APSIPA Transactions on Signal and Information Processing > Vol 7 > Issue 1

Spatio-temporal multidimensional collective data analysis for providing comfortable living anytime and anywhere

Industrial Technology Advances

Naonori Ueda, NTT Communication Science Laboratories, Japan, ueda.naonori@lab.ntt.co.jp , Futoshi Naya, NTT Communication Science Laboratories, Japan
 
Suggested Citation
Naonori Ueda and Futoshi Naya (2018), "Spatio-temporal multidimensional collective data analysis for providing comfortable living anytime and anywhere", APSIPA Transactions on Signal and Information Processing: Vol. 7: No. 1, e4. http://dx.doi.org/10.1017/ATSIP.2018.4

Publication Date: 27 Mar 2018
© 2018 Naonori Ueda and Futoshi Naya
 
Subjects
 
Keywords
Spatio-temporal data analysisIoTSmart citiesProactive navigationMachine learning
 

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This is published under the terms of the Creative Commons Attribution licence.

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In this article:
I. INTRODUCTION 
II. SMART CITY-WIDE DATA COLLECTION USING GOVERNMENT VEHICLES 
III. SPATIO-TEMPORAL MULTIDIMENSIONAL DATA ANALYSIS 
IV. REAL-TIME AND PROACTIVE NAVIGATION BASED ON SPATIO-TEMPORAL PREDICTIONS 
V. CONCLUSIONS 

Abstract

Machine learning is a promising technology for analyzing diverse types of big data. The Internet of Things era will feature the collection of real-world information linked to time and space (location) from all sorts of sensors. In this paper, we discuss spatio-temporal multidimensional collective data analysis to create innovative services from such spatio-temporal data and describe the core technologies for the analysis. We describe core technologies about smart data collection and spatio-temporal data analysis and prediction as well as a novel approach for real-time, proactive navigation in crowded environments such as event spaces and urban areas. Our challenge is to develop a real-time navigation system that enables movements of entire groups to be efficiently guided without causing congestion by making near-future predictions of people flow. We show the effectiveness of our navigation approach by computer simulation using artificial people-flow data.

DOI:10.1017/ATSIP.2018.4