations (ODs). Taking the maximum total revenue of all trains as the objective function, a joint optimization model of multistage pricing and seat allocation is established. The actual operation constraints, including train seat capacity constraints, price time constraints in each period, and price space constraints among products, are fully considered. We reformulate the optimization model as a bilevel multifollower programming model in which the upper-level model solves the seat allocation problem for all trains serving multiple ODs in the whole booking horizon and the lower optimizes the pricing decisions for each train serving each OD in different decision periods. The upper and lower are a large-scale static seat allocation programming and many small-scale multistage dynamic pricing programming which can be solved independently, respectively. The solving difficulty can be significantly reduced by decomposing. Then, we design an effective solution method based on divide-and-conquer strategy. A real instance of the China's Wuhan-Guangzhou high-speed railway is employed to validate the advantages of the proposed model and the solution method. [ABSTRACT FROM AUTHOR]

Calcareous sand is widespread around Nansha Islands, South China Sea. In oceanic and coastal engineering, calcareous sand is usually used as a building foundation and backfill material for airport runway embankments. The engineering characteristics of calcareous sand is different from terrigenous sand because of its irregular grain shape, lower particle strength, and internal voids, which have caused many engineering problems in the last decades. Cement-stabilized soil, as a common foundation reinforcement method, can solve these engineering problems and improve the foundation strength effectively. Therefore, it is very important to estimate the engineering characteristics of cement-stabilized calcareous sand foundations. In this paper, the basic engineering characteristics, bearing capacity, and deformational behavior of calcareous sand were studied by carrying out a series of tests on cement-stabilized calcareous sand. It is found that: (1) the uniaxial compression strength of calcareous sand is higher than that of Guangzhou soft soil but lower than that of filter medium quartz sand; (2) the deformation of the calcareous sand under compression is mainly plastic, and the elastic deformation gradually increases with increasing cement content; (3) the apparent cohesion of calcareous sand increases, while internal friction angle decreases with increasing cement content; (4) cement-stabilized method can significantly improve the bearing capacity of calcareous sand foundation, especially for the saturated state. A cement content equal to or more than 15% and a thickness of 1/8 of the foundation can effectively improve the bearing capacity of the foundation; and (5) the ultimate bearing capacity of the foundation by numerical calculation is higher than that by experiments, while the settlement by calculating is lower. [ABSTRACT FROM AUTHOR]

The indoor living environment of residential buildings is an important part of the habitat environment, affecting the living experience and well-being of the residents, which in turn influences the price of housing. However, few existing studies systematically concentrate on the integrated influence of the various elements of the indoor living environment on prices, and even fewer analyze the relationship between the indoor living environment and housing prices of different houses within the same residential quarter from a microperspective. Therefore, we use the Taojinjiayuan Residential Quarter, located in central Guangzhou City, China, as a case study area and analyze the extent and direction of the effect of the indoor living environment on housing prices. The study found that the quantitative evaluation results of the indoor living environment were reasonable. The integrated indoor living environment factors are closely related to housing prices. Orientation, view, and acoustic environment are significantly and positively related to housing prices, which have a different intensity of influence. These findings are beneficial to real estate developers, building designers, and residential users in quantitatively understanding the value of the indoor living environment. [ABSTRACT FROM AUTHOR]

In this paper, reasonable paths in transit networks are defined as possible paths that satisfy the acceptable time criterion and transfer-walk criterion. A recursive algorithm for finding all of the reasonable paths in a transit network that does not involve a rapid increase in program run-time with network size is presented. Realistic transit networks in Hong Kong and Guangzhou were selected as case studies of the different phases of the development of a trip planning system. Transport planning practitioners and potential users were invited to test the system to evaluate its performance. The results of the prototype evaluation were satisfactory, and the viability of the system as a useful tool for supporting decision-making has been confirmed by the positive feedback that was obtained from survey questionnaires. [ABSTRACT FROM AUTHOR]

We developed a train operation simulation tool to inspect a train operation plan. In applying an improved Petri Net, the train was regarded as a token, and the line and station were regarded as places, respectively, in accordance with the high-speed train operation characteristics and network function. Location change and running information transfer of the high-speed train were realized by customizing a variety of transitions. The model was built based on the concept of component combination, considering the random disturbance in the process of train running. The simulation framework can be generated quickly and the system operation can be completed according to the different test requirements and the required network data. We tested the simulation tool when used for the real-world Wuhan to Guangzhou high-speed line. The results showed that the proposed model can be developed, the simulation results basically coincide with the objective reality, and it can not only test the feasibility of the high-speed train operation plan, but also be used as a support model to develop the simulation platform with more capabilities. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease (COVID-19) pandemic has had a significant impact on transportation. Understanding how the epidemic in China has affected people's travel mode choices can help city managers analyze the travel mode of different populations in the postepidemic phase. Based on a travel behavior questionnaire conducted in Guangzhou, China, during the COVID-19 epidemic and points-of-interest data, this study explored the impact of the built environment, travel characteristics, and socioeconomic factors on changes in commuters' travel modes during the postepidemic phase. We found that gender, age, occupation, the decline in the rate of travel frequency, and built environment characteristics significantly influenced the change in travel mode. When respondents had to give up public transport, those in different professions had different options for alternative means of transportation. The density of residential facilities, bus stations, and government institutions had a more significant impact on the change in the travel mode of commuters. The research results provide a theoretical basis for policy and practice. After the outbreak of a major public health event, urban transport managers and policymakers should consider individual heterogeneity and environmental factors when formulating strategies to address public travel in unconventional situations. [ABSTRACT FROM AUTHOR]

Summary: In this study, multivariate empirical mode decomposition (MEMD) is used to evaluate the dynamic characteristics of super‐tall buildings. Two super‐tall buildings, including Milad Tower, which is located in Tehran, Iran, and Canton Tower, which is located in Guangzhou, China, are used as examples to estimate the capability of multivariate empirical mode decomposition for recognizing the dynamic characteristics of buildings. A method is suggested to extract the frequency of structures automatically. First, the best segment of required data, including acceleration response and wind speed is found, and then wavelet transform is used to eliminate the noise and find proper and wanted natural frequency. Finally, to investigate all signals, that is, acceleration responses of all channels simultaneously, MEMD is applied to identify the frequency of the filtered signals. The extracted frequencies are selected in the order of amplitude power of each mode for each intrinsic mode function (IMF). The obtained results are appropriate, corresponding to other studies. Hence, the proposed method can automatically select the accurate frequency of super‐tall buildings in less time duration by considering all required data simultaneously. [ABSTRACT FROM AUTHOR]

A 3D dynamic finite element model is developed to study the response of the existing tunnels' concrete structure under the influence of different train speeds and undercrossing with fixed shield excavation. The intersection area of Metro Line 3 in Guangzhou and the intercity railway between Foshan and Dongguan is used as the case study. A programming idea simulating oblique moving-axle loads in the numerical model by using function expressions to constrain activity area is proposed. The Hilber–Hughes–Taylor time-integration scheme is used to calculate the dynamic response parameters of the double-channel rectangular cross section tunnels. Considering fixed excavation steps in the process of shield crossing, acceleration and deformation of the existing tunnels were analyzed under the moving-axle loads of trains at different speeds. From the perspective of the vibration and deformation of existing tunnels, shield excavation and moving speed have their own emphasis on the dynamic influence of the existing tunnel. In addition, the calculation results of the numerical simulation are in good agreement with the data obtained from some field tests, which further verifies the reliability of the calculation method of the numerical model. The results of this research can guide the arrangements for reinforcement measures for existing tunnels in the later stage. [ABSTRACT FROM AUTHOR]

Hazardous weather has become a major cause of flight delays in recent years. With the development of satellite navigation systems, the study of flight-path optimization under hazardous weather conditions has become especially important. In this study, radar data were used as the basis for the initial flight-restricted area under hazardous weather conditions, and the Graham algorithm was used to delineate the dynamic flight-restricted area by comprehensively considering the hazardous weather boundary changes along with the speed and direction. Then, under the grid environment model, the range of influence, size, and distribution characteristics of the flight-restricted area was examined, and the path optimization model was created according to constraints related to the path distance, corner size, and number of turning points. An improved F-RRT ∗ algorithm was developed to solve the model. The algorithm can overcome the problems of traditional path planning algorithms, such as strong randomness, poor guidance, slow convergence speed, unsmooth paths, and poor tracing smoothness. Finally, a simulation analysis was conducted on the Guiyang–Guangzhou route in China as an example. This study can address the drawbacks of existing research on route change and provide sufficient theoretical support and reference for the implementation of specific route change plans in the future. [ABSTRACT FROM AUTHOR]

Due to the major energy consumer and carbon emission, China must change the previous extensive economic development method categorized by great energy consumption, high pollution, and great emissions and develop a low-carbon economy. As a result, this research focuses on the measurement and assessment of low-carbon economy growth in Guangzhou. It contains pertinent study findings as well as the most recent research developments at home and abroad. Furthermore, it searches for and investigates limited socioeconomic development theories, structural model systems, and validity and reliability analysis methodologies. Besides, considering the current condition of low-carbon financial growth in Guangzhou and the issues it faces, this study explores and analyses the key variables impacting the growth of a low-carbon economy. Finally, using the statistical analysis program such as LISREL 8.80, the structural equation model is fitted and changed based on the data validity analysis and test. It generates the measurement and evaluation model data of Guangzhou's low-carbon economic development level. [ABSTRACT FROM AUTHOR]

As one of the largest civil engineering systems in China, the network of high-speed railway expanded fast in the last decade. The safety of railway operation is of great concern for the whole country. Railway track irregularity is a potential threat to safety of operation and comfort of passengers, and remains a challenging issue for researchers and engineers. Currently, track irregularity data are recorded by various sensors in the comprehensive inspection cars in China. To reveal relations between the operation of high-speed trains and the track geometry, the data mining and random modeling of track irregularity are needed. In this paper, different methods to evaluate the track irregularities are presented at first. A case study of a section in Beijing-Guangzhou high-speed railway is studied using the Karhunen-Loève expansion. Track geometries that are a representation of this railway network are generated along with statistical and frequency validations. As an application based on generated random track geometries, accelerations of train body under different traveling velocities are calculated and analyzed using a simulation model. [ABSTRACT FROM AUTHOR]

Green building is a hot topic today. The place making and urban cultures are also important issues in postindustrial society. The industrial heritage renovation projects provide a research opportunity in combination with both aspects. This paper tries to shed new light on this issue by interdisciplinary methods, to study six Guangzhou industrial heritage renovation projects, giving aesthetic values for six sites concerning place making and culture creation, especially giving an explanation for old building material’s aesthetic performance in terms of concepts “entropy” and “archetype.” The conclutions regard: the six places are brand spaces of “authentic Guangzhou” that make local experiential knowledge, emotional significance and creative communities in combination with historical and cultural narratives. [ABSTRACT FROM AUTHOR]

This paper is aimed at crowding phenomenon in the subway. As passengers are inclined to choose the route with minimum disutility, we put forward a route choice model which is constructed to achieve minimum objective function of feasibility for the optimal solution. Meanwhile we set passenger volume threshold values according to capacity of facilities. In the case of actual capacity exceeding the threshold, the decision node of constrained route will be selected; computing procedure about searching decision points will be presented. Then we should set rational restrictions at the decision node of the minimum utility function route to prevent too many passengers’ access to platform. Through certification, this series of methods can effectively ensure the safety of the station efficient operation. [ABSTRACT FROM AUTHOR]

The volume of waste produced by aspects of both industrial life and daily life has increased in the past few years in conjunction with the rapidly growing economy in China. In urban areas, citizens consume more resources and produce much waste, which pollutes the environment. Many cities have proposed numerous regulations for waste separation to help build environmentally friendly cities. Plenty of studies tried to reveal the mechanism of residents' waste separation behavior in different theoretical perspectives but were unable to clarify the effect of waste separation factors in university. Moreover, the response and performance of undergraduate students after the waste separation regulations are proposed and are not well discussed. To clarify the mechanism of waste separation behavior in campus and evaluate the response and performance, in this study, a questionnaire is used to sample undergraduate students from the Higher Education Mega Center in Guangzhou. With the use of SPSS 23.0, this study conducts an empirical analysis; the results of which show that (1) environmental awareness and personal responsibility have a significant positive impact on behavior attitude; (2) convenience and economic cost have a significant positive impact on perceived behavior control; (3) intention has a partial mediating role between behavior attitude, subjective norms, and perceived behavior control; and (4) habits directly affect behavior and regulate the relationship between intention and behavior. The results will provide some meaningful implications for the government. [ABSTRACT FROM AUTHOR]

The safety of signalized intersections is of great concern. To allow for an effective evaluation measure on the safety level of intersections, traffic conflict analysis methods are commonly used. However, the existing literature has mainly focused on the statistical prediction of conflicts by using surrogate measurements, among which the spatial-temporal characteristics of the potential conflicts have been less addressed. In addition, most of the relevant studies rely on precise trajectory data, and the results could be limited to engineering applications when real-time/comprehensive trajectory data are not available. To address these issues, this study proposes a SICP (signalized intersection conflict probability) model to predict a straight-left traffic flow conflict with a spatial-temporal distribution in the heat map, which could effectively evaluate the traffic safety of the existing or prebuilt signalized intersections on urban roads. Firstly, the impact of vehicle movement characteristics on traffic conflict at signalized intersections was considered by incorporating the vehicle movement trajectory. Secondly, the signal phase was categorized in several stages (each phase contains switching and nonswitching stages); then, a vehicle-vehicle conflicts probability prediction model was established by integrating both horizontal and vertical arrival probability. Finally, to validate the performance of the proposed model, the measured data were collected from the intersection of Wushan road and Yuehan road in Tianhe District, Guangzhou, China. SSAM（Surrogate Safety Assessment Model）traffic conflict simulation was used to analyze the traffic conflict in the actual data and compared to the SICP model. A case study was conducted to reveal the evolution mechanism of the conflict risk coefficient at the signalized intersection and to estimate the safety status under the various security optimization strategies. The experimental results verified the effectiveness of the SICP model, indicating that the proposed model is effective in evaluating the safety level of existing or prebuilt signalized intersections. [ABSTRACT FROM AUTHOR]

City leading industries are the pillars of urban economic development and are constantly changing as urban economic development enters different stages. The weight setting of many factors in the existing leading industry selection methods and means is mainly set by humans, which is highly subjective and lacks dynamics, integrity, and quantification, and the accuracy of prediction results is not high. Therefore, starting from respecting objective data, the SSM selection method with both dynamic and quantifiable properties is introduced. Based on the SSM mathematical model and principles, 35 manufacturing industries in Guangzhou in 2015 and 2020 are selected as initial variables and stage variables, respectively, taking 35 corresponding industrial sectors in the province as reference variables at the same time point and using the SSM algorithm as an analytical tool to conduct an empirical analysis of the share deviation component, structural deviation component, and competitiveness deviation component of the 35 manufacturing industry sectors in Guangzhou. After drawing the Shift-share analysis chart, it was found that there are 12 industrial sectors most likely to become the city leading industries in Guangzhou, and 4 suggestions for the development planning of city leading industries were put forward; they are, respectively, ➀ accelerate traditional industries technological upgrading, ➁ focus on optimizing automobile manufacturing industry, ➂ promote leading industries independent innovation, and ➃ create leading industry sharing platform. [ABSTRACT FROM AUTHOR]

Since the outbreak of COVID-19, the rapid construction and operation of Wuhan Vulcan Mountain Hospital and Raytheon Hospital have attracted positive responses from local and international observers. At the same time, it has also highlighted the urgency for the construction of emergency medical facilities for public health emergencies. Before construction, the practical location of medical facilities is the basis for improving the city's emergency management ability. Based on the classic susceptible, exposed, infected, and recovered (SEIR) epidemic model and epidemic data in Guangzhou, we established a multi-stage time-delay SEIR epidemic model that is suitable for epidemic research in Guangzhou. According to the results of the model, the five areas with the highest number of infected patients were identified, which included Baiyun District, Panyu District, Haizhu District, Tianhe District, and Zengcheng District. We then centralized infected individuals at five demand points. Based on the distribution of these points and by combining the characteristics of the emergency medical facilities, we built and solved the set covering location decision model, and considered the economy, society, and environment as the starting points to optimize the site location. Finally, based on simulations, we concluded that appropriate site selection can increase the time required to reach the maximum number of patients and reduce the proportion of infected and exposed people by 11.3% and 1.11%, respectively. This is indicative of the effectiveness of the site selection model and the rational selection of facility points in this study. It solves the optimization problem of the location decision of emergency medical facilities for public health emergencies in China, and also provides some valuable references for site selection decisions of emergency medical facilities in other areas. [ABSTRACT FROM AUTHOR]

In order to further enhance the ecological environment construction of smart cities and promote its deep integration with advanced technologies, such as the new generation of artificial intelligence and big data, this study constructs a data fusion framework for the ecological environment of smart cities driven by multisource data and constructs an ecological environment evaluation index system of smart cities in Guangzhou from 2010 to 2018. The ecological environment status of smart cities in Guangzhou is analyzed by principal component analysis, and finally the, correlation influence degree of each principal component content on the ecological environment index of smart cities is analyzed. The results show that environmental excellence ( K 1 ), environmental restoration response ( K 2 ), and environmental pollution pressure ( K 3 ) are the main components of the ecological environment in Guangzhou smart city. The environmental excellence and environmental restoration response level are relatively high, and the environmental pressure system is relatively low, in which the greenland coverage rate increases from 41.3% to 44.0%, and the urban sewage treatment rate and the decontamination rate of urban refuse increase year by year, from 73.1% to 94.6% and from 72.1% to 99.9%, respectively. The results of principal component correlation analysis show that there are interactive influences among environmental excellence ( K 1 ), environmental restoration response (K2), and environmental pollution pressure ( K 3 ). The ecological environment index of smart cities increases with the improvement of environmental excellence and environmental restoration response capacity, but gradually decreases with the increase of environmental pollution pressure. Generally speaking, improving environmental excellence and environmental restoration response will be the key to improve the ecological environment construction capacity of smart cities in the future. [ABSTRACT FROM AUTHOR]

Effective identification of the risk area of the bus bay stop is a prerequisite for the enhancement of traffic safety. This study proposes a method of identifying the risk area based on the distribution of traffic conflicts. Firstly, the traffic flow data of the bus stop is collected by drones and video recognition software, and the traffic flow characteristics of the bus stop are analyzed by the mathematical and statistical methods. Secondly, using the gray clustering evaluation theory, on the basis of the rasterization of the functional area of the bus bay stop, a risk level model based on the index system of conflict rate, conflict severity, and potential conflict risk is proposed. Finally, take a bus stop in Guangzhou as an example to verify the solution. The results show that the constructed model can effectively identify the risk areas of bus bay stops. The risk areas of the bus bay stops are concentrated in the middle and lower reaches of the bus stop, which proves that the impact of bus exiting the stop on the surrounding traffic is greater than the process of bus entering the stop; the traffic risk areas of lanes near the bus stop are concentrated, and the severity of conflicts is low. The traffic risk zone of the lane far away from the bus stop is widely distributed, and the severity of conflict is higher. The research results can provide a basis for the micro safety performance evaluation and safety optimization of bus bay stops, which has strong theoretical and practical significance. [ABSTRACT FROM AUTHOR]

It is important to guarantee the safety of adjacent underground structures during the foundation pit blasting excavation of modern city construction. The blasting excavation construction of a large, deep foundation pit near an existing metro station of Guangzhou Metro Line 3 is used as the example in this study. Based on blasting vibration field test results, the influence of blasting dynamic load on the lining of an adjacent metro tunnel is numerically analyzed in simulation using Fast Lagrangian Analysis of Continua 3D (FLAC3D), and the relationships between the blasting vibration velocity and stress and the displacement of the metro tunnel lining are obtained. The results show that the stress of lining structure is within the allowable range under the experimental blasting conditions, the lining displacement increases linearly with the applied dynamic vibration velocity, and the vertical displacement of the lining is more obviously affected than the horizontal displacement by the dynamic load. This study can be used as a basis for the control of blasting vibration in a complex urban environment. Its practical application shows that the proposed blasting plan and parameters are reasonable and effective. [ABSTRACT FROM AUTHOR]

Due to the high bearing capacity and better controllability of bladder parameters, the multibladder-type inflatable anchor shows a great application prospect in the engineering construction in soft soil areas. In this study, based on a soft soil foundation pit project in Guangzhou Province, China, the multibladder-type inflatable anchor was applied to the engineering practice for the first time, and the working characteristics of the multibladder-type inflatable anchor were analyzed and discussed through field experiments. Primarily, the optimization design and experimental facility of the multibladder-type inflatable anchor were introduced in detail to realize the refinement and perfection of the anchorage technological process. Besides, the influence degrees of the bladder number, diameter, thickness, space, and length on the bearing characteristics and deformation control of the anchor were studied by using the control variable method, as well as the influencing mechanisms. Subsequently, the advantages of the multibladder-type inflatable anchor were presented by comparing its anchoring efficacy with that of the traditional grouting anchor. Ultimately, the analysis results were used to verify the reliability of the multibladder-type inflatable anchor and provide the experimental basis for its popularization and application. [ABSTRACT FROM AUTHOR]