This paper uses weekly data from July 01, 2011 to July 09, 2021 to examine the dynamic nonlinear connectedness between the green bonds, clean energy, and stock price around the COVID-19 outbreak in the global markets. By building a time-varying parameter vector autoregression model (TVP-VAR), the comparison analyses of pre- and during the COVID-19 sample groups verify the existence of nonlinear and dynamic correlation among the three variables. First, prior to the COVID-19 pandemic, the simultaneous impacts of clean energy on stock price increased over time. Second, the results of impulse responses at different horizons indicate that green bonds lead to a short-term increase of clean energy, and it exerts an increasingly positive impacts after the COVID-19 outbreak. The COVID-19 has weakened the negative impacts of green bonds on stock price in the medium term. Finally, through the analysis of impulse responses at different points, we find that stock prices will rise when clean energy is subjected to a positive shock, and this positive effect is stronger during economic recovery period than in the other two periods.

To satisfy the demand of low-carbon transportation, this paper studies the optimization of public transit network based on the concept of low carbon. Taking travel time, operation cost, energy consumption, pollutant emission, and traffic efficiency as the optimization objectives, a bilevel model is proposed in order to maximize the benefits of both travelers and operators and minimize the environmental cost. Then the model is solved with the differential evolution (DE) algorithm and applied to a real network of Baoji city. The results show that the model can not only ensure the benefits of travelers and operators, but can also reduce pollutant emission and energy consumption caused by the operations of buses, which reflects the concept of low carbon.

Dwell time estimation plays an important role in the operation of urban rail system. On this specific problem, a range of models based on either polynomial regression or microsimulation have been proposed. However, the generalization performance of polynomial regression models is limited and the accuracy of existing microsimulation models is unstable. In this paper, a new dwell time estimation model based on extreme learning machine (ELM) is proposed. The underlying factors that may affect urban rail dwell time are analyzed first. Then, the relationships among different factors are extracted and modeled by ELM neural networks, on basis of which an overall estimation model is proposed. At last, a set of observed data from Beijing subway is used to illustrate the proposed method and verify its overall performance.

In urban rail transport, train timetable plays a crucial role, whose quality determines the whole system's performance to a large extent. In practical urban rail operation, two contradictive aspects-service quality and operation cost-should be considered during train scheduling. A good train timetable should achieve considerable service quality with as little operation cost as possible. Previously, many studies have been conducted specific to urban rail train scheduling, although most of them do not put enough emphasis on its multi-objective nature. In this article, therefore, Pareto optimal urban rail train scheduling which can give more instruction to practical operation is studied. First, referring to some existing studies, the problem is reasonably defined, which takes time-dependent origin-destination demand as the input and aims at minimizing the passengers' total travel time and the number of used train stocks. Then, an efficient iteration algorithm and a valid train stock assignment procedure are designed to calculate the passengers' total travel time and required train stock number, respectively. On that basis, the studied problem is reasonably formulated as a bi-objective optimization model and a Pareto-based particle swarm optimization procedure is designed to solve it. Finally, with two different scaled urban rail lines, the whole methodology is illustrated and the algorithm is tested.

With the personalized and diversified development of customer demand in the freight market, road transportation has become a main competitor of railway transportation in container transportation due to its high flexibility, convenience, and low prices. Based on the generalized cost and logit model, this paper constructs a container railway goods transport market competitiveness model including four indicators of economy, timeliness, environmental protection, and safety. Take 20 ft container transportation as an example, the impact of changes in railway goods charge and railway travelling speed on the competitiveness of the railway goods transport market is analysed. Some realistic suggestions, including optimizing the railway tariff system, increasing the travelling speed of railway, innovating container intermodal products, and making full use of policy-oriented advantages are concluded.

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Void fraction is one of the key parameters for gas-liquid study and detection of nuclear power system state. Based on fully convolutional neural network (FCN) and high-speed photography, an indirect void fraction measure approach for flow boiling condition in narrow channels is developed in this paper. Deep learning technique is applied to extract image features and can better realize the identification of gas and liquid phase in channels of complicated flow pattern and high void fraction, and can obtain the instantaneous value of void fraction for analyzing and monitoring. This paper verified the FCN method with visual boiling experiment data. Compared with the time-averaged experimental results calculated by the energy conservation method and the empirical formula, the relative deviations are within 11%, which verifies the reliability of this method. Moreover, the recognition results show that the FCN method has promising improvement in the scope of application compared with the traditional morphological method, and meanwhile saves the design cost. In the future, it can be applied to void fraction measurement and flow state monitoring of narrow channels under complex working conditions.

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