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ASNN-FRR: A traffic-aware neural network for fastest route recommendation

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Abstract

Fastest route recommendation (FRR) is an important task in urban computing. Despite some efforts are made to integrate A∗ 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∗ 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.

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Acknowledgements

This work was supported in part by National Key R&D Program of China (No. 2018YFB2100400), Industrial Internet Innovation and Development Project of China (2019), Guangxi Key Laboratory of Cryptography and Information Security (No.GXIS20 2119), National Natural Science Foundation of China(No.62102276).

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Authors and Affiliations

  1. Cyberspace Institute of Advance Technology, GuangZhou University, Guangzhou, China

    Chaoxiong Wang, Chao Li, Jing Qiu & Lihua Yin

  2. School of Computing and Mathematical Sciences, University of Greenwich, London, UK

    Hai Huang

  3. School of Computer Science and Technology, Soochow University, Suzhou, China

    Jianfeng Qu

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  1. Chaoxiong Wang
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  2. Chao Li
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  3. Hai Huang
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  5. Jianfeng Qu
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  6. Lihua Yin
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Correspondence to Chao Li, Jianfeng Qu or Lihua Yin.

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Wang, C., Li, C., Huang, H. et al. ASNN-FRR: A traffic-aware neural network for fastest route recommendation. Geoinformatica 27, 39–60 (2023). https://doi.org/10.1007/s10707-021-00458-7

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