nach oben

Erschienen in:

2019 | OriginalPaper | Buchkapitel

5. Data, Methods, and Applications of Traffic Source Prediction

verfasst von : Chengcheng Wang, Pu Wang

Erschienen in: Transportation Analytics in the Era of Big Data

Verlag: Springer International Publishing

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

Traffic source prediction provides a new way of mitigating traffic congestion. Since the initial discovery that the major usage of road segments can be traced to surprisingly few driver sources, studies of traffic source prediction have recently experienced rapid development. With more high-resolution traffic data available, dynamical driver sources and passenger sources have been proposed, and the method of targeting traffic sources highly related to congestion has triggered a number of applications ranging from travel demand control to vehicle routing guidance and infrastructure upgrades. Here, we present a comprehensive review of the data, methods, and applications of traffic source prediction.

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

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel A Privacy-Preserving Urban Traffic Estimation System

Nächstes Kapitel Analyzing the Spatial and Temporal Characteristics of Subway Passenger Flow Based on Smart Card Data

P. Wang, T. Hunter, A.M. Bayen, K. Schechtner, M.C. González, Understanding road usage patterns in urban areas. Sci. Rep. 2(12), 1001 (2012)CrossRef

J. Wang, D. Wei, K. He, H. Gong, P. Wang, Encapsulating urban traffic rhythms into road networks. Sci. Rep. 4(7488), 4141 (2014)

J. Wang, Y. Li, J. Liu, K. He, P. Wang, Vulnerability analysis and passenger source prediction in urban rail transit networks. PLoS One 8(11), e80178 (2013)CrossRef

K. He, Z. Xu, P. Wang, L. Deng, Congestion avoidance routing based on large-scale social signals. IEEE Trans. Intell. Transp. Syst. 17, 1–14 (2016)CrossRef

J. Wang, Q. Tan, P. Wang, Analysis of surging passenger flow in urban rail transit network. J. Railw. Sci. Eng. 12(1), 196–202 (2015)

K. He, Z. Xu, P. Wang, A hybrid routing model for mitigating congestion in networks. Phys. A Stat. Mech. Appl. 431, 1–17 (2015)CrossRef

K. He, J. Wang, L. Deng, P. Wang, in Congestion Avoidance Routing in Urban Rail Transit Networks. IEEE, International Conference on Intelligent Transportation Systems (IEEE, 2015), pp. 200–205

M.C. González, C.A. Hidalgo, A.L. Barabási, Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)CrossRef

C. Song, Z. Qu, N. Blumm, A.L. Barabási, Limits of predictability in human mobility. Science 327(5968), 1018 (2010)CrossRef

10.

P. Deville, C. Linard, S. Martin, M. Gilbert, F.R. Stevens, A.E. Gaughan, et al., Dynamic population mapping using mobile phone data. Proc. Natl. Acad. Sci. U. S. A. 111(45), 15888 (2014)CrossRef

11.

K.A.S.A. Khateeb, J.A.Y. Johari, W.F.A. Khateeb, Dynamic traffic light sequence algorithm using rfid. J. Comput. Sci. 4(7), 517–524 (2008)CrossRef

12.

L. Yang, Detection of real-time event in intelligent traffic system based on rfid. Adv. Mater. Res. 926-930, 1314–1317 (2014)CrossRef

13.

W. Wen, An intelligent traffic management expert system with RFID technology. Expert Syst. Appl. 37(4), 3024–3035 (2010)CrossRef

14.

Y. Wei, M.C. Chen, Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transp. Res. Pt. C Emerg. Technol. 21(1), 148–162 (2012)CrossRef

15.

L. Sun, D.H. Lee, A. Erath, X. Huang, in Using Smart Card Data to Extract Passenger’s Spatio-Temporal Density and Train’s Trajectory of MRT System. ACM SIGKDD International Workshop on Urban Computing ACM SIGKDD International Workshop on Urban Computing, vol 4 (ACM, 2012), pp.142–148)

16.

M.S. Iqbal, C.F. Choudhury, P. Wang, M.C. González, Development of origin–destination matrices using mobile phone call data. Transp. Res. Pt. C Emerg. Technol. 40(1), 63–74 (2014)CrossRef

17.

H. Gong, L. Sun, P. Wang, Using mobile phone data to estimate trip distribution of urban mega traffic analysis zones: a case study in San Francisco. Urban Transp. China 14(1), 37–42 (2016)

18.

E.W. Dijkstra, A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)CrossRef

19.

M. Chen, A.S. Alfa, A network design algorithm using a stochastic incremental traffic assignment approach. Transp. Sci. 25(3), 215–224 (1991)CrossRef

20.

M. Frank, P. Wolfe, An algorithm for quadratic programming. Nav. Res. Logist. 3(1-2), 95–110 (1956)CrossRef

21.

S. Çolak, A. Lima, M.C. González, Understanding congested travel in urban areas. Nat. Commun. 7, 10793 (2016)CrossRef

22.

G. Li, S.D.S. Reis, A.A. Moreira, S. Havlin, H.E. Stanley, J.S. Andrade Jr., Towards design principles for optimal transport networks. Phys. Rev. Lett. 104(1), 018701 (2010)CrossRef

23.

P. Crucitti, V. Latora, S. Porta, Centrality measures in spatial networks of urban streets. Phys. Rev. E 73(3), 036125 (2006)CrossRef

24.

Y. Tong, L. Lu, Z. Zhang, Y. He, W. Lu, Vulnerability of long-distance bridges and tunnels in urban roadway networks. J. Eng. Sci. Technol. Rev. 10(1), 123–130 (2017)

25.

B. Yang, W. Guo, B. Chen, G. Yang, J. Zhang, Estimating mobile traffic demand using Twitter. IEEE Wireless Commun. Lett. 5(4), 380–383 (2016)CrossRef

26.

G. Karoń, Travel Demand and Transportation Supply Modelling for Agglomeration without Transportation Model, in International Conference on Transport Systems Telematics, (Springer, Berlin, 2013), pp. 284–293

27.

X. Hu, Y.C. Chiu, J. Shelton, Development of a behaviorally induced system optimal travel demand management system. J. Intell. Transp. Syst. 21(1), 12–25 (2017)CrossRef

28.

J. Wang, Q. Gu, J. Wu, G. Liu, Z. Xiong, in Traffic Speed Prediction and Congestion Source Exploration: A Deep Learning Method. Data Mining (ICDM), 2016 IEEE 16th International Conference on (IEEE, 2016), pp. 499–508

29.

H. Gong, C. Wang, Y. Qu, K. He, P. Wang, in Locating Traffic Information Sources in Urban Areas. Cota International Conference of Transportation Professionals. (2016), pp. 141–151

30.

J.L. Toole, S. Colak, B. Sturt, L.P. Alexander, A. Evsukoff, M.C. González, The path most traveled: travel demand estimation using big data resources. Transp. Res. Pt. C Emerg.Technol. 58, 162–177 (2015)CrossRef

Titel: Data, Methods, and Applications of Traffic Source Prediction
verfasst von: Chengcheng Wang
Pu Wang
Verlag: Springer International Publishing
Buch: Transportation Analytics in the Era of Big Data
Print ISBN: 978-3-319-75861-9

Electronic ISBN: 978-3-319-75862-6

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-319-75862-6_5

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

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

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner