Predicting Taxi and Uber Demand in Cities: Approaching the Limit of Predictability

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 predictio...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 33; no. 6; pp. 2723 - 2736
Main Authors: Zhao, Kai, Khryashchev, Denis, Vo, Huy
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Algorithms
Autoregressive models
Clutter
data mining
Deep learning
Detectors
Economic forecasting
Feature extraction
Machine learning
Measurement
Object recognition
predictability of time-series
predictive algorithm
Questions
Regularity
Shape
Sharing economy
Taxicabs
Three-dimensional displays
Time measurement
Time series
Training
Upper bounds
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary: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.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2019.2955686