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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]

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]

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]

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]

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]

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]

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]

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]

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]

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 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]

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]

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 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]

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]

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]

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]

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]