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ASNN-FRR: A traffic-aware neural network for fastest route recommendation.
- Source :
- GeoInformatica; Jan2023, Vol. 27 Issue 1, p39-60, 22p
- Publication Year :
- 2023
-
Abstract
- Fastest route recommendation (FRR) is an important task in urban computing. Despite some efforts are made to integrate A<superscript>∗</superscript> algorithm with neural networks to learn cost functions by a data driven approach, they suffer from inaccuracy of travel time estimation and admissibility of model, resulting sub-optimal results accordingly. In this paper, we propose an ASNN-FRR model that contains two powerful predictors for g(⋅) and h(⋅) functions of A* algorithm respectively. Specifically, an adaptive graph convolutional recurrent network is used to accurately estimate the travel time of the observed path in g(⋅). Toward h(⋅), the model adopts a multi-task representation learning method to support origin-destination (OD) based travel time estimation, which can achieve high accuracy without the actual path information. Besides, we further consider the admissibility of A* algorithm, and utilize a rational setting of the loss function for h(⋅) estimator, which is likely to return a lower bound value without overestimation. At last, the two predictors are fused into the A<superscript>∗</superscript> algorithm in a seamlessly way to help us find the real-time fastest route. We conduct extensive experiments on two real-world large scale trip datasets. The proposed approach clearly outperforms state-of-the-art methods for FRR task. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13846175
- Volume :
- 27
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- GeoInformatica
- Publication Type :
- Academic Journal
- Accession number :
- 161299141
- Full Text :
- https://doi.org/10.1007/s10707-021-00458-7