Visualization Analysis of Intelligent Vehicles Research Field Based on Mapping Knowledge Domain

He, Yi; Yang, Shuo; Chan, Ching-Yao; Chen, Long; Wu, Chaozhong

doi:10.1109/tits.2020.2991642

Cited by 49 publications

(38 citation statements)

References 48 publications

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“…In bibliometrics analysis, the mapping knowledge domain (MKD) is a method to visually present and explain the newly developed interdisciplinary scientific field using data mining, information analysis, scientific measurement, and graph rendering, aiming at simplifying the information acquisition process in the research field and clarifying the knowledge structure [14]. e MKD has dual properties and characteristics of "graph" and "spectrum," which means that it is not only a visual knowledge map but also a serialized knowledge map.…”

Section: Analysis Methodmentioning

confidence: 99%

Visualization and Analysis of Mapping Knowledge Domain of Heterogeneous Traffic Flow

Feng

Yan

et al. 2022

Computational Intelligence and Neuroscience

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Mapping knowledge domain (MKD) is an important application in bibliometrics, which is a method of visually presenting and explaining newly developed interdisciplinary scientific fields using data mining, information analysis, scientific measurement, and graphic rendering. This study combines applied mathematics, visual analysis technology, information science, and scientometrics to systematically analyze the development status, research distribution, and future trend of the heterogeneous traffic flow by using the MKD software tools VOSviewer and CiteSpace. Based on the MKD and Bibliometrics approaches, 4709 articles have been studied, which were published by Science Citation Index Expanded (SCIE) and Social Sciences Citation Index (SSCI) from 2004 to 2021 in the field of heterogeneous traffic flows. Firstly, this paper presents the annual numbers of articles, origin countries, main research organizations, and groups as well as the source journals on heterogeneous traffic flow studies. Then, cocitation analysis is used to divide heterogeneous traffic flow into three main research directions, which include “heterogeneous traffic flow model,” “traffic flow capacity analysis,” and “traffic flow stability analysis.” The keyword cooccurrence analysis is applied to identify five dominant clusters: “modeling and optimization methods,” “traffic flow characteristics analysis,” “driving behavior analysis,” “simulation experiment,” and “policies and barriers.” Finally, burst keywords were studied according to the publication date to present more clearly the change of research focus and direction over time.

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Section: Analysis Methodmentioning

confidence: 99%

Visualization and Analysis of Mapping Knowledge Domain of Heterogeneous Traffic Flow

Feng

Yan

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

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“…Chu et al proposed an improved curve speed model that takes into account driving style, vehicle, and road factors. On the basis of vehicle-road interaction model, driver behavior factor is introduced to quantify the driving style selected for the speed of curve [ 12 , 24 ]. Deng et al, in order to identify driving styles, reasonably modeled three driving styles (aggressive, moderate, and mild) by means of hidden Markov model according to the driver's braking characteristics and realized efficient analysis of driving styles [ 25 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In recent years, many studies have attempted to evaluate risk driving behavior, but most of them rely on high-demand vehicle and driving data, which is not conducive to largescale application, and most of the previous studies focus on driving safety levels or identify the driving behaviors based on real time or offline with short-term data analysis ( [3][4][5][6][7][8][9][10][11][12]; Yan et al, 2019). However, few studies can dig into the causes and correlations behind individual driving behavior and quantify the driving behaviors portrait for professional driver based on long-term naturalistic driving.…”

Section: Introductionmentioning

confidence: 99%

An Individual Driving Behavior Portrait Approach for Professional Driver of HDVs with Naturalistic Driving Data

Yang

Zhou

et al. 2022

Computational Intelligence and Neuroscience

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More than 50% major road accidents are caused by risk driving behaviors from professional drivers of Heavy Duty Vehicles (HDVs). The quantitative estimation of driving performance and driving behaviors portrait for professional drivers is helpful to measure the driver’s driving risk and inherent driving style. Previous studies have attempted to evaluate risk driving behavior, but most of them rely on high-demand vehicle and driving data. However, few studies can dig into the causes and correlations behind individual driving behavior and quantify the driving behaviors portrait for professional driver based on long-term naturalistic driving. In this study, the data is from On-Board Unit (OBU) devices mounted in the HDVs in China. Based on the driving behavior pattern diagram and the frequency and ranking of drivers’ typical driving patterns, a driving behavior portrait approach is proposed by comprehensively considering the vehicle safety, driving comfort, and fuel economy indicators. The similarities and differences of different drivers’ driving behaviors are quantitatively analyzed. The high precision and sampling frequency data from vehicles are used to verify the proposed approach. The results demonstrated that the driving behavior portrait approach can digitally describe the individual driving behaviors styles and identify the potential driving behaviors with long-term naturalistic driving data. The development of this approach can help quantitatively evaluate the individual characteristic of risk driving behaviors to prevent road accidents.

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“…e safe distances or exclusion zones are based on LNG vapor dispersion data and thermal radiation contours and other considerations as specified in local regulations, environment, and so on. Once the safety zone has been determined, further optimization traffic organization can be realized [29]. (iv) Optimization scheme for traffic organization: optimization scheme for traffic organization includes in-and-out port organization, followed by a comparative analysis and safety precautions.…”

Section: Research Frameworkmentioning

confidence: 99%

Safety or Efficiency? An ECSO Framework of Traffic Organization Optimization for LNG Carriers Entering and Leaving Port

Fan¹,

et al. 2021

Mathematical Problems in Engineering

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Liquefied Natural Gas (LNG) is a clean, efficient, and economic energy, which is mainly transported by LNG carriers in the marine industry. Massive storage of LNG has led to public concern about the possible hazards of LNG carriers for people and facilities. This paper aims to determine the appropriate level of a safety zone and to develop an optimized traffic organization scheme. An innovative ECSO framework is proposed to deal with these two tasks. The results of the comparative analysis show that the influence degree of the optimized scheme is 21%, which is distinctly lower than the previous scheme (33%). To some content, the proposed framework can provide meaningful enlightenments for stakeholders to improve port service level and ensure the safe operation of LNG carriers.

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Visualization Analysis of Intelligent Vehicles Research Field Based on Mapping Knowledge Domain

Cited by 49 publications

References 48 publications

Visualization and Analysis of Mapping Knowledge Domain of Heterogeneous Traffic Flow

Visualization and Analysis of Mapping Knowledge Domain of Heterogeneous Traffic Flow

An Individual Driving Behavior Portrait Approach for Professional Driver of HDVs with Naturalistic Driving Data

Safety or Efficiency? An ECSO Framework of Traffic Organization Optimization for LNG Carriers Entering and Leaving Port

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