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Locating an ambulance base by using social media: a case study in Bangkok

  • S.I.: Applications of OR in Disaster Relief Operations
  • Published:
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Abstract

Response time reduction is a fundamental aspect of ambulance location management. To minimize patient mortality and disability, the response time of emergency medical services is critical. Therefore, real-time management is required to determine the location of an ambulance with a low response time or called or a dynamic allocation system. Dynamic allocation is moving the ambulance bases from low demand areas to high-demand areas that is useful in the operational level. However, the dynamic allocation model for real-time management requires re-allocation of ambulances, resulting in high costs and heavy workloads for the ambulance crews. This paper focuses on a covering model based on social media analysis. The model was used for developing an ambulance reallocation system. In addition to dynamic allocation, the proposed model considers real-time data from a social media application (Twitter) to minimize the response time and cost during emergencies and disasters. Twitter has been used in various ways to communicate during and manage emergencies. In this paper, we formulate the Maximal Covering Location Problem (MCLP), develop a solution procedure based on social media (Twitter application) and show the effect of the approach on the optimal solution by comparing it with the classical approach and also demonstrate our approach on Bangkok EMS.

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References

  • Aboueljinane, L., Sahin, E., & Jemai, Z. (2013). A review on simulation models applied to emergency medical service operations. Computers & Industrial Engineering,66, 734–750.

    Google Scholar 

  • Aytug, H., & Saydam, C. (2002). Solving large-scale maximum expected covering location problems by genetic algorithms: A comparative study. European Journal of Operational Research,141, 480–494.

    Google Scholar 

  • Berman, O., Kalcsics, G., Krass, D., & Nickel, S. (2009). The ordered gradual covering location problem on a network. Discrete Applied Mathematics,157(18), 3689–3707.

    Google Scholar 

  • Boffey, B., & Narula, S. C. (1998). Models for multi-path covering-routing problems. Annals of Operations Research,82, 331–342.

    Google Scholar 

  • Boyd, M. D., & Ellison, B. N. (2008). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication,13, 210–230.

    Google Scholar 

  • Brotcorne, L., Laporte, G., & Semet, F. (2003). Invited review: Ambulance location and relocation models. European Journal of Operational Research,147, 451–463.

    Google Scholar 

  • Chae, J., Thom, D., Bosch, H., Jang, Y., Maciejewski, R., Ebert, D. S., & Ertl, T. (2012). Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition. In Proceedings of the IEEE conference on visual analytics science and technology (pp. 143–152).

  • Church, R., & ReVelle, C. (1974). The maximum covering location problem. Regulation Systems Compliance and Integrity,32, 101–118.

    Google Scholar 

  • Corvey, W. J., Vieweg, S., Rood, T., & Palmer, M. (2010). Twitter in mass emergency: What NLP techniques can contribute. In Proceedings of the NAACL HLT workshop on computational linguistics in a world of social media (pp. 23–24).

  • Curtin, K. M., Hayslett-McCall, K., & Qiu, F. (2010). Determining optimal police patrol areas with maximal covering and backup covering location models. Networks and Spatial Economics,10(1), 125–145.

    Google Scholar 

  • David, G., & Harrington, S. E. (2010). Population density and racial differences in the performance of emergency medical services. Journal of Health Economics,29, 603–615.

    Google Scholar 

  • De Longueville, B., & Smith, R. S. (2009). A use case of mining location based social networks to acquire spatio-temporal data on forest fires. In Proceedings of the first international workshop on location based social networks (73–80).

  • De Maio, V. J., Stiell, I. G., Wells, G. A., & Spaite, D. W. (2003). Optimal defibrillation for maximum out-of-hospital cardiac arrest survival rates. Annals of Emergency Medicine,42(2), 242–250.

    Google Scholar 

  • Dell’Olmo, P., Ricciardi, N., & Sgalambro, A. (2014). A multiperiod maximal covering location model for the optimal location of intersection safety cameras on an urban traffic network. Procedia-Social and Behavioral Sciences,108, 106–117.

    Google Scholar 

  • Erkut, E., Ingolfsson, A., & Erdogan, G. (2008). Ambulance location for maximum survival. Naval Research Logistics, 55, 42–58.

    Google Scholar 

  • Farahani, R. Z., & Asgari, N. (2007). Combination of MCDM and covering techniques in a hierarchical model for facility location: A case study. European Journal of Operational Research,176, 1839–1858.

    Google Scholar 

  • Fuchs, G., Andrienko, N., Andrienko, G., Bothe, S., & Stange, H. (2013). Tracing the German centennial flood in the stream of tweets: First lessons learned. In: SIGSPATIAL international workshop on crowd sourced and volunteered geo-graphic information (pp. 2–10). Orlando.

  • Gendreau, M., Laporte, G., & Semet, F. (2006). The maximal expected coverage relocation problem for emergency vehicles. Journal of the Operational Research Society,57, 22–28.

    Google Scholar 

  • Goldberg, J. (2004). Operations research models for the deployment of emergency service vehicles. EMS Management Journal,1, 20–39.

    Google Scholar 

  • Green, L. V., & Kolesar, P. J. (2004). Improving emergency responsiveness with management science. Management Science,50, 1001–1014.

    Google Scholar 

  • Hecht, B., Hong, L., Suh, B., & Chi E. H. (2011). Tweets from Justin Bieber’s heart: The dynamics of the “location” field in user profiles. In Proceedings of the ACM CHI conference on human factors in computing systems (pp. 237–246).

  • Hiltz, S. R., Diaz, P., & Mark, G. (2011). Introduction: Social media and collaborative systems for crisis management. ACM Transactions on Computer-Human Interaction,18, 18:1–18:6.

    Google Scholar 

  • Iannoni, A. P., Morabito, R., & Saydam, C. (2008). A hypercube queueing model embedded into a genetic algorithm for ambulance deployment on highways. Annals of Operations Research,157(1), 207–224.

    Google Scholar 

  • Jagtenberg, C. J., Bhulai, S., & van der Mei, R. D. (2015). An efficient heuristic for real-time ambulance redeployment. Operations Research for Health Care,4, 27–35.

    Google Scholar 

  • Kaewkitipong, L., Chen, C., & Ractham P., (2012). Lessons learned from the use of social media in combating a crisis: A case study of 2011 Thailand flooding disaster. In Proceedings of the international conference on information systems (ICIS) (pp. 1–17).

  • Kosala, R., & Adi, E. (2012). Harvesting real time traffic information from Twitter. Procedia Engineering,50, 1–11.

    Google Scholar 

  • Lai, L. S. L., & Turban, E. (2008). Groups formation and operations in the Web 2.0 environment and social networks. Group Decision and Negotiation,17(5), 387–402.

    Google Scholar 

  • Laylavi, F., Rajabifard, A., & Kalantari, M. (2017). Event relatedness assessment of Twitter messages for emergency response. Information Processing and Management,53, 266–280.

    Google Scholar 

  • Li, X., Zhao, Z., & Zhu, X. (2011). Covering models and optimization techniques for emergency response facility location and planning: A review. Mathematical Methods of Operations Research,74, 281–310.

    Google Scholar 

  • Lim, C. S., Mamat, R., & Braunl, T. (2011). Impact of ambulance dispatch policies on performance of emergency medical services. IEEE Transactions on Intelligent Transportation Systems,12(2), 624–632.

    Google Scholar 

  • Maxwell, M. S., Henderson, S. G., & Topalogu, H. (2009). Ambulance redeployment: An approximate dynamic programming approach. In: M. D. Rossetti, R. R. Hill, B. Johansson, A. Dunkin, & R. Ingalls (Eds.), Proceedings of 2009 winter simulation conference.

  • Mistovich, J. J., & Karren, K. J. (2014). Prehospital emergency care. New York: Pearson Education.

    Google Scholar 

  • Montgomery, D. C. (2009). Introduction to statistical quality control. Hoboken, NJ: Wiley.

    Google Scholar 

  • Murray, A. T. (2005). Geography in coverage modeling: Exploiting spatial structure to address complementary partial service of areas. Annals of the Association of American Geographers,95, 761–772.

    Google Scholar 

  • Naoum-Sawaya, J., & Elhedhli, S. (2013). A stochastic optimization model for real-time ambulance redeployment. Computers & Operations Research,40, 1972–1978.

    Google Scholar 

  • Palen, L., Vieweg, S., Liu, S. B., & Hughes, A. L. (2009). Crisis in a networked world: Features of computer-mediated communication in the April 16, 2007, Virginia Tech event. Social Science Computer Review,27, 467–480.

    Google Scholar 

  • Pinto, L. R., Silva, P. M. S., & Young, T. P. (2015). A generic method to develop simulation model for ambulance system. Simulation Modelling Practice and Theory,51, 170–183.

    Google Scholar 

  • Rajagopalan, H. K., Saydam, C., & Xiao, J. (2008). A multiperiod set covering location model for dynamic redeployment of ambulances. Computers & Operations Research,35, 814–826.

    Google Scholar 

  • Sarcevic, A., Palen, L., White, J., Starbird, K., Bagdori, M., & Anderson, K. (2012). Beacons of hope in decentralized coordination: Learning from on-the-ground medical twitterers during the 2010 Haiti earthquake. In Proceedings of the ACM 2012 conference on computer supported cooperative work, New York, NY.

  • Schmid, V. (2012). Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming. European Journal of Operational Research,219, 611–621.

    Google Scholar 

  • Schmid, V., & Doerner, K. F. (2010). Ambulance location and relocation problem with time-dependent travel time. European Journal of Operational Research,207, 1293–1303.

    Google Scholar 

  • Shiah, D. M., & Chen, S. W. (2007). Ambulance allocation capacity model. In 2007 9th international conference on e-Health networking, applications and services, Taipei.

  • Sorensen, P., & Church, R. (2010). Integrating expected coverage and local reliability for emergency medical services location problem. Socio-Economic Planning Sciences,44, 8–18.

    Google Scholar 

  • Stefanidis, A., Crooks, A., & Radzikowski, J. (2011). Harvesting ambient geospatial information from social media feeds. GeoJournal,78, 319–338.

    Google Scholar 

  • Steiger, E., Albuquerque, J. P., & Zipf, A. (2015). An advanced systematic literature review on spatiotemporal analyses of Twitter data. Transactions in GIS,19(6), 809–834.

    Google Scholar 

  • Sutton, J. (2009). Twitter service part of disaster communications. Canadian Security Magazine. http://www.canadiansecuritymag.com/RiskManagement/News/Twitter-service-part-of-disaster-communications.html. Accessed 25 Oct 2017.

  • Thomson, R., Ito, N., Suda, H., Lin, F., Liu, Y., Hayasaka, R., Isochi, R., & Wang, Z. (2012). Trusting tweets: The Fukushima disaster and information source credibility on Twitter. In Proceedings of the ninth international conference on information systems for crisis response and management.

  • Toregas, C., Swain, R., ReVelle, C., & Bergman, L. (1971). The location of emergency service facilities. Operations Research,19, 1363–1373.

    Google Scholar 

  • Van den Berg, P. L., & Aardal, K. (2015). Time-dependent MEXCLP with start-up and relocation cost. European Journal of Operations Research,242, 383–389.

    Google Scholar 

  • Van den Berg, P. L., Kommer, G. J., & Zuzakova, B. (2016). Linear formulation for the maximum expected coverage location model with fractional coverage. Operations Research for Health Care,8, 33–41.

    Google Scholar 

  • Wanichayapong, N., Pruthipunyaskul, W., Pattara-Atikom, W., & Chaovalit, P. (2011). Social-based traffic information extraction and classification. In Proceedings of the eleventh international conference on ITS telecommunications (pp. 107–112).

  • Yardi, S., & Boyd, D. (2010). Tweeting from the Town Square: Measuring geographic local networks. In Proceedings of the fourth international AAAI conference on weblogs and social media.

  • 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(1), 6–13.

    Google Scholar 

  • Zarandi, M. H. F., Davari, S., & Haddad Sisakht, S. A. (2011). The large scale maximal covering location problem. Scientia Iranica E,18(6), 1564–1570.

    Google Scholar 

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Acknowledgement

This research is supported by King Mongkut′s Institute of Technology Ladkrabang, KMITL grant no. KREF156004.

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Correspondence to Chumpol Yuangyai.

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Nilsang, S., Yuangyai, C., Cheng, CY. et al. Locating an ambulance base by using social media: a case study in Bangkok. Ann Oper Res 283, 497–516 (2019). https://doi.org/10.1007/s10479-018-2918-8

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