Multitime Resolution Hierarchical Attention-Based Recurrent Highway Networks for Taxi Demand Prediction

Taxi demand forecasting is an important consideration in building up smart cities. However, complex nonlinear spatiotemporal relationships in demand data make it difficult to construct an accurate prediction model. Considering that a single time resolution may not enable accurate learning of the tim...

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Bibliographic Details
Published in:Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-10
Main Authors: Chen, Baiping, Li, Wei
Format: Article
Language:English
Subjects:
Artificial intelligence
Deep learning
Demand
Economic forecasting
Machine learning
Mathematical models
Neural networks
Prediction models
Researchers
Time series
Travel
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Summary:Taxi demand forecasting is an important consideration in building up smart cities. However, complex nonlinear spatiotemporal relationships in demand data make it difficult to construct an accurate prediction model. Considering that a single time resolution may not enable accurate learning of the time pattern of taxi demand, we expand the time series prediction model in our proposed multitime resolution hierarchical attention-based recurrent highway network (MTR-HRHN) model, using three time resolutions to model temporal closeness, period, and trend properties of demand data to capture a more comprehensive time pattern. We evaluate the MTR-HRHN on a taxi trip record dataset and the results show that the forecasting performance of the MTR-HRHN exceeds that of eight well-known methods in the short-term demand prediction in some high-demand regions.
ISSN:1024-123X
1563-5147
DOI:10.1155/2020/4173094