1.3 Background
1.3.2 Collision Avoidance Systems
Este trabajo se ha enfocado en la implementaci ´on de algoritmos de toma de decisi ´on en el marco de las Ciudades Inteligentes. Concretamente, se ha adaptado un algoritmo de toma de decisi ´on existente y ampliamente utilizado en diversos campos, denomi- nado Proceso Anal´ıtico Jer ´arquico (PAJ), en ingl ´es Analytic Hierarchy Process (AHP), al entorno de los Sistemas Inteligentes de Transporte. Para ello, se han fusionado dos variantes del algoritmo de toma de decisi ´on mencionado: el Proceso Anal´ıtico Jer ´arqui- co Difuso, en ingl ´es Fuzzy Analytic Hierarchy Process (FAHP), y el Proceso Anal´ıtico Jer ´arquico Din ´amico, en ingl ´es Dynamic Analytic Hierarchy Process (DAHP).
El algoritmo de toma de decisi ´on propuesto se beneficia del FAHP en el uso de in- tervalos difusos, en lugar de valores ´unicos, para la asignaci ´on de los pesos de las com- paraciones por pares. De esta forma, se modelan las incertidumbres que producen los fen ´omenos f´ısicos del entorno en el que est ´an situados los sistemas de las Ciudades Inteligentes, tales como ruidos reflexiones, difracciones o desvanecimientos, entre otros.
Por otro lado, se beneficia del algoritmo DAHP en el tratamiento de los pesos de los criterios y de las alternativas como variables con el tiempo. Esto proporciona al algoritmo propuesto la posibilidad de recalcular y actualizar los pesos de los criterios y de las alternativas cada cierto periodo de tiempo. Esto, adem ´as, permite que el algoritmo se adapte a los cambios de su entorno optimizando as´ı la respuesta del sistema que lo implementa en funci ´on de la situaci ´on en cada momento.
Para ver el funcionamiento y la idoneidad del algoritmo de toma de decisi ´on propues- to en el entorno de los Sistemas Inteligentes de Transporte, se ha aplicado el algoritmo a un escenario concreto consistente en un sistema inteligente de gesti ´on del tr ´afico. Con- cretamente, se trata de una aplicaci ´on de control del tr ´afico que recibe peri ´odicamente datos de flujo de tr ´afico, velocidad media y tasa de ocupaci ´on, obtenidos de la red de sensores de la DGT. En base a estos datos, se ejecuta el algoritmo de toma de decisi ´on y se obtiene para cada periodo, la mejor carretera en t ´erminos de congesti ´on, es decir, aquella que presente un menor nivel de congesti ´on de acuerdo a los par ´ametros de en- trada (criterios del algoritmo). Los resultado obtenidos en esta validaci ´on del algoritmo demuestran la idoneidad del algoritmo de toma de decisi ´on propuesto para el ´ambito de los Sistemas Inteligentes de Transporte.
Sin embargo, hay a ´un varias modificaciones que se pueden realizar en el algoritmo en un futuro:
• Posibilidad de a ˜nadir nuevos criterios al algoritmo para considerar m ´as factores en la toma de decisi ´on.
• Realizaci ´on de un an ´alisis de los datos hist ´oricos de los pesos finales de las alter- nativas para la observaci ´on y el c ´alculo de tendencias.
• Predicci ´on de congestiones de tr ´afico utilizando el an ´alisis de los pesos de las alter- nativas y las tendencias calculadas. Esto permitir ´a una toma de decisiones con una cierta antelaci ´on para evitar que se produzcan congestiones en una determinada carretera.
Referencias
[1] WASHBURN, D., SINDHU, U., BALAOURAS, S., DINES, R. A., HAYES, N. M.,
NELSON, L. E. Helping CIOs Understand “Smart City¨Initiatives: Defining the Smart City, Its Drivers, and the Role of the CIO. Cambridge, MA: Forrester
Research, Inc, 2010. (http://public.dhe.ibm.com/partnerworld/pub/smb/smarterplan
et/forr help cios und smart city initiatives.pdf)
[2] KANTER, R.M, LITOW, S.S. Informed and interconnected: A manifesto for smarter cities. Harvard Business School General Management Unit Working Paper, 09-141, 2009.
[3] DIRKS, S., KEELING, M. A Vision of Smarter Cities: How Cities Can Lead the Way into a Prosperous and Sustainable Future. IBM Institute for Business Value, 2009. [4] HALL, R.E. The vision of a smart city. Proceedings of the 2nd Inter-
national Life Extension Technology Workshop, 2000. (http://www.osti. gov/bridge/servlets/purl/773961-oyxp82/webviewable/773961.pdf)
[5] GIFFINGER, R., FERTNER, C., KRAMAR, H., KALASEK, R., PICHLER-MILANOVIC, N., MEIJERS, E. Smart Cities: Ranking of European Medium-Sized Cities. Vienna
University of Technology, 2007.
[6] RIOS, P. Creating “The Smart City”. 2008. (http://archive.udmercy
.edu:8080/bitstream/handle/10429/393/2008 rios smart.pdf?se)
[7] PARTRIDGE, H. Developing a human perspective to the digital divide in the smart
city. Proceedings of the Biennial Conference of Australian Library and information
Association, 2004. (http://eprints.qut.edu.au/1299/1/partridge.h.2.paper.pdf)
[8] GIFFINGER, R., GUDRUN, H. Smart cities ranking: An effective instrument for the positioning of cities? ACE: Architecture, City and Environment, 4(12), 7-25, 2010. [9] AL-HADER, M., RODZI, A., SHARIF, A. R., AHMAD, N. Smart city components archi-
tecture Proceedings of the International Conference on Computational Intelligence,
Modelling and Simulation, 2009.
[10] AL-HADER, M., RODZI, A., SHARIF, A. R., AHMAD, N. SOA of smart city geospatial management Proceedings of the 3rd UKSim European Symposium on Computer
Modeling and Simulation, 2009.
[11] ANTHOPOULOS, L., FITSILIS, P. From online to ubiquitous cities: The technical
transformation of virtual communities Next Generation Society. Technological and
Legal Issues,26, 360-372, 2010.
[12] CARAGLIU, A., DEL BO, C., NIJKAMP, P. Smart cities in Europe Proceedings of
the 3rd Central European Conference in Regional Science, 2009. (http://www.inta-
aivn.org/images/cc/Urbanism/background %20documents/01 03 Nijkamp.pdf) [13] YIGITCANLAR, T., O?CONNOR, K., WESTERMAN, C. The making of knowledge ci-
ties: Melbourne?s knowledge-based urban development experience Cities, 25(2),
63-72, 2008.
[14] BOISE SMART CITY INITIATIVE. Boise Smart City Initiative Com- mittee Report Boise, ID: Capital City Development Corp, 2002. (http://www.ccdcboise.com/Documents/SC committee report for screen.pdf)
[15] COE, A., PAQUET, G., ROY, J. E-governance and smart communities: A social lear- ning challenge Social Science Computer Review,19(1), 80-93, 2001.
[16] YIGITCANLAR, T., VELIBEYOGLU, K. Knowledge-based urban development: The lo- cal economic development path of Brisbane, Australia Local Economy, 23(3), 195-
207, 2008.
[17] ODENDAAL, N. Information and communication technology and local governance:
Understanding the difference between cities in developed and emerging economies.
Computers, Environment and Urban Systems,27(6), 585-607, 2003.
[18] MAUHER, M., SMOKVINA, V. Digital to intelligent local government transition frame-
work. Proceedings of the 29th International Convention of MIPRO, 2006.
[19] PASKALEVA, K. A. Enabling the smart city: The progress of city e-governance in
Europe. International Journal of Innovation and Regional Development, 1(4), 405-
422, 2009.
[20] LINDSKOG, H. Smart communities initiatives. Proceedings of the 3rd ISOneWorld
Conference, 2004.
[21] 50 Sensor Applications for a Smarter World. Libelium Comunicaciones Distribuidas
S.L., 2014. (http://www.libelium.com/es/top 50 iot sensor applications ranking/).
[22] PARLIAMENT, T., UNION, T. Directive 2010/40/eu of the european parliament and of the council. Official Journal of the European Union,31, 207, 2010.
[23] KARPINSKI, M., CAHILL, V. Aithne Project. State of the Art Review of Technologies for Implementing Smart Roads. Distributed Systems Group, Department of Compu-
ter Science, Trinity College Dublin, 2006.
[24] SMARTEST Final Report For Publication. Project Part Funded By The European
Commission Under The Transport RTD Programme of the4th Framework Program- me, 2000.
[25] FarianTec Fleet Management. FarianTec, 2013. (http://www.fariantec.com/ fleetma- nagement.php).
[26] Busview. Intelligent Transportation Systems Research University of Washington, 2003. (http://busview.its.washington.edu/busview help.html).
[27] SHLADOVER, S. Review of the State of Development of Advanced Vehicle Control
[28] The Osu Autonomous Vehicle. The Ohio State University, (http://www2.ece.ohio- state.edu/citr/Demo97/osu-av.html).
[29] Advanced Rural Transportation Systems Strategic Plan. U.S. Department of Trans-
portation, 1998. (http://www.itsa.org).
[30] HARRIS, R. Introduction to Decision Making. VirtualSalt, 1998.
(http://www.virtualsalt.com/crebook5.htm)
[31] WIKIPEDIA CONTRIBUTORS. Decision-making. Wikipedia, The Free Encyclopedia, 2015.
[32] PARSONS, J., SAEKI, M., SHOVAL, P., WOO, C., WAND, Y. (Eds.). Conceptual Mo- deling ? ER 2010. 29th International Conference on Conceptual Modeling, Vancou-
ver, BC, Canada,6412, 2010.
[33] BAKER, D., BRIDGES, D., HUNTER, R., JOHNSON, G., KRUPA, J., MURPHY, J., SO-
RENSON, K. Guidebook to Decision-Making Methods. WSRC-IM-2002-00002, De-
partment of Energy, USA, 2001.
[34] WIKIPEDIA CONTRIBUTORS. Weighted sum model. Wikipedia, The Free Encyclope-
dia, 2014.
[35] SAATY, T.L. How to make a decision: the analytic hierarchy process. Interfaces,
24(6), 19-43, 1994.
[36] MIETTINEN, K. Survey of Some Communities and Activities in the Field of MCDM.
European Working Group “Multiple Criteria Decision Aiding”, Series 3, no24, 2011.
[37] HOVANOV, N., YUDAEVA, M., HOVANOV, K. Multicriteria estimation of probabilities
on basis of expert non-numeric, non-exact and non-complete knowledge. European
Journal of Operational Research,195(3), 857-863, 2009.
[38] FIGUEIRA, J., GRECO, S., EHRGOTT, M. (Eds.). Multiple criteria decision analysis: state of the art surveys. European Journal of Operational Research, 78, Springer,
2005.
[39] BRANS, J. P., VINCKE, P. A Preference Ranking Organisation Method: (The PRO- METHEE Method for Multiple Criteria Decision-Making). Management science,
[40] BRANS, J. P., VINCKE, P., MARESCHAL, B. How to select and how to rank projects: The PROMETHEE method. European journal of operational research, 24(2), 228-
238, 1986.
[41] AYHAN, M.B. A Fuzzy AHP approach for supplier selection problem: a case study
in a gearmotor company. International Journal of Managing Value & Supply Chains,
4(3), 11-23, 2013.
[42] GONZALEZ´ -PRIDA, V., VIVEROS, P., BARBERA´, L., M ´ARQUEZ, A. C. Dynamic Analy-
tic Hierarchy Process: AHP method adapted to a changing environment. Journal of
Manufacturing Technology Management,25(4), 457-475, 2014.
[43] NOOR, N.M.M., SABRI, I.A.A., HITAM, M.S., ALI, N.H., ISMAIL, F. Fuzzy Analytic Hierarchy Process (FAHP) Approach for Evaluating Tourism Islands in Terengganu.
International Conference on Computing and Information Technology, 62-66, 2012.
[44] CHOU, S. W., CHANG, Y. C. The implementation factors that influence the ERP (enterprise resource planning) benefits. Decision Support Systems,46(1), 149-157,
2008.