Trafik Ağlarında Anomali Tespiti

Yıl 2018, , 132 - 138, 30.09.2018

Özlem Örnek , Seval Vatan Serpil Sarıoğlu Ahmet Yazıcı

https://doi.org/10.31796/ogummf.440285

Cited By: 2

Öz

Trafik ağlarının gittikçe büyümesiyle beraber izlenmesi ve kontrolünün sağlanması da zorlaşmıştır. Bu izlemenin sağlanmasında da anomali tespiti çok önemli bir yere sahiptir. Trafik ağlarında anomali tespiti yaklaşımları ile olaylar erkenden tespit edilerek hızlı müdahale imkanı sağlanır. Bu ise zaman ve maliyet tasarrufu sağlar. Literatürde farklı alanlardaki problemler için anomali tespitinde sınıflandırma, kümeleme, istatistiksel vb. yaklaşımlar bulunmaktadır. Bu alanda destek vektör makineleri, bayes ağları, bulanık mantık, genetik algoritmalar vb. birçok yöntem anomali tespiti için kullanılmaktadır. Bu çalışmada karar ağacı algoritması ile trafik ağlarında anomali tespiti için bir yöntem önerilmiştir. Önerilen yöntem Britanya Kolumbiyası’na ait trafik veri seti kullanılarak test edilmiştir. Yapılan testlerde trafik ağındaki bazı anormal olayların önerilen yöntem ile tespit edilebileceği görülmüştür. 

Anahtar Kelimeler

Anomali tespiti, karar ağaçları, sınıflandırma algoritmaları, trafik ağları

Anomaly Detection in Traffic Networks

Öz

Along with the growing traffic networks, the control of traffic networks has become increasingly difficult. The detection of anomaly is very important in ensuring this monitoring. With anomaly detection approaches in traffic networks, events are detected early and quick intervention is provided. This saves time and money. There are classification, clustering, statistical, etc. approaches in anomaly detection for problems in the literature. Support vector machines, Bayesian networks, fuzzy logic, genetic algorithms, etc. are main approaches that used to detect anomalies. In this study, a decision tree algorithm is proposed to detect anomaly in traffic networks. The proposed method has been tested using the British Colombia traffic data set. It is seen that some abnormal events in the traffic network can be detected by using the proposed method. 

Anahtar Kelimeler

Anomaly detection, decision trees, classification algorithms, traffic networks

Kaynakça

• Lazarevic, A., Banerjee, A., Chandola, V., Kumar, V., & Srivastava, J. (2008, September). Data mining for anomaly detection. In Tutorial at the European Conference on Principles and Practice of Knowledge Discovery in Databases.
• Chandola, V., Banerjee, A., & Kumar, V. (2007). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 15.
• British Columbia Ministry of Transportation and Infrastructure-Business Management Services. Historical DriveBC Events. https://catalogue.data.gov.bc.ca/dataset/historical-drivebc-events . Erişim Tarihi: Temmuz 2018.
• Chen, S., & Wang, W. (2009). Decision tree learning for freeway automatic incident detection. Expert Systems with Applications, 36(2), 4101-4105.
• Payne, H. J., & Tignor, S. C. (1978). Freeway incident-detection algorithms based on decision trees with states. Transportation Research Record, (682).
• Lu, J., Liu, Q., Yuan, L., & Chen, S. (2014). Grafted Decision Tree for Freeway Incident Detection. In CICTP 2014: Safe, Smart, and Sustainable Multimodal Transportation Systems (pp. 467-477).
• Jiang, G., Niu, S., Li, Q., Chang, A., & Jiang, H. (2010, March). Automated incident detection algorithms for urban expressway. In Advanced Computer Control (ICACC), 2010 2nd International Conference on (Vol. 3, pp. 70-74). IEEE.
• Patel, N., & Upadhyay, S. (2012). Study of various decision tree pruning methods with their empirical comparison in WEKA. International journal of computer applications, 60(12).
• Quinlan, J. R. (2014). C4. 5: programs for machine learning. Elsevier.
• Lior, R. (2014). Data mining with decision trees: theory and applications (Vol. 81). World scientific.
• Brijain, M., Patel, R., Kushik, M., & Rana, K. (2014). A survey on decision tree algorithm for classification.
• Grąbczewski, K. (2014). Meta-learning in decision tree induction(Vol. 1). Springer International Publishing.
• Sewaiwar, P., & Verma, K. K. (2015). Comparative study of various decision tree classification algorithm using WEKA. International Journal of Emerging Research in Management &Technology, 4, 2278-9359.
• Drazin, S., & Montag, M. (2012). Decision tree analysis using weka. Machine Learning-Project II, University of Miami, 1-3.
• Kaur, G., & Chhabra, A. (2014). Improved J48 classification algorithm for the prediction of diabetes. International Journal of Computer Applications, 98(22).
• La-inchua, J., Chivapreecha, S., & Thajchayapong, S. (2013, May). A new system for traffic incident detection using fuzzy logic and majority voting. In Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2013 10th International Conference on (pp. 1-5). IEEE.
• Liu, Q., Lu, J., Chen, S., & Zhao, K. (2014). Multiple Naïve bayes classifiers ensemble for traffic incident detection. Mathematical Problems in Engineering, 2014.
• Chen, L., Cao, Y., & Ji, R. (2010, August). Automatic incident detection algorithm based on support vector machine. In Natural Computation (ICNC), 2010 Sixth International Conference on(Vol. 2, pp. 864-866). IEEE.
• Raiyn, J., & Toledo, T. (2014). Real-time road traffic anomaly detection. Journal of Transportation Technologies, 4(03), 256.
• Kinoshita, A., Takasu, A., & Adachi, J. (2014, October). Real-time traffic incident detection using probe-car data on the Tokyo Metropolitan Expressway. In Big Data (Big Data), 2014 IEEE International Conference on (pp. 43-45). IEEE.
• Barria, J. A., & Thajchayapong, S. (2011). Detection and classification of traffic anomalies using microscopic traffic variables. IEEE Transactions on Intelligent Transportation Systems, 12(3), 695-704.
• Hodge, V., & Austin, J. (2004). A survey of outlier detection methodologies. Artificial intelligence review, 22(2), 85-126.
• Alpaydin, E. (2009). Introduction to machine learning. MIT press.
• Witten, I. H., & Frank, E. (1999). Data mining: Practical machine learning tools and techniques with java implementations. San Francisco: Morgan Kaufmann Publishers. pp. 89–97, 125–127, 159–161.
• Friedman J., Kohavi R., and Yun Y., (1996, August). Lazy decision trees, In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pp. 717–724. Cambridge, MA: AAAI Press/MIT Press.