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

doi:10.1109/TKDE.2021.3070202

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	Periodic Weather-Aware LSTM With Event Mechanism for Parking Behavior Prediction
	Zhang, Feng 1; Liu, Yani 1; Feng, Ningxuan 2; Yang, Cheng 3; Zhai, Jidong 2; Zhang, Shuhao 4; He, Bingsheng 5; Lin, Jiazao 6,7; Zhang, Xiao 1; Du, Xiaoyong 1
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
	2022-12-01
卷号	34 期号:12 页码:5896-5909
通讯作者	Zhai, Jidong(zhaijidong@tsinghua.edu.cn) ; Du, Xiaoyong(duyong@ruc.edu.cn)
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.
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
DOI	10.1109/TKDE.2021.3070202
收录类别	SCI
语种	英语
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]
项目资助者	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
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000880645200024
出版者	IEEE COMPUTER SOC
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/50712
专题	多模态人工智能系统全国重点实验室_互联网大数据与信息安全
通讯作者	Zhai, Jidong; Du, Xiaoyong
作者单位	1.Renmin Univ China, Sch Informat, Key Lab Data Engn & Knowledge Engn, Minist Educ, Beijing 100872, Peoples R China 2.Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China 3.Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China 4.Singapore Univ Technol & Design, Informat Syst Technol & Design Pillar, Singapore 487372, Singapore 5.Natl Univ Singapore, Sch Comp, Singapore 119077, Singapore 6.Peking Univ, Dept Informat Management, Beijing 100871, Peoples R China 7.Chinese Acad Sci, Inst Automat, Beijing 100190, 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.