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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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Vehicle Travel Time Estimation in Transportation Network Using Random Forest and Neural Network
(2025-01-01)
Nakano, Shuya
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Panichpapiboon, Sooksan
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Kulla, Elis
Several technologies in Intelligent Transportation Systems (ITS), such as automatic driving, electric vehicles, vehicular communications, are reshaping the way we travel and use the transportation system. Automatization of ITS mainly consists of traffic management, traffic light control, optimal route selection and so on. In these automatic applications, accurate estimation of vehicle travel time is essential to make efficient decisions. This study uses synthetic traffic data generated by Simulation of Urban MObility (SUMO) to evaluate machine learning models, like Random Forest and Neural Network, for travel time estimation. A variety of traffic-related features were collected, and three feature scenarios were tested. Results show that the Random Forest model outperforms both the neural network and the baseline method based on numerical estimation, highlighting the benefit of feature-rich approaches.
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Color-Aware Structured Parsing and Self-Consistent Reasoning for Chart Question Answering
(2025-01-01)
Vorathammathorn, Supasate
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Sintarasirikulchai, Wassana
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Sakdejayont, Theerat
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Panichpapiboon, Sooksan
Colors are critical elements in charts. They allow us to visualize data in different series or categories effectively. Currently, generative artificial intelligence such as vision language models (VLMs) and large language models (LLMs) can be combined together to form a pipeline for extracting information and answering questions regarding the data presented on a given chart. However, most chart question answering pipelines still treat every chart as if it were rendered in grayscale, ignoring the valuable chromatic attributes. This paper introduces ColorAware Structured Parsing (CASP) with Self-Consistent Reasoning, a two-stage pipeline that extracts both numerical and chromatic information to improve chart understanding. On a 2,500 question Chart Question Answering (ChartQA) benchmark, CASP attains 91.12% exact-match accuracy, a significant gain over the strongest structured baseline. The answers produced by CASP are also auditable. They include explicit pointers to the table cells used for reasoning. By exploiting both numerical and chromatic information on the chart and enforcing agreement across reasoning paths, CASP turns colors into usable evidence, delivering accurate and transparent answers regarding the information embedded in the chart.
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Characterizing the Distributions of Taxi Demand: Is Poisson the Right Model?
(2024-01-01)
Panichpapiboon, Sooksan
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Khunsri, Kavepol
Statistical 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.
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What Separates High-Performing Taxis From the Rest? A Case Study in Bangkok
(2024-01-01)
Panichpapiboon, Sooksan
Taxi is a public transportation system widely used in many cities, including Bangkok, Thailand. It provides a convenient way for people to move around the city. However, past studies have shown that the Bangkok taxi system is highly inefficient. In fact, most taxis are vacant most of the time. Despite this fact, there are highly efficient taxis that outperform the others in terms of gaining higher revenue. Learning what these high-performing taxis do differently is essential to improving the overall system's efficiency. In this paper, we investigate the key factors that differentiate high-performing taxis from low-performing ones. Identifying these factors allows us to draw significant insights into improving each taxi's performance. Based on an analysis of real taxi trajectory data, this study shows that the two crucial factors that separate high-performing taxis from low-performing taxis are the passenger searching time and the ability to select less congested search routes. Thus, an effective solution needs to concentrate on improving these two factors. Enhancing other elements, such as passenger delivery time and delivery route, is much less effective and will not have any critical impact. Lastly, this paper presents an effective regression model for predicting the efficiency of an individual taxi based on its average vacant time and the proportion of time spent in congestion during a passenger search. The model performs well, with only an 8.28% error rate.
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A Big Data Analysis on Urban Mobility: Case of Bangkok
(2022-01-01)
Panichpapiboon, Sooksan
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Khunsri, Kavepol
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.
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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.
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Real-Time Vehicle Maneuvering Detection with Digital Compass
(2021-01-01)
Leakkaw, Puttipong
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Panichpapiboon, Sooksan
Vehicle maneuverings are important pieces of information for many applications such as traffic incident detection and driving behavior recognition. On a macroscopic level, an unusually large number of some maneuvering events (e.g., lane changes) may suggest that a road incident occurs. On a microscopic level, vehicle maneuverings can tell how each individual person drives (e.g., safely or unsafely). A number of smartphone-based maneuvering detection methods have been proposed. However, most of them typically rely on accelerometer and gyroscope, and thus require the smartphone to be placed at a specific position on a vehicle and in a specific orientation. In this work, we take a rather different approach from most of the existing methods. Particularly, we investigate how effective it is to detect vehicle maneuverings by relying only on a signal from a digital compass on a smartphone. To this end, we introduce a simple rule-based maneuvering detection method which only takes the heading angles measured by the digital compass as inputs. This allows the smartphone to be placed freely at any position on the vehicle and in any orientation. Our results show that the digital compass can be used to detect turns and u-turns extremely well, and its accuracy on lane change detection is at an acceptable level.
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Lane Change Detection with Smartphones: A Steering Wheel-Based Approach
(2020-01-01)
Panichpapiboon, Sooksan
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Leakkaw, Puttipong
Lane change is a valuable piece of traffic information. An abnormally high number of lane changes on a road section typically suggests that some lanes are blocked due to traffic incidents. Currently, the lane change information of vehicles on an urban road is typically obtained from over-roadway fixed sensors such as surveillance cameras. However, using fixed sensors has limitations in terms of cost and coverage. It is more effective to collect the lane change information directly from each individual vehicle. In addition, lane changing behavior of a driver can help assess his driving risk. One convenient way to detect a lane change directly from each vehicle is to take advantage of sensors on smart mobile devices such as smartphones. In this paper, we explore a new way to identify a lane change event based on a pattern of steering wheel angles detected by a smartphone. This is distinguishable from all of the existing steering wheel-based lane change detection methods, which need to retrieve the steering wheel angle signal from the On-board Diagnostic (OBD) port of the vehicle via the Controller Area Network-Bus (CAN-Bus). In addition, unlike others, our method does not require a lot of complex features to make an accurate detection. In fact, we demonstrate that a high level of accuracy can already be attained with a single simple feature called rotation span. Results show that the proposed detection method performs very well both in terms of precision and recall.

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