Skip to main content
Top

2025 | OriginalPaper | Chapter

3. Principle and Feature of Mobile Phone Signaling Data

Authors : Fei Yang, Yanchen Wang, Yudong Guo, Haihang Jiang, Zhenxing Yao

Published in: Reliability Evaluation and Its Influence on Traffic Application

Publisher: Springer Nature Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The mobile phone signaling data is the interaction between the mobile terminal and the mobile communication network. Therefore, its quality is closely related to the network evolution. This chapter introduced the mobile communication principle, including the architecture and core technology of 4G-LTE mobile communication, the generation principle of mobile phone signaling data, and the parameters of communication base station. After that, the characteristics of 4G/5G mobile phone signaling data are researched, such as the base station density, data volume, interaction time distribution, positioning frequency, and positioning accuracy. Finally, the chapter comprehensively evaluates the positioning quality of signaling data by analyzing the feasibility and possibility of the identification of individual travel information.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literature
2.
go back to reference Bonnetain L, Furno A, El Faouzi NE et al (2021) TRANSIT: fine-grained human mobility trajectory inference at scale with mobile network signaling data. Trans Res Part C: Emerg Technol 130:103257 Bonnetain L, Furno A, El Faouzi NE et al (2021) TRANSIT: fine-grained human mobility trajectory inference at scale with mobile network signaling data. Trans Res Part C: Emerg Technol 130:103257
3.
go back to reference Chen XG (2020) Research on traffic zone division based on mobile phone data. Southwest Jiaotong University Chen XG (2020) Research on traffic zone division based on mobile phone data. Southwest Jiaotong University
4.
go back to reference Hu YK (2017) Urban rail transit passenger travel behavior analysis methods based on cellular data. Southeast University Hu YK (2017) Urban rail transit passenger travel behavior analysis methods based on cellular data. Southeast University
5.
go back to reference Chin K, Huang H, Horn C et al (2019) Inferring fine-grained transport modes from mobile phone cellular signaling data. Comput Environ Urban Syst 77:101348 Chin K, Huang H, Horn C et al (2019) Inferring fine-grained transport modes from mobile phone cellular signaling data. Comput Environ Urban Syst 77:101348
6.
go back to reference Bolbol A, Cheng T, Tsapakis I et al (2012) Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification. Comput Environ Urban Syst 36(6):526–537 Bolbol A, Cheng T, Tsapakis I et al (2012) Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification. Comput Environ Urban Syst 36(6):526–537
7.
go back to reference Bantis T, Haworth J (2017) Who you are is how you travel: a framework for transportation mode detection using individual and environmental characteristics. Trans Res Part C: Emerg Technol 80:286–309CrossRefMATH Bantis T, Haworth J (2017) Who you are is how you travel: a framework for transportation mode detection using individual and environmental characteristics. Trans Res Part C: Emerg Technol 80:286–309CrossRefMATH
8.
go back to reference Zhou Y, Yang C, Zhu R (2019) Identifying trip ends from raw GPS data with a hybrid spatio-temporal clustering algorithm and random forest model: a case study in Shanghai. Trans Plann Technol 42(8):739–756CrossRefMATH Zhou Y, Yang C, Zhu R (2019) Identifying trip ends from raw GPS data with a hybrid spatio-temporal clustering algorithm and random forest model: a case study in Shanghai. Trans Plann Technol 42(8):739–756CrossRefMATH
9.
go back to reference Yang J, Kang S, Chon K (2005) The map matching algorithm of GPS data with relatively long polling time intervals. J Eastern Asia Soc Trans Stud 6:2561–2573MATH Yang J, Kang S, Chon K (2005) The map matching algorithm of GPS data with relatively long polling time intervals. J Eastern Asia Soc Trans Stud 6:2561–2573MATH
10.
go back to reference Wu YZ (2014) Dynamic OD acquisition method based on mobile phone location information and travel survey. Beijing Jiaotong University Wu YZ (2014) Dynamic OD acquisition method based on mobile phone location information and travel survey. Beijing Jiaotong University
11.
go back to reference Song L (2015) Research on traffic origin-destination distribution based on cell phone data. Southeast University Song L (2015) Research on traffic origin-destination distribution based on cell phone data. Southeast University
12.
go back to reference Zheng Y (2015) Trajectory data mining: an overview. ACM Trans Intell Syst Technol 6(3):1–41CrossRefMATH Zheng Y (2015) Trajectory data mining: an overview. ACM Trans Intell Syst Technol 6(3):1–41CrossRefMATH
Metadata
Title
Principle and Feature of Mobile Phone Signaling Data
Authors
Fei Yang
Yanchen Wang
Yudong Guo
Haihang Jiang
Zhenxing Yao
Copyright Year
2025
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-97-7950-5_3