A Review of Big Data Applications in Urban Transit Systems

Lu, Kai; Liu, Jiangtao; Zhou, Xuesong; Han, Baoming

doi:10.1109/tits.2020.2973365

Cited by 43 publications

(20 citation statements)

References 166 publications

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“…To sum up, the following points can be summarized: on the one hand, the diversification of the application fields and scope of big data technology, on the other hand, the employment and entrepreneurial behavior of college graduates is the focus affecting the national labor structure, and strategic emerging industries are the key link in the national structural reform [22,23]. In terms of practical engineering problems, big data technology does not have a solution to the correlation between strategic emerging industries and college students' entrepreneurship [24,25].…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Analysis of the Correlation between Emerging Industry Development and University Students’ Entrepreneurship Based on Big Data

Yufeng

Huang

2022

Discrete Dynamics in Nature and Society

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With the strong support of local governments for strategic emerging industries such as high-end equipment manufacturing, new materials, and new energy, strategic emerging industries are playing an increasingly important role in the economy and society. With the increasing enthusiasm of college graduates for independent entrepreneurship, college students’ entrepreneurship is constantly integrated with the development of strategic emerging industries. Based on this background, aiming at the practical problems of the development of strategic emerging industries, this study innovatively puts forward the method of using big data technology and GM model to realize the dynamic model analysis of the development of strategic emerging industries and college students’ entrepreneurial behavior. This article analyzes the correlation between dynamic big data such as industrial scale, industrial market, and industrial direction of local strategic emerging industries and university entrepreneurship, so as to provide theoretical support for the development strategy of strategic emerging industries. Through the neural network algorithm, this article evaluates the entrepreneurship of college students, so as to provide a digital basis for the layout of strategic emerging industries to attract talents and entrepreneurship. Experiments show that the big data integration system established by GM correlation analysis and ant colony Elman regression artificial neural network has high accuracy and can well identify the priority relevance of the industrial direction of strategic emerging industries to college students’ entrepreneurship. It provides theoretical support for regional policy makers to better formulate college students’ entrepreneurship strategy and the development plan of emerging industries.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Analysis of the Correlation between Emerging Industry Development and University Students’ Entrepreneurship Based on Big Data

Yufeng

Huang

2022

Discrete Dynamics in Nature and Society

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“…To sum up, it can be seen that, in the process of dynamic statistics and analysis of employment and entrepreneurship of college graduates, there are some problems, such as inability to effectively obtain real-time data of graduates, large error of analysis model, small database of graduates' statistical information, and poor coupling of analysis model [17,18]. In terms of the research on the dynamic model of employment and ntrepreneurship of college graduates, the current research results are mostly implemented based on statistics, but rarely combined with big data technology and artificial intelligence algorithms for diversified and convenient analysis [19][20][21].…”

Section: Related Workmentioning

confidence: 99%

Construction of Graduate Behavior Dynamic Model Based on Dynamic Decision Tree Algorithm

Yang

2022

Discrete Dynamics in Nature and Society

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With the research of machine learning technology and big data intelligent processing technology in engineering application becoming more and more mature, people gradually combine machine learning technology and big data intelligent processing technology. Aiming at the problem of innovative employment in colleges and universities, this paper proposes a dynamic decision tree algorithm based on these two technologies and constructs a dynamic model of graduates’ behavior. Through the analysis of dynamic decision tree algorithm, a big data analysis system is formed. Finally, simulation experiments verify whether the dynamic model can correctly reflect the behavior of college graduates. The results show that the big data integration system based on big data and dynamic decision tree algorithm has high adaptability. Incremental adaptive optimization of the traditional decision tree model can significantly improve the prediction effect and prediction time of dynamic data and provide theoretical support for the industrialization and social significance of big data technology. The dynamic decision tree algorithm of college employment proposed in this paper has good predictability and provides a certain theoretical reference for college graduates’ entrepreneurship.

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“…They reviewed the computing infrastructures applied in IoT big data analysis, which include edge, fog, cloud computing. Lu et al 72 reviewed the data collection technologies in urban transit systems into two groups: traditional and advanced. In this article, the data applications were categorized into three classes: policy application, operation optimization, and passenger behavior.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Leveraging big data in smart cities: A systematic review

Karimi

Kashani

Akbari

et al. 2021

Concurrency and Computation

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Recently, the notion of a smart city, which includes smart well‐being, smart transit, and smart society, has attracted much attention due to its impact on people's quality of living. Data in smart cities are characterized by variety, velocity, volume, value, and veracity that are the well‐known characteristics of big data. The fast pace expanding of IoT devices and sensors in smart cities generates a huge volume of data that can help decision‐makers and managers in city management. The aim of this article is to wholly and systematically review big data handling approaches in smart cities, in which we analyze research efforts published between 2013 and February 2021, where these techniques are categorized based on their algorithms and architectures. Further, the main ideas, evaluation techniques, tools, evaluation metrics, algorithm types, advantages, and disadvantages are explored. Additionally, essential evaluation factors are introduced in which scalability and availability by 16%, time by 15% and accuracy by 11% are more in focus, and finally, some of the challenges, open issues, and future trends that are valuable for further research are suggested in big data handling approaches in smart cities.

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A Review of Big Data Applications in Urban Transit Systems

Cited by 43 publications

References 166 publications

[Retracted] Analysis of the Correlation between Emerging Industry Development and University Students’ Entrepreneurship Based on Big Data

[Retracted] Analysis of the Correlation between Emerging Industry Development and University Students’ Entrepreneurship Based on Big Data

Construction of Graduate Behavior Dynamic Model Based on Dynamic Decision Tree Algorithm

Leveraging big data in smart cities: A systematic review

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