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2019 | OriginalPaper | Buchkapitel

The Use of Social Computing in Travelers’ Activities Preference Analysis

verfasst von : Charis Chalkiadakis, Panagiotis Iordanopoulos, Evangelos Mitsakis, Eleni Chalkia

Erschienen in: Data Analytics: Paving the Way to Sustainable Urban Mobility

Verlag: Springer International Publishing

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Abstract

Each traveler moves across the physical plane to perform activities. It is known that each trip connects two distinct activities. Travelers, during their trips, make various choices in order to decide mode, route and time of departure. These choices depend on factors that are either predetermined or emotional. Other factors such as existence of various events can also affect the choices of the travelers. During the last decade, information related to the factors mentioned above, are addressed through the social networks. The amount of information provided in the social media is important and crucial in addressing the way travelers move around. On the other hand, understanding and, more importantly, predicting activities is a crucial matter in order to predict traffic conditions as well as to provide improved trip advice to travelers.

The present paper studies the possibilities and capabilities exist in order to proceed to transport modelling techniques by deriving information from the social media status updates of the users. More specifically the study reviews methodologies and techniques that can collect information from the users’ status updates in order to estimate their preferences.

In the present study the development of a methodology which integrates the gathered information from the social media status updates with stated activities’ preferences is being investigated. The review takes into account the social computing paradigm where humans and machines collaborate to solve a social problem. Also, multiple data sources are examined in order more integrated results to be returned.

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Nächstes Kapitel Development of a Methodology, Using Multi-Criteria Decision Analysis (MCDA), to Choose Between Full Pedestrianization and Traffic Calming Area (Woonerf Zone Type)

Parameswaran, M., Whinston, A.B.: Social computing: an overview. Commun. Assoc. Inf. Syst. 19, Article 37, 762–780 (2007). https://doi.org/10.17705/1CAIS.01937

Hanna, R., Rohm, A., Crittenden, V.L.: We’re all connected: the power of the social media ecosystem. Bus. Horiz. 54, 265–273 (2011). https://doi.org/10.1016/j.bushor.2011.01.007CrossRef

Rashidi, T.H., Abbasi, A., Maghrebi, M., Hasan, S., Waller, T.S.: Exploring the capacity of social media data for modelling travel behaviour: opportunities and challenges. Transp. Res. Part C Emerg. Technol. 75, 197–211 (2017). https://doi.org/10.1016/j.trc.2016.12.008CrossRef

Facebook. www.facebook.com. Accessed 21 Jan 2018

Twitter. www.twitter.com. Accessed 21 Jan 2018

Foursquare. https://foursquare.com/. Accessed 21 Jan 2018

Chaniotakis, E., Antoniou, C., Pereira, F.: Mapping social media for transportation studies. IEEE Intell. Syst. 31, 64–70 (2016). https://doi.org/10.1109/MIS.2016.98CrossRef

Zheng, X., Chen, W., Wang, P., Shen, D., Chen, S., Wang, X., Zhang, Q., Yang, L.: Big data for social transportation. IEEE Trans. Intell. Transp. Syst. 17, 620–630 (2015). https://doi.org/10.1109/TITS.2015.2480157CrossRef

D’Andrea, E., Ducange, P., Lazzerini, B., Marcelloni, F.: Real-time detection of traffic from Twitter stream analysis. IEEE Trans. Intell. Transp. Syst. 16, 2269–2283 (2015). https://doi.org/10.1109/TITS.2015.2404431CrossRef

10.

Gal-Tzur, A., Grant-Muller, S.M., Kuflik, T., Minkov, E., Nocera, S., Shoor, I.: The potential of social media in delivering transport policy goals. Transp. Policy 32, 115–123 (2014). https://doi.org/10.1016/j.tranpol.2014.01.007CrossRef

11.

Kuflik, T., Minkov, E., Nocera, S., Grant-Muller, S., Gal-Tzur, A., Shoor, I.: Automating a framework to extract and analyse transport related social media content: the potential and the challenges. Transp. Res. Part C Emerg. Technol. 77, 275–291 (2017). https://doi.org/10.1016/j.trc.2017.02.003CrossRef

12.

Ruiz, T., Mars, L., Arroyo, R., Serna, A.: Social networks, big data and transport planning. Transp. Res. Procedia 18, 446–452 (2016). https://doi.org/10.1016/j.trpro.2017.01.122CrossRef

13.

Wanichayapong, N., Pruthipunyaskul, W., Pattara-Atikom, W., Chaovalit, P.: Social-based traffic information extraction and classification. In: International Conference on ITS Telecommununications, pp. 107–112 (2011). https://doi.org/10.1109/itst.2011.6060036

14.

Chaniotakis, E., Antoniou, C., Aifadopoulou, G., Dimitriou, L.: Inferring activities from social media data. In: 96th Annual Meet Transportation Research Board, pp. 1–8 (2016). https://doi.org/10.3141/2666-04

15.

Chaniotakis, E., Antoniou, C.: Use of geotagged social media in urban settings: empirical evidence on its potential from Twitter. In: IEEE Conference on Intelligent Transport System Proceedings, ITSC 2015, pp. 214–219 (2015). https://doi.org/10.1109/itsc.2015.44

16.

Sinha, M., Varma, P., Sivakumar, G., Singh, M., Mukherjee, T., Chander, D., Dasgupta, K.: Improving urban transportation through social media analytics. In: Proceedings of the 3rd IKDD Conference on Data Science, CODS 2016, pp. 1–2 (2016)

17.

Chaniotakis, E., Antoniou, C., Mitsakis, E.: Data for leisure travel demand from social networking services. In: Heart Conference (2015)

18.

Twitter API. https://developer.twitter.com/. Accessed 21 Jan 2018

19.

Efthymiou, D., Antoniou, C.: Use of social media for transport data collection. Procedia Soc. Behav. Sci. 48, 775–785 (2012). https://doi.org/10.1016/j.sbspro.2012.06.1055CrossRef

20.

Cottrill, C., Gault, P., Yeboah, G., Nelson, J.D., Anable, J., Budd, T.: Tweeting transit: an examination of social media strategies for transport information management during a large event. Transp. Res. Part C Emerg. Technol. 77, 421–432 (2017). https://doi.org/10.1016/j.trc.2017.02.008CrossRef

21.

The R Project for Statistical Computing. https://www.r-project.org/. Accessed 21 Jan 2018

22.

TwitterXML. https://twitterxml.codeplex.com/. Accessed 21 Jan 2018

23.

TwitterXML BlogSpot. http://twitterxml.blogspot.gr/. Accessed 21 Jan 2018

24.

Toumpalidis, I.: Physical Spaces and Digital Flows: Navigating through the Informational Matrix, A dissertation submitted in partial fulfillment of the requirements for the degree of Master of Research of Spatial Data Science and Visualisation. The Bartlett Centre for Advanced Spatial Analysis University College London (2017)

25.

Toumpalidis, I., Karanikolas, N.: Spatial Data Mining from Social Media Services. Aristotle University of Thessaloniki (2015)

26.

Toumpalidis, I., Karanikolas, N.: Spatial Data Analysis from Social Media Services, Research study, School of Planning and Development. Aristotle University of Thessaloniki (2015)

27.

Grau, J.M.S., Toumpalidis, I., Chaniotakis, E., Karanikolas, N., Aifadopoulou, G.: A spatio-temporal correlation between digital and physical world, case study in Thessaloniki (under review)

28.

Grau, J.M.S., Chaniotakis, E., Toumpalidis, I., Karanikolas, N., Aifadopoulou, G.: Big data for transportation analysis and trip generation (under review)

29.

Facebook API. https://developers.facebook.com/. Accessed 21 Jan 2018

30.

Cambridge Dictionary. https://dictionary.cambridge.org/dictionary/english/trend. Accessed 21 Jan 2018

Titel: The Use of Social Computing in Travelers’ Activities Preference Analysis
verfasst von: Charis Chalkiadakis
Panagiotis Iordanopoulos
Evangelos Mitsakis
Eleni Chalkia
Verlag: Springer International Publishing
Buch: Data Analytics: Paving the Way to Sustainable Urban Mobility
Print ISBN: 978-3-030-02304-1

Electronic ISBN: 978-3-030-02305-8

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-02305-8_37

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