Browsing by Author "Lei, Po-Ruey"

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A Framework for Discovering Maritime Traffic Conflict from AIS Network
(2017-01-01)
Lei, Po-Ruey
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Tsai, Tzu-Hao
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Wen, Yu-Ting
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Peng, Wen-Chih
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交大名義發表
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.
A Framework of Mining Semantic Regions from Trajectories
(2011)
Lu, Chun-Ta
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Lei, Po-Ruey
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Peng, Wen-Chih
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Su, Ing-Jiunn
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交大名義發表
With the pervasive use of mobile devices with location sensing and positioning functions, such as Wi-Fi and GPS, people now are able to acquire present locations and collect their movement. As the availability of trajectory data prospers, mining activities hidden in raw trajectories becomes a hot research problem. Given a set of trajectories, prior works either explore density-based approaches to extract regions with high density of GPS data points or utilize time thresholds to identify users' stay points. However, users may have different activities along with trajectories. Prior works only can extract one kind of activity by specifying thresholds, such as spatial density or temporal time threshold. In this paper, we explore both spatial and temporal relationships among data points of trajectories to extract semantic regions that refer to regions in where users are likely to have some kinds of activities. In order to extract semantic regions, we propose a sequential clustering approach to discover clusters as the semantic regions from individual trajectory according to the spatial-temporal density. Based on semantic region discovery, we develop a shared nearest neighbor (SNN) based clustering algorithm to discover the frequent semantic region where the moving object often stay, which consists of a group of similar semantic regions from multiple trajectories. Experimental results demonstrate that our techniques are more accurate than existing clustering schemes.
A Velocity-Preserving Trajectory Simplification Approach
(2016-01-01)
Lin, Chih-Yu
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Hung, Chih-Chieh
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Lei, Po-Ruey
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交大名義發表
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National Chiao Tung University
By the rise of mobile devices, trajectory data could be easily collected and used in several applications, like destination prediction, public transportation optimization, and travel route recommendation. However, due to the spatio-temporal nature, raw trajectory data usually contain redundant movement information. This observation motivates the trajectory simplification approaches which discard some points with preserving some specific features, such as position features, direction features, and so on. Most of existing simplifications ignore the importance of velocity features. This paper proposes an adaptive trajectory approaches while taking the velocity feature into account. Specifically, the Adaptive Trajectory Simplification (ATS) algorithm is proposed, which not only preserves the position feature, but the velocity feature from the given trajectories. ATS algorithm groups the velocity values into several intervals, which are used to partition trajectories into velocity-preserving segments. The simplified trajectory could be derived by applying the position-preserving simplification approach on each segment, where the threshold in a position-preserving approach could be determined without manual setting. Extensive experiments are conducted by using a real trajectory dataset in Porto. The experimental results show ATS algorithm could simplify trajectories effectively while preserving the velocity feature and the position feature at the same time.
AppNow: Predicting Usages of Mobile Applications on Smart Phones
(2012)
Liao, Zhung-Xun
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Lei, Po-Ruey
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Shen, Tsu-Jou
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Li, Shou-Chung
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Peng, Wen-Chih
Due to the proliferation of mobile applications (abbreviated as Apps) on smart phones, users can install many Apps to facilitate their life. Usually, users browse their Apps by swiping touch screen on smart phones, and are likely to spend much time on browsing Apps. In this paper, we design an AppNow widget that is able to predict users' Apps usage. Therefore, users could simply execute Apps from the widget. The main theme of this paper is to construct the temporal profiles which identify the relation between Apps and their usage times. In light of the temporal profiles of Apps, the AppNow widget predicts a list of Apps which are most likely to be used at the current time. In our experiments, we collected real usage traces to show that the accuracy of AppNow could reach 86% for identifying temporal profiles and 90% for predicting App usage.
CAPatternMiner: Mining Ship Collision Avoidance Behavior from AIS Trajectory Data
(2018-01-01)
Lei, Po-Ruey
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Xiao, Li-Pin
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Wen, Yu-Ting
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Peng, Wen-Chih
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交大名義發表
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 anticollision actions are actually carried out by the navigators' own experience according to the local encounter situation. In this paper, different from the existing works, we discover the collision avoidance behavior from real ships' movement, i.e., AIS trajectory data. However, the uncertainty of maritime trajectory data brings the challenge of collision avoidance behavior mining. To achieve our goal, we propose CAPatternMiner to provide a framework to discover the ships' anti-collision behavior, which is effective in the encounter situation, and generate the discovered behavior in form of collision avoidance pattern. Furthermore, a prototype of CAPatternMiner is built for pattern analysis and visualization and also benefits a deeper understanding of collision avoidance behavior on maritime traffic. The proposed framework will be applied to the developing of pattern-aware collision avoidance system to improve the maritime traffic safety.
ConflictFinder: Mining Maritime Traffic Conflict from Massive Ship Trajectories
(2017-01-01)
Lei, Po-Ruey
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Tsai, Tzu-Hao
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Wen, Yu-Ting
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Peng, Wen-Chih
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交大名義發表
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.
Discovering Maritime Traffic Route from AIS Network
(2016)
Lei, Po-Ruey
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Tsai, Tzu-Hao
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Peng, Wen-Chih
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資訊工程學系
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Department of Computer Science
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.
Dummy-Based Schemes for Protecting Movement Trajectories
(2012-03-01)
Lei, Po-Ruey
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Peng, Wen-Chih
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Su, Ing-Jiunn
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Chang, Chien-Ping
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資訊工程學系
Dummy-based anonymization techniques for protecting the location privacy of mobile users have appeared in the literature. By generating dummies that move in human-like trajectories, this approach shows that the location privacy of mobile users can be preserved. However, the trajectories of mobile users can still be exposed by monitoring the long-term movement patterns of users. We argue that, once the trajectory of a user is identified, the locations of the user are exposed. Thus, it is critical to protect the movement trajectories of mobile users in order to preserve user location privacy. We propose two schemes that generate consistent movement patterns in the long run. Guided by three parameters in a user specified privacy profile, namely, short-term disclosure, long-term disclosure and distance deviation, the proposed schemes derive movement trajectories for dummies. The experimental results show that our proposed schemes are more effective than existing works in protecting movement trajectories.
Exploring Sequential Probability Tree for Movement-Based Community Discovery
(2014-11-01)
Zhu, Wen-Yuan
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Peng, Wen-Chih
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Hung, Chih-Chieh
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Lei, Po-Ruey
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Chen, Ling-Jyh
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.
Exploring Social Influence on Location-Based Social Networks
(2014)
Wen, Yu-Ting
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Lei, Po-Ruey
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Peng, Wen-Chih
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Zhou, Xiao-Fang
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資訊工程學系
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 Gowalla 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 Temporal Profiles of Mobile Applications for Usage Prediction
(2012)
Liao, Zhung-Xun
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Lei, Po-Ruey
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Shen, Tsu-Jou
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Li, Shou-Chung
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Peng, Wen-Chih
Due to the proliferation of mobile applications (abbreviated as Apps) on smart phones, users can install many Apps to facilitate their life. Usually, users browse their Apps by swiping touch screen on smart phones, and are likely to spend much time on browsing Apps. In this paper, we design an AppNow widget that is able to predict users' Apps usage. Therefore, users could simply execute Apps from the widget. The main theme of this paper is to construct the temporal profiles which identify the relation between Apps and their usage times. In light of the temporal profiles of Apps, the AppNow widget predicts a list of Apps which are most likely to be used at the current time. AppNow consists of three components, the usage logger, the temporal profile constructor and the Apps predictor. First, the usage logger records every App start time. Then, the temporal profiles are built by applying Discrete Fourier Transform and exploring usage periods and specific times. Finally, the system calculates the usage probability at current time for each App and shows a list of Apps with highest probability. In our experiments, we collected real usage traces to show that the accuracy of AppNow could reach 86% for identifying temporal profiles and 90% for predicting App usage.
Mining Top-K Relevant Stay Regions from Historical Trajectories
(2014-01-01)
Lin, Yung-Hsiang
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Lai, Chien-Hsiang
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Lei, Po-Ruey
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資訊工程學系
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Department of Computer Science
With increasingly prevalent mobile positioning devices, such as GPS loggers, smart phones, and GPS navigation devices, a huge amount of trajectories data is collected. Users are able to obtain the various location-based services by uploading their trajectories. In this paper, we address that a user\'s movement behavior is able to discover by their similar shape trajectories and resulted in some regions frequently stay in common, called relevant stay regions. Once a set of stay regions discovered, we can predict the next region where the user intends to go and provide location-based information of the next stay in advance, such as traffic status, targeted advertises, sightseeing recommendations, and so on. Prior works have elaborated on discovering stay region from the whole crowd trajectories and then exploring the relations between the regions to describe the movement patterns for location prediction. However, the trajectories pass the same region may not have the similar movement behavior. Thus, we propose a framework to discover stay regions relevant to the specific movement behavior and then applied in location prediction, called Region Modeling and Mobility Prediction. The proposed framework includes two modules: region modeling and mobility prediction. In the region modeling module, we develop shape clustering method to group the similar trajectories from historical data and then explore the stay region model from trajectory clusters. Based on the discovered region model, the mobility prediction module provide a cluster selection algorithm and several prediction strategies to generate the top-k relevant stay regions. Experiments results on real datasets demonstrate the effectiveness and accuracy of our proposed model on detecting next stay region, comparing with other baseline methods.
QS-STT: QuadSection clustering and spatial-temporal trajectory model for location prediction
(2013-06-01)
Lei, Po-Ruey
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Li, Shou-Chung
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Peng, Wen-Chih
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交大名義發表
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National Chiao Tung University
Location prediction is a crucial need for location-aware services and applications. Given an object's recent movement and a future time, the goal of location prediction is to predict the location of the object at the future time specified. Different from traditional location prediction using motion function, some research works have elaborated on mining movement behavior from historical trajectories for location prediction. Without loss of generality, given a set of trajectories of an object, prior works on mining movement behaviors will first extract regions of popularity, in which the object frequently appears, and then discover the sequential relationships among regions. However, the quality of the frequent regions extracted affects the accuracy of the location prediction. Furthermore, trajectory data has both spatial and temporal information. To further enhance the accuracy of location prediction, one could utilize not only spatial information but also temporal information to predict the locations of objects. In this paper, we propose a framework QS-STT (standing for QuadSection clustering and Spatial-Temporal Trajectory model) to capture the movement behaviors of objects for location prediction. Specifically, we have developed QuadSection clustering to extract a reasonable and near-optimal set of frequent regions. Then, based on the set of frequent regions, we propose a spatial-temporal trajectory model to explore the object's movement behavior as a probabilistic suffix tree with both spatial and temporal information of movements. Note that STT is not only able to discover sequential relationships among regions but also derives the corresponding probabilities of time, indicating when the object appears in each region. Based on STT, we further propose an algorithm to traverse STT for location prediction. By enhancing the quality of the frequent region extracted and exploring both the spatial and temporal information of STT, the accuracy of location prediction in QS-STT is improved. QS-STT is designed for individual location prediction. For verifying the effectiveness of QS-STT for location prediction under the different spatial density, we have conducted experiments on four types of real trajectory datasets with different speed. The experimental results show that our proposed QS-STT is able to capture both spatial and temporal patterns of movement behaviors and by exploring QS-STT, our proposed prediction algorithm outperforms existing works.
RouteMiner: Mining Ship Routes from a Massive Maritime Trajectories
(2014-01-01)
Wen, Yu-Ting
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Lai, Chien-Hsiang
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Lei, Po-Ruey
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Peng, Wen-Chih
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資訊工程學系
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, RouteMiner system provides a framework of ship route mining for maritime traffic analysis. Given a set of ship trajectories in a maritime area, RouteMiner 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 RouteMiner. In the future, RouteMiner is going to serve as the phototype for exploring the solutions of the challenges those related to anomaly detection and traffic management in the maritime domain.