Affiliation:
1. Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, 10125 Turin, Italy
Abstract
Automatic passenger counting (APC) systems in public transport are useful in collecting information that can help improve the efficiency of transport networks. Focusing on video-based passenger counting, the aim of this study was to evaluate and compare an existing APC system, claimed by its manufacturer to be highly accurate (98%), with a newly developed low-cost APC system operating under the same real-world conditions. For this comparison, a low-cost APC system using a Raspberry Pi with a camera and a YOLOv5 object detection algorithm was developed, and an in-field experiment was performed in collaboration with the public transport companies operating in the cities of Turin and Asti in Italy. The experiment shows that the low-cost system was able to achieve an accuracy of 72.27% and 74.59%, respectively, for boarding and alighting, while the tested commercial APC system had an accuracy, respectively, of 53.11% and 55.29%. These findings suggest that current APC systems might not meet expectations under real-world conditions, while low-cost systems could potentially perform at the same level of accuracy or even better than very expensive commercial systems.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference73 articles.
1. Combining ITS and Optimization in Public Transportation Planning: State of the Art and Future Research Paths;Iliopoulou;Eur. Transp. Res. Rev.,2019
2. A Comprehensive View of Intelligent Transport Systems for Urban Smart Mobility;Mangiaracina;Int. J. Logist. Res. Appl.,2017
3. Dimitrakopoulos, G., Uden, L., and Varlamis, I. (2020). The Future of Intelligent Transport Systems, Elsevier.
4. Computer Vision Applications in Construction: Current State, Opportunities & Challenges;Paneru;Autom. Constr.,2021
5. Dhou, S., Alnabulsi, A., Al-Ali, A.R., Arshi, M., Darwish, F., Almaazmi, S., and Alameeri, R. (2022). An IoT Machine Learning-Based Mobile Sensors Unit for Visually Impaired People. Sensors, 22.
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