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

Zeng, Chao; Ma, Changxi; Wang, Ke; Cui, Zihao

doi:10.1109/access.2022.3171330

Cited by 60 publications

(27 citation statements)

References 25 publications

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“…Furthermore, many researchers try to combine neural networks for accuracy improvement, e.g. a hybrid POP method [25] which stacks gate recurrent unit (GRU) and Long short‐term memory (LSTM) together. This method combines the advantages of LSTM and GRU in terms of prediction accuracy and prediction efficiency, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

“…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%

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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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Section: Literature Reviewmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…LSTM is considered an improved version of the recurrent neural network (RNN) [ 25 ]. It is proposed to expand the RNN memory.…”

Section: Machine Learning and Neural Network Algorithmsmentioning

confidence: 99%

Evaluation of Machine Leaning Algorithms for Streets Traffic Prediction: A Smart Home Use Case

Feng

Ahvar

Lee

2023

Sensors

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This paper defines a smart home use case to automatically adjust home temperature and/or hot water. The main objective is to reduce the energy consumption of cooling, heating and hot water systems in smart homes. To this end, the residents set a temperature (i.e., X degree Celsius) for home and/or hot water. When the residents leave homes (e.g., for work), they turn off the cooling or heating devices. A few minutes before arriving at their residences, the cooling or heating devices start working automatically to adjust the home or water temperature according to the residents’ preference (i.e., X degree Celsius). This can help reduce the energy consumption of these devices. To estimate the arrival time of the residents (i.e., drivers), this paper uses a machine learning-based street traffic prediction system. Unlike many related works that use machine learning for tracking and predicting residents’ behaviors inside their homes, this paper focuses on predicting resident behavior outside their home (i.e., arrival time as a context) to reduce the energy consumption of smart homes. One main objective of this paper is to find the most appropriate machine learning and neural network-based (MLNN) algorithm that can be integrated into the street traffic prediction system. To evaluate the performance of several MLNN algorithms, we utilize an Uber’s dataset for the city of San Francisco and complete the missing values by applying an imputation algorithm. The prediction system can also be used as a route recommender to offer the quickest route for drivers.

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“…Based on the empirical parking data, Mo et al [26] found that adjusting the price of parking fees can effectively change the parking behavior of travelers. Zeng et al [27] considered three factors, i.e., occupancy, weather conditions, and holiday, to achieve higher-precision parking space occupancy prediction than previous models.…”

Section: Influencing Factors Of Parkingmentioning

confidence: 99%

Multicriteria Model for Shared Parking and Parking Route Recommender Systems

Zhao

Wei

Pan

et al. 2022

Journal of Advanced Transportation

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In many congested areas, shared parking has gotten increasing attention because of its potential to alleviate parking resource shortages. However, managing parking resources remains a challenge when simultaneously considering multiple decision-making criteria of public travelers in allocating parking spaces and recommending optimal parking routes. To fill this gap, from four perspectives, i.e., driving, among shared parking lots, at a shared parking lot, between shared parking spaces and destinations, we proposed nine criteria for shared parking space allocations and parking route recommendations, and we also gave the quantitative models for different criteria. Furthermore, an analytic hierarchy process Entropy-TOPSIS grey relational analysis (AHP-Entropy-TOPSIS-GRA) method and an improved ant colony algorithm were proposed to solve the proposed allocation of parking spaces and recommend optimal parking routes, respectively. Finally, the validity of our proposed models and algorithms was tested by empirical parking data and road traffic data collected in Huai’an City, Jiangsu province, China. The research helps provide a theoretical foundation for implementing shared parking initiatives and improving public travelers’ parking satisfaction.

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

Cited by 60 publications

References 25 publications

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

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

Evaluation of Machine Leaning Algorithms for Streets Traffic Prediction: A Smart Home Use Case

Multicriteria Model for Shared Parking and Parking Route Recommender Systems

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