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

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 dem...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2019-06, Vol.11 (11), p.1265
Main Authors: Kuang, Li, Yan, Xuejin, Tan, Xianhan, Li, Shuqi, Yang, Xiaoxian
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Car sharing
convolutional neural network
Data mining
Deep learning
Demand
Economic forecasting
Embedding
Feature extraction
International conferences
Learning
Long short-term memory
Machine learning
Methods
multi-task learning
Natural language
Neural networks
Predictions
Remote sensing
Short term
spatiotemporal data
taxi demand prediction
Taxicabs
Traffic control
Voice recognition
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs11111265