nach oben

Erschienen in:

2025 | OriginalPaper | Buchkapitel

2. Review of Mobile Phone Data in Travel Characteristics Recognition

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

Erschienen in: Reliability Evaluation and Its Influence on Traffic Application

Verlag: Springer Nature Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This chapter provides a systematic review of the evolutionary history and developmental stages of mobile phone signaling data analysis technologies.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

Vorheriges Kapitel Introduction

Nächstes Kapitel Principle and Feature of Mobile Phone Signaling Data

Calabrese F, Di Lorenzo G, Liu L et al (2011) Estimating origin-destination flows using mobile phone location data. IEEE Pervasive Comput 10(4):36–44

Tsui SYA, SHALABY AS (2006) Enhanced system for link and mode identification for personal travel surveys based on global positioning systems. Transp Res Rec: J Transp Res Board 1972(1): 38–45

Schlaich J, Otterstatter T, Friedrich M (2010) Generating trajectories from mobile phone data. In: 89th annual meeting compendium of papers, J Transp Res Board Natl Acad

Wang MH, Schrock SD, Vander Broek N et al (2013) Estimating dynamic origin-destination data and travel demand using cell phone network data. Int J Intell Transp Syst Res 11(2): 76-86

Wu YZ (2014) Dynamic OD acquisition method based on mobile phone location information and travel survey. Beijing Jiaotong University

Song L (2015) Research on traffic origin-destination distribution based on cell phone data. Southeast University

Zheng Y (2015) Trajectory data mining: An overview. ACMTrans Intell Syst Technol 6(3): 1–41

Ni L, Wang XC, Chen XM (2018) A spatial econometric model for travel flow analysis and real-world applications with massive mobile phone data. Transp Res Part C: Emerg Technol 86: 510–526

Yang F, Jiang HH, Yao ZX et al (2021) Evaluation of activity location recognition using cellular signaling data. J Southwest Jiaotong Univ, 56(5): 928–936

10.

Poonawala H, Kolar V, Blandin S et al (2016) Singapore in motion: Insights on public transport service level through farecard and mobile data analytics. In: The 22nd ACM SIGKDD international conference

11.

Wang H, Calabrese F, Lorenzo GD et al (2010) Transportation mode inference from anonymized and aggregated mobile phone call detail records. In: International IEEE conference on intelligent transportation systems

12.

Alexander L, Jiang S, Murga M et al (2015) Origin-destination trips by purpose and time of day inferred from mobile phone data. Transp Res Part C: Emerg Technol 58: 240–250

13.

Chen C, Bian L, Ma J (2014) From traces to trajectories: How well can we guess activity locations from mobile phone traces? Transp Res Part C: Emerg Technol 46:326–337

14.

Wu ZX (2019) Travel chain estimation based on cell phone data. Urban Transp China 17(3):11–18

15.

Jiang HH, Yang F, Zhu X et al (2022) Improved F-DBSCAN for trip end identification using mobile phone data in combination with base station density. J Adv Transp 2022:1–17

16.

Bernstein D, Kornhauser AL (1996) An introduction to map matching for personal navigation assistants

17.

White CE, Bernstein D, Kornhauser AL (2000) Some map matching algorithms for personal navigation assistants. Transp Res Part C: Emerg Technol 8(1–6):91–108

18.

Taylor G, Blewitt G, Steup D et al (2001) Road reduction filtering for GPS-GIS navigation. Trans GIS 5(3):193–207

19.

Yin HB, Wolfson O (2004) A weight-based map matching method in moving objects databases. In: 16th International conference on scientific and statistical database management. Santorini Island, Greece, pp 437–438

20.

Blazquez CA, Vonderohe AP (2005) Simple map-matching algorithm applied to intelligent winter maintenance vehicle data. Transp Res Rec: J Transp Res Board 1935(1):68–76

21.

Quddus MA, Noland RB, Ochieng WY (2006) A high accuracy fuzzy logic based map matching algorithm for road transport. J Intell Transp Syst 10(3):103–115

22.

Pink O, Hummel B (2008) A statistical approach to map matching using road network geometry, topology and vehicular motion constraints. In: 11th International IEEE conference on intelligent transportation systems. Beijing, China, pp 862–867

23.

Obradovic D, Lenz H, Schupfner M (2006) Fusion of map and sensor data in a modern car navigation system. J VLSI Signal Proc Syst Signal Image Video Technol 45(1–2):111–122

24.

Wu C, Thai J, Yadlowsky S et al (2015) Cell path: fusion of cellular and traffic sensor data for route flow estimation via convex optimization. Transp Res Procedia 7:212–232

25.

Bonnetain L, Furno A, El Faouzi NE et al (2021) TRANSIT: Fine-grained human mobility trajectory inference at scale with mobile network signaling data. Transp Res Part C: Emerg Technol 130: 103257

26.

Yang F (2013) Link travel speed data capture technology based on cellular handoff information: method, algorithm and evaluation. Science Press, Beijing

27.

Lai JH (2014) Research on data mining and analysis in transportation based on mobile communication location. Beijing University of Technology

28.

Lv M, Chen L, Shen Y et al (2015) Measuring cell-id trajectory similarity for mobile phone route classification. Knowl-Based Syst 89:181–191

29.

Zhou CY (2016) On the cellular signaling based transport trajectory matching technologies in urban area. Southwest Jiaotong University

30.

Li S, Li G, Cheng Y, Ran B (2020) Urban arterial traffic status detection using cellular data without cellphone GPS information. Transp Res Part C: Emerg Technol 114:446–462

31.

Guo Y, Yang F, Jin PJ et al (2022) Vehicle travel path recognition in urban dense road network environments by using mobile phone data. Transportmetrica A Transport Sci 18(3): 1496–1516

32.

Sohn T, Varshavsky A, Lamarca A et al (2006) Mobility detection using everyday gsm traces. In: Dourish P, Friday A UbiComp: ubiquitous computing: Berlin, Heidelberg: Springer Berlin Heidelberg, 4206: 212–224

33.

Zhang B (2010) Research on trip modal split of OD survey based on the cellular positioning system. Beijing Jiaotong University

34.

Xu D, Song G, Gao P et al (2011) Transportation modes identification from mobile phone data using probabilistic models. In: Tang J, King I, Chen L et al Advanced data mining and applications: Berlin, Heidelberg: Springer Berlin Heidelberg, 7121, 359–371

35.

Ho KC, Chan YT (1993) Solution and performance analysis of geolocation by TDOA. IEEE Trans on Aerosp and Electron Syst 29(4):1311–1322

36.

Danafar S, Piorkowski M, Krysczcuk K (2017) Bayesian framework for mobility pattern discovery using mobile network events. In: 25th European signal processing conference (EUSIPCO). IEEE, Kos, Greece, pp 1070–1074

37.

Zhong SQ, Deng RF, Deng HP et al (2020) Recognition of traffic mode of mobile phone data based on the combination of point of interest data and navigation data. Acta Scientiarum Naturalium Univ Sunyatseni 59(3):10

38.

Lai WK, Kuo TH, Chen CH (2016) Vehicle speed estimation and forecasting methods based on cellular floating vehicle data. Appl Sci 6(2):47–47

39.

Li S, Li GF, Cheng Y, Ran B (2020) Urban arterial traffic status detection using cellular data without cellphone GPS information. Transp Res Part C: Emerg Technol 114:446–462

40.

Xu D, Wei C, Peng P et al (2020) GE-GAN: a novel deep learning framework for road traffic state estimation. Transp Res Part C: Emerg Technol 117:102635

41.

Yang Z, Zheng J, and Yu W et al (2024) Car-following behavior based on LiDAR trajectory data at urban intersections. IEEE Syst J 18(1):438–449

42.

Deng Y, Cao Q, Ren G et al (2023) Vehicle trajectory reconstruction incorporating probe and fixed sensor data. J Transp Eng Part A: Syst (9):149

Titel: Review of Mobile Phone Data in Travel Characteristics Recognition
verfasst von: Fei Yang
Yanchen Wang
Yudong Guo
Haihang Jiang
Zhenxing Yao
Verlag: Springer Nature Singapore
Buch: Reliability Evaluation and Its Influence on Traffic Application
Print ISBN: 978-981-9779-49-9

Electronic ISBN: 978-981-9779-50-5

Copyright-Jahr: 2025
DOI: https://doi.org/10.1007/978-981-97-7950-5_2