As one of the most strategically important natural resources, crude oil can be dangerous to transport by sea. The resilience of a maritime transport network denotes the ability of the system to withstand damage and remain operational after disturbances, representing the invulnerability of the network and its ability to recover from harm. In this study, we built a crude oil transportation network titled the Maritime Silk Road using Automatic Identification System (AIS) sensor data, designed a resilience assessment framework based on complex network theory, and assessed the resilience of the maritime crude oil transportation network from both qualitative and quantitative perspectives with the help of complex network metrics and a resilience model. The results show that the topology of the crude oil transportation network has a significant impact on its resilience, in that network density and centrality are negatively related to network resilience, whereas network connectivity and size are positively related to resilience. Subsequently, the resilience of crude oil transportation networks declines at a steady rate under random attacks but declines sharply under intentional attacks. Finally, a comprehensive analysis of invulnerability and recovery in the context of resilience concludes that strengthening small and medium-sized ports in the network is important to enhance network resilience. These results can provide reference and decision support for port planning, route design, optimization, and form a foundation for a more secure and reliable maritime transportation network system.

Travel recommendations form a major part of tourism service. Traditional collaborative filtering and Markov model are not appropriate for expressing the trajectory features, for travel preferences of tourists are dynamic and affected by previous behaviors. Inspired by the success of deep learning in sequence learning, a personalized recurrent neural network (P-RecN) is proposed for tourist route recommendation. It is data-driven and adaptively learns the unknown mapping of historical trajectory input to recommended route output. Specifically, a trajectory encoding module is designed to mine the semantic information of trajectory data, and LSTM neural networks are used to capture the sequence travel patterns of tourists. In particular, a temporal attention mechanism is integrated to emphasize the main behavioral intention of tourists. We retrieve a geotagged photo dataset in Shanghai, and evaluate our model in terms of accuracy and ranking ability. Experimental results illustrated that P-RecN outperforms other baseline approaches and can effectively understand the travel patterns of tourists.

Maritime traffic can reflect the diverse and complex relations between countries and regions, such as economic trade and geopolitics. Based on the AIS (Automatic Identification System) trajectory data of ships, this study constructs the Maritime Silk Road traffic network. In this study, we used a complex network theory along with social network analysis and network flow analysis to analyze the spatial distribution characteristics of maritime traffic flow of the Maritime Silk Road; further, we empirically demonstrate the traffic inequality in the route. On this basis, we explore the role of the country in the maritime traffic system and the resulting traffic relations. There are three main results of this study. (1) The inequality in the maritime traffic of the Maritime Silk Road has led to obvious regional differences. Europe, west Asia, northeast Asia, and southeast Asia are the dominant regions of the Maritime Silk Road. (2) Different countries play different maritime traffic roles. Italy, Singapore, and China are the core countries in the maritime traffic network of the Maritime Silk Road; Greece, Turkey, Cyprus, Lebanon, and Israel have built a structure of maritime traffic flow in the eastern Mediterranean Sea, and Saudi Arabia serves as a bridge for maritime trade between Asia and Europe. (3) The maritime traffic relations show the characteristics of regionalization; countries in west Asia and the European Mediterranean region are clearly polarized, and competition–synergy relations have become the main form of maritime traffic relations among the countries in the dominant regions. Our results can provide a scientific reference for the coordinated development of regional shipping, improvement of maritime competition, cooperation strategies for countries, and adjustments in the organizational structure of ports along the Maritime Silk Road.

Nowadays, tourism practices face increasingly intensified flows of people, making it imperative to explore the tourism space through the lens of mobility. To examine nationwide tourist mobility, this study collected online travel blog data from Qunar.com, a leading travel services platform in China, to construct tourist mobility networks across China. In these networks, attractions are represented as nodes, while tourist movements between them, derived from blog data, are represented as weighted and directed edges. To capture different travel contexts, the study also develops mobility networks categorized by departure season and travel partners. All networks are released in a simple, accessible format to support future research.