Prediction of On-Street Parking Level of Service Based on Random Undersampling Decision Trees

Pozo, Rubén Fernández; González, Ana Belén Rodríguez; Wilby, Mark Richard; Díaz, Juan José Vinagre; Matesanz, Miguel Viana

doi:10.1109/tits.2021.3077985

Cited by 9 publications

(6 citation statements)

References 44 publications

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“…To tackle the aforementioned challenges, several solutions are proposed. First and foremost, in order to address data shortages, the main focus of some solutions remains on data collection and analysis to better interpret and infer the current and future parking statuses [12,13,26]. Examples include: (1) CoPASample [27] and BATF [28], which utilize heuristics-based covariance and Bayesian augmented tensor factorization, respectively, to generate synthetic samples that look close to the original data; and (2) WoT-NNs [14], which leverages the techniques of Web of Things (WoT) to collect additional information and incorporate them into neural networks.…”

Section: Related Solutionsmentioning

confidence: 99%

“…where S i represents the state matrix of federation member i, who has a dimension of 2 × N, η 1×N i and τ 1×N i indicate the density and type feature vectors of state respectively, η i and τ i are the density scalar and type code of member i, η and Γ represents the density vector and type matrix of the federation, N is the total number of members, λ denotes a positive constant, and * denotes the Hadamard product. (12).…”

Section: Data Declarationmentioning

confidence: 99%

“…Traditional data-hungry models are unable to predict with high accuracy and stability in a data-poor scenario, resulting in insufficient quality of service provided, which seriously undermines users' trust [11]. Although, with the development of Smart Cities, more and more sensing devices have been deployed for real-time data collection of all kinds, limited-sensing parking facilities still account for the majority [12], e.g., on-street and public-free parking lots. As a result, an effective and efficient POP method capable of addressing the small-sample issue is required to enable full coverage of smart parking services while reducing the cost of building sensing infrastructures.…”

Section: Introductionmentioning

confidence: 99%

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Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction

Liu

et al. 2022

Mathematics

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Parking occupancy prediction (POP) plays a vital role in many parking-related smart services for better parking management. However, an issue hinders its mass deployment: many parking facilities cannot collect enough data to feed data-hungry machine learning models. To tackle the challenges in small-sample POP, we propose an approach named Adaptation and Learning to Learn (ALL) by adopting the capability of advanced deep learning and federated learning. ALL integrates two novel ideas: (1) Adaptation: by leveraging the Asynchronous Advantage Actor-Critic (A3C) reinforcement learning technique, an auto-selector module is implemented, which can group and select data-scarce parks automatically as supporting sources to enable the knowledge adaptation in model training; and (2) Learning to learn: by applying federated meta-learning on selected supporting sources, a meta-learner module is designed, which can train a high-performance local prediction model in a collaborative and privacy-preserving manner. Results of an evaluation with 42 parking lots in two Chinese cities (Shenzhen and Guangzhou) show that, compared to state-of-the-art baselines: (1) the auto-selector can reduce the model variance by about 17.8%; (2) the meta-learner can train a converged model 102x faster; and (3) finally, ALL can boost the forecasting performance by about 29.8%. Through the integration of advanced machine learning methods, i.e., reinforcement learning, meta-learning, and federated learning, the proposed approach ALL represents a significant step forward in solving small-sample issues in parking occupancy prediction.

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Section: Related Solutionsmentioning

confidence: 99%

Section: Data Declarationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction

Liu

et al. 2022

Mathematics

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“…In the current literature, there are two types of methods to extract the change features of parking occupancy to improve the prediction accuracy: 1) data augmentation by generating fake samples that look like original data, introducing extra heterogeneous data, or decomposing patterns [7][8][9][10]; 2) structure optimization by using advanced machine learning or deep learning methods [11,12]. However, these methods still suffer from two major shortages.…”

Section: Introductionmentioning

confidence: 99%

A parking occupancy prediction method incorporating time series decomposition and temporal pattern attention mechanism

Ye,

Kuang,

et al. 2023

IET Intelligent Trans Sys

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Parking occupancy prediction is an important reference for travel decisions and parking management. However, due to various related factors, such as commuting or traffic accidents, parking occupancy has complex change features that are difficult to model accurately, thus making it difficult for parking occupancy to be accurately predicted. Moreover, how to give appropriate weights to these changing features in prediction becomes a new challenge in the era of machine learning. To tackle these challenges, a parking occupancy prediction method called time series decomposition–long and short‐term memory neural network (LSTM)–temporal pattern attention mechanism, which consists of three modules, namely 1) time series decomposition: modelling parking occupancy changes by extracting features such as trend, period, and effect; 2) encoder: extracting temporal correlations of feature sequences with LSTM; 3) temporal pattern attention mechanism: assigning attention to different features, are proposed. The evaluation results of 30 parking lots in Guangzhou city show that the proposed model 1) improves accuracy over the baseline model LSTM by 9.14% on average; 2) performs outstanding in four prediction time intervals and six types of parking lots, proving its validity and generality; 3) demonstrates its rationality and interpretability through ablation experiments and Shapley additive explanation.

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“…[5] applies classification methods to images captured by fixed cameras in order to estimate the occupancy status. A prediction of parking lot occupancy is performed by [6] based on data from smart parking meters. In these approaches, data analysis is limited to those areas covered by the respective sensor technology.…”

Section: Introductionmentioning

confidence: 99%

Parking space inventory from above: Detection on aerial images and estimation for unobserved regions

Hellekes

Kehlbacher

Díaz

et al. 2022

IET Intelligent Trans Sys

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Parking is a vital component of today's transportation system and descriptive data are therefore of great importance for urban planning and traffic management. However, data quality is often low: managed parking places may only be partially inventoried, or parking at the curbside and on private ground may be missing. This paper presents a processing chain in which remote sensing data and statistical methods are combined to provide parking area estimates. First, parking spaces and other traffic areas are detected from aerial imagery using a convolutional neural network. Individual image segmentations are fused to increase completeness. Next, a Gamma hurdle model is estimated using the detected parking areas and OpenStreetMap and land use data to predict the parking area adjacent to streets. A systematic relationship is found between the road length and type and the parking area obtained. It is suggested that these results are informative to those needing information on parking in structurally similar regions.

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Prediction of On-Street Parking Level of Service Based on Random Undersampling Decision Trees

Cited by 9 publications

References 44 publications

Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction

Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction

A parking occupancy prediction method incorporating time series decomposition and temporal pattern attention mechanism

Parking space inventory from above: Detection on aerial images and estimation for unobserved regions

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