Panichpapiboon, Sooksan
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Panichpapiboon, Sooksan
Main Affiliation
Email
sooksan.pa@kmitl.ac.th
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Item type:Publication, A Big Data Analysis on Urban Mobility: Case of Bangkok(2022-01-01); Khunsri, KavepolDesigning 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Characterizing the Distributions of Taxi Demand: Is Poisson the Right Model?(2024-01-01); Khunsri, KavepolStatistical distribution of taxi demand is essential for modeling the dynamics of taxi services. An accurate demand prediction not only helps the drivers lessen their searching time but also helps the passengers shorten their waiting time. Moreover, the temporal distribution of taxi demand is a critical component in traffic simulation. Obviously, a simulator needs to know how many new taxi pickup events to schedule after the others. In most studies, a poisson distribution is often used to model the temporal distribution of taxi pickups. However, this assumption has mostly been used without validation with empirical data. Therefore, it is unclear whether such an assumption is appropriate for modeling the statistical distribution of taxi demand. In this study, we characterize the temporal distribution of taxi pickups based on real taxi trip data from Bangkok, Thailand, and Chicago, IL, USA. It is shown that, in most cases, the poisson distribution is not suitable for modeling the temporal distribution of taxi pickups. On the contrary, this study demonstrates that a geometric distribution is more appropriate in modeling the temporal distribution of taxi pickups. To our knowledge, this has not been discovered in any prior studies.