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.
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- Taxi Demand Forecasting Based on Taxi Probe Data by Neural Network
- Springer Berlin Heidelberg
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