A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale

Zhao, Ziyao; Zhang, Yi

doi:10.1155/2020/5624586

Cited by 27 publications

(15 citation statements)

References 21 publications

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“…By analyzing the parking lot historical data, Richter et al [10] clustered the time and space of the parking occupancy data and selected the optimal time and space characteristics to further improve prediction accuracy. Zheng et al [11] and Zhao et al [12] predicted the parking availability of the support vector regressions under conditions considered, and verified the accuracy and stability of its predictive performance. Zhang [13] proposed a multi-step prediction method for Fourier transform-minimum multiplier support to verify the chaotic characteristics of the occupancy sequence, and used multi Agent to simulate the economic benefits and social benefits of occupancy prediction.…”

Section: Literature Reviewmentioning

confidence: 85%

Parking Occupancy Prediction Method Based on Multi Factors and Stacked GRU-LSTM

Zeng

Wang

et al. 2022

IEEE Access

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With the development of society and the continuous advancement of urbanization, motor vehicles have increased rapidly, which exacerbates the imbalance between parking supply and demand. Therefore, it is very important to excavate knowledge from historical parking data and forecast the parking volume in different time periods so as to optimize parking resource utilization and improve traffic conditions. This paper proposes a new hybrid model that stacks gated recurrent unit (GRU) and long-short term memory (LSTM). The proposed stacked GRU-LSTM model combines LSTM's advantage in prediction accuracy and GRU's advantage in prediction efficiency, and uses multi factors, including occupancy, weather conditions and holiday, as input to predict parking availability. When compared against other predictive models such as stacked simple RNN, stacked LSTM-RNN, and stacked LSTM-Bi-LSTM, our experimental results indicate that the stacked GRU-LSTM model has better performance for parking occupancy prediction as it not only improves prediction accuracy, but also reduces prediction time.

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Section: Literature Reviewmentioning

confidence: 85%

Parking Occupancy Prediction Method Based on Multi Factors and Stacked GRU-LSTM

Zeng

Wang

et al. 2022

IEEE Access

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“…In the last decade, machine learning methods have attracted much attention to traffic condition prediction due to their excellent ability to model complex relationships between features [21][22][23]. The forecasting task for parking occupancy is no exception.…”

Section: Literature Reviewmentioning

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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“…As shown by Figure 5, 30 car parks in Guangzhou (P1-P30) are set as Train tasks, while 12 car parks in Shenzhen (Target1-Target12) are Target (Test) tasks. Further, these parks are classified into six types according to the land use attributes of the area in which they are located [43]: Commercial, Hospital, Office, Residential, Recreational, and Tourism. Another important parking-related feature is the density of points of interest (POI).…”

Section: Data Declarationmentioning

confidence: 99%

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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A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale

Cited by 27 publications

References 21 publications

Parking Occupancy Prediction Method Based on Multi Factors and Stacked GRU-LSTM

Parking Occupancy Prediction Method Based on Multi Factors and Stacked GRU-LSTM

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

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

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