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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Aboueljinane, L., Sahin, E., & Jemai, Z. (2013). A review on simulation models applied to emergency medical service operations. Computers & Industrial Engineering,66, 734–750.
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.
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.
Boffey, B., & Narula, S. C. (1998). Models for multi-path covering-routing problems. Annals of Operations Research,82, 331–342.
Boyd, M. D., & Ellison, B. N. (2008). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication,13, 210–230.
Brotcorne, L., Laporte, G., & Semet, F. (2003). Invited review: Ambulance location and relocation models. European Journal of Operational Research,147, 451–463.
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.
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.
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.
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.
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.
Erkut, E., Ingolfsson, A., & Erdogan, G. (2008). Ambulance location for maximum survival. Naval Research Logistics, 55, 42–58.
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.
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.
Goldberg, J. (2004). Operations research models for the deployment of emergency service vehicles. EMS Management Journal,1, 20–39.
Green, L. V., & Kolesar, P. J. (2004). Improving emergency responsiveness with management science. Management Science,50, 1001–1014.
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.
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.
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.
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.
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.
Laylavi, F., Rajabifard, A., & Kalantari, M. (2017). Event relatedness assessment of Twitter messages for emergency response. Information Processing and Management,53, 266–280.
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.
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.
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.
Montgomery, D. C. (2009). Introduction to statistical quality control. Hoboken, NJ: Wiley.
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.
Naoum-Sawaya, J., & Elhedhli, S. (2013). A stochastic optimization model for real-time ambulance redeployment. Computers & Operations Research,40, 1972–1978.
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.
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.
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.
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.
Schmid, V., & Doerner, K. F. (2010). Ambulance location and relocation problem with time-dependent travel time. European Journal of Operational Research,207, 1293–1303.
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.
Stefanidis, A., Crooks, A., & Radzikowski, J. (2011). Harvesting ambient geospatial information from social media feeds. GeoJournal,78, 319–338.
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.
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.
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.
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.
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.
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.
Acknowledgement
This research is supported by King Mongkut′s Institute of Technology Ladkrabang, KMITL grant no. KREF156004.
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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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DOI: https://doi.org/10.1007/s10479-018-2918-8