Version 1
: Received: 2 July 2024 / Approved: 2 July 2024 / Online: 3 July 2024 (10:45:15 CEST)
How to cite:
Radovan, A.; Đambić, G.; Mihaljević, B. A Review of Passenger Counting in Public Transport Concepts based on Image Processing and Machine Learning. Preprints2024, 2024070263. https://doi.org/10.20944/preprints202407.0263.v1
Radovan, A.; Đambić, G.; Mihaljević, B. A Review of Passenger Counting in Public Transport Concepts based on Image Processing and Machine Learning. Preprints 2024, 2024070263. https://doi.org/10.20944/preprints202407.0263.v1
Radovan, A.; Đambić, G.; Mihaljević, B. A Review of Passenger Counting in Public Transport Concepts based on Image Processing and Machine Learning. Preprints2024, 2024070263. https://doi.org/10.20944/preprints202407.0263.v1
APA Style
Radovan, A., Đambić, G., & Mihaljević, B. (2024). A Review of Passenger Counting in Public Transport Concepts based on Image Processing and Machine Learning. Preprints. https://doi.org/10.20944/preprints202407.0263.v1
Chicago/Turabian Style
Radovan, A., Goran Đambić and Branko Mihaljević. 2024 "A Review of Passenger Counting in Public Transport Concepts based on Image Processing and Machine Learning" Preprints. https://doi.org/10.20944/preprints202407.0263.v1
Abstract
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 frequency 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, with a focus on image processing and machine learning techniques and concepts. The paper explores various algorithms, including traditional computer vision methods and advanced deep learning approaches, highlighting their strengths and limitations. Additionally, the paper examines the integration of these technologies with other sensing modalities such as infrared sensors, Wi-Fi, and RFID. 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 paper also analyzed the current GDPR regulation 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.
Keywords
public transport; passenger counting; image processing; machine learning
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
