Abstract
In this paper we study the deviation of bus trip duration and its causes. Deviations are obtained by comparing scheduled times against actual trip duration and are either delays or early arrivals. We use distribution rules, a kind of association rules that may have continuous distributions on the consequent. Distribution rules allow the systematic identification of particular conditions, which we call contexts, under which the distribution of trip time deviations differs significantly from the overall deviation distribution. After identifying specific causes of delay the bus company operational managers can make adjustments to the timetables increasing punctuality without disrupting the service.
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References
Azevedo, P.J.: CAREN - class project association rule engine (2008), http://www.di.uminho.pt/~pja/class/caren.html
Bowman, L.A., Turnquist, M.A.: Service frequency, schedule reliability and passenger wait times at transit stops. Transportation Research Part A 15, 465–471 (1981)
Carey, M.: Optimizing scheduled times, allowing for behavioural response. Transportation Research Part B 32(5), 329–342 (1998)
Conover, W.J.: Practical Nonparametric Statistics, 3rd edn. John Wiley & Sons, New York (1999)
Duarte, E., Mendes-Moreira, J., Belo, O.: Exploração de técnicas de classificação associativa no planeamento de horários de transportes públicos (exploitation of associative classification techniques in the planning the schedules of public transports). In: 9 Conferência da Associação Portuguesa de Sistemas de Informação, Viseu - Portugal (2009)
Frank, A., Asuncion, A.: UCI machine learning repository (2010)
Hand, D., Manila, H., Smyth, P.: Principles of Data Mining. MIT Press (2001)
Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning. Springer (August 2001)
Jorge, A.M., Azevedo, P.J., Pereira, F.: Distribution Rules with Numeric Attributes of Interest. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 247–258. Springer, Heidelberg (2006)
Kavšek, B., Lavrač, N., Jovanoski, V.: APRIORI-SD: Adapting Association Rule Learning to Subgroup Discovery. In: Berthold, M., Lenz, H.-J., Bradley, E., Kruse, R., Borgelt, C. (eds.) IDA 2003. LNCS, vol. 2810, pp. 230–241. Springer, Heidelberg (2003)
Klősgen, W.: Explora: A multipattern and multistrategy discovery assistant. In: Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining. AAAI Press, Menlo Park (1996)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: KDD 1998: Proceedings of the fourth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 80–86. ACM Press, New York (1998)
Mendes-Moreira, J., Duarte, E., Belo, O.: A decision support system for timetable adjustments. In: 13th EURO Working Group on Transportation Meeting (EWGT 2009), Padua - Italy (2009)
Mendes-Moreira, J., Jorge, A., de Sousa, J.F., Soares, C.: Comparing state-of-the-art regression methods for long term travel time prediction. Intelligent Data Analysis 16(3), 427–449 (2012)
Zhao, J., Dessouky, M., Bukkapatnam, S.: Optimal slack time for schedule-based transit operations. Transportation Science 40(4), 529–539 (2006)
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Jorge, A.M., Mendes-Moreira, J., de Sousa, J.F., Soares, C., Azevedo, P.J. (2012). Finding Interesting Contexts for Explaining Deviations in Bus Trip Duration Using Distribution Rules. In: Hollmén, J., Klawonn, F., Tucker, A. (eds) Advances in Intelligent Data Analysis XI. IDA 2012. Lecture Notes in Computer Science, vol 7619. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34156-4_14
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DOI: https://doi.org/10.1007/978-3-642-34156-4_14
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