• Journal of Korean Society of Transportation
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• v.32 no.2
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• pp.152-161
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• 2014

The total boarding and alighting time models have been developed by applying the multiple regression analysis with three variables; numbers of boarding or alighting passengers, non-sitting passengers, and the step-height from the ground. Such variables have influenced to the total boarding time model with the most influential in the numbers of boarding or alighting passengers and the least in the step-height. On the total alighting time model, the numbers of alighting passengers are the most strongest while the step-heights the least. The total boarding and alighting time models can be used in practices for the prediction of current and future bus stops' capacities in TOD-based towns.

• Journal of the Korean Society for Railway
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• v.17 no.3
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• pp.210-215
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• 2014

Various methods have been developed in an effort to increase the scheduled speeds of the urban railways. Reducing the train dwell times by extending door widths is one such method. However, there is thus far no domestic model of boarding and alighting that is appropriate to lead to boarding and alighting time reductions if the door width is extended. Foreign models are not suitable because human behaviors, which are important factors when assessing boarding and alighting times, differ from country to country. In this study, a boarding and alighting model for domestic urban railways is proposed and related equations and parameters are derived from measured and experimental data. The model can be employed to assess time reductions in Korean railroad system if the door widths are extended.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.13-18
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• 2018

In this study, we classify Seoul metro stations according to boarding and alighting patterns using machine earning technique. The target data is the number of boarding and alighting passengers per hour every day at 233 subway stations from 2008 to 2017 provided by the public data portal. Gaussian mixture model (GMM) and K-means clustering are used as machine learning techniques in order to classify subway stations. The distribution of the boarding time and the alighting time of the passengers can be modeled by the Gaussian mixture model. K-means clustering algorithm is used for unsupervised learning based on the data obtained by GMM modeling. As a result of the research, Seoul metro stations are classified into four groups according to boarding and alighting patterns. The results of this study can be utilized as a basic knowledge for analyzing the characteristics of Seoul subway stations and analyzing it economically, socially and culturally. The method of this research can be applied to public data and big data in areas requiring clustering.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.131-146
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• 2023

Smart card data is widely used in the public transportation field. Despite the inevitability of various errors occur during the data collection and storage; however, smart card data errors have not been extensively studied. This paper investigates inherent errors in boarding and alighting station information in smart card data. A comparison smart card data and bus boarding and alighting survey data for the same time frame shows that boarding station names differ by 6.2% between the two data sets. This indicates that the error rate of smart card data is 6.2% in terms of boarding station information, given that bus boarding and alighting survey data can be considered as ground truth. This paper propose 6-step algorithm for correcting errors in smart card boarding station information, linking them to corresponding information in Bus Information System(BIS) Data. Comparing BIS data and bus boarding and alighting survey data for the same time frame reveals that boarding station names correspond by 98.3% between the two data sets, indicating that BIS data can be used as reliable reference for ground truth. To evaluate its performance, applying the 6-step algorithm proposed in this paper to smart card data set shows that the error rate of boarding station information is reduced from 6.2% to 1.0%, resulting in a 5.2%p improvement in the accuracy of smart card data. It is expected that the proposed algorithm will enhance the process of adjusting bus routes and making decisions related to public transportation infrastructure investments.

• Journal of the Korean Society for Railway
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• v.16 no.2
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• pp.144-150
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• 2013

This paper presents an analysis of door design for the Ui LRT based on experiments to predict doorway flow-time and their analyses results. A similar railway vehicle (from Gimhae LRT) and operational conditions are utilized to assess the doorway flow-time through repetitive experiments. Design of the experiments consists of four scenarios, and the experiments are repeated 39 times in total. We use the results of the experiments to verify the design of doors of Ui LRT (e.g. the required number of doors and their width). Various statistical analyses are carried out for the flow-time with respect to the number of boarding/alighting passengers. We make three category levels of boarding/alighting passengers, and analyze the mean and variance for each category, and then carry out One-Way ANOVA to analyze how the number of boarding/alighting and onboard passengers impact flow-time. The results of this paper can be used for making decisions about doors of the LRT vehicle.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1D
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• pp.29-34
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• 2008

The Korean society has been changed to the aging one. The number of elderly people has been in creasing rapidly. For their social and economic activity, more convenient transport services have to be offered. Specifically, increasing mobility is one of the most urgent policies for them. One action of the policy in Seoul has introduced low floor buses since 2003. This paper shows how low floor buses affect on passengers' boarding and alighting time through the field survey. In the analysis of the survey results it has been found that the low floor buses can reduce average boarding time by 0.8 sec, especially, by 1.1 sec (about 36%) for elderly passengers. These outcomes expect total bus operating hours as well as headways could be reduced, and operating cost and passengers' waiting time could be saved.

• Journal of the Korean Society of Safety
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• v.36 no.1
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• pp.72-79
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• 2021

This basic study introduces the concept of S-BRT and develops dwell time estimation models that consider road geometry and S-BRT characteristics for a signal operation strategy to meet the S-BRT's operational goal of high speed and punctuality. Field surveys of low-floor buses similar in shape to S-BRTs and data collection of passengers, station elements, vehicle elements, and other factors that can affect stop times were used in a regression analysis to establish statistically significant dwell time estimation models. These dwell time estimation models are developed by categorizing according to the locations of the signal or sidewalk that have the most impact on the dwell time. In this way, the number of people boarding and alighting the bus at the crowded door and the number of people boarding and alighting the bus at the front door considering the internal congestion was analyzed to affect the dwell time. The estimation dwell time models in this study can be used in the establishment of strategies that provide priority signals to S-BRTs.

• Smart Media Journal
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• v.12 no.11
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• pp.57-66
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• 2023

In this paper, we present a study on an AI-based system utilizing the CCTV system within city buses to predict the intentions of boarding and alighting passengers, with the aim of preventing accidents. The proposed system employs the YOLOv7 Pose model to detect passengers, while utilizing an LSTM model to predict intentions of tracked passengers. The system can be installed on the bus's CCTV terminals, allowing for real-time visual confirmation of passengers' intentions throughout driving. It also provides alerts to the driver, mitigating potential accidents during passenger transitions. Test results show accuracy rates of 0.81 for analyzing boarding intentions and 0.79 for predicting alighting intentions onboard. To ensure real-time performance, we verified that a minimum of 5 frames per second analysis is achievable in a GPU environment. his algorithm enhance the safety of passenger transitions during bus operations. In the future, with improved hardware specifications and abundant data collection, the system's expansion into various safety-related metrics is promising. This algorithm is anticipated to play a pivotal role in ensuring safety when autonomous driving becomes commercialized. Additionally, its applicability could extend to other modes of public transportation, such as subways and all forms of mass transit, contributing to the overall safety of public transportation systems.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.731-738
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• 2006

Facing congestion, people want to know traffic information about their routes, especially real-time link travel time (LTT). In this paper, as a sequel paper of the previous non-taxi based LTT generating study by Choi et al. (1998), taxi based GPS probes have been tried to produce LTT for urban arterials. Taxis in itself are good deployment mode of GPS probes although it by nature experiences boarding and alighting time noises which should be accounted. A heuristic real-time dynamic outlier filter algorithm for taxi GPS probe has been developed focusing on urban arterials. An actual traffic survey for dynamic link travel times has been conducted using license plate method for the test arterials of Seoul city transportation network. With the algorithm, it is estimated that 70% of outliers have been filtered and the relative error has been improved by 73.7%. The filtering algorithm developed here would be expected to be in use for other spatial sites with some calibration efforts. Some limitations and future research agenda have also been discussed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.67-83
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• 2018

This research is motivated by the question, "Where, when, and through what mode does an individual passenger moving within a subway station use to travel from starting to final destinations ?" To answer this, the stations passed by the individual passenger, the path taken, and modes used need to be known beforehand. In the metropolitan integrated public transportation fare system, Automated Fare Collection System(AFC) can be a source of information on transit modes, stations, and paths of individual passengers. AFC calculates a fare for the passenger based on travel data such as boarding and alighting stations, time, and mode used. In this research, an Selected Station Analysis(SSA) method, in which AFC data is used to observe passenger movement in the metropolitan public transportation subway station from the perspective of subway transfer stations, is proposed. SSA subdivides individual passenger movement in transfer stations and analyzes initial station/time and final destination station/time information using the trip chain perspective.