Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Beyond Geotagged Tweets: Exploring the Geolocalisation of Tweets for Transportation Applications Read chapter Read first chapter

2019 | OriginalPaper | Chapter

2. Social Media in Transportation Research and Promising Applications

Authors : Zhenhua Zhang, Qing He

Published in: Transportation Analytics in the Era of Big Data

Publisher: Springer International Publishing

Log in

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

search-config
loading …

Abstract

The newly emerged social media data can collect large quantities of location, time information, as well as the fully detailed text messages, which in turn contribute to existing transportation studies. With the wide spread of mobile device, information acquired from social media appears to be easier and larger than the traditional data collection methods and the related topics cover a wide range of transportation-related events.
This chapter uses one of the social media tools: Twitter to demonstrate the promises of social media in complementing traditional transportation studies. Three major applications in transportation research are examined: traffic event detection, human mobility exploration, and trip purpose and travel demand forecasting. In these applications, we detail the process how to leverage the GPS information to extract displacement; how to automatically extract topics from text messages; how to forecast travel demands toward a social event. The state-of-the-art methods are employed to process the Big Data of social media and the results show the advantages as well as the deficiencies of social media in transportation research and applications.

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!

inform now

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!

inform now

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!

inform now
previous chapter Beyond Geotagged Tweets: Exploring the Geolocalisation of Tweets for Transportation Applications
next chapter Ground Transportation Big Data Analytics and Third Party Validation: Solutions for a New Era of Regulation and Private Sector Innovation
Literature
1.
R. Buettner, Getting a job via career-oriented social networking sites: the weakness of ties, in 2016 49th Hawaii International Conference on System Sciences (HICSS), 2016, pp. 2156–2165
2.
3.
T. Aichner, F. Jacob, Measuring the degree of corporate social media use. Int. J. Mark. Res. 57, 257–275 (2015)CrossRef
4.
Z. Zhang, Fusing Social Media and Traditional Traffic Data for Advanced Traveler Information and Travel Behavior Analysis (State University of New York at Buffalo, Buffalo, 2017)
5.
Z. Xiang, U. Gretzel, Role of social media in online travel information search. Tour. Manag. 31, 179–188 (2010)CrossRef
6.
K.S.S.N.K. Ueno, K. Cho. Feasibility study on detection of transportation information exploiting twitter as a sensor, 2012
7.
J. Evans-Cowley, G. Griffin, Microparticipation with social media for community engagement in transportation planning. Transp. Res. Rec.: J. Transp. Res. Board 2307, 90–98 (2012)CrossRef
8.
J.H. Lee, S. Gao, K. Janowicz, K.G. Goulias, Can Twitter data be used to validate travel demand models?, in IATBR 2015-WINDSOR, 2015
9.
L. Lin, M. Ni, Q. He, J. Gao, A.W. Sadek, T. I. T. I. Director, Modeling the impacts of inclement weather on freeway traffic speed: an exploratory study utilizing social media data, in Transportation Research Board 94th Annual Meeting, 2015
10.
A.M. Sadri, S. Hasan, S.V. Ukkusuri, Joint Inference of User Community and Interest Patterns in Social Interaction Networks, arXiv preprint arXiv:1704.01706, 2017
11.
Y. Chen, H.S. Mahmassani, Use of social networking data to explore activity and destination choice behavior in two metropolitan areas. Transp. Res. Rec.: J. Transp. Res. Board 2566, 71–82 (2016)CrossRef
12.
Y. Chen, A. Talebpour, H.S. Mahmassani, Friends don’t let friends drive on bad routes: modeling the impact of social networks on drivers’ route choice behavior, in Transportation Research Board 94th Annual Meeting, 2015
13.
Y. Chen, H.S. Mahmassani, Exploring activity and destination choice behavior in two metropolitan areas using social networking data, in Transportation Research Board 95th Annual Meeting, 2016
14.
T. Sakaki, M. Okazaki, Y. Matsuo, Earthquake shakes twitter users: real-time event detection by social sensors, in Proceedings of the 19th international conference on World wide web, 2010, pp. 851–860
15.
M. Krstajic, C. Rohrdantz, M. Hund, A. Weiler, Getting there first: Real-time detection of real-world incidents on twitter, 2012
16.
A. Schulz, P. Ristoski, H. Paulheim, I See a Car Crash: Real-Time Detection of Small Scale Incidents in Microblogs, in The Semantic Web: ESWC 2013 Satellite Events, (Springer, Berlin, 2013), pp. 22–33CrossRef
17.
S. Zhang, J. Tang, H. Wang, Y. Wang, Enhancing traffic incident detection by using spatial point pattern analysis on social media. Transp. Res. Rec.: J. Transp. Res. Board 2528, 69–77 (2015)CrossRef
18.
A.M. Sadri, S. Hasan, S.V. Ukkusuri, J.E.S. Lopez, Analyzing Social Interaction Networks from Twitter for Planned Special Events, arXiv preprint arXiv:1704.02489, 2017
19.
H. Gao, G. Barbier, R. Goolsby, D. Zeng, Harnessing the crowdsourcing power of social media for disaster relief, DTIC Document 2011
20.
S. Ukkusuri, X. Zhan, A. Sadri, Q. Ye, Use of social media data to explore crisis informatics: study of 2013 Oklahoma Tornado. Transp. Res. Rec.: J. Transp. Res. Board 2459, 110–118 (2014)CrossRef
21.
A.M. Sadri, S. Hasan, S.V. Ukkusuri, M. Cebrian, Understanding information spreading in social media during Hurricane Sandy: user activity and network properties, arXiv preprint arXiv:1706.03019, 2017
22.
Y. Kryvasheyeu, H. Chen, E. Moro, P. Van Hentenryck, M. Cebrian, Performance of social network sensors during Hurricane Sandy. PLoS One 10, e0117288 (2015)CrossRef
23.
Y. Kryvasheyeu, H. Chen, N. Obradovich, E. Moro, P. Van Hentenryck, J. Fowler, et al., Rapid assessment of disaster damage using social media activity. Sci. Adv. 2, e1500779 (2016)CrossRef
24.
A.M. Sadri, S.V. Ukkusuri, H. Gladwin, The role of social networks and information sources on hurricane evacuation decision making. Nat. Hazards Rev. 18, 04017005 (2017)CrossRef
25.
A.M. Sadri, S.V. Ukkusuri, H. Gladwin, Modeling joint evacuation decisions in social networks: the case of Hurricane Sandy. J. Choice Modell. 25, 50–60 (2017)CrossRef
26.
S. Hasan, S.V. Ukkusuri, X. Zhan, Understanding social influence in activity location choice and lifestyle patterns using geolocation data from social media. Front. ICT 3(10) (2016)
27.
S. Hasan, S.V. Ukkusuri, Urban activity pattern classification using topic models from online geo-location data. Transp. Res. Pt. C 44, 363–381 (2014)CrossRef
28.
S. Hasan, X. Zhan, S.V. Ukkusuri, Understanding urban human activity and mobility patterns using large-scale location-based data from online social media, in Proceedings of the 2nd ACM SIGKDD international workshop on urban computing, 2013, p. 6
29.
T. Wall, G. Macfarlane, K. Watkins, Exploring the use of egocentric online social network data to characterize individual air travel behavior. Transp. Res. Rec.: J. Transp. Res. Board 2400, 78–86 (2013)CrossRef
30.
S. Camay, L. Brown, M. Makoid, Role of social media in environmental review process of national environmental policy act. Transp. Res. Rec.: J. Transp. Res. Board 2307, 99–107 (2012)CrossRef
31.
C. Stambaugh, Social media and primary commercial service airports. Transp. Res. Rec.: J. Transp. Res. Board 2325, 76–86 (2013)CrossRef
32.
J. Gelernter, S. Balaji, An algorithm for local geoparsing of microtext. GeoInformatica 17, 635–667 (2013)CrossRef
33.
B. Pender, G. Currie, A. Delbosc, N. Shiwakoti, International study of current and potential social media applications in unplanned passenger rail disruptions. Transp. Res. Rec.: J. Transp. Res. Board 2419, 118–127 (2014)CrossRef
34.
R. Chan, J. Schofer, Role of social media in communicating transit disruptions. Transp. Res. Rec.: J. Transp. Res. Board 2415, 145–151 (2014)CrossRef
35.
M. Ni, Q. He, J. Gao, Forecasting the subway passenger flow under event occurrences with social media. IEEE Trans. Intell. Transp. Eng. 18, 1623–1632 (2017)
36.
L. Adam, L. Andrew, 2016 U.S. cross-platform future in focus, 2016
37.
M. Duggan, J. Brenner, The Demographics of Social Media Users, 2012, vol 14 (Pew Research Center’s Internet & American Life Project, Washington, DC, 2013)
38.
39.
Statista, Number of active Twitter users in the United States from 2010 to 2014, by gender (in millions), 2016
40.
H. Purohit, A. Hampton, S. Bhatt, V.L. Shalin, A. Sheth, J. Flach, An information filtering and management model for twitter traffic to assist crises response coordination, Special Issue on Crisis Informatics and Collaboration, 2013
41.
E. Aramaki, S. Maskawa, M. Morita, Twitter catches the flu: detecting influenza epidemics using Twitter, in Proceedings of the conference on empirical methods in natural language processing, 2011, pp. 1568–1576
42.
C. Shirky, The political power of social media. Foreign Aff. 90, 28–41 (2011)
43.
E. Mai, R. Hranac, Twitter interactions as a data source for transportation incidents, in Proc. Transportation Research Board 92nd Ann. Meeting, 2013
44.
A. Gal-Tzur, S.M. Grant-Muller, T. Kuflik, E. Minkov, S. Nocera, I. Shoor, The potential of social media in delivering transport policy goals. Transp. Policy 32, 115–123 (2014)CrossRef
45.
Y. Gu, Z.S. Qian, F. Chen, From twitter to detector: real-time traffic incident detection using social media data. Transp. Res. Pt. C 67, 321–342 (2016)CrossRef
46.
E. D’Andrea, P. Ducange, B. Lazzerini, F. Marcelloni, Real-time detection of traffic from twitter stream analysis. Intell. Transp. Syst. IEEE Trans. 16, 2269–2283 (2015)CrossRef
47.
Z. Zhang, M. Ni, Q. He, J. Gao, J. Gou, X. Li, An exploratory study on the correlation between twitter concentration and traffic surge 2. Transp. Res. Rec.: J. Transp. Res. Board 35, 36 (2016)CrossRef
48.
N. Wanichayapong, W. Pruthipunyaskul, W. Pattara-Atikom, P. Chaovalit, Social-based traffic information extraction and classification, in ITS Telecommunications (ITST), 2011 11th International Conference on, 2011, pp. 107–112
49.
R. Li, K. H. Lei, R. Khadiwala, and K. C.-C. Chang, Tedas: a twitter-based event detection and analysis system, in Data engineering (ICDE), 2012 IEEE 28th international conference on, 2012, pp. 1273–1276
50.
51.
H. Cramér, Mathematical Methods of Statistics, vol 9 (Princeton University Press, Princeton, 1999)
52.
R. Agrawal, R. Srikant, Fast algorithms for mining association rules. in Proc. 20th int. conf. very large data bases, VLDB, 1994, pp. 487–499
53.
M. Hahsler, B. Grün, K. Hornik, Introduction to arules–mining association rules and frequent item sets. in SIGKDD Explor, 2007
54.
T.J. Ypma, Historical development of the Newton–Raphson method. SIAM Rev. 37, 531–551 (1995)CrossRef
55.
Y. Freund, R.E. Schapire, Large margin classification using the perceptron algorithm. Mach. Learn. 37, 277–296 (1999)CrossRef
56.
Z. Zhang, Q. He, J. Gao, M. Ni, A. Deep Learning, Approach for detecting traffic accidents from social media data. Transp. Res. Pt. C 86, 580–596 (2016)CrossRef
57.
58.
D. Brockmann, L. Hufnagel, T. Geisel, The scaling laws of human travel. Nature 439, 462–465 (2006)CrossRef
59.
C. Song, Z. Qu, N. Blumm, A.-L. Barabási, Limits of predictability in human mobility. Science 327, 1018–1021 (2010)CrossRef
60.
M.C. Gonzalez, C.A. Hidalgo, A.-L. Barabasi, Understanding individual human mobility patterns. Nature 453, 779–782 (2008)CrossRef
61.
Z. Zhang, Q. He, S. Zhu, Potentials of using social media to infer the longitudinal travel behavior: a sequential model-based clustering method. Transp. Res. Pt. C 85, 396–414 (2016)CrossRef
62.
K. Fatima, P. Anne, H. Cahill M.L. Erik, B. Khamthakone, Demographic Reports 2015, County of Fairfax, Virginia. in Countywide Service Integration and Planning Management (CSIPM), Economic, Demographic and Statistical Research January, 2016, 2016
63.
C. Kang, X. Ma, D. Tong, Y. Liu, Intra-urban human mobility patterns: an urban morphology perspective. Phys. A: Stat. Mech. Appl. 391, 1702–1717 (2012)CrossRef
64.
W. Bohte, K. Maat, Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: a large-scale application in the Netherlands. Transp. Res. Pt. C 17, 285–297 (2009)CrossRef
65.
A. Moiseeva, J. Jessurun, H. Timmermans, Semiautomatic imputation of activity travel diaries: use of global positioning system traces, prompted recall, and context-sensitive learning algorithms. Transp. Res. Rec.: J. Transp. Res. Board 2183, 60–68 (2010)CrossRef
66.
L. Shen, P.R. Stopher, A process for trip purpose imputation from global positioning system data. Transp. Res. Pt. C 36, 261–267 (2013)CrossRef
67.
L. Stenneth, O. Wolfson, P.S. Yu, B. Xu, Transportation mode detection using mobile phones and GIS information. in Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2011, pp. 54–63
68.
T.H. Rashidi, A. Abbasi, M. Maghrebi, S. Hasan, T.S. Waller, Exploring the capacity of social media data for modelling travel behaviour: opportunities and challenges. Transp. Res. Pt. C 75, 197–211 (2017)CrossRef
Metadata
Title
Social Media in Transportation Research and Promising Applications
Authors
Zhenhua Zhang
Qing He
Copyright Year
2019
Book
Transportation Analytics in the Era of Big Data

Print ISBN: 978-3-319-75861-9

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

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-75862-6_2