ASNN-FRR: A traffic-aware neural network for fastest route recommendation
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...
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| Published in | GeoInformatica Vol. 27; no. 1; pp. 39 - 60 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.01.2023
Springer Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1384-6175 1573-7624 |
| DOI | 10.1007/s10707-021-00458-7 |
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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. |
|---|---|
| AbstractList | 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. Fastest route recommendation (FRR) is an important task in urban computing. Despite some efforts are made to integrate A.sup.* 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.sup.* 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. 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. |
| Audience | Academic |
| Author | Huang, Hai Li, Chao Qu, Jianfeng Yin, Lihua Wang, Chaoxiong Qiu, Jing |
| Author_xml | – sequence: 1 givenname: Chaoxiong surname: Wang fullname: Wang, Chaoxiong organization: Cyberspace Institute of Advance Technology, GuangZhou University – sequence: 2 givenname: Chao surname: Li fullname: Li, Chao email: lichao@gzhu.edu.cn organization: Cyberspace Institute of Advance Technology, GuangZhou University – sequence: 3 givenname: Hai surname: Huang fullname: Huang, Hai organization: School of Computing and Mathematical Sciences, University of Greenwich – sequence: 4 givenname: Jing surname: Qiu fullname: Qiu, Jing organization: Cyberspace Institute of Advance Technology, GuangZhou University – sequence: 5 givenname: Jianfeng surname: Qu fullname: Qu, Jianfeng email: jfqu@suda.edu.cn organization: School of Computer Science and Technology, Soochow University – sequence: 6 givenname: Lihua surname: Yin fullname: Yin, Lihua email: yinlh@gzhu.edu.cn organization: Cyberspace Institute of Advance Technology, GuangZhou University |
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| CitedBy_id | crossref_primary_10_1007_s10707_024_00517_9 crossref_primary_10_1016_j_inffus_2024_102413 crossref_primary_10_3390_math12192977 crossref_primary_10_1007_s40747_024_01611_z |
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| Keywords | Traffic speed prediction Travel time prediction Fastest route planning Heuristic search |
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A
∗
algorithm with neural networks to... Fastest route recommendation (FRR) is an important task in urban computing. Despite some efforts are made to integrate A.sup.* algorithm with neural networks... Fastest route recommendation (FRR) is an important task in urban computing. Despite some efforts are made to integrate A∗ algorithm with neural networks to... |
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| SubjectTerms | Algorithms Analysis Computer Science Cost function Data Structures and Information Theory Geographical Information Systems/Cartography Information Storage and Retrieval Lower bounds Multimedia Information Systems Neural networks Travel time |
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| Title | ASNN-FRR: A traffic-aware neural network for fastest route recommendation |
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