Analysis of Suburbanites' Travel Behavior Based on Big Data

CICTP 2018 ◽  
2018 ◽  
Author(s):  
Xiaozhe Wu ◽  
Kai Zhang ◽  
Jinping Guan ◽  
Bokui Chen ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Big Data ◽  
Travel Behavior ◽  
Behavior Based

Role of Urban Big Data in Travel Behavior Research

2020 ◽  
pp. 036119812097502
Author(s):  
Chihuangji Wang ◽  
Daniel Baldwin Hess
Keyword(s):  
Big Data ◽  
Survey Data ◽  
Travel Behavior ◽  
Data Science ◽  
Location Based Service ◽  
Labor Costs ◽  
Public And Private ◽  
Public And Private Sector ◽  
Data Analytic

Understanding urban travel behavior (TB) is critical for advancing urban transportation planning practice and scholarship; however, traditional survey data is expensive (because of labor costs) and error-prone. With advances in data collection techniques and data analytic approaches, urban big data (UBD) is currently generated at an unprecedented scale in relation to volume, variety, and speed, producing new possibilities for applying UBD for TB research. A review of more than 50 scholarly articles confirms the remarkable and expanding role of UBD in TB research and its advantages over traditional survey data. Using this body of published work, a typology is developed of four key types of UBD—social media, GPS log, mobile phone/location-based service, and smart card—focusing on the features and applications of each type in the context of TB research. This paper discusses in significant detail the opportunities and challenges in the use of UBD from three perspectives: conceptual, methodological, and political. The paper concludes with recommendations for researchers to develop data science knowledge and programming skills for analysis of UBD, for public and private sector agencies to cooperate on the collection and sharing of UBD, and for legislators to enforce data security and confidentiality. UBD offers both researchers and practitioners opportunities to capture urban phenomena and deepen knowledge about the TB of individuals.

scholarly journals Cooperative Innovation Behavior Based on Big Data

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Guojun Ji ◽  
Muhong Yu ◽  
Kim Hua Tan
Keyword(s):  
Big Data ◽  
Rapid Change ◽  
Competitive Advantages ◽  
Evolution Path ◽  
Collaborative Innovation ◽  
Cooperative Innovation ◽  
Behavior Based ◽  
Method Approach

With the rapid change in technology, cooperative innovation based on data sharing has become an imminent tactic for enterprises to gain competitive advantages. This paper adopted a mixed method approach (case study-modelling-case study) to study firms’ co-opetition behavior based on their data analytics capabilities for innovation. We show that firms favor cooperation among peers with same capabilities, i.e., when each firm’s data level is comparable to their partners. We further establish that data transferability and incentive have high impact on cooperation decisions. Finally, we explain the evolution path of firms’ cooperation decisions and discuss the best options for them to sustain long-term growth and competitiveness. The results provide a basis for firms to decide how best to utilize big data for collaborative innovation, so as to improve customers’ product adoption and reduce costs.

Railway passengers travel behavior based on bounded rationality by rough set weight

2017 ◽  
Vol 22 (S4) ◽  
pp. 10019-10029
Author(s):  
Hai-jun Li ◽  
Hong-chang Zhou ◽  
Jian-rong Feng ◽  
Xiao-hong Chen ◽  
Wei Zhang
Keyword(s):  
Bounded Rationality ◽  
Rough Set ◽  
Travel Behavior ◽  
Behavior Based

scholarly journals RESEARCH ON EARLY WARNING SYSTEM OF COLLEGE STUDENTS’ BEHAVIOR BASED ON BIG DATA ENVIRONMENT

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 659-665
Author(s):  
C. Y. Yang ◽  
J. Y. Liu ◽  
S. Huang
Keyword(s):  
College Students ◽  
Big Data ◽  
Early Warning ◽  
Behavioral Problems ◽  
Early Warning System ◽  
Warning System ◽  
Safety Control ◽  
Education Management ◽  
Data Environment ◽  
Behavior Based

Abstract. Because most schools have been using traditional methods to manage students, there is a lack of effective monitoring of students' behavioral problems. In order to solve this problem, this paper analyses the characteristics of big data in University campus, adopts K-Means algorithm, a traditional clustering analysis algorithm, and proposes an early warning system of College Students' behavior based on Internet of Things and big data environment under the mainstream Hadoop open source platform. The system excavates and analyses the potential connections in the massive data of these campuses, studies the characteristics of students' behavior, analyses the law of students' behavior, and clusters the categories of students' behavior. It can provide students, colleges, schools and logistics management departments with multi-dimensional behavior analysis and prediction, early warning and safety control of students' behavior, realize the informatization of students' management means, improve the scientific level of students' education management, and promote the construction of intelligent digital campus.

Big data analytics for MOOC video watching behavior based on Spark

2019 ◽  
Vol 32 (11) ◽  
pp. 6481-6489
Author(s):  
Hui Hu ◽  
Guofeng Zhang ◽  
Wanlin Gao ◽  
Minjuan Wang
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Behavior Based
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