scholarly journals Research on the Integration of Urban Traffic and Big Data

2020 ◽  
Vol 6 (2) ◽  
pp. 38
Author(s):  
Wangshu He
Keyword(s):  
Big Data ◽  
Smart Cities ◽  
Urban Transportation ◽  
Research Direction ◽  
Urban Traffic ◽  
Enterprise Management ◽  
Future Research ◽  
Application Research ◽  
Administrative Supervision ◽  
Application Requirements

The powerful data processing ability of big data technology can allocate traffic resources more efficiently, and can deal with various sudden traffic problems flexibly. It is an unprecedented opportunity and challenge for urban transportation and smart cities to effectively collect and utilize traffic big data to meet the application requirements of high timeliness traffic administrative supervision, traffic enterprise management and traffic citizen service. This article expounds the concept and characteristics of big data, discusses the application research of big data in urban transportation at home and abroad in recent years, summarizes its application research scope and trend, points out that intelligent transportation is the focus of the application of big data in urban transportation, and finally looks forward to the future research direction.

Application research of urban subway traffic mode based on behavior entropy in the background of big data

2021 ◽  
pp. 1-14
Author(s):  
Wanxin Hu ◽  
Fen Cheng
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Rapid Development ◽  
Urban Transportation ◽  
Transportation System ◽  
Urban Traffic ◽  
Time Optimization ◽  
The City

With the development of society and the Internet and the advent of the cloud era, people began to pay attention to big data. The background of big data brings opportunities and challenges to the research of urban intelligent transportation networks. Urban transportation system is one of the important foundations for maintaining urban operation. The rapid development of the city has brought tremendous pressure on the traffic, and the congestion of urban traffic has restricted the healthy development of the city. Therefore, how to improve the urban transportation network model and improve transportation and transportation has become an urgent problem to be solved in urban development. Specific patterns hidden in large-scale crowd movements can be studied through transportation networks such as subway networks to explore urban subway transportation modes to support corresponding decisions in urban planning, transportation planning, public health, social networks, and so on. Research on urban subway traffic patterns is crucial. At the same time, a correct understanding of the behavior patterns and laws of residents’ travel is a key factor in solving urban traffic problems. Therefore, this paper takes the metro operation big data as the background, takes the passenger travel behavior in the urban subway transportation system as the research object, uses the behavior entropy to measure the human behavior, and actively explores the urban subway traffic mode based on the metro passenger behavior entropy in the context of big data. At the same time, the congestion degree of the subway station is analyzed, and the redundancy time optimization model of the subway train stop is established to improve the efficiency of the subway operation, so as to provide important and objective data and theoretical support for the traveler, planner and decision maker. Compared to the operation graph without redundant time, the total travel time optimization effect of passengers is 7.74%, and the waiting time optimization effect of passengers is 6.583%.

scholarly journals Studi On Big Data Analytics Framework in Smart City Context

2021 ◽  
Vol 328 ◽  
pp. 04022
Author(s):  
Rahmawati Dinda ◽  
Arief Assaf ◽  
Do Abdullah Saiful Saiful
Keyword(s):  
Big Data ◽  
Smart City ◽  
Data Analytics ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Future Research ◽  
Technology Governance ◽  
Advantages And Disadvantages ◽  
The Government ◽  
Data Analytic

The issue of global urbanization, which is a separate problem faced by the government, is the very rapid growth of population density in cities. To face this challenge, the government launched a smart city project by targeting sustainable economic growth and improving the quality of life. Information and Communication Technology governance is the key to realizing a smart city. However, each of these I.C.T. tools produce large amounts of data known as Big Data. Data processing with the Big Data approach is becoming a trend in information systems to provide better public services and provide references in the policy-making process. However, to obtain important information in the scope of big data, a Big Data Analytics process is needed, also known as Big Data Value Chain. Extracting knowledge from the related literature can identify the characteristics of the big data analytic framework for smart cities. This paper reviews several big data analytic frameworks applied to smart cities. This paper is to find the advantages and disadvantages of each framework so that it can be a direction for future research

A Collaborative Approach of IoT, Big Data, and Smart City

2019 ◽  
pp. 25-37 ◽  
Author(s):  
K. Jayashree ◽  
R. Abirami ◽  
R. Babu
Keyword(s):  
Economic Development ◽  
Big Data ◽  
Internet Of Things ◽  
Smart City ◽  
Life Quality ◽  
Research Direction ◽  
Future Research ◽  
Collaborative Approach ◽  
Future Research Direction ◽  
Sensor Devices

During the last two decades, a number of new nations emerged and played their intense role in changing human lifestyle. The growing demand for smart city and big data stimulates innovation, and the development of new smart applications is becoming important. Internet of things comprises billions of devices, people, and services, and entitles each to connect through sensor devices. The economic development of a city leads to better life quality and improved citizen services. Thus, this chapter discusses the background of big data, IoT, and smart city. It also discusses the collaborative approach of all the above. The various related work and future research direction for implementing smart city with the concept of big data and IoT would be addressed in this chapter.

Customer Analytics Capabilities in the Big Data Spectrum

2020 ◽  
pp. 1-17 ◽  
Author(s):  
Md Afnan Hossain ◽  
Shahriar Akter ◽  
Venkata Yanamandram
Keyword(s):  
Big Data ◽  
Research Direction ◽  
Business Environment ◽  
Vital Role ◽  
Future Research ◽  
Managerial Decision ◽  
Sustainable Business ◽  
Innovation Product ◽  
Customer Analytics ◽  
Analytics Capability

Customer analytics plays a vital role in generating insights from big data to improve service innovation, product development, personalization, and managerial decision-making; yet, no academic study has investigated customer analytics capability through which it is possible to achieve sustainable business growth. To close this gap, this chapter explores the constructs of the customer analytics capability by drawing on a systematic review of the literature in the big data spectrum. The chapter's interpretive framework portrays a definitional aspect of customer analytics, the importance of customer analytics, and customer analytics capability constructs. The study proposes a customer analytics capability model, which consists of four principal constructs and some important sub-constructs. The chapter briefly discusses the challenges and future research direction for developing the customer analytics capability model in the data rich competitive business environment.

Methods for Assessing, Predicting, and Improving Data Veracity: A survey

2020 ◽  
Vol 9 (4) ◽  
pp. 5-30
Author(s):  
Fatmah Assiri
Keyword(s):  
Big Data ◽  
Web Applications ◽  
Smart Cities ◽  
Future Research ◽  
Research Directions ◽  
Future Research Directions ◽  
Software Construction ◽  
Technical Solutions ◽  
The Internet Of Things

Data is an essential part of smart cities, and data can play an important role indecision making processes. Data generated through web applications and devicesutilize the Internet of Things (IoT) and related technologies. Thus, it is also importantto be able to create big data, which has historically been defined as having threekey dimensions: volume, variety, and velocity. However, recently, veracity has beenadded as the fourth dimension. Data veracity relates to the quality of the data. Anypotential issues with the quality of the data must be corrected because low-quality dataleads to poor software construction, and ultimately bad decision making. In this work,we reviewed the existing literature on related technical solutions that address dataveracity based on the domain of its application, including social media, web, and IoTapplications. The challenges or limitations and related gaps in existing work will bediscussed, and future research directions will be proposed to address the critical issuesof data veracity in the era of big data

scholarly journals Applying Big Data Analytics in Higher Education

2021 ◽  
Vol 17 (3) ◽  
pp. 29-51
Author(s):  
Adel Alkhalil ◽  
Magdy Abd Elrahman Abdallah ◽  
Azizah Alogali ◽  
Abdulaziz Aljaloud
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Research Direction ◽  
Future Research ◽  
Systematic Mapping Study ◽  
Mapping Study ◽  
Ongoing Research ◽  
Motivation Factors

Higher education systems (HES) have become increasingly absorbed in applying big data analytics due to competition as well as economic pressures. Many studies have been conducted that applied big data analytics in HES; however, a systematic review (SR) of the research is scarce. In this paper, the authors conducted a systematic mapping study to address this deficiency. The qualitative and quantitative analysis of the mapping study resulted in highlighting the research progression over the last decade, and identification of three major themes, 12 subthemes, 10 motivation factors, 10 major challenges, three categories of tools and support techniques, and 16 models for applying big data analytics in higher education. This result contributes to the ongoing research on applying big data analytics in HES. It provides a better understanding of the level of contribution to research as well as identifies gaps for future research direction.

scholarly journals Multiple Dimensions of Smart Cities’ Infrastructure: A Review

Buildings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 73
Author(s):  
Ana Paula P. Kasznar ◽  
Ahmed W. A. Hammad ◽  
Mohammad Najjar ◽  
Eduardo Linhares Qualharini ◽  
Karoline Figueiredo ◽  
...  
Keyword(s):  
Land Use ◽  
Big Data ◽  
Traffic Control ◽  
Smart City ◽  
Smart Cities ◽  
Future Research ◽  
Social Challenges ◽  
Smart Systems ◽  
Long Run ◽  
Land Use Development

In recent years, there has been significant focus on smart cities, on how they operate and develop, and on their technical and social challenges. The importance of infrastructure as a major pillar of support in cities, in addition to the rapid developments in smart city research, necessitate an up-to-date review of smart cities’ infrastructure issues and challenges. Traditionally, a majority of studies have focused on traffic control and management, transport network design, smart grid initiatives, IoT (Internet of Things) integration, big data, land use development, and how urbanization processes impact land use in the long run. The work presented herein proposes a novel review framework that analyzes how smart city infrastructure is related to the urbanization process while presenting developments in IoT sensor networks, big data analysis of the generated information, and green construction. A classification framework was proposed to give insights on new initiatives regarding smart city infrastructure through answering the following questions: (i) What are the various dimensions on which smart city infrastructure research focuses? (ii) What are the themes and classes associated with these dimensions? (iii) What are the main shortcomings in current approaches, and what would be a good research agenda for the future? A bibliometric analysis was conducted, presenting cluster maps that can be used to understand different research trends and refine further searches. A bibliographic analysis was then followed, presenting a review of the most relevant studies over the last five years. The method proposed serves to stress where future research into understanding smart systems, their implementation and functionality would be best directed. This research concluded that future research on the topic should conceptualize smart cities as an emergent socio-techno phenomenon.

ECONOMIC GROWTH ANALYSIS: A STUDY ON MALAYSIAN MARKET

2019 ◽  
Vol 8 (2) ◽  
Author(s):  
Suhaily Maizan Abdul Manaf ◽  
Shuhada Mohamed Hamidi ◽  
Nur Shafini Mohd Said ◽  
Siti Rapidah Omar Ali ◽  
Nur Dalila Adenan
Keyword(s):  
Economic Growth ◽  
Foreign Direct Investment ◽  
Direct Investment ◽  
Inflation Rate ◽  
Government Expenditure ◽  
Correlation Method ◽  
Research Direction ◽  
Least Square ◽  
Future Research ◽  
Internal And External Factors

Economic performance of a country is mostly determined by the growth and any other internal and external factors. In this study, researchers purposely focused on Malaysian market by examining the relationship between export, inflation rate, government expenditure and foreign direct investment towards economic growth in Malaysia by applying the yearly data of 47 years from 1970 to 2016 using descriptive statistics, regression model and correlation method analysis. By applying Ordinary Least Square (OLS) method, the result suggests that export, government expenditure and foreign direct investment are positively and significantly correlated with the economic growth. However, inflation rate has negative and insignificant relationship with the economic growth. The outcome of the study is suggested to be useful in providing the future research direction towards the economic growth in Malaysia. Keywords: economic growth; export; inflation rate; government expenditure

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