A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning
The accurate counting of passengers in public transport systems is crucial for optimizing operations, improving service quality, and planning infrastructure. It can also contribute to reducing the number of public transport lines where a high number of vehicles is not needed in certain periods durin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Eng |
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| Online Access: | https://www.mdpi.com/2673-4117/5/4/172 |
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| author | Aleksander Radovan Leo Mršić Goran Đambić Branko Mihaljević |
| author_facet | Aleksander Radovan Leo Mršić Goran Đambić Branko Mihaljević |
| author_sort | Aleksander Radovan |
| collection | DOAJ |
| description | The accurate counting of passengers in public transport systems is crucial for optimizing operations, improving service quality, and planning infrastructure. It can also contribute to reducing the number of public transport lines where a high number of vehicles is not needed in certain periods during the year, but also by increasing the number of lines where the need is increased. This paper provides a comprehensive review of current methodologies and technologies used for passenger counting, without the actual implementation of the automatic passenger counting system (APC), but with a proposal based on image processing and machine learning techniques and concepts, since it represents one of the most used approaches. The research explores various technologies and algorithms, like card swiping, infrared, weight and ultrasonic sensors, RFID, Wi-Fi, Bluetooth, LiDAR, thermos cameras, including CCTV cameras and traditional computer vision methods, and advanced deep learning approaches, highlighting their strengths and limitations. By analyzing recent advancements and case studies, this review aims to offer insights into the effectiveness, scalability, and practicality of different passenger counting solutions and offers a solution proposal. The research also analyzed the current General Data Protection Regulation (GDPR) that applies to the European Union and how it affects the use of systems like this. Future research directions and potential areas for technological innovation are also discussed to guide further developments in this field. |
| format | Article |
| id | doaj-art-1ca0bd7b3aa749da8e6576ce63ce8da3 |
| institution | DOAJ |
| issn | 2673-4117 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Eng |
| spelling | doaj-art-1ca0bd7b3aa749da8e6576ce63ce8da32025-08-20T02:50:59ZengMDPI AGEng2673-41172024-12-01543284331510.3390/eng5040172A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine LearningAleksander Radovan0Leo Mršić1Goran Đambić2Branko Mihaljević3Department of Software Engineering, Algebra University College, 10000 Zagreb, CroatiaDepartment of Program Engineering, Algebra University College, 10000 Zagreb, CroatiaDepartment of Software Engineering, Algebra University College, 10000 Zagreb, CroatiaDepartment of Information Sciences & Technologies, Rochester Institute of Technology (RIT Croatia), 10000 Zagreb, CroatiaThe accurate counting of passengers in public transport systems is crucial for optimizing operations, improving service quality, and planning infrastructure. It can also contribute to reducing the number of public transport lines where a high number of vehicles is not needed in certain periods during the year, but also by increasing the number of lines where the need is increased. This paper provides a comprehensive review of current methodologies and technologies used for passenger counting, without the actual implementation of the automatic passenger counting system (APC), but with a proposal based on image processing and machine learning techniques and concepts, since it represents one of the most used approaches. The research explores various technologies and algorithms, like card swiping, infrared, weight and ultrasonic sensors, RFID, Wi-Fi, Bluetooth, LiDAR, thermos cameras, including CCTV cameras and traditional computer vision methods, and advanced deep learning approaches, highlighting their strengths and limitations. By analyzing recent advancements and case studies, this review aims to offer insights into the effectiveness, scalability, and practicality of different passenger counting solutions and offers a solution proposal. The research also analyzed the current General Data Protection Regulation (GDPR) that applies to the European Union and how it affects the use of systems like this. Future research directions and potential areas for technological innovation are also discussed to guide further developments in this field.https://www.mdpi.com/2673-4117/5/4/172public transportpassenger countingimage processingmachine learning |
| spellingShingle | Aleksander Radovan Leo Mršić Goran Đambić Branko Mihaljević A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning Eng public transport passenger counting image processing machine learning |
| title | A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning |
| title_full | A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning |
| title_fullStr | A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning |
| title_full_unstemmed | A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning |
| title_short | A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning |
| title_sort | review of passenger counting in public transport concepts with solution proposal based on image processing and machine learning |
| topic | public transport passenger counting image processing machine learning |
| url | https://www.mdpi.com/2673-4117/5/4/172 |
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