Passenger flow forecast of tourist attraction based on MACBL in LBS big data environment
The existing scenic spot passenger flow prediction models have poor prediction accuracy and inadequate feature extraction ability. To address these issues, a multi-attentional convolutional bidirectional long short-term memory (MACBL)-based method for predicting tourist flow in tourist scenic locati...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2023-12-01
|
| Series: | Open Geosciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/geo-2022-0577 |
| _version_ | 1827398612292730880 |
|---|---|
| author | Tang Qili Yang Li Pan Li |
| author_facet | Tang Qili Yang Li Pan Li |
| author_sort | Tang Qili |
| collection | DOAJ |
| description | The existing scenic spot passenger flow prediction models have poor prediction accuracy and inadequate feature extraction ability. To address these issues, a multi-attentional convolutional bidirectional long short-term memory (MACBL)-based method for predicting tourist flow in tourist scenic locations in a location-based services big data environment is proposed in this study. First, a convolutional neural network is employed to identify local features and reduce the dimension of the input data. Then, a bidirectional long short-term memory network is utilized to extract time-series information. Second, the multi-head attention mechanism is employed to parallelize the input data and assign weights to the feature data, which deepens the extraction of important feature information. Next, the dropout layer is used to avoid the overfitting of the model. Finally, three layers of the above network are stacked to form a deep conformity network and output the passenger flow prediction sequence. In contrast to the state-of-the-art models, the MACBL model has enhanced the root mean square error index by at least 2.049, 2.926, and 1.338 for prediction steps of 24, 32, and 60 h, respectively. Moreover, it has also enhanced the mean absolute error index by at least 1.352, 1.489, and 0.938, and the mean absolute percentage error index by at least 0.0447, 0.0345, and 0.0379% for the same prediction steps. The experimental results indicate that the MACBL is better than the existing models in evaluating indexes of different granularities, and it is effective in enhancing the forecasting precision of tourist attractions. |
| first_indexed | 2024-03-08T19:32:13Z |
| format | Article |
| id | doaj.art-2ff48dc8d2a448a6930e05a5bba86ef5 |
| institution | Directory Open Access Journal |
| issn | 2391-5447 |
| language | English |
| last_indexed | 2024-03-08T19:32:13Z |
| publishDate | 2023-12-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Geosciences |
| spelling | doaj.art-2ff48dc8d2a448a6930e05a5bba86ef52023-12-26T07:40:09ZengDe GruyterOpen Geosciences2391-54472023-12-0115122707110.1515/geo-2022-0577Passenger flow forecast of tourist attraction based on MACBL in LBS big data environmentTang Qili0Yang Li1Pan Li2School of Economics and Management, Aba Teachers University, Aba, Sichuan, 623002, ChinaSchool of Automation, Chengdu University of Information Technology, Chengdu, Sichuan, 610225, ChinaPersonnel Department, Zhengzhou Institute of Engineering and Technology, Zhenzhou450044, ChinaThe existing scenic spot passenger flow prediction models have poor prediction accuracy and inadequate feature extraction ability. To address these issues, a multi-attentional convolutional bidirectional long short-term memory (MACBL)-based method for predicting tourist flow in tourist scenic locations in a location-based services big data environment is proposed in this study. First, a convolutional neural network is employed to identify local features and reduce the dimension of the input data. Then, a bidirectional long short-term memory network is utilized to extract time-series information. Second, the multi-head attention mechanism is employed to parallelize the input data and assign weights to the feature data, which deepens the extraction of important feature information. Next, the dropout layer is used to avoid the overfitting of the model. Finally, three layers of the above network are stacked to form a deep conformity network and output the passenger flow prediction sequence. In contrast to the state-of-the-art models, the MACBL model has enhanced the root mean square error index by at least 2.049, 2.926, and 1.338 for prediction steps of 24, 32, and 60 h, respectively. Moreover, it has also enhanced the mean absolute error index by at least 1.352, 1.489, and 0.938, and the mean absolute percentage error index by at least 0.0447, 0.0345, and 0.0379% for the same prediction steps. The experimental results indicate that the MACBL is better than the existing models in evaluating indexes of different granularities, and it is effective in enhancing the forecasting precision of tourist attractions.https://doi.org/10.1515/geo-2022-0577scenic area passenger flow forecastdeep learningmulti-head attention mechanismconvolutional neural networkbidirectional long short-term memory networklbs big data |
| spellingShingle | Tang Qili Yang Li Pan Li Passenger flow forecast of tourist attraction based on MACBL in LBS big data environment Open Geosciences scenic area passenger flow forecast deep learning multi-head attention mechanism convolutional neural network bidirectional long short-term memory network lbs big data |
| title | Passenger flow forecast of tourist attraction based on MACBL in LBS big data environment |
| title_full | Passenger flow forecast of tourist attraction based on MACBL in LBS big data environment |
| title_fullStr | Passenger flow forecast of tourist attraction based on MACBL in LBS big data environment |
| title_full_unstemmed | Passenger flow forecast of tourist attraction based on MACBL in LBS big data environment |
| title_short | Passenger flow forecast of tourist attraction based on MACBL in LBS big data environment |
| title_sort | passenger flow forecast of tourist attraction based on macbl in lbs big data environment |
| topic | scenic area passenger flow forecast deep learning multi-head attention mechanism convolutional neural network bidirectional long short-term memory network lbs big data |
| url | https://doi.org/10.1515/geo-2022-0577 |
| work_keys_str_mv | AT tangqili passengerflowforecastoftouristattractionbasedonmacblinlbsbigdataenvironment AT yangli passengerflowforecastoftouristattractionbasedonmacblinlbsbigdataenvironment AT panli passengerflowforecastoftouristattractionbasedonmacblinlbsbigdataenvironment |