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Author: search.filters.author.Panichpapiboon, SooksanSubject: search.filters.subject.Big data analytics

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    Characterization of Passenger Search Time Under Varying Supply-Demand Imbalance
    (2026-08-01)
    Panichpapiboon, Sooksan
    Passenger search time is an essential variable in a street-hail taxi operation. Fundamentally, the statistical distributions of passenger search time form the basis of analytical and simulation models for describing the taxi system’s dynamics. To create accurate analytical and simulation models that realistically represent the dynamics of the actual taxi system, it is inevitable to characterize the type of statistical distributions that can effectively model the empirical distributions. While the probability distribution functions of passenger search time are critical elements of analytical and simulation models, their empirical distributions under different levels of supply-demand imbalance have not been characterized in any existing studies. In this paper, based on more than eight million real taxi trips in Bangkok, Thailand, and formal statistical analysis, we identify the probability distributions that can effectively model the empirical distributions of passenger search time under different levels of taxi supply-demand imbalance.
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    Modeling the Distributions of Taxi Supply: A Case Study in Bangkok
    (2025-04-01)
    Panichpapiboon, Sooksan
    Taxi is one of the most popular public transportation systems that is commonly available in many cities. It allows passengers to travel to their destinations conveniently. Nonetheless, traditional street-hail taxi systems are known to have low efficiency. Passengers often have to wait for a long time, while many vacant taxis spend a considerable amount of time searching for passengers. This inefficiency is a result of an imbalance between supply and demand. Understanding the statistical distributions of taxi supply and demand is vital to solving this imbalance problem. Moreover, to realistically simulate the number of vacant and busy taxis with a traffic simulator, it is necessary to know the types of distributions that can properly represent the empirical variation of taxi supply and demand. In our previous work, we have successfully characterized the temporal distribution of taxi demand. In this study, we investigate the supply side and characterize the temporal distribution of vacant taxis. Contrary to the conventional belief, we show that modeling the temporal distribution of taxi supply with a Poisson distribution is mostly invalid. Finally, we demonstrate that a geometric distribution is suitable for modeling the distribution of taxi supply in most scenarios.
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    A big data analysis on efficiency of bangkok taxi system
    (2021-05-19)
    Khunsri, Kavepol
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    Panichpapiboon, Sooksan
    Taxi is one of the most popular urban public transportation systems. Many people rely on taxi service on a daily basis. However, the Bangkok taxi system may not be operating at its full potentials. Obviously, there are plenty of vacant taxis on the road searching for passengers while the passengers have to spend a lot of time waiting for taxis. This inefficiency wastes valuable resources such as time and fuel energy. Nonetheless, to our knowledge, the efficiency of the Bangkok taxi system has not been measured quantitatively. In this paper, we quantitatively analyze the performance of the Bangkok taxi system based on the big data collected from real taxi traces. Both the system-level efficiency and the agent-level efficiency are investigated.