Abstract
The taxi is a flexible transportation system that everyone can move to any destination. However, in Japan, the charge for the taxi is more expensive than other transportation facilities. The taxi business is in a very tough situation because the cost of crude oil suddenly increased in addition to the influence of the oversupply of the taxi market. Recently, the application of Information Technologies has advanced on taxi industries (e.g., the fare payment by non-contact IC and car navigation system). One of the technologies that gain such the attention is a probe system which can store a large amount of customer trajectory data. The probe system will improve the profitability of taxi companies if the demand in the future can be forecasted from the statistics. Therefore, in this paper, we try to forecast the taxi demands from the taxi probe data by a neural network (i.e., multilayer perceptron). First, we analyze the statistics of the taxi demands and make the training data set for the neural network. Then, the back-propagation learning is applied to the neural network to reveal the relationship of regions in the Tokyo(i.e., 23-words, Mitaka-shi, and Musashino-shi). Finally, we report our discussion about the result.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Araki, H., Kimura, A., Arizono, I., Ohta, H.: Demand forecasting based on differences of demands via neural networks. Journal of Japan Industrial Management Association 47(2), 59–68 (1996)
Kanazawa, F., Sawada, Y., Wakatsuki, T., Iwasaki, K.: Applying the probe data, accumulated by the its-spot, to the road governance. In: Proceedings of ITS Symposium 2011, pp. 73–76 (2011)
Kimura, A., Arizono, I., Ohta, H.: An application of layered neural networks to demand forecasting. Journal of Japan Industrial Management Association 44(5), 401–407 (1993)
Nakajima, Y., Makimura, K.: Study on improvement of road time table using taxi probe vehicle data. Journal of Japan Society of Civil Engineering 29 (2004)
Nishimura, S., Suzuki, K., Kobayashi, M., Matsumoto, O.H.H., Nagashima, Y.: An improvement on traffic signal control through use of probe vehicle data. In: Proceedings of ITS Symposium 2010, pp. 365–370 (2010)
Taguchi, K., Yoshida, S., Sadohara, S.: Time spatial analysis of taxi demand using probe. In: Summaries of Technical Papers of Annual Meeting Architectural Institute of Japan, vol. 2009, pp. 519–520 (2009)
Xu, J.X., Lim, J.S.: A new evolutionary neural network for forecasting net flow of a car sharing system. In: Proceedings of IEEE Congress on Evolutionary Computation 2007, pp. 1670–1676 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mukai, N., Yoden, N. (2012). Taxi Demand Forecasting Based on Taxi Probe Data by Neural Network. In: Watanabe, T., Watada, J., Takahashi, N., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia: Systems and Services. Smart Innovation, Systems and Technologies, vol 14. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29934-6_57
Download citation
DOI: https://doi.org/10.1007/978-3-642-29934-6_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29933-9
Online ISBN: 978-3-642-29934-6
eBook Packages: EngineeringEngineering (R0)