Social Media Use during Unplanned Transit Network Disruptions: A Review of Literature

References

• Association of Train Operating Companies. (2012, March). Approved code of practice — passenger information during disruption.London: Association of Train Operating Companieso.
   Google Scholar
• Awazu, Y., & Desouza, K. (2004). Open knowledge management: Lessons from the open source revolution. Journal of the American Society for Information Science and Technology, 55(11), 1016–1019. doi: 10.1002/asi.20050
   Web of Science ®Google Scholar
• Blainey, S., Hickford, A., & Preston, J. (2012). Barriers to passenger rail use: A review of the evidence. Transport Reviews, 32(6), 675–696. doi: 10.1080/01441647.2012.743489
   Web of Science ®Google Scholar
• Bonsall, P. (2005). Stimulating modal shift. In K. Button & D. Hensher (Eds.), Handbook of transport strategy, policy and institutions (pp. 613–634). Oxford: Elsevier.
   Google Scholar
• Boyd, A., Maier, P., & Caton, J. (1998). Critical incident management guidelines (Report FTA-MA-26–7009–98–1). Washington, DC.
   Google Scholar
• Boyd, D., & Ellison, N. (2007). Social network sites: Definition, history and scholarship. Journal of Computer-Mediated Communication, 13(1), 210–230. doi: 10.1111/j.1083-6101.2007.00393.x
   Web of Science ®Google Scholar
• Brabham, D. (2012). Motivations for participation in a crowdsourcing application to improve public engagement in transit planning. Journal of Applied Communication Research, 40(3), 307–328. doi: 10.1080/00909882.2012.693940
   Web of Science ®Google Scholar
• Bregman, S. (2012). Uses of social media in public transportation. Transit cooperative research program (TCRP) synthesis 99. Washington, DC: Transportation Research Board.
   Google Scholar
• Britton, N. (1989). Anticipating the unexpected: Is the bureaucracy able to come to the dance? Working Paper. Sydney, Australia: Cumberland College of Health Sciences, Disaster Management Studies Centre.
   Google Scholar
• Bruns, A. (2011). Tracking crises on Twitter: Analysing #qldfloods and #eqnz. Canberra: Emergency Media and Public Affiars Conference.
   Google Scholar
• Budd, L., & Vorley, T. (2013). Airlines, apps and business travel: A critical examination. Research in Transportation Business and Management, 9, 41–49. doi: 10.1016/j.rtbm.2013.08.004
   Google Scholar
• Chan, R., & Schofer, J. (2014). The role of social media in communicating transit disruptions. Washington, DC: Transportation Research Board (TRB) — 93rd Annual Meeting.
   Google Scholar
• Cheng, Y. (2010). Exploring passenger anxiety associated with train travel. Transportation, 37, 875–896. doi: 10.1007/s11116-010-9267-z
   Web of Science ®Google Scholar
• Codina, E., Marin, A., & Lopez, F. (2013). A model for setting services on auxiliary bus lunes under congestion. TOP — Spanish Society of Statistics and Operations Research, 21, 48–83.
   Google Scholar
• Collins, C., Hasan, S., & Ukkusuri, S. (2012). A novel transit riders’ satisfaction metric: Riders’ sentiments measured from online social media data. Washington, DC: Transportation Research Board 91st Annual Meeting.
   Google Scholar
• Comfort, L. (2007). Crisis management in hindsight: Cognition, communication, coordination and control. Public Administration Review, 671(1), 189–197. doi: 10.1111/j.1540-6210.2007.00827.x
   Web of Science ®Google Scholar
• Constantinides, E., & Fountain, S. (2008). Web 2.0: Conceptual foundations and marketing issues. Journal of Direct, Data and Digital Marketing Practice, 9, 231–244. doi: 10.1057/palgrave.dddmp.4350098
   Google Scholar
• Currie, G., & Rose, J. (2008). Growing patronage: Challenges and what has been found to work. Research in Transportation Economics, 22, 5–11. doi: 10.1016/j.retrec.2008.05.003
   Google Scholar
• De-Los-Santos, A., Laporte, G., Mesa, J., & Perea, F. (2012). Evaluating passenger robustness in a rail transit network. Transportation Research Part C, 20(1), 34–46. doi: 10.1016/j.trc.2010.09.002
   Web of Science ®Google Scholar
• Dutzik, T. (2013). A new direction: Our changing relationship with driving and the implications for America's future. Santa Barbara: US Education Fund and Frontier Group.
   Google Scholar
• Dynes, R. (1985). Organized behaviour in disaster. Delaware: Disaster Research Center, University of Delaware.
   Google Scholar
• Elizer, R., Squier, D., Brydia, R., & Beaty, C. (2012). ACRP report 70: Guidebook for implementing intelligent transportation systems elements to improve airport traveler access information. Washington, DC: T. R. B. o. t. N. Academies.
   Google Scholar
• Evans-Cowley, J. S., & Griffin, G. (2012). Micro-participation: Community engagement in transportation planning with social media. Washington, DC: Transportation Research Board 91st Annual Meeting.
   Google Scholar
• Fischer, H. (1998). Response to disaster: Fact versus fiction and perpetuation (2nd ed.). New York, NY: University Press of America.
   Google Scholar
• Fouarce, P., Dunkerley, C., & Gardner, G. (2003). Mass rapid transit systems for cities in the developing world. Transport Reviews, 23(3), 299–310. doi: 10.1080/0144164032000083095
   Web of Science ®Google Scholar
• Gao, H., Barbier, G., & Goolsby, R. (2011). Harnessing the crowdsourcing power of social media for disaster relief. IEE Proceedings Intelligent Transportation Systems, 26(3), 10–14.
   Web of Science ®Google Scholar
• Giannopoulos, G. (2004). The application of information and communication technologies in transport. European Journal of Operational Research, 152, 302–320. doi: 10.1016/S0377-2217(03)00026-2
   Web of Science ®Google Scholar
• Goodchild, M., & Glennon, J. (2010). Crowdsourcing geographic information for disaster response: A research frontier. International Journal of Digital Earth, 3(3), 231–241. doi: 10.1080/17538941003759255
   Web of Science ®Google Scholar
• Hagar, C. (2006). Using research to aid the design of a crisis information management course. ALISE Annual Conference SIG Multicultural, Ethnic and Humanistic Concerns (MEH). Informatin Seeking and Service Delivery for Communities in Disaster/Crisis, San Antonio.
   Google Scholar
• Hagar, C. (2013). Crisis informatics: Perspective of trust — is social media a mixed blessing? SLIS Student Research Journal, 2(2). Retrieved from http://scholarworks.sjsu.edu/slissrj/vol2/iss2/2
   Google Scholar
• Harazeen, S. (2011). Effects of information on decisions of passengers during service disruptions (MSc Civil Engineering). London: University College London.
   Google Scholar
• Hendler, J., & Golbeck, J. (2008). Metcalfe's law, Web 2.0 and the semantic Web. Web Semantics: Science, Services and Agents on the World Wide Web, 6(1), 14–20. doi: 10.1016/j.websem.2007.11.008
   Web of Science ®Google Scholar
• Heverin, T., & Zach, L. (2010). Microblogging for crisis communication: Examination of Twitter use in response to a 2009 Violent Crisis in Seattle-Tacoma, Washington Area. Seattle: Information Systems for Crisis Response and Management Conference.
   Google Scholar
• Hossman, T., Legendre, F., Carta, P., Gunningberg, P., & Rohner, C. (2011). Twitter in disaster mode: Opportunistic communication and distribution of sensor data in emergencies. ExtremeCom2011, Manaus, Brazil.
   Google Scholar
• Howe, J. (2006). Crowdsourcing: A definition. Crowdsourcing: Tracking the Rise of the Amateur (weblog, 2 June). Retrieved from http://crowdsourcing.typepad.com/cs/2006/06/crowdsourcing_a.html
   Google Scholar
• Hughes, A., & Palen, L. (2009). Twitter adoption and use in mass convergence and emergency events. International Journal of Emergency Management, 6, 248–260. doi: 10.1504/IJEM.2009.031564
   Google Scholar
• Hughes, A., & Palen, L. (2012). The evolving role of the public information officer: An examination of social media in emergency management. Journal of Homeland Security and Emergency Management, 9(1), 1–20. doi: 10.1515/1547-7355.1976
   Web of Science ®Google Scholar
• Janarthanan, N., & Schneider, J. (1984). Computer aided design as applied to transit system emergency contingency planning. Computer Environment Urban Systems, 9(1), 32–52. doi: 10.1016/0198-9715(84)90004-8
   Web of Science ®Google Scholar
• Java, A., Song, X., Finin, T., & Tseng, B. (2007). Why we Twitter: Understanding microblogging usage and communities. Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis San Jose, California.
   Google Scholar
• Keim, M., & Noji, E. (2011). Emergent use of social media: A new age of opportunity for disaster resilience. American Journal of Disaster Medicine, 6(1), 47–54.
   PubMedGoogle Scholar
• Kepaptsoglou, K., & Karlaftis, M. (2009). The bus bridging problem in metro operations: Conceptual framework, models & algorithms. Public Transport, 1(4), 275–297. doi: 10.1007/s12469-010-0017-6
   Google Scholar
• Kepaptsoglou, K., & Karlaftis, M. (2010). A model for analyzing metro station platform condition following a service disruption. Intelligent Transport Systems (ITSC) 13th International IEEE, Madeira Island, Portugal, 189–1794.
   Google Scholar
• Krishnamurthy, B., Gill, P., & Arlitt, M. (2008). A few chirps about Twitter. Proceedings of the First Workshop on Online Social Networks Seattle, Washington.
   Google Scholar
• Kruger, L., & Shannon, M. (2000). Getting to know ourselves and our places through participation in civic social assessment. Society and Natural Resources, 13, 461–478. doi: 10.1080/089419200403866
   Web of Science ®Google Scholar
• Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a social network or news media? Raleigh, North California: World Wide Web Conference.
   Google Scholar
• Lane, B. (2008). Significant characteristics of the urban rail renaissance in the United States: A discriminant analysis. Transportation Research Part A, 42, 279–295.
   Google Scholar
• Levy, P. (1997). Collective intelligence: Mankind's emerging world in cyberspace. New York, NY: Plenum.
   Google Scholar
• Lindsay, B. (2011). Social media and disasters: Current uses, future options and policy considerations. Washington, DC: Congressional Research Service Report for Congress.
   Google Scholar
• Lyons, G. (2006). The role of information in decision-making with regard to travel. IEE Proceedings Intelligent Transportation Systems, 153(3), 199–212. doi: 10.1049/ip-its:20060001
   Google Scholar
• May, A. (2006). First informers in the disaster zone: The lessons of Katrina. Washington, DC: Aspen Institute.
   Google Scholar
• Molin, E., & Timmermans, H. (2006). Traveler expectations and willingness-to-pay for web-enabled public transport information services. Transportation Research Part C, 14, 57–67. doi: 10.1016/j.trc.2006.05.003
   Web of Science ®Google Scholar
• Nakanishi, Y., Kim, K., Ulusoy, Y., & Bata, A. (2003). Assessing emergency preparedness of transit agencies: Focus on performance indicators. Transportation Research Record: Journal of the Transportation Research Board, 1822, 24–32. doi: 10.3141/1822-04
   Google Scholar
• Nielsen, L., Kroon, L., & Maroti, G. (2012). A rolling horizon approach for disruption management of railway rolling stock. European Journal of Operational Research, 220, 496–509. doi: 10.1016/j.ejor.2012.01.037
   Web of Science ®Google Scholar
• O'Dell, S., & Wilson, N. (1997). Optimal real-time control strategies for rail transit operations during disruptions. In N. Wilson (Ed.), Lecture notes in economics and mathematical systems (pp. 299–324). Cambridge: Sprinfield.
   Google Scholar
• Outlook Research Limited. (2012). Short and Tweet — how passengers want social media during disruption. London, for Passenger Focus and Abellio, UK.
   Google Scholar
• Palen, L., & Liu, S. (2007a). Citizen communications in crisis: Anticipating a future of ICT-supported participation. Proceedings of the ACM Conference on Human Factors in Computing Systems CHI 2007, San Jose, California, ACM Press.
   Google Scholar
• Palen, L., & Liu, S. (2007b). Citizen communications in crisis: Anticipating a future of ICT-supported public participation. San Jose: ACM 2007 Conference on Human Factors in Computing Systems.
   Google Scholar
• Palen, L., Vieweg, S., Liu, S., & Hughes, A. (2009). Crisis in a networked world: Features of computer-mediated communication in the April 16 2007, Virginia tech event. Social Science Computer Review, 27(4), 467–480. doi: 10.1177/0894439309332302
   Web of Science ®Google Scholar
• Passenger Focus. (2006). Passenger focus response to department for transport proposals for the east midlands franchise. London: Passenger Focus.
   Google Scholar
• Passenger Focus. (2011). Information: Rail passengers needs during unplanned disruption — qualitative research. London. Retrieved July 13, 2012, from http://www.passengerfocus.org.uk/media/dca7e0b50f3ad90e6475953c74997ea3c4b0fa59/pf_unplanned_disruption_report__final.pdf
   Google Scholar
• Pender, B., Currie, G., Delbosc, A., & Shiwakoti, N. (2013). Social media utilisation during unplanned passenger rail disruption – What's not to “like”?Australasian Transport Research Forum, Brisbane, Australia.
   Google Scholar
• Pender, B., Currie, G., Delbosc, A., & Wang, Y. (2012). Pro-active recovery from rail disruptions through provision of track crossovers and bus bridging. Transportation Research Record: Journal of the Transportation Research Board, 2275(2), 68–76. doi: 10.3141/2275-08
   Web of Science ®Google Scholar
• Perng, S., Buscher, M., Halvorsrud, R., Wood, L., Stiso, M., Ramirez, L., & Al-Akkad, A. (2012). Peripheral response: Microblogging during the 22/7/2011 Norway attacks. Vancouver: Information Systems for Crisis Response and Management Conference.
   Google Scholar
• Procopio, C., & Procopio, S. (2007). Do you know what it means to miss New Orleans? Internet communication, geographical community and social capital in crisis. Journal of Applied Communication Research, 35(1), 67–87. doi: 10.1080/00909880601065722
   Web of Science ®Google Scholar
• Qu, Y., Huang, C., Zhang, P., & Zhang, J. (2011). Microblogging after a major disaster in China: A case study of the 2010 Yushu Earthquake. Hangzhou: ACM 2011 Conference on Computer Supported Cooperative Work.
   Google Scholar
• Qu, Y., Wu, P., & Wang, X. (2009). Online community response to major disaster: A study of Tianya Forum in the 2008 Sichuan Earthquake. 42nd Hawaii International Conference on System Sciences, 1–11.
   Google Scholar
• Racine, N., & Bregman, S. (2014). From hurricanes to Carmageddon: Social media for real-time communication. In S. Bregman & K. Watkins (Eds.), Best practices for transportation agency use of social media (pp. 109–131). Boca Raton, FL: CRC Press — Taylor & Francis Group.
   Google Scholar
• Rogers, A. (2013). Airline apps-room for improvement. Retrieved May 13, 2013, from www.flightviewfromthesky.com
   Google Scholar
• Sakaki, T., Okazaki, M., & Matsuo, Y. (2010). Earthquake shakes Twitter users: Real-time event detection by social sensors. Raleigh, North California: World Wide Web Conference.
   Google Scholar
• Schmocker, J.-D., Cooper, S., & Adeney, W. (2005). Metro service delay recovery: Comparison of strategies and constraints across systems. Journal of Transportation Research Board, 1930, 30–37. doi: 10.3141/1930-04
   Google Scholar
• Seeger, M., Vennette, S., Ulmer, R., & Sellnow, T. (Eds.). (2002). Media use, information seeking and reported needs in post crisis contexts. Communication and Terrorism: Public and Media Responses to 9/11, Cresskill, NJ, Hampton Press.
   Google Scholar
• Slater, D., Keiko, N., & Kindstrand, L. (2011). Social media in disaster Japan. In J. Kingston (Ed.), Natural disaster and nuclear crisis in Japan (pp. 94–108). Oxford: Routledge.
   Google Scholar
• Smith, R., Atkins, S., & Sheldon, R. (1994). London transport buses: ATT in action and the London countdown route 18 project. First World Congress on Applications of Transport Telematics and Intelligent Vehicle-Highway Systems, Paris, France.
   Google Scholar
• Sreenivasan, N., Lee, C., & Goh, D. (2011). Tweet me home: Exploring information use on Twitter in crisis situations. Online Communities and Social Computing: Lecture Notes in Computer Science, 6778, 120–129. doi: 10.1007/978-3-642-21796-8_13
   Google Scholar
• Stambaugh, C. (2013). Social media and primary commercial service airports. Transportation Research Record, 2235, 76–86. doi: 10.3141/2325-08
   Web of Science ®Google Scholar
• Starbird, K., Palen, L., Liu, B., Vieweg, S., Hughes, A., Schram, A., … Schenk, C. (2012). Promoting structured data in citizen communications during disaster response: An account of strategies for diffusion of the “Tweak the Tweet” syntax. In C. Hagar (Ed.), Crisis information management: Communication and technologies (pp. 43–61). Oxford: Chandos Publishing.
   Google Scholar
• Steinfeld, A., Maisel, J., & Steinfeld, E. (2009). The value of citizen science to promote transit accessibility. Pittsburgh: First International Symposium on Quality of Life Technology.
   Google Scholar
• Steinfeld, A., Zimmerman, J., Tomasic, A., Yoo, D., & Dar Aziz, R. (2011). Mobile transit information from universal design and crowdsourcing. Transportation Research Record, 2217, 95–102. doi: 10.3141/2217-12
   Web of Science ®Google Scholar
• Surowiecki, J. (2004). The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies and nations. New York, NY: Doubleday.
   Google Scholar
• Sutton, J., Palen, L., & Shlklovski, I. (2008). Backchannels on the front lines: Emergent use of social media in the 2007 California wildfires. The Netherlands: Information Systems for Crisis Response and Management.
   Google Scholar
• Tierney, K., Lindell, M., & Perry, R. (2001). Facing the unexpected: Disaster preparedness and response in the United States. Washington, DC: John Henry Press.
   Google Scholar
• Tierney, K., & Quarantelli, E. (1989). Needed innovation in the delivery of emergency medical services in disasters: Present and future. Disaster Management, 2(2), 70–76.
   Google Scholar
• Tinker, T., & Vaughan, E. (2012). Risk and crisis communications: Best practices for government agencies and non-profit organisations. McLean: Booz Allen Hamilton.
   Google Scholar
• Tornquist, J. (2012). Design of an effective algorithm for fast response to re-scehduling of railway traffic during distrurbances. Transportation Research Part C, 20, 62–78. doi: 10.1016/j.trc.2010.12.004
   Web of Science ®Google Scholar
• Tsuchiya, R., Sugiyama, Y., Yamauchi, K., Fujinami, K., Arisawa, R., & Nakagawa, T. (2006). Route-choice support system for passengers in the face of unexpected disturbance of train operations. In J. Allan (Ed.), Computers in railways X: Computer system design and operation in the railways and other transit systems (pp. 189–197). Boston: WIT Press.
   Google Scholar
• Van de Walle, B., & Turoff, M. (2007). Emergency response information systems: Emerging trends and technologies. Communications of the Association for Computing Machinery, 50(3), 29–31. doi: 10.1145/1226736.1226760
   Web of Science ®Google Scholar
• Viewag, S., Hiughes, A., Starbird, K., & Palen, L. (2010). Microblogging during two natural hazards events: What Twitter may contribute to situational awareness. Atlanta: Conference on Human Factors in Computing Systems.
   Google Scholar
• Vuchic, V. (2005). Urban transit: Operations, planning and economics. Hoboken, NJ: John Wiley & Sons Inc.
   Google Scholar
• Whitelaw, G., Vaughan, H., Craig, B., & Atkinson, D. (2003). Establishing the Canadian community monitoring network. Environmental Monitoring and Assessment, 88, 409–418. doi: 10.1023/A:1025545813057
   PubMed Web of Science ®Google Scholar
• Wright, K. (2002). Social support within an on-line cancer community: An assessment of emotional support, perceptions of advantages and disadvantages and motives for using the community from a communication perspective. Journal of Applied Communication Research, 30, 195–209. doi: 10.1080/00909880216586
   Web of Science ®Google Scholar
• Yates, D., & Paquette, S. (2011). Emergency knowledge management and social media technologies: A case study of the 2010 Haitian Earthquake. International Journal of Information Management, 31, 6–13. doi: 10.1016/j.ijinfomgt.2010.10.001
   Web of Science ®Google Scholar
• Yates, D., Wagner, C., & MAjchrzak, A. (2010). Factors affecting shapers of organisational Wikis. Journal of the American Society for Information Science and Technology, 61(3), 543–554.
   Web of Science ®Google Scholar