Abstract
The purpose of this paper is the development and application of patterns and behavioral models of time series data collected by sensors belonging to the Spanish Directorate General for Traffic. The extraction of these patterns will be used to predict the behavior and effects on the system as accurately as possible to facilitate early notifications of traffic congestions, therefore minimizing the response time and providing alternatives to the circulation of vehicles. Decision trees, artificial neural networks and nearest neighbors algorithms have been successfully applied to a particular location in Sevilla, Spain.
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References
Ahmane M, Abbas-Turki A, Perronnet F, Wu J, El-Moudni A, Buisson J, Zeo R (2013) Modeling and controlling an isolated urban intersection based on cooperative vehicles. Transp Res Part C Emerg Technol 28:44–62
Alcalá-Fdez J, Sánchez L García S, del Jesús MJ, Ventura S, Garrell JM, Otero J, Romero C, Bacardit J, Rivas VM, Fernández JC, Herrera F (2008) KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13(3):307–318
Carlson RC, Papamichail I, Papageorgiou M, Messmer A (2010) Optimal mainstream traffic flow control of large scale motorway networks. Transp Res Part C Emerg Technol 18:193–212
Collins JF (1993) Automatic incident detection: experience with TRRL algorithm HIOCC. TRRL Suppl Rep 775:1–6
Dunkel J, Fernandez A, Ortiz R, Ossowski S (2011) Event-driven architecture for decision support in traffic management systems. Expert Syst Appl 38:6530–6539
Hart PE (1968) The condensed nearest neighbor rule. IEEE Trans Inf Theory 18:515–516
Hernández JZ, Ossowski S, García-Serrano A (2002) Multiagent architectures for intelligent traffic management system. Transp Res Part C Emerg Technol 10(5):473–506
Lee WH, Tseng SS, Shieh WY (2010) Collaborative real-time traffic information generation and sharing framework for the intelligent transportation system. Inf Sci 180:62–702
Mahmud K, Gope K, Chowdhury SMR (2012) Possible causes and solutions of traffic jam and their impact on the economy of Dhaka city. J Manage Sustain 2(2):p112
Martínez-Álvarez F, Reyes J, Morales-Esteban A, Rubio-Escudero C (2013) Determining the best set of seismicity indicators to predict earthquakes. Two case studies: Chile and the Iberian Peninsula. Knowl-Based Syst 50:198–210
McCulloch WS, Pitts W (1943) A logical calulus of ideas immanent in nervous activity. Bull Math Biophys 5:115–133
Morales-Esteban A, Martínez-Álvarez F, Reyes J (2013) Earthquake prediction in seismogenic areas of the Iberian Peninsula based on computational intelligence. Tectonophysics 593:121–134
Portugais B, Khanal M (2014) Adaptive traffic speed estimation. Proc Comput Sci 32:356–363
Wei Qu Z, Xing Y, Song XM, Duan YZ, Wei F (2012) A study on the coordination of urban traffic control and traffic assignment. Discr Dyn Nat Soc 2012(12):367–368
Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers
Acknowledgments
The authors wish to thank the Dirección General de Tráfico (DGT) for providing the data and to the Junta de Andalucía through research project P12-TIC-1728.
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Florido, E., Castaño, O., Troncoso, A., Martínez-Álvarez, F. (2015). Data Mining for Predicting Traffic Congestion and Its Application to Spanish Data. In: Herrero, Á., Sedano, J., Baruque, B., Quintián, H., Corchado, E. (eds) 10th International Conference on Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol 368. Springer, Cham. https://doi.org/10.1007/978-3-319-19719-7_30
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DOI: https://doi.org/10.1007/978-3-319-19719-7_30
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