Intelligent Transit Management and Capacity Insights

The "Intelligent Transit Management and Capacity Insights" project offers a novel way to deal with the problems of crowding and discomfort among passengers on public transit. The technology offers real-time monitoring of seat occupancy on buses by utilizing cameras linked to a Raspberry Pi and machine learning techniques. The technology precisely counts the number of occupied seats by evaluating the images taken by the cameras. This information is then subtracted from the overall seating capacity to provide correct information about seat availability. This feature greatly improves passenger comfort while traveling in addition to helping to reduce congestion. Because of the system's compatibility with Raspberry Pi, existing bus infrastructures may be seamlessly integrated, guaranteeing dependable data processing. Additionally, the system's use of machine learning enables steady performance and accuracy improvements over time. By combining advanced technologies such as machine learning, cameras, and Raspberry Pi, this integrated solution represents a significant step forward in the enhancement of public transportation systems, ultimately leading to improved operational efficiency, safety measures, and passenger satisfaction.

Authors

Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-03-24
DOI
https://doi.org/10.22214/ijraset.2025.67732
Primary Topic
Traffic Prediction and Management Techniques
Type
article
Field-Weighted Citation Impact
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article

Intelligent Transit Management and Capacity Insights

T. H. Akshaya
International Journal for Research in Applied Science and Engineering Technology
Traffic Prediction and Management Techniques
article

Intelligent Transit Management and Capacity Insights

T. H. Akshaya
article en

Abstract

The "Intelligent Transit Management and Capacity Insights" project offers a novel way to deal with the problems of crowding and discomfort among passengers on public transit. The technology offers real-time monitoring of seat occupancy on buses by utilizing cameras linked to a Raspberry Pi and machine learning techniques. The technology precisely counts the number of occupied seats by evaluating the images taken by the cameras. This information is then subtracted from the overall seating capacity to provide correct information about seat availability. This feature greatly improves passenger comfort while traveling in addition to helping to reduce congestion. Because of the system's compatibility with Raspberry Pi, existing bus infrastructures may be seamlessly integrated, guaranteeing dependable data processing. Additionally, the system's use of machine learning enables steady performance and accuracy improvements over time. By combining advanced technologies such as machine learning, cameras, and Raspberry Pi, this integrated solution represents a significant step forward in the enhancement of public transportation systems, ultimately leading to improved operational efficiency, safety measures, and passenger satisfaction.

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(3)
Openalex Percentile: Top 95%
Traffic Prediction and Management Techniques
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Intelligent Transit Management and Capacity Insights — T. H. Akshaya · International Journal for Research in Applied Science and Engineering Technology (2025) | TGRS Research Map | TGRS