A Hybrid Convolutional Approach for Parking Availability Prediction

Jomaa, Hadi S.; Grabocka, Josif; Schmidt-Thieme, Lars; Borek, Alexander

doi:10.1109/ijcnn.2019.8852400

Cited by 6 publications

(11 citation statements)

References 23 publications

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“…As our aim is to predict availability of parking lots at non pre-defined multiple time steps simultaneously, existing algorithms are not applicable for our problem. The sequence-to-sequence models that we used as our baselines can be considered as the extension of existing works [12,23], and we validated that our method achieves higher accuracy than them. Some existing methods use not only historical parking data, but also other data sources for predicting parking lot availability.…”

Section: Related Workmentioning

confidence: 83%

Section: Related Workmentioning

confidence: 99%

“…Several neural network-based prediction models have been proposed nowadays [6,12,23,25,32] 4 . For example, Shao et al [23], Jomaa et al [12], Xiao et al [30] use LSTM, CNN, and GCN with GLU, respectively. Existing works predict the availability of parking lots at either a single step (i.e., current or near future) or pre-defined multiple steps.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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

Sasaki¹,

Takayama²,

Santana³

et al. 2022

Preprint

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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.

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Section: Related Workmentioning

confidence: 83%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Sasaki¹,

Takayama²,

Santana³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The latter can even help drivers decide whether it is wise to take their vehicles. The state-ofthe-art OSPI systems are mostly developed using complex machine learning techniques [7,8,10,[12][13][14][15][16][17][18]. The majority of models aim to achieve real-time prediction, but there has also been a study on estimating parking availability for a given time interval, like 10-20 min [19].…”

Section: Use Case Background: On-street Parking Information (Ospi)mentioning

confidence: 99%

“…OSPI services exist as a guidance system to smartly navigate drivers in search for on-street parking. A couple of benefits of OSPI are the reduction of traffic congestion caused by cruising drivers [7][8][9][10] and pre-departure information of parking situation at destination that increases the chances of finding a parking spot [11]. The latter can even help drivers decide whether it is wise to take their vehicles.…”

Section: Use Case Background: On-street Parking Information (Ospi)mentioning

confidence: 99%

Prioritization‐based subsampling quality assessment methodology for mobility‐related information systems

Gomari

Knoth

Antoniou

2022

IET Intelligent Trans Sys

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Mobility‐related information systems, such as on‐street parking information (OSPI) systems have become more popular in the original equipment manufacturer (OEM) industry over the last decade. However, there is a lack of methods to assess their quality at a large scale. This paper introduces a data‐driven methodology to measure the true quality by fleet data prioritization‐based subsampling strategies (PSSs). It is applied to the use case of OSPI using parking events (PE), but is applicable to other mobility‐related information systems utilizing their respective fleet data. PSSs are defined based on neighbourhoods and time periods. Each PSS generates a unique set of spatio‐temporally important areas at different quadkey zoom levels over 168 week‐hours, called slices. The importance weight in each slice depends on the volume of PE within them. The algorithm for each PSS automatically selects important areas and time frames that are vital to be observed. Sample prediction models are used for the benefits assessment of the methodology by comparing it against non‐prioritized randomized selection of ground truth. It is proven that the methodology can lessen the effort of ground truth collection, while maintaining the amount of information necessary to assess the true quality of a prediction model.

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Disk Failure Prediction with Multiple Channel Convolutional Neural Network

Yang

et al. 2021

2021 International Joint Conference on Neural Networks (IJCNN)

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A Hybrid Convolutional Approach for Parking Availability Prediction

Cited by 6 publications

References 23 publications

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

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

Prioritization‐based subsampling quality assessment methodology for mobility‐related information systems

Disk Failure Prediction with Multiple Channel Convolutional Neural Network

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