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Port-centric intermodal transshipment hubs are significant nodes in the global freight network and are likewise the gateway to a country's external communications. It is vital to increase the service capacity of PCITHs, and it is necessary to assess the service capacity of port-centric intermodal transshipment hubs to respond to the growth of economies and global freight needs. This study provides a detailed definition of port-centric intermodal transshipment hubs through a review of relevant kinds of works from the literature and analyzes their primary functions. Based on the research perspective of sea–rail intermodal transportation, the three evaluation dimensions of service capacity of port-centric intermodal transshipment hubs are divided into radiation scale capacity, transportation connection capacity, and resource integration capacity, focusing on the functions of cargo aggregation, cargo transfer, and connection of different transportation modes. The service capacity evaluation indicators were then selected based on the three dimensions. The subjective and objective weightings were calculated by the G1 weighting method and the modified CRITIC method, and the combination weightings were determined based on game theory. The service capability of port-centric intermodal transshipment hubs was evaluated by the fuzzy matter element method, and the evaluation results were quantified by the Euclidean closeness degree. Finally, through the barrier degree model, the current indicators of PCITHs that urgently need improvement were explored, and targeted improvement suggestions are proposed in this paper. The results show that Tianjin Port has the highest service capacity, followed by Ningbo Zhoushan Port. The port rail dedicated line mileage is the most critical area that needs attention in Ningbo Zhoushan Port and Qingdao Port. Tianjin Port needs to improve the container sea–rail transportation volume, while Guangzhou Port and Xiamen Port need to improve the sea–rail container handling capacity. [ABSTRACT FROM AUTHOR]

As China's economy has entered the development stage of a "new normal," the traditional path relying on investment scale expansion for economic growth can no longer sustain. Thus the key of the supply-side structural reform lies in breaking through the long-term path dependence and facilitating the transformation and innovation of socio-economic and urban development. This paper takes Shenzhen as the benchmark of the comparative study, and Guangzhou and Tianjin as the comparative object to analyze the contributions of the demand-side "troika" - investment, consumption, and export, to economic growth from 1986 to 2015. Then, it introduces multiple growth elements on the supply-side to analyze the significant contribution elements in urban economic growth and their contribution degrees. Meanwhile, in combination with the economic development background and the policy guidance since the reform and opening up, the paper analyzes the characteristics of the development paths and their trends of the three cities. In the end, based on the comparative study, it carries out a discussion on the development path for mega-cities and their urban planning strategies at the new era from three perspectives. [ABSTRACT FROM AUTHOR]

The analysis of the multi-scale evaluation of port city's international trade goal is conducive to the sustainable development of port city's international trade. In order to make a more in-depth study on the realization ability of port city's international trade sustainable development goal, this paper proposes a new multi-scale evaluation method of port city's international trade goal. This method selects the evaluation indexes, uses the improved normalization method to process the indexes, uses the combination of AHP and factor analysis method to form the subjective and objective combination weighting method, brings the processed indexes into the least square optimization combination evaluation model, calculates the index weight, and uses the fuzzy evaluation method to carry out multi-scale index evaluation on the international trade of port cities to realize its multi-scale evaluation and analysis. The results show that the standardized index of Shanghai's foreign trade dependence is 0.0056, indicating its independence in international trade. In the comprehensive evaluation, the evaluation values of Shanghai, Tianjin, and Guangzhou are 92.56, 87.89, and 88.45, respectively, which are very close to the actual results, which shows that the accuracy of the evaluation method is high, and provides a theoretical basis for the sustainable development of international trade in port cities. [ABSTRACT FROM AUTHOR]

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The quantitative relationship between the spatial variation of building's height and the associated land surface temperature (LST) change in six Chinese megacities is investigated in this paper. The six cities involved are Beijing, Shanghai, Tianjin, Chongqing, Guangzhou, and Shenzhen. Based on both remote sensing and building footprint data, we retrieved the LST using a single-channel (SC) algorithm and evaluate the heating/cooling effect caused by building-height difference via correlation analysis. The results show that the spatial distribution of high-rise buildings is mainly concentrated in the center business districts, riverside zones, and newly built-up areas of the six megacities. In the urban area, the number and the floor-area ratio of high to super high-rise buildings (>24m) account for over 5% and 4.74%, respectively. Being highly urbanized cities, most of urban areas in the six megacities are associated with high LST. Ninety-nine percent of the city areas of Shanghai, Beijing, Chongqing, Guangzhou, Shenzhen, and Tianjin are covered by the LST in the range of 30.2~67.8°C, 34.8~50.4°C, 25.3~48.3°C, 29.9~47.2°C, 27.4~43.4°C, and 33.0~48.0°C, respectively. Building's height and LST have a negative logarithmic correlation with the correlation coefficients ranging from -0.701 to -0.853. In the building's height within range of 0~66m, the LST will decrease significantly with the increase of building's height. This indicates that the increase of building's height will bring a significant cooling effect in this height range. When the building's height exceeds 66m, its effect on LST will be greatly weakened. This is due to the influence of building shadows, local wind disturbances, and the layout of buildings. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) can obtain traffic information through a large number of data collected by sensors. However, the lack of data, abnormal data, and other low-quality problems have seriously restricted the development and application of the IoV. To solve the problem of missing data in a large-scale road network, the previous research achievements show that tensor decomposition method has the advantages in solving multi-dimensional data imputation problems, so we adopt this tensor mode to model traffic velocity data. A new method of data missing estimation with tensor heterogeneous ensemble learning based on FNN (Fuzzy Neural Network) named FNNTEL is proposed in this paper. The performance of this method is evaluated by our experiments and analysis. The proposed method is applied to be tested by the real data captured in Guangzhou and Tianjin of China respectively. A large number of experimental tests show that the performance of the new method is better than other commonly used technologies and different missing data generation models. [ABSTRACT FROM AUTHOR]

This study assessed the regional differences of PM2.5 (fine particulate matter) concentration and secondary components in China. Through chemical analysis (Thermal/Optical Carbon Analyzer and ion chromatography) and statistical tools (SPSS and Origin software), PM2.5 samples' concentration and main components were estimated and compared in Tianjin and Guangzhou in December 2016. The average PM2.5 mass concentration was 148 μg/m3 in Tianjin and 62 μg/m3 in Guangzhou, indicating heavy air pollution in both cities. Organic carbon constituted 15% of PM2.5 in Tianjin, while that accounted for 25% in Guangzhou. The concentration of the secondary organic carbon accounted for 40% and 33% of organic carbon in Tianjin and Guangzhou, respectively. The ionic components in Tianjin made up 49% of PM2.5, while those of Guangzhou were responsible for 36% locally. The sulfate oxidation ratio reached 0.41 in Tianjin and 0.53 in Guangzhou while the nitrate oxidation ratio was 0.23 and 0.14 in the two cities, respectively. These results indicated high secondary conversion rate in PM2.5 pollution in both cities and suggested that PM2.5 mass concentration could not fully reflect its environmental and health effects, and so more attentions should be paid to the effects of its components, especially secondary components. In addition, the results could provide a scientific basis for making policies concerning air pollution control and developing strategies of air quality improvement. [ABSTRACT FROM AUTHOR]

Intra-urban surface water (IUSW) is an indispensable resource for urban living. Accurately acquiring and updating the distributions of IUSW resources is significant for human settlement environments and urban ecosystem services. High-resolution optical remote sensing data are used widely in the detailed monitoring of IUSW because of their characteristics of high resolution, large width, and high frequency. The lack of spectral information in high-resolution remote sensing data, however, has led to the IUSW misclassification problem, which is difficult to fully solve by relying only on spatial features. In addition, with an increasing abundance of water products, it is equally important to explore methods for using water products to further enhance the automatic acquisition of IUSW. In this study, we developed an automated urban surface-water area extraction method (AUSWAEM) to obtain accurate IUSW by fusing GaoFen-1 (GF-1) images, Landsat-8 Operational Land Imager (OLI) images, and GlobeLand30 products. First, we derived morphological large-area/small-area water indices to increase the salience of IUSW features. Then, we applied an adaptive segmentation model based on the GlobeLand30 product to obtain the initial results of IUSW. Finally, we constructed a decision-level fusion model based on expert knowledge to eliminate the problem of misclassification resulting from insufficient information from high-resolution remote sensing spectra and obtained the final IUSW results. We used a three-case study in China (i.e., Tianjin, Shanghai, and Guangzhou) to validate this method based on remotely sensed images, such as those from GF-1 and Landsat-8 OLI. We performed a comparative analysis of the results from the proposed method and the results from the normalized differential water index, with average kappa coefficients of 0.91 and 0.55, respectively, which indicated that the AUSWAEM improved the average kappa coefficient by 0.36 and obtained accurate spatial patterns of IUSW. Furthermore, the AUSWAEM displayed more stable and robust performance under different environmental conditions. Therefore, the AUSWAEM is a promising technique for extracting IUSW with more accurate and automated detection performance. [ABSTRACT FROM AUTHOR]