The research on the relationship between risk and return of new energy stocks is the focus of financial research. Related research focuses more on the relationship between idiosyncratic fluctuation ...risk and stock returns. In the Chinese stock market, some Chinese investors clearly prefer stocks with high risk characteristics, which leads to overvalued stocks. However, the short-selling restrictions in the Chinese stock market and the heterogeneity of investors have also led to a significant negative correlation between idiosyncratic volatility and cross-sectional yield. There are many studies on the relationship between idiosyncratic volatility and stock returns, but no consistent conclusions have been drawn, and there is a lack of relevant research on new energy stocks. Therefore. This paper collates the data of 70 listed companies in the new energy and new energy automobile industry from 2017 to 2019, tracks the stock returns of sample companies for 3 years (36 months), and conducts in-depth research on the relationship between idiosyncratic fluctuation risks and new energy stock returns. To further verify and supplement the risk-return relationship of China's new energy stock market and provide a certain basis for the company's decision-making behaviour.
Under the background of urbanization and the rapid development of urban rail transit (URT), serious attention has been focused on URT network reliability in recent years. In this work, in order to ...measure network reliability, three indicators are constructed based on passengers’ tolerable travel paths, passenger travel efficiency and passenger travel realization on a URT network. The passenger tolerability coefficient, which is the ratio of passengers’ tolerable travel time to the shortest possible travel time, is proposed and added to the indicators. It reflects passengers’ behavior with respect to choice of travel paths. The ratio of affected passenger volume (RAPV) is proposed to identify important stations. Finally, the connectivity reliability of Wuhan’s subway network is analyzed by simulating attacks on stations. The results show that the degree centrality, betweenness centrality and RAPV indicators of stations can effectively identify the important stations that have a significant impact on the connectivity reliability of the network. In particular, the RAPV indicator effectively identifies stations that have the greatest influence on passenger travel realization. The connectivity reliability of Wuhan’s subway network is sensitive to passenger tolerability coefficient, and reliability is greater during non-peak hours than during peak hours. In addition, the stations that are important to the connectivity reliability of the Wuhan subway have two features, i.e., they are located at the center of the city, and they are important for connecting subgraphs of the network.
The location of wagon gravity center for a loaded wagon is underestimated in a vehicle–track coupled system. The asymmetric wheel load distribution due to loading offset significantly affects the ...wheel-rail contact state and seriously deteriorates the curving performance in conjunction with the height of gravity center and cant deficiency. Optimizing the location of gravity center and cruising velocity, therefore, is of interest to prevent the derailment and promote the transport capacity of railway wagons. This study aims to reveal the three-dimensional influencing mechanism of mass distribution on vehicle curving performance under different velocities. The wheel unloading ratio is regarded as the evaluation index. A simplified quasi-static model is established considering essential assumptions to highlight the influence of lateral and vertical offset on curving performance. For a more accurate description, the MBS models with various locations of wagon gravity center are built and then negotiate curves in different simulation cases. The simulation results reveal that the distribution of wheel unloading ratio determined by loading offset is like contour lines of ‘basin’. Based on the conclusions of quasi-static analysis and dynamics simulations, the regression equation is proposed and the fitting parameters are calculated for each simulation case. This paper demonstrates the necessity of optimizing the location of wagon gravity center according to the running condition and offers a novel strategy to load and transport the cargo by railway wagons.
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.
•Three factors are ascertained to measure the effects of disruptions.•Real-time prediction requirements are particularly considered in the model.•The model shows high accuracy in predicting the ...effects of disruptions.•The model shows strong generalizability on two different high-speed railway lines.
Based on the Bayesian network (BN) paradigm, we propose a hybrid model to predict the three main consequences of disruptions and disturbances during train operations, namely, the primary delay (L), the number of affected trains (N), and the total delay times (T). To obtain an effective BN structure, we first analyze the dependencies of the involved factors on each station and among adjacent stations, given domain knowledge and expertise about operational characteristics. We then put forward four candidate BN structures, integrating expert knowledge, the interdependencies learned from real-world data, and real-time prediction and operational requirements. Next, we train the candidate structures based on a 5-fold cross-validation method, using the operational data from Wuhan-Guangzhou (W-G) and Xiamen-Shenzhen (X-S) high-speed railway (HSR) lines in China. The best performing structure is nominated to predict the consequences of disruptions and disturbances in the two HSR lines. Comparisons results show that the proposed model outperforms three other commonly used predictive models, reaching an average prediction accuracy of 96.6%, 74.8%, and 91.0% on the W-G HSR line, and 94.8%, 91.1%, and 87.9% on the X-S HSR line for variables L, N, and T, respectively.
Chinese high-speed railway has implemented large-scale network operation with an urgent need for capacity improvement. The concept of virtual coupling seems to be a promising solution that provides a ...new operational scenario for high-speed railway, where trains are formed into a cooperative convoy and run synchronously with small train headways. The train-following principles under the virtual coupling signalling are quite different from those under conventional train control systems. Therefore, train headway analysis for different operational scenarios should be carried out to ensure railway safety and evaluate capacity benefits brought by virtual coupling. This paper proposes a potential virtual coupling architecture with reference to ETCS/ERTMS specifications. We compare blocking time models under different train control systems, and eight typical train-following scenarios are investigated for virtual coupling, including train arrival and departure cases. A detailed multiscenario-based train headway analysis is provided based on the microscopic infrastructure of the station and technological characteristics of virtual coupling. All computational outcomes are based on the train dynamic motion model. A comparative analysis of train headways under virtual coupling and CTCS-3 is provided in the case study. Results show that train headways can be substantially reduced under virtual coupling and are related to the station infrastructure layout.
Rail transit network design is an important strategic problem in determining the layout of infrastructure and improving operating performance. A core transit network with multiclass rail transit ...systems has been constructed in many metropolitan areas worldwide. In this study, we aimed to expand an existing network to shorten travel time and improve service quality under the restriction of limited transport supply. We formulate the studied problem as a mixed-integer linear model to obtain optimal construction links, the number of trains required on each link, and the path selected by each traveler such that the weighted sum of total costs from the perspective of travelers, operators, and investors is minimized. The formulated model is path-based, where feasible paths for each traveler are generated to describe the full door-to-door journey, including the first/last mile, transfers, and multiclass transit modes. Owing to the complexity of the network design problem and because it is impractical to enumerate all feasible paths for each traveler in real-size problems, we propose a column generation-based algorithm to find both tight lower bounds and good-quality solutions efficiently by considering only a subset of feasible paths. We prove that the pricing subproblem in column generation can be decomposed into multiple shortest path problems, which can be solved efficiently and separately, based on O/D pairs instead of individual travelers. A rail transit network along a metropolitan corridor was studied as an example. Multiple computational experiments were conducted, and the results illustrate the validity and practicality of the proposed methodology for solving the problem.
The construction industry is a high-risk industry with many safety accidents. The popularity of Internet information technology has led to an explosion in the amount of data obtained in various ...engineering fields, and it is of necessary significance to explore the current situation of the application of big data technology in construction safety management. This paper systematically reviews 66 articles closely related to the research topic and objectives, describes the current status of big data application to various construction safety issues from the perspectives of both big data collection and big data analysis for engineering and construction projects, and categorically lists the breakthrough results of big data analysis technology in improving construction safety. Finally, the trends and challenges of big data in the field of construction safety are discussed in three directions: the application of big data to worker behavior, the prospect of integrating big data technologies, and the integration of big data technologies with construction management. The aim of this paper is to demonstrate the current state of research on big data technology fueling construction safety management, providing valuable insight into improving safety at engineering construction sites and providing guidance for future research in this field.
Train timetables and operations are defined by the train running time in sections, dwell time at stations, and headways between trains. Accurate estimation of these factors is essential to ...decision-making for train delay reduction, train dispatching, and station capacity estimation. In the present study, we aim to propose a train dwell time model based on an averaging mechanism and dynamic updating to address the challenges in the train dwell time prediction problem (e.g., dynamics over time, heavy-tailed distribution of data, and spatiotemporal relationships of factors) for real-time train dispatching. The averaging mechanism in the present study is based on multiple state-of-the-art base predictors, enabling the proposed model to integrate the advantages of the base predictors in addressing the challenges in terms of data attributes and data distributions. Then, considering the influence of passenger flow on train dwell time, we use a dynamic updating method based on exponential smoothing to improve the performance of the proposed method by considering the real-time passenger amount fluctuations (e.g., passenger soars in peak hours or passenger plunges during regular periods). We conduct experiments with the train operation data and passenger flow data from the Chinese high-speed railway line. The results show that due to the advantages over the base predictors, the averaging mechanism can more accurately predict the dwell time at stations than its counterparts for different prediction horizons regarding predictive errors and variances. Further, the experimental results show that dynamic smoothing can significantly improve the accuracy of the proposed model during passenger amount changes, i.e., 15.4% and 15.5% corresponding to the mean absolute error and root mean square error, respectively. Based on the proposed predictor, a feature importance analysis shows that the planned dwell time and arrival delay are the two most important factors to dwell time. However, planned time has positive influences, whereas arrival delay has negative influences.
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