mlsanityData to 14 Oct 2026

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

arXiv 2206.10160 ↗First posted 21 Jun 2022cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 41 references that are in the map. First posted on arXiv 21 Jun 2022.

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References · 14

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2 more
  1. 2020Sequenced Route Query with Semantic Hierarchycited by 2
  2. 2021Smart City Data Analysis via Visualization of Correlated Attribute Patternscited by 1

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