Predicting Parking Lot Availability by Graph-to-Sequence Model: A Case Study with SmartSantander

• Published in: 2023 24th IEEE International Conference on Mobile Data Management (MDM) pp. 73 - 80
• Main Authors: Sasaki, Yuya, Takayama, Junya, Santana, Juan Ramon, Yamasaki, Shohei, Okuno, Tomoya, Onizuka, Makoto
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2023
• Subjects: Graph neural network; Internet of Things; Neural networks; Predictive models; Roads; Smart cities; Smart city; Spatio-temporal analysis; User interfaces; Wireless sensor networks
• ISSN: 2375-0324
• DOI: 10.1109/MDM58254.2023.00023

Nowadays, so as to improve services and urban area livability, multiple smart city initiatives are being carried out throughout the world. SmartSantander is a smart city project in Santander, Spain, which has relied on wireless sensor network technologies to deploy heterogeneous sensors within the city to measure multiple parameters, including outdoor parking information. In this paper, we study the prediction of parking lot availability using historical data from more than 300 outdoor parking sensors with SmartSantander. We design a graph-to-sequence model to capture the periodical fluctuation and geographical proximity of parking lots. For developing and evaluating our model, we use a 3-year dataset of parking lot availability in the city of Santander. Our model achieves a high accuracy compared with existing sequence-to-sequence models, which is accurate enough to provide a parking information service in the city. We apply our model to a smartphone application to be widely used by citizens and tourists.