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Taxi Demand Prediction Using Parallel Multi-Task Learning Model
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DOI：10.1109/TITS.2020.3015542.png)
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Abstract
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Accurate and real-time taxi demand prediction can help managers pre-allocate taxi resources in cities, which assists drivers quickly finding passengers and reduce passengers' waiting time. Most of the existing studies focus on mining spatial-temporal characteristics of taxi demand distributions, while lacking in modeling the correlations between taxi pick-up demand and the drop-off demand from the perspective of multi-task learning. In this article, we propose a multi-task learning model containing three parallel LSTM layers to co-predict taxi pick-up and drop-off demands, and compare the performance of single demand prediction methodology and that of two demands' co-prediction methodology. Experimental results on real-world datasets demonstrate that the pick-up demand and the drop-off demand do depend on each other, and the effectiveness of the proposed co-prediction methods.
Keywords:
Public transportation
Predictive models
Urban areas
Task analysis
Deep learning
Data mining
Correlation
Taxi demand prediction
pick-up
drop-off demand
multi-task learning
LSTM
deep learning
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