Linking granular computing, big data and decision making: a case study in urban path planning

Granular computing, an emerging information processing paradigm transforming complex data into information granules at different scales so that different features and regularities can be revealed, offers an essential linkage between big data and decision making. By using innovative technologies of g...

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Vydáno v:Soft computing (Berlin, Germany) Ročník 24; číslo 10; s. 7435 - 7450
Hlavní autoři: Li, Xiang, Zhou, Jiandong, Pedrycz, Witold
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2020
Springer Nature B.V
Témata:
Artificial Intelligence
Big Data
Case studies
Computation
Computational Intelligence
Control
Data processing
Decision making
Engineering
Fuzzy sets
Granular materials
Information processing
Knowledge
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Optimization
Optimization models
Path planning
Problem solving
Random variables
Robotics
Social networks
Traffic
Traffic information
Big data
Granular computing
Urban path planning
Decision making
Social media
ISSN:1432-7643, 1433-7479
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Shrnutí:Granular computing, an emerging information processing paradigm transforming complex data into information granules at different scales so that different features and regularities can be revealed, offers an essential linkage between big data and decision making. By using innovative technologies of granular computing that transforms big data collections into information granules, we would be at position of recognizing and exploiting the meaningful pieces of knowledge present in data, and produce sound, and practically supported decisions. In this study, we first summarize a general scheme of big data–granular computing–decision making and then present a case study where we detect the important traffic event information by collecting and analyzing social media data, and transform them into probabilistic information granules that can be used for urban routing navigation. We propose a robust fastest path optimization model to incorporate the impact of traffic events and generate the optimal routing strategy. Real-life experiments are carried out in regional Chaoyang District, Beijing, as well as the backbone roadway network of Beijing, which illustrate the effectiveness of our proposed big data-driven decision-making method. Our study provides new evidence demonstrating that big data can be efficiently used to enhance decisions and granular computing with this regard. The concept of the proposed scheme can be easily extended for decision-making modeling in other domains.
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-019-04369-6