A Big Data Analysis on Urban Mobility: Case of Bangkok

Designing an efficient on-demand mobility service requires comprehensive knowledge of the statistical characteristics of trips. In other words, it is critical to know how long passengers typically spend on a trip and how far they usually travel. Likewise, it is important to learn how much time a dri...

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Bibliographic Details
Published in:IEEE access 2022, Vol.10, p.44400-44412
Main Authors: Panichpapiboon, Sooksan, Khunsri, Kavepol
Format: Article
Language:English
Subjects:
big data
Data analysis
data analytics
Data models
Empirical analysis
Goodness of fit
Passengers
Predictive models
Public transportation
Statistical analysis
statistical modeling
Statistical tests
Taxicabs
Urban mobility
Vehicles
Visualization
Weibull distribution
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Summary:Designing an efficient on-demand mobility service requires comprehensive knowledge of the statistical characteristics of trips. In other words, it is critical to know how long passengers typically spend on a trip and how far they usually travel. Likewise, it is important to learn how much time a driver spends searching for passengers. This study presents a statistical analysis of taxi trips in Bangkok based on real traces of 5,853 taxis over the period of three months. Significant insights on trip volume, trip time, trip distance, and origin-destination distance are derived. In addition, the probability distributions of trip time, trip distance, and origin-destination distance are also characterized based on two goodness-of-fit tests. To our knowledge, this characterization is done for the first time for Bangkok taxi trips. It is shown that a lognormal distribution can best describe the empirical trip time distribution. On the other hand, a Weibull distribution can best describe the empirical trip distance distribution and the empirical origin-destination distance distribution. These distributions are essential to traffic simulation. Finally, the efficiency of the Bangkok taxi system is also quantified both at the system level and at the agent level.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3170068