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Location advantages of ports refer to the current developments of ports based on their conditions, such as geographic location, traffic accessibility and hinterland economy, etc., and the spatial pattern of ports’ location advantages reflects the spatial distributions, the regularities and the correlations among their conditions for development. A good understanding of the spatial patterns of ports’ location advantages can help to better identify the relative advantages of ports, position ports’ functions and make strategic plans for development. This paper selected 1259 ports from 63 countries along the Maritime Silk Road as research objects and builds an accessing model to analyze their location advantages on the bases of six factors: the influence of strategic shipping pivot, the competitiveness of port location potential, port network status, the influence of city, the influence of traffic trunk, and road network density in hinterland. The study has the following three findings. Firstly, the location advantages of ports show a “high-low-high” distribution pattern from the west to the east, displaying an obvious “core-periphery” regionalized distribution. Secondly, most ports have high location advantages, mainly located in Strait of Malacca, the United Arab Emirates, northern Mediterranean coastal region and China-Japan region, the top 10 ports are mainly located in Singapore, China, Malaysia and Japan, indicating that the shipping industry in Asia-Pacific region has stepped to the far front of the global competition; slow economic growths, wars, far away from the Belt and Road countries or bad climate have low location advantages, mainly located in African coastal areas, Oceania, Northeast Europe and Russia. Thirdly, compared with the landward location advantages, the seaward location advantages have a higher influence, and different indicators of location advantages have different influences on the evaluation results, the competitiveness of port location potential being the core indicator.

The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transportation network based on the most recent Automatic Identification System (AIS) data available. First, we subdivide three typical cargo ship transportation networks (i.e., oil tanker, container ship and bulk carrier) from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, including random attack and three intentional attacks (i.e., degree-based attack, betweenness- based attack and flux-based attack) to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1) compared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation; 2) different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3) small-scale intentional attacks may have significant influence on the integrity of the container network but a minor impact on the bulk carrier and oil tanker transportation networks. These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation system.

Sensored traffic data in modern cities have been collected and applied for various purposes in the domain of intelligent transportation systems (ITS). However, analyzing these traffic data often lacks in priori knowledge due to the dynamics of transportation systems, making it hard to cope with diverse scenarios with specific models. In view of the limitations of traditional approaches, in this paper, we propose the Stepwise Heterogeneous Ensemble (SHE) for citywide traffic analysis based on stacked generalization. We first prove SHE's effectiveness using error-ambiguity decomposition technique. Secondly we analyze the optimal linear combination of SHE and present the stepwise iterating strategy. We also demonstrate its validity based on Kullback-Leibler divergence analysis. Thirdly we integrate six classical approaches into SHE framework, including linear least squares regression (LLSR), autoregressive moving average (ARMA), historical mean (HM), artificial neural network (ANN), radical basis function neural network (RBFNN), support vector machine (SVM). We further compare SHE's performance with other four linear combination models, namely equal weights method (EW), optimal weights method (OW), minimum error method (ME) and minimum variance method (MV). A series of experiments are conducted with a real city traffic dataset in Beijing city. The results show that the proposed SHE method behaves more robust and precise than other six single methods. Moreover, this method also outperforms other four different combination strategies both in variance and bias. In addition, the SHE method provides an open-ending framework for citywide traffic analysis, which means any new promising models can be easily incorporated into it in the future.

Floating-car data (FCD) are never perfectly accurate due to various noises. To make FCD available, we propose a novel spatio-temporal particle filter ST-PF for FCD pre-processing. First we analyze the causes of errors and the shortcomings of previous studies. Second, we introduce the spatio-temporal constraints into the modeling of ST-PF. We also devise a novel iterating strategy for the recurrence of particle filtering based on sequential-importance sampling (SIS). We further design a series of experiments and compare the performances with that of other four traditional filters, namely, the mean filter, the median filter, the Kalman filter, and the original particle filter. The final results show ST-PF is much more effective for noise reduction and improvement of map-matching performance and shows a promising direction for FCD pre-processing.

In this article, it designs an escape system for vehicles in water crashes. The structure mainly contains sensors, control organs and actuating mechanism for both doors and windows. Sensors judge whether the vehicle falls into water or is in the falling process. The actuating mechanism accepts the signal delivered by the control organs, then open the electronic central lock on doors and meanwhile lower the window. The water escape system is able to anticipate drowning situations for vehicles and controls both doors and windows in such an emergency. Under the premise of doors staying in an undamaged state, it is for sure that people in the vehicle can open the door while drowning in the water and safely escape.