The study aims to predict the location of taxi users, based on an algorithm that was built using a map learning method, which is one of the techniques of deep learning. As the location data of taxi riders showed sequential characteristics over time, learning was performed using a recurrent neural network, which is suitable for predicting dynamic changes over time. The main data used in the analysis were the Seoul Metropolitan Government's taxi tachometer data. These data were collected over a span of six months, from February 2018 to July 2018. Seoul Metropolitan Government's building data and Seoul public transportation smart card data were used as secondary data sources to reflect taxi traffic characteristics. Deep learning results were reviewed using different accuracy values based on combinations of the data sources, such as taxi data only, taxi data and building data and taxi data and smart card data. As a result, the algorithm was able to accurately obtain the distribution of taxi passengers’ boarding positions compared to the actual taxi riding pattern through statistical analysis. On the basis of these predictions, the asymmetric characteristics of taxi traffic in terms of transport planning and management can be solved.

中文翻译：

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使用循环神经网络实时预测出租车需求

该研究旨在基于使用地图学习方法构建的算法来预测出租车用户的位置，这是深度学习的技术之一。由于出租车乘客的位置数据随着时间的推移表现出顺序特征，因此使用循环神经网络进行学习，该网络适合预测随时间的动态变化。分析中使用的主要数据是首尔市政府的出租车转速表数据。这些数据是在 2018 年 2 月至 2018 年 7 月的六个月内收集的。首尔市政府的建筑数据和首尔公共交通智能卡数据被用作辅助数据源，以反映出租车交通特征。使用基于数据源组合的不同准确度值来审查深度学习结果，例如出租车数据、出租车数据和建筑物数据以及出租车数据和智能卡数据。结果，该算法通过统计分析能够准确地获得与实际出租车乘坐模式相比的出租车乘客上车位置的分布。在这些预测的基础上，可以解决出租车交通在交通规划和管理方面的不对称特征。