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The cooperative tracking control problem is investigated for the virtually coupled train set subject to gradient terrain and actuator saturation. This paper proposes an adaptive event-triggered cooperative tracking control scheme, where a distributed asynchronous event-triggered sampling is designed based on the relative states between adjacent trains. In addition, the unknown parameters of high-speed train system are estimated by the adaptive method. By the proposed control scheme, the velocity and position tracking errors between any adjacent trains are ultimately bounded while realizing on-demand interaction between trains. It is proved that the Zeno behavior can be excluded by quantitatively expressing the strictly positive minimum lower bound of the inter-event interval. Finally, a simulation of the Beijing-Shanghai high-speed railway is used to verify the feasibility and effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

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]

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]

Abstract: An integrated ecological evaluation of the Beijing economy was presented in the paper based on the emergy accounting with the data in 2004. Through calculating environmental and economic inputs within and outside the Beijing economy, this paper discusses the Beijing’s resource structure, economic situation and trade status based on a series of emergy indicators. Through the comparison of the systematic indicators of Beijing with those of the selected Chinese cities, the general status of the Beijing economy in China is identified. The results also show that most indicators of Beijing are located at middle levels among the selected Chinese cities. Particularly, the environmental impacts, expressed by the ratio of waste to the renewable emergy, and the ratio of waste to the total emergy use, are 84.2 and 0.26, respectively in Beijing in 2004, which indicate that the Beijing economy is greatly reliant on the imported intensive fuels, goods and services with high empower density and environmental loading. [Copyright &y& Elsevier]

Abstract: For urban economies, an ecological endowment embodiment analysis has to be supported by endowment intensities at both the international and domestic scales to reflect the international and domestic imports of increasing importance. A three-scale input–output modeling for an urban economy to give nine categories of embodiment fluxes is presented in this paper by a case study on the carbon dioxide emissions by the Beijing economy in 2007, based on the carbon intensities for the average world and national economies. The total direct emissions are estimated at 1.03E+08t, in which 91.61% is energy-related emissions. By the modeling, emissions embodied in fixed capital formation amount to 7.20E+07t, emissions embodied in household consumption are 1.58 times those in government consumption, and emissions in gross capital formation are 14.93% more than those in gross consumption. As a net exporter of carbon emissions, Beijing exports 5.21E+08t carbon embodied in foreign imported commodities and 1.06E+08t in domestic imported commodities, while emissions embodied in foreign and domestic imported commodities are 3.34E+07 and 1.75E+08t respectively. The algorithm presented in this study is applicable to the embodiment analysis of other environmental resources for regional economies characteristic of multi-scales. [Copyright &y& Elsevier]

Abstract: Ecological systems modelling based on the unified biophysical measure of cosmic emergy in terms of embodied cosmic exergy is illustrated in this paper with ecological accounting, simulation and scenario analysis, by a case study for the regional socio-economic ecosystem associated with the municipality of Beijing. An urbanized regional ecosystem model with eight subsystems of natural support, agriculture, urban production, population, finance, land area, potential environmental impact, and culture is representatively presented in exergy circuit language with 12 state variables governing by corresponding ecodynamic equations, and 60 flows and auxiliary variables. To characterize the regional socio-economy as an ecosystem, a series of ecological indicators based on cosmic emergy are devised. For a systematic ecological account, cosmic exergy transformities are provided for various dimensions including climate flows, natural resources, industrial products, cultural products, population with educational hierarchy, and environmental emissions. For the urban ecosystem of Beijing in the period from 1990 to 2005, ecological accounting is carried out and characterized in full details. Taking 2000 as the starting point, systems modelling is realized to predict the urban evolution in a one hundred time horizon. For systems regulation, scenario analyses with essential policy-making implications are made to illustrate the long term systems effects of the expected water diversion and rise in energy price. [Copyright &y& Elsevier]

Within a Collaboration Agreement between INFN and CAS (Chinese Academy of Science), the ARGO-YBJ experiment is completely installed and has been in stable data taking since November 2007 at the YangBaJing Cosmic Ray Laboratory (Tibet, P.R. China, 4300 m a.s.l.). In this paper we report a few selected results in γ-ray Astronomy and Cosmic Ray Physics. [Copyright &y& Elsevier]

Abstract: Ecological treatment engineering has been widely accepted as an artificially designed work to deal with the deteriorating ecological environment with low energy and resource consumption. To measure the energy and resource consumption and environmental support contained in the constructed wetland as a kind of ecological treatment engineering, emergy as embodied solar energy based assessment is performed and relative emergy-based indices including emergy yield ratio (EYR), emergy load ratio (ELR), emergy sustainability index (ESI), net economic benefit index (Np), and renewable percentage index (Pr), are also modified to evaluate the local sustainability of the constructed wetland in this paper. A case study on Longdao River constructed wetland compared with those of some earlier conventional treatment systems indicate that more local renewable resources and less ecological cost are involved, thus promoting the economic benefit due to less energy and resource consumption and simultaneously lowering the environmental stress of the treatment system on the local areas. [Copyright &y& Elsevier]

Abstract: Presented in this paper is an integrated cost and efficiency analysis of a pilot vertical subsurface-flow constructed wetland (CW) built up in 2004 near the Longdao River in Beijing, China. The CW has been monitored over one year and proved to be a good solution to treat the polluted water and restored the ecosystem health of the Longdao River. The modified CW system in accordance with local conditions costs less in construction, operation and maintenance than traditional wastewater treatment system and occupies less land than conventional CW. Also, derived from the efficiency analysis, the Longdao River CW provides better elimination effects for nutrient substances in the polluted river water and has stable performances in cold seasons. [Copyright &y& Elsevier]