Transport Policy: Social Media and User-Generated Content in a Changing Information Paradigm

  • S. M. Grant-MullerEmail author
  • A. Gal-Tzur
  • E. Minkov
  • T. Kuflik
  • S. Nocera
  • I. Shoor


Rapid and recent developments in social media networks are providing a vision amongst transport suppliers, governments and academia of ‘next-generation’ information channels. This chapter identifies the main requirements for a social media information harvesting methodology in the transport context and highlights the challenges involved. Three questions are addressed concerning (1) The ways in which social media data can be used alongside or potentially instead of current transport data sources, (2) The technical challenges in text mining social media that create difficulties in generating high quality data for the transport sector and finally, (3) Whether there are wider institutional barriers in harnessing the potential of social media data for the transport sector. The chapter demonstrates that information harvested from social media can complement, enrich (or even replace) traditional data collection. Whilst further research is needed to develop automatic or semi-automatic methodologies for harvesting and analysing transport-related social media information, new skills are also needed in the sector to maximise the benefits of this new information source.


Social media Transport planning Transport policy Text mining 


  1. 1.
    AASHTO (2012, September). Third annual state DOT social media survey.
  2. 2.
    Aggarwal, C. C., & Zhai C. -X. (2012). Mining text data. Berlin: Springer.Google Scholar
  3. 3.
    Amitay, E., Har’El, N., Sivan, R., & Soffer, A. (2004). Web-a-Where: “Geotagging Web Content”. In SIGIR ’04 Proceedings of the 27th Annual International ACM SIGIR Conference on Research and development in Information Retrieval (pp. 273–280).Google Scholar
  4. 4.
    Austin, J. (2013). Use of social networking to promote public transport and sustainable travel.$26+Sustainable+Travel.pdf. Accessed August 1, 2013.
  5. 5.
    Barron, E., Peck, S., Venner, M., & Malley, W. G. (2013, September). Suggested practices guidance resource. NCHRP 25–25 TASK 80.Google Scholar
  6. 6.
    Becker, M., & Smith, S. F. (1997). An ontology for multi-modal transportation planning and scheduling. Technical report CMU-RI-TR-98–15, Robotics Institute, Carnegie Mellon University.Google Scholar
  7. 7.
    Bickerstaff, K., & Walker, G. (2001). Participatory local governance and transport planning. Environment and Planning A, 33(3), 431–452.CrossRefGoogle Scholar
  8. 8.
    Bie, J., Bijlsma, M., Broll, G., et al. (2012). Move better with tripzoom. International Journal on Advances in Life Sciences, 4, 125–135.Google Scholar
  9. 9.
    Bollacker, K., Evans, C., Paritosh, P., Sturge, T., & Taylor, J. (2008). Freebase: A collaboratively created graph database for structuring human knowledge. In Proceedings of the ACM SIGMOD International Conference on Management of Data, Vancouver, BC, Canada (pp. 1247–1250). ACM (2008). ISBN 978-1-60558-102-6.Google Scholar
  10. 10.
    Bollen, J., Pepe, A., & Mao, H. (2011a). Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (ICWSM) (pp. 450–453), Barcelona, Spain, July 17–21.Google Scholar
  11. 11.
    Bollen, J., Mao, H., & Zeng, X. J. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2, 1–8.CrossRefGoogle Scholar
  12. 12.
    Bregman, S. (2012). Uses of Social Media in Public Transportation, TCRP SYNTHESIS 99.Google Scholar
  13. 13.
    Bry, F., Lorenz, B., Ohlbach, H.J., & Rosner, M. (2005). A geospatial world model for the semantic web. In Principles and Practice of Semantic Web Reasoning, Vol. 3703. Lecture Notes in Computer Science (pp. 145–159). Berlin: Springer.Google Scholar
  14. 14.
    Caceres, N., Romero, L. M., Benitez, F. G., & del Castillo, J. M. (2012). Traffic flow estimation models using cellular phone data. IEEE Transactions on Intelligent Transportation Systems, 13(3), 1430–1441.CrossRefGoogle Scholar
  15. 15.
    Carrasco, J. A., Hogan, B., Wellman, B., & Miller, E. J. (2008). Collecting social network data to study social activity-travel behavior: An egocentric approach. Environment and Planning B: Planning and Design, 35(6), 961–980.CrossRefGoogle Scholar
  16. 16.
    Castells, M. (2011). The power of identity: The information age: Economy, society, and culture, Vol. 2. Wiley-Blackwell.Google Scholar
  17. 17.
    Chen, M., Jin, X., & Shen, D. (2011). Short text classification improved by learning multi-granularity topics. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI).Google Scholar
  18. 18.
    Cheng, Z., Caverlee, J., & Lee, K. (2010). You are where you tweet: A content-based approach to geo-locating twitter users. In Proceeding of CIKM ’10 Proceedings of the 19th ACM International Conference on Information and Knowledge Management (pp. 759–768). New York.Google Scholar
  19. 19.
    Chenliang, L., Weng, J., He, Q. et al. (2012). TwiNER: named entity recognition in targeted twitter stream. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval.Google Scholar
  20. 20.
    Cho, S., Kang, J. Y., Yasar, A., Luk, Knapen L., Bellemans, T., Janssens, D., et al. (2013). An activity-based carpooling microsimulation using ontology. Procedia Computer Science, 2013(19), 48–55.CrossRefGoogle Scholar
  21. 21.
    Chung, J., & Mustafaraj, E. (2011). Can collective sentiment expressed on twitter predict political elections? In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence. San Francisco, CA, USA (pp. 1770–1771).Google Scholar
  22. 22.
    Collins, C., Hasan, S., & Ukkusuri, S. V. (2013). A novel transit rider satisfaction metric: Rider sentiments measured from online social media data. Journal of Public Transportation, 16(2), 21–45.CrossRefGoogle Scholar
  23. 23.
    Corley, C., Cook, D., Mikler, A., & Singh, K. (2010). Text and structural data mining of influenza mentions in web and social media. International Journal of Environmental Research and Public Health, 7(2), 596–615.CrossRefGoogle Scholar
  24. 24.
    Cotey, A. (2011). Social media: Transit agencies connect with riders in new ways. Progressive Railroading, January 2011.–25447
  25. 25.
    Davidov, D., Sur, O., & Rappoport, A. (2010). Semi-supervised recognition of sarcastic sentences in Twitter and Amazon. In Proceedings of the Fourteenth Conference on Computational Natural Language Learning (pp. 107–116). Uppsala, Sweden.Google Scholar
  26. 26.
    Davis Jr, C. A., Pappa, G. L., de Oliveira, D. R. R., & de L Arcanjo, F. (2011). Inferring the location of twitter messages based on user relationships. Transactions in GIS, 15 (6), 735–751.Google Scholar
  27. 27.
    Denecke, K., & Nejdi, W. (2009). How valuable is medical social media data? Content analysis of the medical web. Information Sciences, 179(12), 1870–1880.CrossRefGoogle Scholar
  28. 28.
    DuBose, C. (2011). The social media revolution. Radiologic Technology, 83(2), 112–119.Google Scholar
  29. 29.
    Eboli, L., & Mazzulla, G. (2012). Performance indicators for an objective measure of public transport service quality. European Transport/Trasporti Europei, 51, 1–21.Google Scholar
  30. 30.
    Efthymiou, D. & Antoniou, C. (2012). Use of social media for transport data collection. Procedia—Social and Behavioral Sciences, Vol. 48, pp. 775–785. doi: ISSN 1877-0428.
  31. 31.
    Eisenstein, J., O’Connor, B., Smith, N. A., & Xing, E. P. (2010). A latent variable model for geographic lexical variation. In Proceedings of Empirical Methods in Natural Language Processing, Stroudsburg, PA, USA, pp. 1277–1287.Google Scholar
  32. 32.
    European Commission. (2001). A sustainable Europe for a better world: A European Union strategy for sustainable development. Belgium: Brussels.Google Scholar
  33. 33.
    Gal-Tzur, A., Grant-Muller, S. M., Minkov, E., & Nocera, S. (2014). The impact of social media usage on transport policy: Issues, challenges and recommendations. Procedia—Social and Behavioral Science, 111, 937–946.CrossRefGoogle Scholar
  34. 34.
    Gal-Tzur, A., Grant-Muller, S. M., Kuflik, T., Minkov, E., Nocera, S., & Shoor, I. (2014). The potential of social media in delivering transport policy goals. Transport Policy, 32, 115–123.CrossRefGoogle Scholar
  35. 35.
    Gao, L., & Wu, H. (2013). Verb-Based Text Mining of Road Crash Report, TRB 92nd Annual Meeting.Google Scholar
  36. 36.
    Gao, L., Zhang, Z., & Wu, H. (2013b). Analyzing the Use of Facebook Page Among State DOTs. In TRB 92nd Annual Meeting Compendium of Papers.Google Scholar
  37. 37.
    Giannopoulos, G. A. (2004). The application of information and communication technologies in transport. European Journal of Operational Research, 152(2), 302–320.zbMATHCrossRefGoogle Scholar
  38. 38.
    Grant-Muller, S. M., Gal-Tzur, A., Minkov, E., Nocera, S., Kuflik, T., & Shoor, I. (2014a). Enhancing transport data collection through social media sources: Methods, challenges and opportunities for textual data. IET Intelligent Transport Systems. doi: 10.1049/iet-its.2013.0214.
  39. 39.
    Grant-Muller, S. M., & Usher, M. (2013). Intelligent transport systems: The propensity for environmental and economic benefits. Technological Forecasting and Social Change. doi: 10.1016/j.techfore.2013.06.010 Google Scholar
  40. 40.
    Grosenick, S. (2012). Real-time traffic prediction improvement through semantic mining of social networks. Thesis (Master’s)—University of Washington. url:
  41. 41.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: An update. SIGKDD Explorations, 11(1), 10–18.CrossRefGoogle Scholar
  42. 42.
    Houda, M., Khemaja, M., Oliveira, K., & Abed, M. (2010). A public transportation ontology to support user travel planning. In Proceedings of the Fourth International Conference on Research Challenges in Information Science (RCIS) (pp. 127–136). Nice, France.Google Scholar
  43. 43. (2013). Common Highways Agency Rijkswaterstaat Model (CHARM) (online). Available at:–4287-84e9-c00d266a15b3. Accessed 11 Dec 2013.
  44. 44.
    Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of SocialMedia, Business Horizons, 53(1), 59–68.Google Scholar
  45. 45.
    Kaur, A., & Gupta, V. (2013). A survey on sentiment analysis and opinion mining techniques. Journal of Emerging Technologies in Web Intelligence, Vol. 5, No. 4, November 2013.Google Scholar
  46. 46.
    Kenyon, S., & Lyons, G. (2003). The value of integrated multimodal traveller information and its potential contribution to modal change. Transportation Research Part F: Traffic Psychology and Behaviour, 6(1), 1–21.CrossRefGoogle Scholar
  47. 47.
  48. 48.
    Khanwalkar, S., Seldin, M., Srivastava, A., Kumar, A., & Colbath, S. (2013). Content-based geo-location detection for placing tweets pertaining to trending news on map. In 4th International Workshop on Mining Ubiquitous and Social Environments (MUSE), Prague, Czech Republic, September 2013.Google Scholar
  49. 49.
    Lanagan, J., & Smeaton, A. F. (2011). Using twitter to detect and tag important events in live sports. In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (ICWSM).Google Scholar
  50. 50.
    Leetaru, K., Wang, S., Cao, G., Padmanabhan, A., & Shook, E. (2013). Mapping the global Twitter heartbeat: The geography of Twitter. First Monday, 18 (5), doi: 10.5210/fm.v18i5.4366
  51. 51.
    Li, L., Wu, W., & Liu, N. (2013). Ontology model for situation awareness of city tunnel traffic. In Proceedings of the 2nd International Symposium on Computer, Communication, Control and Automation (ISCCCA-13) (pp. 601–603). Paris, France: Atlantis Press.Google Scholar
  52. 52.
    Libardo, A., & Nocera, S. (2008). Transportation elasticity for the analysis of Italian transportation demand on a regional scale. Traffic Engineering and Control, 49(5), 187–192.Google Scholar
  53. 53.
    Liu, X., Lang, B., Yu, W., Lou, J., Huang, L. (2011). AUDR: An advanced unstructured data repository, pervasive computing and applications (ICPCA). In 2011 6th International Conference, Conference Publication, pp. 462–469.Google Scholar
  54. 54.
    Madkour, M., & Maach, A. (2011). Ontology-based context modeling for vehicle-aware services. Journal of Theoretical and Applied Information Technology, 34(2), 158–166.Google Scholar
  55. 55.
    Musakwa, W. (2014). The use of social media in public transit systems: the case of the Gautrain, Gauteng Province, South Africa: Analysis and Lessons Learnt. In Proceedings REAL CORP 2014 Tagungsband, 21–23 May 2014, Vienna, Austria.
  56. 56.
    Mai, E., & Hranac, R. (2013). Twitter Interactions as a Data Source for Transportation Incidents. In TRB 92nd Annual Meeting Compendium of Papers, 2013.Google Scholar
  57. 57.
    Manning, C., & Schütze, H. (1999). Foundations of statistical natural language processing. Cambridge, MA: MIT Press.zbMATHGoogle Scholar
  58. 58.
    Manning, C., Raghavan, P., & Schtze, H. (2008). Introduction to information retrieval. New York, USA: Cambridge university Press.zbMATHCrossRefGoogle Scholar
  59. 59.
    Mazzulla, G., & Forciniti, C. (2012). Spatial association techniques for analysing trip distribution in an urban area. European Transport Research Review, 4(4), 217–233.CrossRefGoogle Scholar
  60. 60.
    Metro. (2011). West Yorkshire LTP3 Network Management Plan, 2011. Accessed January 2015.
  61. 61.
    Minnesota Department of Transportation, Office of Policy Analysis (2011). Use of Social Media by Minnesota Cities and Counties. Transportation Research Synthesis.Google Scholar
  62. 62.
    Minkov, E., Wang, R. C., Cohen, W. W. (2005). Extracting personal names from email: applying named entity recognition to informal text. In Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 443–450.Google Scholar
  63. 63.
    Moss, M. L., & Kaufman, S. (2013). How Social Media Moves in New York—Final report. Accessed August 1, 2013.
  64. 64.
    Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: market-structure surveillance through text mining. Marketing Science, 31(3), 521–543.CrossRefGoogle Scholar
  65. 65.
    Niaraki, A. S., & Kim, K. (2009). Ontology based personalized route planning system using a multi-criteria decision making approach. Expert Systems with Applications, 36, 2250–2259.CrossRefGoogle Scholar
  66. 66.
    Nocera, S. (2010). An operational approach for quality evaluation in public transport services. Ingegneria Ferroviaria, 65(4), 363–383.Google Scholar
  67. 67.
    Nocera, S. (2011). The key role of quality assessment in public transport policy. Traffic Engineering and Control, 52(9), 394–398.Google Scholar
  68. 68.
    Nocera, S., & Cavallaro, F. (2011). Policy effectiveness for containing CO2 emissions in transportation. Procedia—Social and Behavioral Science, 20, 703–713.CrossRefGoogle Scholar
  69. 69.
    Nugroho, A. S., Endarnoto, S. K., Pradipta. S., & Purnama J. (2011). Traffic condition information extraction and visualization from social media twitter for android mobile application. In Proceedings of the International Conference on Electrical Engineering and Informatics (ICEEI).Google Scholar
  70. 70.
    O’Connor, B., Balasubramanyan, R., Routledge, B. R., & Smith, N. A. (2010). From tweets to polls: Linking text sentiment to public opinion time series. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (ICWSM).Google Scholar
  71. 71.
    Oppenheim, N. (1995). Urban travel demand modeling: From individual choices to general equilibrium. New York: Wiley.Google Scholar
  72. 72.
    Pak, A., & Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. Computer, 10, 1320–1326.Google Scholar
  73. 73.
    Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends Information Retrieval, 2(1–2), 1–135.CrossRefGoogle Scholar
  74. 74.
    Paradesi, S. (2011). Geotagging Tweets Using Their Content. In Proceedings of the Twenty-Fourth. International Florida Artificial Intelligence Research Society Conference, 2011 (pp. 335–356).Google Scholar
  75. 75.
    Pender, B., Currie, G., Delbosc, A., & Shiwakoti, N. (2014). Social Media Use in Unplanned Passenger Rail Disruptions—An International Study. IN TRB 93rd Annual Meeting, 2014.Google Scholar
  76. 76.
    Piskorski, J., & Yangarber, R. (2013). Information extraction: past, present and future. In Theory and Applications of Natural Language Processing (pp 23–49).Google Scholar
  77. 77.
    Priedhorsky, R., Culotta, A., Del Valle, S. Y. (2014). Inferring the origin locations of tweets with quantitative confidence. In Proceedings of the 17th ACM conference on Computer Suppostive Cooperative Work and Social Computing (CSCW), Baltimore, MD, Feb 15–19.Google Scholar
  78. 78.
    Ritter, A., Clark, S., Mausam, & Etzioni, O. (2011). Named entity recognition in tweets: an experimental study. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP).Google Scholar
  79. 79.
    Schulz, A., Ristoski, P., & Paulheim. H (2013). I see a car crash: Real-time detection of small scale incidents in microblogs. In P. Cimiano, M. Fernández, V. Lopez, S. Schlobach, J. Völker, (Eds.), The Semantic Web: ESWC 2013 Satellite Events, Vol. 7955. Lecture Notes in Computer Science, pp. 22–33. Berlin: Springer.Google Scholar
  80. 80.
    Schweitzer, L. (2012). How are we doing? Opinion mining customer sentiment in US transit agencies and airlines via twitter. In TRB 91th Annual Meeting.Google Scholar
  81. 81.
    Shepherd, P. A. (2013). The transportation world should embrace social media… carefully. In Eno Center of Transportation. Accessed August 1, 2013.
  82. 82.
    Sood, S., Owsley, S., Hammond, K., & Birnbaum, L. (2007). Reasoning through search: A novel approach to sentiment classification. In WWW2007, North Western University, Electrical Engineering and Computer Science Department Technical Report NWU-EECS-07-05, Banff, Canada, July 21, 2007. Accessed July 7th, 2013.
  83. 83.
    Steiger, E., Ellersiek, T., Zipf, A. (2014). Explorative public transport flow analysis from uncertain social media data. In GeoCrowd ’14 Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Crowd sourced and Volunteered Geographic Information (pp. 1–7).Google Scholar
  84. 84.
    Steinwart, I., & Christmann, A. (2008). Support vector machines. New York: Springer.zbMATHGoogle Scholar
  85. 85.
    Sterne, J. (2010). Social media metrics: How to measure and optimize your marketing investment, Scholar
  86. 86.
    Tapscott, D., Williams, A. D., & Herman, D. (2013). Government 2.0: Transforming government and governance for the twenty-first century, New Paradigm, January 2008
  87. 87.
    Transportation Research Board [TRB] (2004). User information systems; developments and issues for the 21st century. In TRB Millennium Papers (Washington, DC: National Academy of Sciences).Google Scholar
  88. 88.
    Transportation Safety Board of Canada (2013). Social media terms of use. Accessed August 1, 2013.
  89. 89.
    Trappey, C., Wu, H. Y., & Liu K. L. (2012). Knowledge discovery of customer satisfaction and dissatisfaction using ontology-based text analysis of critical incident dialogues. In Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design, Wuhan, 2012 (pp. 470–475).Google Scholar
  90. 90.
    Virginia Department of Transportation (2013). VDOT on Social Media. Accessed August 1, 2013.
  91. 91.
    Wang, J., Ding, Z., & Jiang, C. (2005). An ontology-based public transport query system. In Proceedings of the First International Conference on semantics and Grid, SKG, 2005.Google Scholar
  92. 92.
    Wiegand, M., Balahur, A., Roth, B., Klakow, D., & Montoyo, A. (2010). A survey on the role of negation in sentiment analysis. In Proceedings of the Workshop on Negation and Speculation in Natural Language Processing (NeSp-NLP ’10), Association for Computational Linguistics (pp. 60–68). Stroudsburg, PA, USA, 2010.Google Scholar
  93. 93.
    Wilson, T., Wiebe, J., & Hoffmann, P. (2009). Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Computational Linguistics, 35(3), 399–433.CrossRefGoogle Scholar
  94. 94.
    Yang, W. D., & Wang, T. (2012). The fusion model of intelligent transportation systems based on the urban traffic ontology. Physics Procedia, 25, 917–923.CrossRefGoogle Scholar
  95. 95.
    Zimmer, C. G. (2012). Social media use in local public agencies: A study of California’s cities. Master Thesis, Department of Public Policy and Administration, California State University, Sacramento.Google Scholar
  96. 96.
    Grant-Muller, S. M., Gal-Tzur, A., Minkov, E., Nocera, S., Kuflik, T., & Shoor, I. (2014). Efficacy of mining social media data for transport policy and practice. In Proceedings of the 93th Transportation Research Board Meeting, paper no. 14–1716. Washington, D.C., USA, January 12–16, 2014.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • S. M. Grant-Muller
    • 1
    Email author
  • A. Gal-Tzur
    • 4
  • E. Minkov
    • 2
  • T. Kuflik
    • 2
  • S. Nocera
    • 3
  • I. Shoor
    • 5
  1. 1.Institute for Transport StudiesUniversity of LeedsLeedsUK
  2. 2.Department of Information SystemsUniversity of HaifaHaifaIsrael
  3. 3.Department of Architecture and ArtsIUAV University of VeniceVeniceItaly
  4. 4.Transportation Research InstituteTechnion - Israel Institute of TechnologyTechnion CityIsrael
  5. 5.Department of Computer ScienceUniversity of HaifaHaifaIsrael

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