Taxi Demand Forecasting Based on Taxi Probe Data by Neural Network
Taxi Demand Forecasting Based on Taxi Probe Data by Neural Network
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DOI：
10.1007/978-3-642-29934-6_57
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发表时间：
2012
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影响因子：
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通讯作者：
Naoto Mukai;Naoto Yoden
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文献类型：
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作者：
Naoto Mukai;Naoto Yoden
出租车是一种灵活的交通系统，每个人都可以移动到任何目的地。在日本，出租车的价格比其他交通工具要贵。由于原油成本突然上涨，加上出租车市场供过于求的影响，出租车业务的形势非常坚韧。最近，信息技术的应用已经在出租车行业（例如，非接触式IC卡和汽车导航系统支付车费）。获得这种关注的技术之一是可以存储大量客户轨迹数据的探测系统。如果能从统计数据中预测出未来的需求，那么探测系统将提高出租车公司的盈利能力。因此，在本文中，我们试图预测出租车需求从出租车探测数据的神经网络（即，多层感知器）。首先，我们分析出租车需求的统计数据，并为神经网络的训练数据集。然后，将反向传播学习应用于神经网络，以揭示东京（即，23个字，Mitaka-shi和Musashino-shi）。最后，我们报告了我们对结果的讨论。
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.