Research Area:  Machine Learning

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.

Keywords:  

Author(s) Name:  Feng Zhang; Yani Liu; Ningxuan Feng; Cheng Yang; Jidong Zhai; Shuhao Zhang; Bingsheng He; Jiazao Lin; Xiao Zhang; Xiaoyong Du

Publisher name:  IEEE

DOI:  10.1109/TKDE.2021.3070202

Volume Information:  Page(s): 1 - 1

Paper Link:   https://ieeexplore.ieee.org/abstract/document/9392279