期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 23, 期 2, 页码 794-803出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2020.3015542
关键词
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
资金
- National Key Research and Development Program of China [2018YFB1004803]
- NSFC [U1811463, U1909204, 61773381, 61876011]
- Guandong Grant [2019B1515120030]
- China Railway [N2019G020]
- Open Program of the Zhejiang Lab [2019KE0AB03]
This article investigates the importance of accurate and real-time taxi demand prediction for pre-allocating taxi resources in cities, and proposes a multi-task learning model to co-predict taxi pick-up and drop-off demands. Experimental results demonstrate the effectiveness of the proposed model.
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.
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