With the rapid increase in global maritime shipping, there is a great demand for the technology of maritime traffic monitoring to detect inappropriate encountering interaction between ships and prevent ship collision accidents. The Automatic Identification System (AIS) network makes it possible to collect a large volume of maritime traffic data and investigate the collision avoidance behavior of real-world ships. Most collision avoidance systems are based on expert systems and simulations based on the International Regulations for Preventing Collisions at Sea (COLREGs). Those regulations outline the general principles underlying collision avoidance; however, they do not provide specific guidance and fail to account for the complexity of many real-world situations. Furthermore, guidance systems coordinating the movement of a ship must have the capacity to predict the movement behavior of all ships involved in potential encounter situations, and do so as early as possible for anti-collision reaction. Our objective in this study was to model the collision avoidance behaviors of human operators in order to formulate a set of realistic trajectory predictions for encountering near collision scenarios. By machine learning approach, the proposed framework is able to learn a model of interaction movement behavior from collected AIS historical traffic data involving near collision situations and then generate a set of predicted trajectories while ships encountering. The proposed model eliminates the need for a priori information related to environmental conditions and the rules governing encounter situations. The resulting projections can be used to suggest anti-collision paths for navigators or to guide the selection of collision-free paths for maritime autonomous surface ships.

The avoidance of collisions between ships in encounter situations is crucial to maritime traffic safety. Most research on maritime collision avoidance has focused on planning a safe path by which to avoid approaching ships in accordance with the requirements laid out in the International Regulations for Preventing Collision at Sea (COLREGs). The resulting solution provides reference for the navigator in planning movements to avoid collisions. Nonetheless, specific anti-collision actions are generally based on the experience of the navigator. This study differed from existing works in that we sought to derive collision avoidance behavior from the trajectory data of actual ships. The Automatic Identification System (AIS) network makes it possible to collect an enormous volume of trajectory data and investigate real-world ship behavior. Unfortunately trajectory data that introduces uncertainty can hinder behavior mining for collision avoidance. Irregular and/or asynchronous location sampling can lead to situations in which the movement of a ship does not necessarily follow a given trajectory, even if its movement behavior is similar to that of other ships. In this study, we developed a framework to decipher the anti-collision behavior of ships in encounter situations from a large database of trajectory data collected by AIS network, and to present this behavior in the form of anti-collision patterns. A prototype of the proposed framework was built to enable pattern analysis and visualization functions, thereby providing a deeper understanding of collision avoidance behavior in maritime traffic. The proposed framework is applicable to the development of pattern-aware collision avoidance systems aimed at improving maritime traffic safety.

The improvement of collision avoidance for ship navigation in encounter situation is an important topic in maritime traffic safety. Most research on maritime collision avoidance has focused on planning a safe path for a ship to keep away from the approaching ship under the requirements of the International Regulations for Preventing Collision at Sea (COLREGs). However, the specific anti-collision actions are actually carried out by the navigators' own experience according to the local encounter situation.

Thanks for the common use of Automatic Identification System (AIS) network has made a large number of the maritime traffic data to be available. Ships equipped with AIS automatically exchange navigational information with nearby ships and terrestrial AIS receivers to facilitate the tracking and monitoring of ships' location and movement for collision avoidance and control. Obviously, with increasing amount of maritime shipping traffic, the navigational collisions are one of the growing safety concerns in maritime traffic situation awareness. To understand the collision situations can help the maritime traffic managers to improve the safety control of maritime traffic. However, it is difficult to statistically analyze such collision due to the number of collected real cases of collisions are relatively low within a short period of time. To overcome the problem of low sample size, we discover traffic conflict from data collected by AIS network to substitute the real collision. Given a set of maritime traffic data collected from AIS network, we try to discover ships' movements that have conflict behaviors and these behaviors may bring a possible collision if they do not take any evasive action. We propose a framework of Clustering-and-Detection to automatically discover the clusters of conflict trajectory from AIS trajectory data in an unsupervised way. Based on real AIS data, the experimental results show that the proposed framework is able to effectively discover sets of trajectory with conflict situation from maritime AIS traffic data. The statistical analysis on the discovered sets of conflict trajectory is able to provide useful knowledge for maritime traffic monitoring.

Collision-free is one of the major safety concerns for maritime traffic management. To analyze the collision data and understand the cause of the collision can contribute the improvement of the maritime traffic safety and management. However, the real collisions is not always available to analyze. Based on a massive AIS trajectory data collected, we focus on mining the ships' movement behaviors those may bring a possible collision if they do not take any avoidance, called Maritime Traffic Conflict. Even though the maritime traffic conflict is a non-accident incident, the movement behaviors of maritime traffic conflict may have the similar behaviors of navigational collision for analysis. Thus, we propose ConflictFinder to provide a framework for maritime traffic conflict mining. Different from existing methods those focus on detecting the conflicts between two ships in a restricted water way, we discover the conflicts occurred by multi-ships in open sea. For analysis of maritime traffic conflicts, a prototype of ConflictFinder is implemented which helps with gaining a better understanding of traffic conflicts discovered and can be applied to the improvement of maritime traffic safety evaluation and management.

The recent build-up network of Automatic Identification System (AIS) equipped on vessels provides a rich source of vessel movement information. AIS is originally designed for automatically exchanging navigation information, such as their unique identification, position, course, and speed, with nearby vessels and terrestrial receivers to affect collision avoidance and safety control. The collected sequences of AIS logs can be considered as maritime trajectory data, i.e., the sequences of location points with timestamps. This vast amount of AIS trajectory data can be collected and employed to achieve an awareness of maritime traffic knowledge. This paper is devoted to discovery of maritime traffic route from trajectory data generated by AIS networks. However, AIS trajectory data discovery is a challenging task because of the trajectory data is available with uncertainty. Furthermore, unlike the vehicles' movements are constrained by road networks, there is no such a sea route for vessels to follow in marine areas. To overcome the challenges, we propose a framework of Maritime Traffic Route Discovery (abbreviated as MTRD) to generate pattern-aware routes to achieve an effective understanding of maritime traffic awareness. The proposed framework is evaluated on real AIS data and the experimental results shows that the proposed MTRD is able to extract the marine traffic route effectively and provides a cornerstone of maritime traffic knowledge for traffic management, anomaly detection, and conflict analysis in the future.

With the popularity of location-based social networks (LBSNs), users would like to share their check-ins with their friends for more social interactions. These check-in records reflect not only when and where they are, but also what they are doing. If we can capture the relations of the location, time, and activity factors in LBSNs, the location-based social platforms can provide more personalized location-based services to users. In this paper, we aim to infer individual activity and mobility based on their check-in records in LBSNs. For these two inference problems, we analyze check-in records, and utilize Bayesian network to represent the relations among location, time, and activity factors of check-in records. Based on the proposed network model, the two inference problems can be simplified to two modules, the activity-time and the location-activity relation. For the activity-time relation, we propose Order-1 Activity Transition Model to capture the activity-time relations of check-in records. Moreover, for the location-activity relation, we exploit the Gaussian mixture model to capture individual mobility features in different activities. To evaluate the proposed network model for the two inference problems, we conduct extensive experiments on two real datasets, and the experimental results show that our proposed Bayesian-based approach has higher performance than the state-of-the-art approaches for activity and mobility inference in LBSNs.

Recently, with the advent of location-based social networking services (LBSNs), travel planning and location-aware information recommendation based on LBSNs have attracted much research attention. In this paper, we study the impact of social relations hidden in LBSNs, i.e., The social influence of friends. We propose a new social influence-based user recommender framework (SIR) to discover the potential value from reliable users (i.e., Close friends and travel experts). Explicitly, our SIR framework is able to infer influential users from an LBSN. We claim to capture the interactions among virtual communities, physical mobility activities and time effects to infer the social influence between user pairs. Furthermore, we intend to model the propagation of influence using diffusion-based mechanism. Moreover, we have designed a dynamic fusion framework to integrate the features mined into a united follow probability score. Finally, our SIR framework provides personalized top-k user recommendations for individuals. To evaluate the recommendation results, we have conducted extensive experiments on real datasets (i.e., The Go Walla dataset). The experimental results show that the performance of our SIR framework is better than the state-of the-art user recommendation mechanisms in terms of accuracy and reliability.

Mining trajectory data has been attracting significant interest in the last years. By analyzing trajectory data, we are able to discover the movement behavior and location-aware knowledge, and then develop many interesting applications such as movement behavior discovery, location prediction, traffic analysis, and so on. However, trajectory data mining is a challenge task because of the trajectory data is available with uncertainty. Furthermore, discovering the valuable knowledge from maritime trajectory is made even more difficult due to the maritime area is a free moving space. Unlike the vehicles' movements are constrained by road networks, there is no such a sea route for ships to follow in maritime area. A ship's movement may not exactly repeat the same trajectory even the ship has the similar movement behavior with others. In this work, Route Miner system provides a framework of ship route mining for maritime traffic analysis. Given a set of ship trajectories in a maritime area, Route Miner explore the movement behavior from those massive trajectories in a free moving space. Then, ship routes are detected based on those behavioral pattern. Finally, the system generates a set of ships routes to provide operators a better understanding from ship trajectory data. We conduct the experiments on real maritime trajectories to show the effectiveness of proposed Route Miner. In the future, Route Miner is going to serve as the photo type for exploring the solutions of the challenges those related to anomaly detection and traffic management in the maritime domain.

In this paper, we tackle the problem of discovering movement-based communities of users, where users in the same community have similar movement behaviors. Note that the identification of movement-based communities is beneficial to location-based services and trajectory recommendation services. Specifically, we propose a framework to mine movement-based communities which consists of three phases: 1) constructing trajectory profiles of users, 2) deriving similarity between trajectory profiles, and 3) discovering movement-based communities. In the first phase, we design a data structure, called the Sequential Probability tree (SP-tree), as a user trajectory profile. SP-trees not only derive sequential patterns, but also indicate transition probabilities of movements. Moreover, we propose two algorithms: BF (standing for breadth-first) and DF (standing for depth-first) to construct SP-tree structures as user profiles. To measure the similarity values among users’ trajectory profiles, we further develop a similarity function that takes SP-tree information into account. In light of the similarity values derived, we formulate an objective function to evaluate the quality of communities. According to the objective function derived, we propose a greedy algorithm Geo-Cluster to effectively derive communities. To evaluate our proposed algorithms, we have conducted comprehensive experiments on two real data sets. The experimental results show that our proposed framework can effectively discover movement-based user communities.

Predicting Apps usage has become an important task due to the proliferation of Apps, and the complex of Apps. However, the previous research works utilized a considerable number of different sensors as training data to infer Apps usage. To save the energy consumption for the task of predicting Apps usages, only the temporal information is considered in this paper. We propose a Temporal-based Apps Predictor (abbreviated as TAP) to dynamically predict the Apps which are most likely to be used. First, we extract three Apps usage features, global usage feature, temporal usage feature, and periodical usage feature from the Apps usage trace. Then, based on those explored features, we dynamically derive an Apps usage probability model to estimate the current usage probability of each App in each feature. Finally, we investigate the usage probability in each feature and select k Apps with highest usage probability from the probability model. In this paper, we propose two selection algorithms, MaxProb and MinEntropy. To evaluate the performance of TAP, we use two real mobile Apps usage traces and assess the accuracy and efficiency. The experimental results show that the proposed TAP with the MinEntropy selection algorithm could have shorter response time of Apps prediction. Moreover, the accuracy reaches to 80% when k is 5, and when k is 7, the accuracy achieves almost 100% in both of the two real datasets.

As security requirements in coastal water and sea ports, maritime surveillance increases the duty. In this research, we focus on the maritime trajectory data to explore movement behavior for anomaly detection in maritime traffic. Trajectory data records the moving objects' true movement and provides the opportunity to discover the movement behavior for anomaly detection. The multidimensional outlying features are first identified and defined. To deal with the uncertain property of trajectory, a maritime trajectory modeling is developed to explore the movement behavior from historical trajectories and build a maritime trajectory model for anomaly detection. Then, our ongoing work is developing an anomaly detection algorithm to detect anomalous moving objects from real time maritime trajectory stream effectively. This work should contribute the area of maritime security surveillance by trajectory data mining.

Location prediction has attracted a significant amount of research effort. Given an object's recent movements and a future time, the goal of location prediction is to predict the location of this object at the future time specified. Prior works have elaborated on mining association relationships among regions, in which objects frequently appear, to predict locations. Association relationships among regions are represented as association rules. By exploring association relationships among regions, prior works are able to have a good accuracy for location prediction. However, with a large amount of trajectory data produced, a huge amount of association rules is expected. Furthermore, trajectory data has both the spatial and temporal information. To further enhance the accuracy of location prediction, one could utilize not only spatial information but also temporal information to estimate locations of objects. In this paper, we propose a spatial-temporal trajectory model (abbreviated as STT) to capture movement behaviors of objects. STT is represented as a probabilistic suffix tree with both spatial and temporal information of movements. Note that STT is able to discover sequential traversal relationships among regions and, for each region, STT derives the corresponding probabilities about the time when objects appear. With the nature of probabilistic suffix tree, we could use a compact structure to capture movement behavior of objects compared to association rules proposed. In light of STT, we further propose an algorithm to traverse STT for location prediction. By exploring both the spatial and temporal information of STT, the accuracy of location prediction is improved. To evaluate our proposed STT and prediction algorithm, we conduct experiments on the synthetic dataset generated from real datasets. Experimental results shows that our proposed STT is able to capture both spatial and temporal patterns of movement behaviors and, by exploring STT, our proposed prediction algorithm outperforms existing works.