periodic weather aware lstm with event mechanism for parking behavior prediction CORD-Papers-2021-10-25 (Version 1)

Title: Periodic Weather-Aware LSTM with Event Mechanism for Parking Behavior Prediction
Abstract: There are plenty of parking spaces in big cities, but we often find nowhere to park 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 consists of two parts: a periodic weather-aware LSTM prediction module and an event prediction module, for predicting parking behaviors in regular days and events 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 We have also analyzed parking behaviors in events like holidays and COVID-19;PewLSTM can also handle parking behavior prediction in events and reaches 90 68% accuracy IEEE
Published: 2021
Journal: IEEE Transactions on Knowledge and Data Engineering
Author Name: Zhang, F
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhang_f
Author Name: Liu, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Feng, N
Author link: https://covid19-data.nist.gov/pid/rest/local/author/feng_n
Author Name: Yang, C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/yang_c
Author Name: Zhai, J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhai_j
Author Name: Zhang, S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhang_s
Author Name: He, B
Author link: https://covid19-data.nist.gov/pid/rest/local/author/he_b
Author Name: Lin, J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/lin_j
Author Name: Zhang, X
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhang_x
Author Name: Du, X
Author link: https://covid19-data.nist.gov/pid/rest/local/author/du_x
license: unk
license_url: [unknown license]
source_x: WHO
source_x_url: https://www.who.int/
who_covidence_id: #1183132
has_full_text: FALSE
G_ID: periodic_weather_aware_lstm_with_event_mechanism_for_parking_behavior_prediction
S2 ID: 233559942