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

TL;DR

This paper presents a graph-to-sequence deep learning model that accurately predicts parking lot availability in Santander using three years of sensor data, enhancing urban parking services.

Contribution

The study introduces a novel graph-to-sequence model that captures spatial and temporal patterns for parking prediction, outperforming existing sequence models.

Findings

High prediction accuracy achieved with the model.

Effective utilization of 3-year sensor data.

Model successfully integrated into a smartphone app.

Abstract

Nowadays, so as to improve services and urban areas 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.