An Integrated Approach for the Near Real-Time Parking Occupancy Prediction

Li, Jun; Qu, Haohao; You, Linlin

doi:10.1109/tits.2022.3230199

Cited by 10 publications

(4 citation statements)

References 35 publications

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“…It directly exploits the powerful property of convolution to extract features across time steps. MGRU [30]: an recent developed model for parking occupancy prediction that combines empirical mode decomposition and GRU. Whereof, GRU, LSTM, BiLSTM, and MGRU are recurrent neural network (RNN)‐based models, while TCN is convolutional neural network (CNN)‐based model.…”

Section: Methodsmentioning

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: Methodsmentioning

confidence: 99%

“…Moreover, even in the same context, the parking type of parking facilities may also affect the occupancy, e.g. shopping centers may have a higher parking ratio on Valentine's Day than hospitals [30].…”

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

Self Cite

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“…A parking availability prediction model based on neural networks and random forests is proposed to demonstrate the role of WoT and AI in smart cities [22]. In recent years, computational and storage capabilities have been evolving, and deep learning has been widely used for intelligent traffic status prediction and parking occupancy prediction [23][24][25][26]. For example, a novel long short-term memory recurrent neural network (LSTM-NN) model is proposed to make multistep prediction for parking occupancy based on historical information [27].…”

Section: Related Workmentioning

confidence: 99%

A Multi-View Approach for Regional Parking Occupancy Prediction with Attention Mechanisms

Ye,

Kuang,

Lai

et al. 2023

Mathematics

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The near-future parking space availability is informative for the formulation of parking-related policy in urban areas. Plenty of studies have contributed to the spatial–temporal prediction for parking occupancy by considering the adjacency between parking lots. However, their similarities in properties remain unspecific. For example, parking lots with similar functions, though not adjacent, usually have similar patterns of occupancy changes, which can help with the prediction as well. To fill the gap, this paper proposes a multi-view and attention-based approach for spatial–temporal parking occupancy prediction, namely hybrid graph convolution network with long short-term memory and temporal pattern attention (HGLT). In addition to the local view of adjacency, we construct a similarity matrix using the Pearson correlation coefficient between parking lots as the global view. Then, we design an integrated neural network focusing on graph structure and temporal pattern to assign proper weights to the different spatial features in both views. Comprehensive evaluations on a real-world dataset show that HGLT reduces prediction error by about 30.14% on average compared to other state-of-the-art models. Moreover, it is demonstrated that the global view is effective in predicting parking occupancy.

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“…[48], and unlike other modes of transportation, bus passenger fow is subject to greater uncertainty and can be afected by multiple contingencies. Terefore, in order to accurately represent the time series information, a time series decomposition (TSD) model is used to extract the temporal features [49,50].…”

Section: Problem Statement Te Transit Passenger Fow Predictionmentioning

confidence: 99%

A Dual-View Approach for Multistation Short-Term Passenger Flow Prediction in Bus Transit Systems

Luo,

Kuang,

Zhang

et al. 2023

Journal of Advanced Transportation

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Timely and accurate prediction of bus passenger flow plays a crucial role in uncovering real-time traffic demand, presenting an essential and formidable challenge in the realm of bus scheduling and management. The extensive application of deep learning methods in transit passenger flow prediction can be attributed to their exceptional ability to effectively capture spatiotemporal features, resulting in superior performance. However, prevailing deep learning models in transit passenger flow prediction tend to ignore the data enhancement. Additionally, the predominant focus on a single station in the prediction task presents challenges in effectively capturing the spatiotemporal features of the entire network. A model named TSD-ST is proposed to better accomplish the task of predicting short-term transit passenger flow at multistation. The TSD-ST model leverages time series decomposition for data enhancement. Simultaneously, in addition to considering the adjacency graph, the similarity of all the stations of the entire transit network is also considered and uses multigraph convolution and graph fusion modules. This approach enables the TSD-ST model to effectively capture spatiotemporal dependencies. Experiments based on real-world bus transit datasets confirm that the TSD-ST model shows better performance in prediction tasks at 30-min, 60-min, and 90-min time scales, with an average improvement of 21.87%. The effectiveness of each component has been verified through ablation experiments.

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An Integrated Approach for the Near Real-Time Parking Occupancy Prediction

Cited by 10 publications

References 35 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

A Multi-View Approach for Regional Parking Occupancy Prediction with Attention Mechanisms

A Dual-View Approach for Multistation Short-Term Passenger Flow Prediction in Bus Transit Systems

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