A Framework of Vehicular Security and Demand Service Prediction Based on Data Analysis Integrated with Blockchain Approach
The prediction of taxi demand service has become a recently attractive area of research along with large-scale and potential applications in the intelligent transportation system. The demand process is divided into two main parts: Picking-up and dropping-off demand based on passenger habit. Taxi dem...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/10/3314 |
| _version_ | 1797534598178013184 |
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| author | Zeinab Shahbazi Yung-Cheol Byun |
| author_facet | Zeinab Shahbazi Yung-Cheol Byun |
| author_sort | Zeinab Shahbazi |
| collection | DOAJ |
| description | The prediction of taxi demand service has become a recently attractive area of research along with large-scale and potential applications in the intelligent transportation system. The demand process is divided into two main parts: Picking-up and dropping-off demand based on passenger habit. Taxi demand prediction is a great concept for drivers and passengers, and is designed platforms for ride-hailing and municipal managers. The majority of research has focused on forecasting the pick-up part of demand service and specifying the interconnection of spatial and temporal correlations. In this study, the main focus is to overcome the access point of non-registered users for having fake transactions using taxi services and predicting taxi demand pick-up and drop-off information. The integration of machine learning techniques and blockchain framework is considered a possible solution for this problem. The blockchain technique was selected as an effective technique for protecting and controlling the real-time system. Historical data analysis was processed by extracting the three higher related sections for the intervening time, namely closeness and trend. Next, the pick-up and drop-off taxi prediction task was processed based on constructing the components of multi-task learning and spatiotemporal feature extraction. The combination of feature embedding performance and Long Short-Term Memory (LSTM) obtain the pick-up and drop-off correlation by fusing the historical data spatiotemporal features. Finally, the taxi demand pick-up and drop-off prediction were processed based on the combination of the external factors. The experimental result is based on a real dataset in Jeju Island, South Korea, to show the proposed system’s efficacy and performance compared with other state-of-art models. |
| first_indexed | 2024-03-10T11:32:46Z |
| format | Article |
| id | doaj.art-8079833d4a464fbdb9389f6942850d3b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:32:46Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8079833d4a464fbdb9389f6942850d3b2023-11-21T19:07:05ZengMDPI AGSensors1424-82202021-05-012110331410.3390/s21103314A Framework of Vehicular Security and Demand Service Prediction Based on Data Analysis Integrated with Blockchain ApproachZeinab Shahbazi0Yung-Cheol Byun1Department of Computer Engineering, Institute of Information Science Technology, Jeju National University, Jejusi 63243, KoreaDepartment of Computer Engineering, Institute of Information Science Technology, Jeju National University, Jejusi 63243, KoreaThe prediction of taxi demand service has become a recently attractive area of research along with large-scale and potential applications in the intelligent transportation system. The demand process is divided into two main parts: Picking-up and dropping-off demand based on passenger habit. Taxi demand prediction is a great concept for drivers and passengers, and is designed platforms for ride-hailing and municipal managers. The majority of research has focused on forecasting the pick-up part of demand service and specifying the interconnection of spatial and temporal correlations. In this study, the main focus is to overcome the access point of non-registered users for having fake transactions using taxi services and predicting taxi demand pick-up and drop-off information. The integration of machine learning techniques and blockchain framework is considered a possible solution for this problem. The blockchain technique was selected as an effective technique for protecting and controlling the real-time system. Historical data analysis was processed by extracting the three higher related sections for the intervening time, namely closeness and trend. Next, the pick-up and drop-off taxi prediction task was processed based on constructing the components of multi-task learning and spatiotemporal feature extraction. The combination of feature embedding performance and Long Short-Term Memory (LSTM) obtain the pick-up and drop-off correlation by fusing the historical data spatiotemporal features. Finally, the taxi demand pick-up and drop-off prediction were processed based on the combination of the external factors. The experimental result is based on a real dataset in Jeju Island, South Korea, to show the proposed system’s efficacy and performance compared with other state-of-art models.https://www.mdpi.com/1424-8220/21/10/3314blockchainmulti-task learningmachine learningtaxi demand servicelong short-term memory |
| spellingShingle | Zeinab Shahbazi Yung-Cheol Byun A Framework of Vehicular Security and Demand Service Prediction Based on Data Analysis Integrated with Blockchain Approach Sensors blockchain multi-task learning machine learning taxi demand service long short-term memory |
| title | A Framework of Vehicular Security and Demand Service Prediction Based on Data Analysis Integrated with Blockchain Approach |
| title_full | A Framework of Vehicular Security and Demand Service Prediction Based on Data Analysis Integrated with Blockchain Approach |
| title_fullStr | A Framework of Vehicular Security and Demand Service Prediction Based on Data Analysis Integrated with Blockchain Approach |
| title_full_unstemmed | A Framework of Vehicular Security and Demand Service Prediction Based on Data Analysis Integrated with Blockchain Approach |
| title_short | A Framework of Vehicular Security and Demand Service Prediction Based on Data Analysis Integrated with Blockchain Approach |
| title_sort | framework of vehicular security and demand service prediction based on data analysis integrated with blockchain approach |
| topic | blockchain multi-task learning machine learning taxi demand service long short-term memory |
| url | https://www.mdpi.com/1424-8220/21/10/3314 |
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