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Predicting Taxi-Calling Demands Using Multi-Feature and Residual Attention Graph Convolutional Long Short-Term Memory Networks

文献类型：期刊论文

作者Mi, Chunlei1,2; Cheng, Shifen1,2; Lu, Feng1,2
刊名ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
出版日期2022-03-01
卷号11期号:3页码:14
关键词taxi-calling demands prediction residual attention graph convolutional long short-term memory networks deep learning pattern dependence
DOI10.3390/ijgi11030185
通讯作者Cheng, Shifen(chengsf@lreis.ac.cn)
英文摘要Predicting taxi-calling demands at the urban area level is vital to coordinate the supply-demand balance of the urban taxi system. Differing travel patterns, the impact of external data, and the expression of dynamic spatiotemporal demand dependence pose challenges to predicting demand. Here, a framework using residual attention graph convolutional long short-term memory networks (RAGCN-LSTMs) is proposed to predict taxi-calling demands. It consists of a spatial dependence (SD) extractor, which extracts SD features; an external dependence extractor, which extracts traffic environment-related features; a pattern dependence (PD) extractor, which extracts the PD of demands for different zones; and a temporal dependence extractor and predictor, which leverages the abovementioned features into an LSTM model to extract temporal dependence and predict demands. Experiments were conducted on taxi-calling records of Shanghai City. The results showed that the prediction accuracies of the RAGCN-LSTMs model were a mean absolute error of 0.8664, a root mean square error of 1.4965, and a symmetric mean absolute percentage error of 43.11%. It outperformed both classical time-series prediction methods and other deep learning models. Further, to illustrate the advantages of the proposed model, we investigated its predicting performance in various demand densities in multiple urban areas and proved its robustness and superiority.
资助项目National Natural Science Foundation of China[42101423] ; China Postdoctoral Science Foundation[2020M680655] ; China Postdoctoral Science Foundation[2021T140656]
WOS研究方向Computer Science ; Physical Geography ; Remote Sensing
语种英语
WOS记录号WOS:000776829400001
出版者MDPI
资助机构National Natural Science Foundation of China ; China Postdoctoral Science Foundation
源URL[http://ir.igsnrr.ac.cn/handle/311030/173612]  
专题中国科学院地理科学与资源研究所
通讯作者Cheng, Shifen
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Mi, Chunlei,Cheng, Shifen,Lu, Feng. Predicting Taxi-Calling Demands Using Multi-Feature and Residual Attention Graph Convolutional Long Short-Term Memory Networks[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2022,11(3):14.
APA Mi, Chunlei,Cheng, Shifen,&Lu, Feng.(2022).Predicting Taxi-Calling Demands Using Multi-Feature and Residual Attention Graph Convolutional Long Short-Term Memory Networks.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,11(3),14.
MLA Mi, Chunlei,et al."Predicting Taxi-Calling Demands Using Multi-Feature and Residual Attention Graph Convolutional Long Short-Term Memory Networks".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 11.3(2022):14.

入库方式： OAI收割

来源：地理科学与资源研究所

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