Improving Parking Availability Information Using Deep Learning Techniques

Arjona, Jamie; Linares, M. Paz; Garcia, Josep Casanovas; Vázquez, J. J. Rodríguez

doi:10.1016/j.trpro.2020.03.113

Cited by 21 publications

(13 citation statements)

References 17 publications

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“…As previous research mentioned, 23,54 parking availability information is not only closely related with historical pattern, the attributes information and the variance of prediction time slots (i.e., 5 min ahead and 2 h ahead) also shows significant impact to the utility level. So, another key issues worth to review is hybridizing attention and LSTM networks for sequential data-related research.…”

Section: Machine-learning-based Methodsmentioning

confidence: 89%

“…Combining the characteristics and skills of the neural network with the features and challenges of the parking perdition problem, several customized neural networks are designed to complete the forecasting task. Generally, current research mainly uses existing machine learning models, such as, Convolution Neural Network (CNN), 53,57 Recurrent Neural Network (RNN), 23,54,58,59 and then adopts them to address the real parking challenges. These models always include several features extraction parts: spatial features extraction, temporal features extraction, and attribute information.…”

Section: Machine-learning-based Methodsmentioning

confidence: 99%

“…5 min or 10 min ahead) and ignore long‐term prediction, which is not suitable for general use of parking prediction and management. To propose a method for general parking utility prediction, the attributes information, especially the long term pattern (workday or weekend, night time or day time, weather condition) significantly impact the prediction result 11,13,53 . How to effectively integrated the heterogeneous information sources, including spatial‐temporal features, the attribute features and environmental information, becomes a new topic for neural network design and algorithm development.…”

Section: Literature Review On Parking Prediction Methodologymentioning

confidence: 99%

See 2 more Smart Citations

Toward a real‐time Smart Parking Data Management and Prediction (SPDMP) system by attributes representation learning

Yang

Cui

et al. 2021

Int J of Intelligent Sys

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Managing and estimating the availability information of parking lots is of great importance to travelers and managers. However, the task is very challenging since the occupancy rate is affected by various factors, including spatial-temporal features, parking lot attributes features, and environmental changes. Previous studies mostly focus on the short-term prediction by capturing the historical sequential dependencies among inputs and outputs, which leads to low estimation accuracy for long-term prediction and limited scalability for real deployment in parking lots. To address the challenges, a comprehensive framework for real-time Smart Parking Data Management and Prediction (SPDMP) system is proposed. Three types of data sources, including historical sequential data, real-time sequential data, and attributes category data, are sufficiently integrated into a customized Parking Availability Prediction (PAP) neural network by representation learning and heterogeneous feature embedding. Specifically, instead of using parking lot property information and environmental data directly, the authors design an Attributes Tensor Embedding Component (ATEC) to integrate the intra-class affection and interclass representations and correlations by two steps of customized embedding process. To balance the impact of the indiscriminate features for various

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Section: Machine-learning-based Methodsmentioning

confidence: 89%

Section: Machine-learning-based Methodsmentioning

confidence: 99%

Section: Literature Review On Parking Prediction Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Toward a real‐time Smart Parking Data Management and Prediction (SPDMP) system by attributes representation learning

Yang

Cui

et al. 2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

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“…Apart from that, Multi-layer Perceptrons (MLPs) have been also proposed and used as a baseline to compare with other algorithms, especially deep learning algorithms [23,30,31]. GRU and LSTM have been proven to be the best RNN architectures for solving a wide set of sequential data problems [32]. In [12], the GRU architecture achieves better results in nearly all scenarios (4 countries and different exogenous variables) compared to the LSTM version for 6 h-ahead prediction.…”

Section: Applied Methodsmentioning

confidence: 99%

Parking Availability Prediction with Coarse-Grained Human Mobility Data

González-Vidal¹,

Terroso-S醗nz²,

Skarmeta³

2022

Computers, Materials &Amp; Continua

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Nowadays, the anticipation of parking-space demand is an instrumental service in order to reduce traffic congestion levels in urban spaces. The purpose of our work is to study, design and develop a parking-availability predictor that extracts the knowledge from human mobility data, based on the anonymized human displacements of an urban area, and also from weather conditions. Most of the existing solutions for this prediction take as contextual data the current road-traffic state defined at very high temporal or spatial resolution. However, access to this type of fine-grained location data is usually quite limited due to several economic or privacy-related restrictions. To overcome this limitation, our proposal uses urban areas that are defined at very low spatial and temporal resolution. We conducted several experiments using three Artificial Neural Networks: Multilayer Perceptron, Gated Recurrent Units and bidirectional Long Short Term Memory networks and we tested their suitability using different combinations of inputs. Several metrics are provided for the sake of comparison within our study and between other studies. The solution has been evaluated in a real-world testbed in the city of Murcia (Spain) integrating an open human-mobility dataset showing high accuracy. A MAPE between 4% and 10% was reported in horizons of 1 to 3 h.

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“…Systematic Literature Review of Smart Cities Information Services to Support the Mobility of Persons with Disabilities researched by Rocha [10] Parking Information Guidance System and Application of Intelligent Technology Used in Urban Areas and Multi-storey Parking Lots were researched by Hanzl [11]. Parking behavior cluster analysis: A case study in Munich was investigated by Arjona [12]. Designing an integrated smart parking application was researched by Fabusuyi [13].…”

Section: Introductionmentioning

confidence: 99%

Vehicle Parking Security System with Face Recognition Detection Based on Eigenface Algorithm

Raharja

Fathansyah

Chamim

2021

Journal of Robotics and Control

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RFID (Radio Frequency Identification) card technology is used for intelligent parking systems. Officers no longer need to record and check manually and repeatedly every vehicle that comes in and out with this system. The system has a weakness, namely if the RFId card is dropped or lost, the person who finds it can use the card. For the purpose of increasing security for users, one of the latest technologies is the use of facial recognition methods. The eigenface algorithm system is an algorithm used for face recognition. The algorithm is used as a training process for the previously inputted employee faces. The results of the training data are stored in a database which is then used as a key to recognize the face of the owner of the motorized vehicle who took the motorcycle. In the trial results of the data application, motorized vehicle owners enter when the facial recognition level is above 70%.

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Improving Parking Availability Information Using Deep Learning Techniques

Cited by 21 publications

References 17 publications

Toward a real‐time Smart Parking Data Management and Prediction (SPDMP) system by attributes representation learning

Toward a real‐time Smart Parking Data Management and Prediction (SPDMP) system by attributes representation learning

Parking Availability Prediction with Coarse-Grained Human Mobility Data

Vehicle Parking Security System with Face Recognition Detection Based on Eigenface Algorithm

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