Abstract

The measurement of the origin-destination (OD) flow of individual passengers within the public transportation system plays a vital role in understanding resident mobility and facilitating route planning. Particularly in resource-limited communities, where access to advanced technologies like smart card systems may not be available, bus transportation systems play a crucial role in daily life. Traditional methods, such as driver logs, are not only time-consuming but also difficult to provide precise measurements of individualized OD pairs. Therefore, the primary objective of this study is to propose an automatic passenger sensing system (software) capable of accurately measuring OD pairs for individual passengers while ensuring the preservation of privacy information. The devised method incorporates Global Positioning System (GPS) sensing and utilizes state-of-the-art computer vision techniques, including person detection, tracking, and re-identification models. To enhance practical performance, the system is customized based on environment-specific properties. In this study, various detection and re-identification (ReID) models were compared, and optimal models were chosen for our specific case study. Pretrained models were employed, and transfer learning techniques were utilized to fine-tune the models using datasets from the case study. The proposed sensing systems (hardware) were installed and operated on the public bus system in Benton Harbor, Michigan. The associated algorithm was developed and improvements were implemented to address some of the identified issues. Finally, the results demonstrate that the proposed sensing system effectively and accurately detects OD pairs and provides accurate passenger counts on buses. This research work contributes to a more comprehensive understanding of individual passenger movements within the public transportation system, thus facilitating informed decision-making for route planning and improving the overall efficiency of the transportation network.

Notes

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Graduation Date

2023

Semester

Summer

Advisor

Sun, Patrick

Degree

Master of Science in Computer Engineering (M.S.Cp.E.)

College

College of Engineering and Computer Science

Department

Civil, Environmental, and Construction Engineering

Degree Program

Civil Engineering

Identifier

CFE0009794; DP0027902

URL

https://purls.library.ucf.edu/go/DP0027902

Language

English

Release Date

August 2024

Length of Campus-only Access

1 year

Access Status

Masters Thesis (Campus-only Access)

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