Browsing by Author "Tsai, Tzu-Hao"

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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.
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.
Customer Purchase Behavior Prediction from Payment Datasets
(2018-01-01)
Wen, Yu-Ting
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Yeh, Pei-Wen
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Tsai, Tzu-Hao
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Peng, Wen-Chih
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Shuai, Hong-Han
With the advances in the development of mobile payments, a huge amount of payment data are collected by banks. User payment data offer a good dataset to depict customer behavior patterns. A comprehensive understanding of customers' purchase behavior is crucial to developing good marketing strategies, which may trigger much greater purchase amounts. For example, by exploring customer behavior patterns, given a target store, a set of potential customers is able to be identified. In other words, personalized campaigns at the right time and in the right place can be treated as the last stage of consumption. Here we propose a probability graphical model that exploits the payment data to discover customer purchase behavior in the spatial, temporal, payment amount and product category aspects, named STPC-PGM. As a result, the mobility behavior of an individual user could be predicted with a probabilistic graphical model that accounts for all aspects of each customer's relationship with the payment platform. To achieve real time advertising, we then develop an online framework that efficiently computes the prediction results. Our experiment results show that STPC-PGM is effective in discovering customers' profiling features, and outperforms the state-of-the-art methods in purchase behavior prediction. In addition, the prediction results are being deployed in the marketing of real-world credit card users, and have presented a significant growth in the advertising conversion rate.
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.