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This paper studies the dynamic performance of subway transportation systems. A three-layer model, consisting of a rail layer, a train layer, and a passenger layer, is proposed to describe the structure and operation of a metro system. Two parameters, namely, time efficiency and maximum load ratio, are proposed to assess the transport efficiency and the train utilization rate, respectively. Case studies of the metro networks in Beijing, Tokyo, and Hong Kong show that the time efficiency decreases nonlinearly with the increase of vehicle resource and passenger demand, whereas maximum load ratio varies in an opposite manner. For the three metro networks under study, the Tokyo metro system has the highest time efficiency when passengers adopt a shortest-path (SP) routing strategy, while the Hong Kong system’s highest time efficiency exceeds the others’ when passengers adopt a minimum-transfer-path (MTP) strategy. In general, the maximum time efficiency is higher when passengers adopt SP routing rather than MTP routing. Moreover, the Beijing metro system has the highest maximum load ratio, regardless of the passengers’ routing behavior. This paper can be applied to a metro network to optimize the train departure interval under a certain passenger entrance rate, with the aim to maximize the time efficiency and maximum load ratio. Our model permits assessment of the operational effectiveness of metro systems, which helps the metro operators to improve the performance and reduce the cost. [ABSTRACT FROM AUTHOR]

The haze problem in cities poses a great threat to human health. Although there are many factors that cause fog and haze, the main reason is the increase of the PM2.5 concentration. Due to the complexity of the particles motion, it is difficult to use traditional methods obtain information about PM2.5 (including its sources, influencing factors, and distribution forecast). This paper presents a cellular automata (CA) model based on a multivariate regression model and several physical models to analyze the generation and diffusion of PM2.5. In Beijing for example, after the researches, the multiple regression confirmed that the major source of PM2.5 is vehicle pollution, which accounts for 39.2% of the pollution generated in Beijing. The secondary source is from the residential areas, accounting for 27.5% in the winter. Besides, 32% of the total pollution comes from nearby areas. In addition, it is also confirmed that the weather factors, such as, temperature, wind, pressure and relative humidity, and radiation are all have a great impact on PM2.5. The CA model is demonstrated to be an effective simulation and prediction method for PM2.5 since it allows for the estimation of the governance results by simulating the control scheme and predicting the concentration of PM2.5 accurately in the next 48 h. In the prediction experiment, 77.5% of prediction error is less than $20~ {\mu }\text{g} / {\text {m}}^{3}$. [ABSTRACT FROM AUTHOR]