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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]

Recent years have witnessed the world-wide emergence of mega-metropolises with incredibly huge populations. Understanding residents mobility patterns, or urban dynamics, thus becomes crucial for building modern smart cities. In this paper, we propose a Neighbor-Regularized and context-aware Non-negative Tensor Factorization model (NR-cNTF) to discover interpretable urban dynamics from urban heterogeneous data. Different from many existing studies concerned with prediction tasks via tensor completion, NR-cNTF focuses on gaining urban managerial insights from spatial, temporal, and spatio-temporal patterns. This is enabled by high-quality Tucker factorizations regularized by both POI-based urban contexts and geographically neighboring relations. NR-cNTF is also capable of unveiling long-term evolutions of urban dynamics via a pipeline initialization approach. We apply NR-cNTF to a real-life data set containing rich taxi GPS trajectories and POI records of Beijing. The results indicate: 1) NR-cNTF accurately captures four kinds of city rhythms and seventeen spatial communities; 2) the rapid development of Beijing, epitomized by the CBD area, indeed intensifies the job-housing imbalance; 3) the southern areas with recent government investments have shown more healthy development tendency. Finally, NR-cNTF is compared with some baselines on traffic prediction, which further justifies the importance of urban contexts awareness and neighboring regulations. [ABSTRACT FROM AUTHOR]

Urban black and odorous water (BOW) is a serious global environmental problem. Since these waters are often narrow rivers or small ponds, the detection of BOW waters using traditional satellite data and algorithms is limited both by a lack of spatial resolution and by imperfect retrieval algorithms. In this paper, we used the Chinese high-resolution remote sensing satellite Gaofen-2 (GF-2, 0.8 m). The atmospheric correction showed that the mean absolute percentage error of the derived remote sensing reflectance ($R_{\mathrm {rs}}$) in visible bands is 25.19%. We first measured $R_{\mathrm {rs}}$ spectra of two classes of BOW [BOW with high concentrations of iron (II) sulfide, i.e., BOW1 and BOW with high concentrations of total suspended matter, i.e., BOW2] and ordinary water in Shenyang. Then, in situ $R_{\mathrm {rs}}$ data were converted into $R_{\mathrm {rs}}$ corresponding to the wide GF-2 bands using the spectral response functions. We used the converted $R_{\mathrm {rs}}$ data to calculate several band combinations, including the baseline height, [ $R_{\mathrm {rs}}$ (green) $- R_{\mathrm {rs}}$ (red))/($R_{\mathrm {rs}}$ (green) $+ R_{\mathrm {rs}}$ (red)], and the color purity on a Commission Internationale de L’Eclairage (CIE) chromaticity diagram. The color purity was found to be the best index to extract BOW from ordinary water. Then, $R_{\mathrm {rs}}$ (645) was applied to categorize BOW into BOW1 and BOW2. We applied the algorithm to two synchronous GF-2 images. The recognition accuracy of BOW2 and ordinary water are both 100%. The extracted river water type near Weishanhu Road was BOW1, which agreed well with ground truth. The algorithm was further applied to other GF-2 data for Shenyang and Beijing. [ABSTRACT FROM AUTHOR]