Predicting Taxi and Uber Demand in Cities: Approaching the Limit of Predictability
Predicting Taxi and Uber Demand in Cities: Approaching the Limit of Predictability
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DOI：
10.1109/tkde.2019.2955686
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发表时间：
2021-06
影响因子：
8.9
通讯作者：
Kai Zhao;Denis Khryashchev;H. Vo
中科院分区：
文献类型：
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作者：
Kai Zhao;Denis Khryashchev;H. Vo
时间序列预测在股票价格预测、产品需求预测、经济预测等领域有着广泛的应用。在本文中，我们将每个位置的出租车和Uber需求视为时间序列，并将出租车和Uber需求预测问题简化为时间序列预测问题。我们回答了这一领域的两个关键问题。首先，时间序列具有不同的时间规律性。有些很容易被预测，有些则不然。给定一个预测算法，如LSTM（深度学习）或ARIMA（时间序列），如果它捕获了该时间序列的所有时间模式，它可以达到的最大预测精度是多少？第二，给定最大可预测性，哪种算法可以在预测精度方面接近上界？为了回答这两个问题，我们使用时间相关熵来度量时间序列的规律性，并获得最大的可预测性。通过对1400万个数据样本的测试，我们发现深度学习算法并不总是预测的最佳算法。当时间序列具有高可预测性时，简单的马尔可夫预测算法（训练时间0.5秒）可以优于深度学习算法（训练时间6小时）。可预测性可以帮助确定在准确性和计算成本方面使用哪个预测器。我们还发现，由于不同的巡航策略，优步的需求更容易预测出租车的需求相比，前者是需求驱动的时间规律性更高。
Time series prediction has wide applications ranging from stock price prediction, product demand estimation to economic forecasting. In this article, we treat the taxi and Uber demand in each location as a time series, and reduce the taxi and Uber demand prediction problem to a time series prediction problem. We answer two key questions in this area. First, time series have different temporal regularity. Some are easy to be predicted and others are not. Given a predictive algorithm such as LSTM (deep learning) or ARIMA (time series), what is the maximum prediction accuracy that it can reach if it captures all the temporal patterns of that time series? Second, given the maximum predictability, which algorithm could approach the upper bound in terms of prediction accuracy? To answer these two question, we use temporal-correlated entropy to measure the time series regularity and obtain the maximum predictability. Testing with 14 million data samples, we find that the deep learning algorithm is not always the best algorithm for prediction. When the time series has a high predictability a simple Markov prediction algorithm (training time 0.5s) could outperform a deep learning algorithm (training time 6 hours). The predictability can help determine which predictor to use in terms of the accuracy and computational costs. We also find that the Uber demand is easier to be predicted compared the taxi demand due to different cruising strategies as the former is demand driven with higher temporal regularity.