Parking Volume Forecast of Railway Station Garages Based on Passenger Behaviour Analysis Using the LSTM Network

Gai, Songxue; Zeng, Xiaoqing; Yuan, Tengfei

doi:10.1155/2021/6688609

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…Compared with other deep learning-based algorithm architectures, the multipose human action recognition architecture mainly has the advantages of high recognition accuracy, low complexity, and strong complementarity of action information between multiple modalities. Human action recognition is an important technology in the field of video understanding and analysis [34]. In recent years, with the rapid development of video-based position estimation algorithms, bone-based methods for human action recognition have become popular.…”

Section: Multiview Reobservation Fusion Action Recognition Model Base...mentioning

confidence: 99%

Research on Human Action Feature Detection and Recognition Algorithm Based on Deep Learning

Hua-ying

2022

Mobile Information Systems

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With the improvement of computer computing power and storage capacity, the emergence of massive data makes the methods based on human action feature detection and recognition unable to meet people’s needs due to poor generalization ability. Based on the detection and recognition of human action features based on deep learning algorithms, a suitable neural network can be constructed to identify locked human action features from surveillance video and analyze whether it is a specific behavior. In this paper, a deep learning algorithm is proposed to optimize the detection of human action features, and a multiview reobservation fusion action recognition model of 3D pose is designed. Several factors affecting the recognition of human action features are analyzed, and a detailed summary is made from the detection environment. Experiments show that adding one or two layers of feature attention enhancement to the multiview observation fusion network can improve the accuracy by 1% to 3%. In this way, the model can integrate action features from multiple observation angles to judge actions and learn to find observation angles suitable for action recognition, thereby improving the performance of action recognition.

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Section: Multiview Reobservation Fusion Action Recognition Model Base...mentioning

confidence: 99%

Research on Human Action Feature Detection and Recognition Algorithm Based on Deep Learning

Hua-ying

2022

Mobile Information Systems

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“…According to formulas ( 9) and ( 10) and the above model calibration results, the influence of the waiting time change of each connection mode on the selection results of passenger connection modes in the aggregation and relief connection journey is obtained, as shown in Tables 6 and 7 [31].…”

Section: Waiting Time Adjustment Strategymentioning

confidence: 99%

Modeling of Mobility as a Service (MaaS) Collaborative Dispatching System of Railway Passenger Transport Hub Based on Neural Network Algorithm

Dan

Yun

Chen

et al. 2022

Wireless Communications and Mobile Computing

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In order to solve the modeling problem of travel as a service (MaaS) collaborative dispatching system of railway passenger transport hub based on neural network algorithm, meet people’s needs, make up for the lack of high traffic travel pressure, and improve people’s living standards, through 30 random questionnaires, it is found that 28 people think that travel convenience improves their quality of life, and 2 people think that owning a car has little effect on travel convenience. Travel has always been people’s basic living needs. In recent years, with the improvement of people’s living standards, there are more and more urban vehicles. At the same time, the increase of vehicles also directly leads to the increasing pressure of urban traffic travel, and the problems of vehicle emission pollution and traffic congestion are becoming more and more obvious. With the national low-carbon environmental protection policies, green transportation has become the theme of the times. With the continuous development of shared transportation mode and intelligent information technology, the new transportation concept of “Mobility as a Service” based on this technical mode will highly integrate the existing transportation modes and travel services. With the support of MaaS system, people’s travel modes will change greatly. Starting with the MaaS cooperative dispatching system of railway passenger transport hub, a multiparameter fuzzy neural network control system dispatching algorithm is proposed to better help the modeling of MaaS cooperative dispatching system of railway passenger transport hub.

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“…e results showed that the parking demand predicted by the deep learning neural network is greatly improved in comparison with the parking demand predicted by conventional basic models. Based on the analysis of passenger travel behaviors, Gai et al [4] proposed a parking demand prediction model with long short-term memory (LSTM) as the core to predict the parking demand of parking lots in railway stations. e results showed that the LSTM model achieves a better prediction effect than the traditional models.…”

Section: Introductionmentioning

confidence: 99%

Parking Demand Prediction Method of Urban Commercial-Office Complex Buildings Based on the MRA-BAS-BP Algorithm

Tang

Zhou³

et al. 2022

Journal of Advanced Transportation

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With the increasingly significant trend of developing urban land for mixed-use, an increasing number of urban commercial and office complexes have been built. The parking demand characteristics of such buildings are more complex than the parking demand characteristics of single-use buildings due to more diverse influencing factors. As there are complicated linear and nonlinear relationships between parking demand and influencing factors, it is difficult to accurately predict parking demand using a single multiple regression analysis (MRA) model. Hence, in this paper, a combined algorithm based on the MRA model, beetle antennae search (BAS) algorithm, and BP neural network is proposed for demand prediction. In this paper, a two-level and ten-category index system is established and then mixed with the BP algorithm through the MRA model to improve the overall robustness and accuracy of the algorithm. Then, the BAS algorithm is used to search for optimal parameters involved in the BP neural network to avoid local optimization and improve the accuracy and efficiency of prediction. Finally, an instance analysis is carried out for verification, and the result indicates that the parking demand prediction accuracy of the MRA-BAS-BP algorithm is higher than the prediction accuracy of the traditional algorithm.

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Parking Volume Forecast of Railway Station Garages Based on Passenger Behaviour Analysis Using the LSTM Network

Cited by 5 publications

References 33 publications

Research on Human Action Feature Detection and Recognition Algorithm Based on Deep Learning

Research on Human Action Feature Detection and Recognition Algorithm Based on Deep Learning

Modeling of Mobility as a Service (MaaS) Collaborative Dispatching System of Railway Passenger Transport Hub Based on Neural Network Algorithm

Parking Demand Prediction Method of Urban Commercial-Office Complex Buildings Based on the MRA-BAS-BP Algorithm

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