Online Predictive Model for Taxi Services
In recent years, both companies and researchers have been exploring intelligent data analysis to increase the profitability of the taxi industry. Intelligent systems for online taxi dispatching and time saving route finding have been built to do so. In this paper, we propose a novel methodology to produce online predictions regarding the spatial distribution of passenger demand throughout taxi stand networks. We have done so by assembling two well-known time series short-term forecast models: the time-varying Poisson models and ARIMA models. Our tests were performed using data gathered over a period of 6 months and collected from 63 taxi stands within the city of Porto, Portugal. Our results demonstrate that this model is a true major contribution to the driver mobility intelligence: 78% of the 253745 demanded taxi services were correctly forecasted in a 30 minutes horizon.
KeywordsARIMA Time-Varying Poisson Model Taxi Services Time Series Data Streams
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- 1.Glashenko, A., Ivaschenko, A., Rzevski, G., Skobelev, P.: Multiagent real time scheduling system for taxi companies. In: Proceedings of 8th Int. Conf. on Autonomous Agents and Multi-Agent Systems, Budapest, Hungary (2009)Google Scholar
- 3.Junghoon, L., Inhye, S., Park, G.: Analysis of the Passenger Pick-Up Pattern for Taxi Location Recommendation. In: Conference on Networked Computing and Advanced Information Management, vol. 1, pp. 199–204 (2008)Google Scholar
- 4.Bin, L., Daqing, Z., Lin, S., Chao, C., Shijian, L., Guande, Q., Qiang, Y.: 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, pp. 63–68 (2011)Google Scholar
- 5.Ihler, A., Hutchins, J., Smyth, P.: Adaptive Event Detection with Time-Varying Poisson Processes. In: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA, pp. 207-216 (2006)Google Scholar
- 7.Matias, L., Gama, J., Mendes-Moreira, J., Sousa, J.F.: Validation of both number and coverage of bus Schedules using AVL data. In: Proceedings of IEEE Conference on Intelligent Transportation Systems, Funchal, Portugal, pp. 131–136 (2010)Google Scholar
- 16.R Development Core Team, R: A Language and Environment for Statistical Computing, Vienna, Austria (2005)Google Scholar
- 17.Yeasmin, K., Hyndman, R.: Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software 27 (2008)Google Scholar