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This paper studies the problem of rescheduling trains with speed management in uncertain disruptions that lead to temporary speed restrictions (TSRs) in high-speed railway systems. The disruption uncertainties are that the coverage and speed limit value of TSRs change randomly over time, and the start and end times of TSRs are unknown in advance. First, a mixed-integer linear programming model is formulated to reduce the total train traveling times and improve the passenger comfortability. The solutions of the model provide simultaneously the optimal train rescheduling strategies and train speed control strategies. Second, a rolling horizon algorithm is applied in consideration of the disruption uncertainties. The rolling horizon algorithm updates and solves the model in every time horizon, according to the disruption information newly detected. Therefore, the train rescheduling orders and speed control orders can be adjusted according to the real-time situation to guarantee effectiveness. The rolling horizon approach is terminated automatically once the disruption ends. Finally, based on the practical data of the Beijing-Tianjin intercity high-speed railway line, numerical experiments are carried out to test the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

• The paper proposes a novel dynamic graph recurrent convolutional neural network model, named Dynamic-GRCNN, to deeply capture the spatio-temporal traffic flow features for more accurately predicting urban passenger traffic flows. • The paper presents incidence dynamic graph structures based on historically passenger traffic flows to model traffic station relationships. Different from existing traffic transportation network topological structures based graph relationships between stations, the incidence dynamic graph structures firstly model the traffic relationships from historical passenger flows. • For real urban passenger traffic flows, the paper demonstrates that dynamic spatial-temporal incidence graphs are more suitable to model external changes and influences. • The paper compares Dynamic-GRCNN with state-of-the-art deep learning approaches on three benchmark datasets which contain different types of passenger traffic flows for evaluation. The results show that Dynamic-GRCNN significantly outperforms all the baselines in both effectiveness and efficiency in urban passenger traffic flows prediction. Accurate and real-time traffic passenger flows forecasting at transportation hubs, such as subway/bus stations, is a practical application and of great significance for urban traffic planning, control, guidance, etc. Recently deep learning based methods are promised to learn the spatial-temporal features from high non-linearity and complexity of traffic flows. However, it is still very challenging to handle so much complex factors including the urban transportation network topological structures and the laws of traffic flows with spatial and temporal dependencies. Considering both the static hybrid urban transportation network structures and dynamic spatial-temporal relationships among stations from historical traffic passenger flows, a more effective and fine-grained spatial-temporal features learning framework is necessary. In this paper, we propose a novel spatial-temporal incidence dynamic graph neural networks framework for urban traffic passenger flows prediction. We first model dynamic traffic station relationships over time as spatial-temporal incidence dynamic graph structures based on historically traffic passenger flows. Then we design a novel dynamic graph recurrent convolutional neural network, namely Dynamic-GRCNN, to learn the spatial-temporal features representation for urban transportation network topological structures and transportation hubs. To fully utilize the historical passenger flows, we sample the short-term, medium-term and long-term historical traffic data in training, which can capture the periodicity and trend of the traffic passenger flows at different stations. We conduct extensive experiments on different types of traffic passenger flows datasets including subway, taxi and bus flows in Beijing. The results show that the proposed Dynamic-GRCNN effectively captures comprehensive spatial-temporal correlations significantly and outperforms both traditional and deep learning based urban traffic passenger flows prediction methods. [ABSTRACT FROM AUTHOR]

High-precision short-term inbound passenger flow prediction is of great significance to the daily crowd management and line rescheduling in urban rail systems. Although current models have been applied to prediction, most methods need optimization to meet refined passenger flow management demand. In order to better predict the passenger flow, a novel Wave-LSTM model, based on long short-term memory network (LSTM) and wavelet, is introduced in this paper. In an empirical study with practical passenger flow data of Dongzhimen Station in the Beijing Subway system, the hybrid model exhibited more effective performance in terms of prediction accuracy than the existing algorithms, e.g., autoregressive integrated moving average (ARIMA), nonlinear regression (NAR), and traditional LSTM model. The study illustrates that our newly adopted model is a promising approach for predicting high-precision short-term inbound passenger flow. [ABSTRACT FROM AUTHOR]

In modern scientific applications, more and more data sets contain natural matrix predictors and traditional regression methods are not directly applicable. Matrix regression has been adapted to such data structure and received increasing attention in recent years. In this paper, we consider estimation of the conditional quantile in high-dimensional regularized matrix regression with a nuclear norm penalty and establish the convergence rate of the estimator. In order to construct a quantile matrix regression estimator in the distributed setting or for massive data sets, we propose a regularized communication-efficient surrogate loss (CSL) function. The proposed CSL method only needs the worker machines to compute the gradient based on local data and the central machine solves a regularized estimation problem. We prove that the estimation error based on the proposed CSL method matches the estimation error bound of the centralized method that analyzes the entire data set. An alternating direction method of multipliers algorithm is developed to efficiently obtain the distributed CSL estimator. The finite-sample performance of the proposed estimator is studied through simulations and an application to Beijing Air Quality data set. [ABSTRACT FROM AUTHOR]

As extensions of vector data with ultrahigh dimensionality and complex structures, matrix data are fast emerging in a large variety of scientific applications. In this paper, we consider the matrix regression with streaming data and propose two-stage online regularized estimators with nuclear norm (NN) and adaptive nuclear norm (ANN) penalties, respectively. In the first stage, an equivalent form of offline matrix regression loss function using current raw data and summary statistics from historical data is established. In the second stage, gradient descent algorithm and soft thresholding methods are implemented iteratively to obtain the proposed online NN and ANN estimators. We establish the asymptotic properties of the resulting online regularized estimators and show the rank selection consistency for the online ANN estimator. The finite-sample performance of the proposed estimators is studied through simulations and an application to Beijing Air Quality data set. • We consider the matrix regression with streaming data. • We establish the asymptotic properties and rank selection consistency. • The finite-sample performance of the proposed estimators is studied through simulations. • A real example on Beijing Air Quality data set is provided to show the performance of the proposed estimators. [ABSTRACT FROM AUTHOR]

• GPS and IC card data are used to simulate the time-dependent travel time and passenger demand. • An improved passenger selection model is proposed by introducing a preference coefficient. • A working hours constraint is first formulated in bus timetabling problem. • The model is transformed into an integer linear program, followed by a model contraction approach. • A two-stage solution method is proposed for large-scale bus timetabling problems. Bus lines are suffering from serious decline in passenger volume due to the rapid development of urban rail transit and shared transport, and big data intelligence may help them change the status quo. However, the tremendous amount of travel data collected in recent years have not got effectively utilization. In order to improve passenger volume for bus lines, this paper devotes to develop a data-driven bus timetable to substitute the existing experience-based bus timetable, which is now widely used by bus lines. Driven by the bus GPS data and IC card data, a timetable optimization model with time-dependent passenger demand and travel time among stops is proposed. The objective of maximizing passenger volume is based on a new preference-based passenger selection model. The working hours constraint is initially formulated, and the headway constraint and departure time constraints are also taken into account. For handling the step functions in both objective and constraints, we introduce a set of 0–1 variables to transform the proposed model into an integer linear programming. A model contraction approach is provided for solving the medium-scale problems and a two-stage solution method is proposed for the large-scale problems. The proposed model and methodology are tested on a real-world bus line in Beijing. The results show that it is able to produce a satisfactory timetable that outperforms the previously used experience-based one in terms of raising the average passenger volume by 8.2%. [ABSTRACT FROM AUTHOR]

In this paper, a novel model-free adaptive control (MFAC) algorithm based on a dual successive projection (DuSP)-MFAC method is proposed, and it is analyzed using the introduced DuSP method and the symmetrically similar structures of the controller and its parameter estimator of MFAC. Then, the proposed DuSP-MFAC scheme is successfully implemented in an autonomous car “Ruilong” for the lateral tracking control problem via converting the trajectory tracking problem into a stabilization problem by using the proposed preview-deviation-yaw angle. This MFAC-based lateral tracking control method was tested and demonstrated satisfactory performance on real roads in Fengtai, Beijing, China, and through successful participation in the Chinese Smart Car Future Challenge Competition held in 2015 and 2016. [ABSTRACT FROM AUTHOR]

As extensions of vector and matrix data with ultrahigh dimensionality and complex structures, tensor data are fast emerging in a large variety of scientific applications. In this paper, a two-stage estimation procedure for linear tensor quantile regression (QR) with longitudinal data is proposed. In the first stage, we account for within-subject correlations by using the generalized estimating equations and then impose a low-rank assumption on tensor coefficients to reduce the number of parameters by a canonical polyadic decomposition. To avoid the asymptotic analysis and computation problems caused by the non-smooth QR score function, kernel smoothing method is applied in the second stage to construct the smoothed tensor QR estimator. When the number of rank is given, a block-relaxation algorithm is proposed to estimate the regression coefficients. A modified BIC is applied to estimate the number of rank in practice and show the rank selection consistency. Further, a regularized estimator and its algorithm are investigated for better interpretation and efficiency. The asymptotic properties of the proposed estimators are established. Simulation studies and a real example on Beijing Air Quality data set are provided to show the performance of the proposed estimators. [ABSTRACT FROM AUTHOR]

The nine papers in this special section consists of expanded versions of papers presented at the International Symposium on Circuits and Systems, held in Beijing, China, in May 2013. [ABSTRACT FROM AUTHOR]

The author comments on the findings of a survey conducted by the Beijing Municipal Bureau of Statistics regarding the average monthly wage of city employees in 2008. It revealed that the average age of these employees reached 3,726 yuan or a 12% increase as compared to 2007. The author reveals the findings of an online survey stating that 74.2% noted that their incomes were below the local average. He thinks that the bureau's claim indicates the fact that ordinary people enjoy a rich life only in terms of statistics.

The article focuses on the piece of paper team, an environmental protection group organized by Yuan Rishe, a middle-school student, that advocated using both sides of a piece of paper. The members of the group also collected and sold used paper to raise money for tree planting. The student presented 2008 trees to the Beijing Olympic Committee on March 12, 2007, the 23rd Tree-planting Day in Beijing, China, as a gift and called them Young Pioneer Woods. It cites that her story calls for everybody's participation in saving energy.

Information about several papers discussed at a conference on the need for the development of agricultural and rural areas in China, which was conducted in Beijing on December 2005 is presented. The economy in the country can be developed in the right direction if problems relating to agriculture, rural areas and the farmers have been solved properly. The conference was attended by Chinese Premier Wen Jiabao and Vice-Premier Huang Ju. INSET: OVERPOPULATION: A MAIN CAUSE OF RURAL POVERTY.

The article discusses the cleanup campaign of Chinese government in the city of Beijing that includes demolitions of illegal structures made by building walls and digging holes, aimed at driving out as many of the city's incomers as possible. It mentions that the campaign leaves a new level of legitimacy for shop owners to save their businesses, that is the right papers in the right zone, including personal residence permit listed in the right city. Other topics mentioned include Beijing's nominal population, the impact of breaking small businesses on the poor, and government's promises to control migration and to prevent peasants from using hospitals and schools of metropolises.

The article offers brief information on the Institute of Electrical & Electronics Engineers World Congress on Computational Intelligence (IEEE WCCI) technical event to be held in Beijing, China from July 6-11, 2014, and invites papers, tutorials and special sesion proposals for the event.

The article discusses the highlights of the Symposium on China's Peaceful Development, held in Beijing, China on September 15, 2011. The symposium has gathered 200 officials and scholars who discussed the country's future development path. It was held after the State Council Information Office has released a white paper, titled China's Peaceful Development. According to Chinese Councilor Dai Bingguo, China's peaceful development is not a challenge, but an opportunity, to the rest of the world.

The article focuses on the All Artisans Shop museum in Beijing, China. The museum gathers more than 100 folk art masters who have set up workshops to both make art and cultivate students. According to the article, museum visitors can see demonstrations of the skills needed for making glass and baccaro teapots, Beijing embroidery and paper cuttings. It also features several artists, including Zhao Caixuan, a fifth-generation hand-torn artist.

Reports that visitors hang pieces of paper, on which they write down their wishes, on the big Christmas trees erected in the Xidan business pedestrian in Beijing, China.

Features Wang Chengfeng, chief resident physician of the Beijing, China-based Cancer Hospital. Career background; Achievements in the field of diagnosis and treatment of cancer; Papers published by Wang.

Highlights the 16th World Computer Congress in Beijing, China. Statement of Chinese President Jiang Zemin on the importance of information technology (IT) to China's economic development; Number of IT experts participating in the conference; Volume of research papers submitted and discussed.

The article highlights the Olympics opening ceremony at the National Stadium in Beijing, China on August 8, 2008. One of the most stunning performances was the demonstration of printing. A huge scroll painting, which represented the ancient invention of papermaking, was rolled open at the center of the stadium. Gunpowder was represented in the event by the fireworks displays.

In this article the author describes the difficulties she associated with her move to Beijing to set up a bureau for Great Britain's "Channel 4 News." She is required to adopt a Chinese name, corresponding phonetically to her English one, and explains the problems of buying a "chop" or rubber stamp inscribed with Chinese characters representing her company. She rents an apartment in the diplomatic quarter of the city.

The article deals with the use of cutting-edge technology in the opening ceremony of the 2008 Olympics in Beijing, China. Yu Jianping, head of the technology group for the opening ceremony, relates that advanced technologies were employed for the ceremony, including materials for spaceships. It describes how light-emitting diode screens were used in the ceremony. It discusses the Chinese cultural elements utilized in the opening ceremony such as papermaking and movable type printing.