A Method for Driving Route Predictions Based on Hidden Markov Model

Ye, Ning; Wang, Zhongqin; Malekian, Reza; Lin, Qian; Wang, Ruchuan

doi:10.1155/2015/824532

Cited by 35 publications

(28 citation statements)

References 13 publications

(33 reference statements)

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“…After describing the DNMD procedure, this paper illustrates the effectiveness of the proposed method by considering the following simulation signals [19,20]. All the tests are carried out using MATLAB R2012b on a desktop Intel Core i7-455U PC with Windows 8 system.…”

Section: Numerical Simulation and Analysismentioning

confidence: 99%

A Method of Multi-component Signal Detection Based on Differential Nonlinear Mode Decomposition

Yang

Shao

Huang

et al. 2018

JCOMSS

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Section: Numerical Simulation and Analysismentioning

confidence: 99%

A Method of Multi-component Signal Detection Based on Differential Nonlinear Mode Decomposition

Yang

Shao

Huang

et al. 2018

JCOMSS

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“…(1) As is well known, there are a variety of communication styles such as VHF, RFID, GPRS and 3G [78,79]. The data of these communication styles is similar but not compatible because of the lack of unified interaction interfaces.…”

Section: The Limit Of Transmission For the Waterway Traffic Informationmentioning

confidence: 99%

The Development of Key Technologies in Applications of Vessels Connected to the Internet

Tian

Liu

et al. 2017

Symmetry

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Abstract:With the development of science and technology, traffic perception, communication, information processing, artificial intelligence and the shipping information system have become important in supporting the realization of intelligent shipping transportation. Against this background, the Internet of Vessels (IoV) is proposed to integrate all these advanced technologies into a platform to meet the requirements of international and regional transportations. The purpose of this paper is to analyze how to benefit from the Internet of Vessels to improve the efficiency and safety of shipping, and promote the development of world transportation. In this paper, the IoV is introduced and its main architectures are outlined. Furthermore, the characteristics of the Internet of Vessels are described. Several important applications that illustrate the interaction of the Internet of Vessels' components are proposed. Due to the development of the Internet of Vessels still being in its primary stage, challenges and prospects are identified and addressed. Finally, the main conclusions are drawn and future research priorities are provided for reference and as professional suggestions for future researchers in this field.

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“…To make the classification more practical, appropriate weight coefficients [26][27][28] can be obtained with a certain objective and verified samples, as discussed in Step 4, Section 2. With the verified AIS data gathered from 1 June 2014 to 31 October 2014, including 106 samples for direction 1, 88 samples for direction 2, 116 samples for direction 3, 1151 samples for direction 4, 82 samples for direction 5, and 1472 samples for direction 6, weight coefficients can be trained based on the Equations (10) and (11).…”

Section: Step 4: Non-linear Optimization Of Evidential Weightsmentioning

confidence: 99%

An Internet of Things Approach for Extracting Featured Data Using AIS Database: An Application Based on the Viewpoint of Connected Ships

Malekian

et al. 2017

Symmetry

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Automatic Identification System (AIS), as a major data source of navigational data, is widely used in the application of connected ships for the purpose of implementing maritime situation awareness and evaluating maritime transportation. Efficiently extracting featured data from AIS database is always a challenge and time-consuming work for maritime administrators and researchers. In this paper, a novel approach was proposed to extract massive featured data from the AIS database. An Evidential Reasoning rule based methodology was proposed to simulate the procedure of extracting routes from AIS database artificially. First, the frequency distributions of ship dynamic attributes, such as the mean and variance of Speed over Ground, Course over Ground, are obtained, respectively, according to the verified AIS data samples. Subsequently, the correlations between the attributes and belief degrees of the categories are established based on likelihood modeling. In this case, the attributes were characterized into several pieces of evidence, and the evidence can be combined with the Evidential Reasoning rule. In addition, the weight coefficients were trained in a nonlinear optimization model to extract the AIS data more accurately. A real life case study was conducted at an intersection waterway, Yangtze River, Wuhan, China. The results show that the proposed methodology is able to extract data very precisely.

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A Method for Driving Route Predictions Based on Hidden Markov Model

Cited by 35 publications

References 13 publications

A Method of Multi-component Signal Detection Based on Differential Nonlinear Mode Decomposition

A Method of Multi-component Signal Detection Based on Differential Nonlinear Mode Decomposition

The Development of Key Technologies in Applications of Vessels Connected to the Internet

An Internet of Things Approach for Extracting Featured Data Using AIS Database: An Application Based on the Viewpoint of Connected Ships

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