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Periodic Weather-Aware LSTM With Event Mechanism for Parking Behavior Prediction

文献类型：期刊论文

作者Zhang, Feng7; Liu, Yani7; Feng, Ningxuan6; Yang, Cheng5; Zhai, Jidong6; Zhang, Shuhao4; He, Bingsheng3; Lin, Jiazao1,2; Zhang, Xiao7; Du, Xiaoyong7
刊名IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
出版日期2022-12-01
卷号34期号:12页码:5896-5909
ISSN号1041-4347
关键词Meteorology Predictive models COVID-19 Logic gates Urban areas Recurrent neural networks Mathematical model Periodic weather-aware LSTM event mechanism parking behavior prediction COVID-19
DOI10.1109/TKDE.2021.3070202
通讯作者Zhai, Jidong(zhaijidong@tsinghua.edu.cn) ; Du, Xiaoyong(duyong@ruc.edu.cn)
英文摘要There are plenty of parking spaces in big cities, but we often find nowhere to park. For example, New York has 1.4 million cars and 4.4 million on-street parking spaces, but it is still not easy to find a parking place near our destination, especially during peak hours. The reason is the lack of prediction of parking behavior. If we could provide parking behavior in advance, we can ease this parking problem that affects human well-being. We observe that parking lots have periodic parking patterns, which is an important factor for parking behavior prediction. Unfortunately, existing work ignores such periodic parking patterns in parking behavior prediction, and thus incurs low accuracy. To solve this problem, we propose PewLSTM, a novel periodic weather-aware LSTM model that successfully predicts the parking behavior based on historical records, weather, environments, weekdays, and events. PewLSTM includes a periodic weather-aware LSTM prediction module and an event prediction module, for predicting parking behaviors in regular days and events. PewLSTM is extremely useful for drivers and parking lot owners to improve customer experience. For example, the probability of parking space that will be available soon can be provided even if the parking lot is full. Based on 910,477 real parking records in 904 days from 13 parking lots, PewLSTM yields 93.84% parking prediction accuracy, which is about 30% higher than the state-of-the-art parking behavior prediction method. Additionally, we have analyzed parking behaviors in events like holidays and COVID-19. PewLSTM can handle parking behavior prediction in events and reaches 90.68 percent accuracy.
资助项目National Key Research and Development Program of China[2018YFB1004401] ; National Natural Science Foundation of China[U20A20226] ; National Natural Science Foundation of China[61732014] ; National Natural Science Foundation of China[61802412] ; National Natural Science Foundation of China[62002029] ; Beijing Natural Science Foundation[4202031] ; Beijing Natural Science Foundation[L192027] ; Tsinghua University Initiative Scientific Research Program[20191080594] ; MoE AcRF[T1 251RES1824] ; MoE AcRF[MOE2017-T2-1-122] ; SUTD Start-up Research in Singapore[SRT3IS21164]
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE COMPUTER SOC
WOS记录号WOS:000880645200024
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Tsinghua University Initiative Scientific Research Program ; MoE AcRF ; SUTD Start-up Research in Singapore
源URL[http://ir.ia.ac.cn/handle/173211/50711]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者Zhai, Jidong; Du, Xiaoyong
作者单位1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Peking Univ, Dept Informat Management, Beijing 100871, Peoples R China
3.Natl Univ Singapore, Sch Comp, Singapore 119077, Singapore
4.Singapore Univ Technol & Design, Informat Syst Technol & Design Pillar, Singapore 487372, Singapore
5.Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China
6.Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China
7.Renmin Univ China, Sch Informat, Key Lab Data Engn & Knowledge Engn, Minist Educ, Beijing 100872, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Feng,Liu, Yani,Feng, Ningxuan,et al. Periodic Weather-Aware LSTM With Event Mechanism for Parking Behavior Prediction[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2022,34(12):5896-5909.
APA Zhang, Feng.,Liu, Yani.,Feng, Ningxuan.,Yang, Cheng.,Zhai, Jidong.,...&Du, Xiaoyong.(2022).Periodic Weather-Aware LSTM With Event Mechanism for Parking Behavior Prediction.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,34(12),5896-5909.
MLA Zhang, Feng,et al."Periodic Weather-Aware LSTM With Event Mechanism for Parking Behavior Prediction".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 34.12(2022):5896-5909.

入库方式： OAI收割

来源：自动化研究所

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