Abstract
Informed driving is becoming a key feature to increase the sustainability of taxi companies. Some recent works are exploring the data broadcasted by each vehicle to provide live information for decision making. In this paper, we propose a method to employ a learning model based on historical GPS data in a real-time environment. Our goal is to predict the spatiotemporal distribution of the Taxi-Passenger demand in a short time horizon. We did so by using learning concepts originally proposed to a well-known online algorithm: the perceptron [1]. The results were promising: we accomplished a satisfactory performance to output the next prediction using a short amount of resources.
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References
Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review 65(6), 386 (1958)
Schaller, B.: Entry controls in taxi regulation: Implications of us and canadian experience for taxi regulation and deregulation. Transport Policy 14(6), 490–506 (2007)
Wong, K., Wong, S., Bell, M., Yang, H.: Modeling the bilateral micro-searching behavior for urban taxi services using the absorbing markov chain approach. Journal of Advanced Transportation 39(1), 81–104 (2005)
Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L.: Online predictive model for taxi services. In: Hollmén, J., Klawonn, F., Tucker, A. (eds.) IDA 2012. LNCS, vol. 7619, pp. 230–240. Springer, Heidelberg (2012)
Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., Huang, Y.: T-drive: driving directions based on taxi trajectories. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 99–108. ACM (2010)
Deng, Z., Ji, M.: Spatiotemporal structure of taxi services in Shanghai: Using exploratory spatial data analysis. In: 2011 19th International Conference on Geoinformatics, pp. 1–5. IEEE (2011)
Liu, L., Andris, C., Biderman, A., Ratti, C.: Uncovering taxi drivers mobility intelligence through his trace. IEEE Pervasive Computing 160, 1–17 (2009)
Yue, Y., Zhuang, Y., Li, Q., Mao, Q.: Mining time-dependent attractive areas and movement patterns from taxi trajectory data. In: 2009 17th International Conference on Geoinformatics, pp. 1–6. IEEE (2009)
Li, B., Zhang, D., Sun, L., Chen, C., Li, S., Qi, G., Yang, Q.: Hunting or waiting? Discovering passenger-finding strategies from a large-scale real-world taxi dataset. In: 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 63–68 (March 2011)
Lee, J., Shin, I., Park, G.: Analysis of the passenger pick-up pattern for taxi location recommendation. In: Fourth International Conference on Networked Computing and Advanced Information Management (NCM 2008), pp. 199–204. IEEE (2008)
Moreira-Matias, L., Gama, J., Ferreira, M., Damas, L.: A predictive model for the passenger demand on a taxi network. In: 15th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 1014–1019 (2012)
Cryer, J., Chan, K.: Time Series Analysis with Applications in R. Springer, USA (2008)
Ihler, A., Hutchins, J., Smyth, P.: Adaptive event detection with time-varying poisson processes. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 207–216. ACM (2006)
Burke, E., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Woodward, J.: A classification of hyper-heuristic approaches. In: Handbook of Metaheuristics, vol. 146, pp. 449–468. Springer, US (2010)
Chalup, S.: Incremental learning in biological and machine learning systems. International Journal of Neural Systems 12(06), 447–465 (2002)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Real-time learning capability of neural networks. IEEE Transactions on Neural Networks 17(4), 863–878 (2006)
Holt, C.: Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting 20(1), 5–10 (2004)
Wang, H., Fan, W., Yu, P., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 226–235. ACM (2003)
Gama, J., Pinto, C.: Discretization from data streams: applications to histograms and data mining. In: Proceedings of the 2006 ACM Symposium on Applied Computing, pp. 662–667. ACM (2006)
R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2012)
Makridakis, S., Hibon, M.: The m3-competition: results, conclusions and implications. International Journal of Forecasting 16(4), 451–476 (2000)
Jaynes, E.: Probability theory: The logic of science. Cambridge University Press (2003)
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Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., Damas, L. (2013). On Predicting the Taxi-Passenger Demand: A Real-Time Approach. In: Correia, L., Reis, L.P., Cascalho, J. (eds) Progress in Artificial Intelligence. EPIA 2013. Lecture Notes in Computer Science(), vol 8154. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40669-0_6
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DOI: https://doi.org/10.1007/978-3-642-40669-0_6
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