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Predicting Taxi Demand Based on 3D Convolutional Neural Network and Multi-task Learning

1
School of Computer Science and Engineering, Central South University, Changsha 410075, China
2
School of Computer and Information Engineering, Shanghai Polytechnic University, Shanghai 201209, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1265; https://doi.org/10.3390/rs11111265
Received: 20 April 2019 / Revised: 22 May 2019 / Accepted: 25 May 2019 / Published: 28 May 2019
(This article belongs to the Special Issue Advanced Communication and Networking Techniques for Remote Sensing)
Taxi demand can be divided into pick-up demand and drop-off demand, which are firmly related to human’s travel habits. Accurately predicting taxi demand is of great significance to passengers, drivers, ride-hailing platforms and urban managers. Most of the existing studies only forecast the taxi demand for pick-up and separate the interaction between spatial correlation and temporal correlation. In this paper, we first analyze the historical data and select three highly relevant parts for each time interval, namely closeness, period and trend. We then construct a multi-task learning component and extract the common spatiotemporal feature by treating the taxi pick-up prediction task and drop-off prediction task as two related tasks. With the aim of fusing spatiotemporal features of historical data, we conduct feature embedding by attention-based long short-term memory (LSTM) and capture the correlation between taxi pick-up and drop-off with 3D ResNet. Finally, we combine external factors to simultaneously predict the taxi demand for pick-up and drop-off in the next time interval. Experiments conducted on real datasets in Chengdu present the effectiveness of the proposed method and show better performance in comparison with state-of-the-art models. View Full-Text
Keywords: taxi demand prediction; deep learning; spatiotemporal data; convolutional neural network; multi-task learning taxi demand prediction; deep learning; spatiotemporal data; convolutional neural network; multi-task learning
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MDPI and ACS Style

Kuang, L.; Yan, X.; Tan, X.; Li, S.; Yang, X. Predicting Taxi Demand Based on 3D Convolutional Neural Network and Multi-task Learning. Remote Sens. 2019, 11, 1265.

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