• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.40-53
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• 2021

Recently, shared dockless e-scooter usage has rapidly increased, rather than the station-based shared mobility service, because of convenience. This transition leads to new social problems in urban areas such as increased traffic accidents and hindrance of pedestrian environments. In this study, we analyze the usage characteristics of shared e-scooters in Seoul, and identify factors influencing demand for shared e-scooters by developing a negative binomial regression model. As a result, the usage characteristics show that the average trip distance, the average trip duration, and the average trip speed were 1.5km, 9.4min, and 10.3km/h, respectively. Demographic factor, transport facility factors, land use factors, and weather factors have statistically significant impacts on demand for shared e-scooters. The results of this study will be used as basic data for suggesting effective operation strategies for areas with higher shared e-scooter demand and for establishing transport policies for facilitating shared e-scooter usage.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.51-61
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• 2023

Travel demand estimation of E-Scooter is the start point of solving the regional demand-supply imbalance problem and plays pivotal role in a linked transportation system such as Mobility-as-a-Service (a.k.a. MaaS). Most focuses on developing trip generation model of shared E-Scooter but it is no study on selection of an appropriate zone scale when it comes to estimating travel demand of E-Scooter. This paper aimed for selecting an optimal TAZ scale for developing trip distribution model for shared E-Scooter. The TAZ scale candidates were selected in 250m, 500m, 750m, 1,000m square grid. The shared E-Scooter usage historical data were utilized for calculating trip distance and time, and then applying to developing gravity model. Mean Squared Error (MSE) is applied for the verification step to select the best suitable gravity model by TAZ scale. As a result, 250m of TAZ scale is the best for describing practical trip distribution of shared E-Scooter among the candidates.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.54-69
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• 2021

Policy improvement such as the revision of the Road Traffic Act are proposed for personal mobility(especially e-scooter) usage. However, there is not enough discussion to solve the problem of using shared e-scooter. In this study, we analyze the influencing factors that amount of pick-up and drop-off of shared e-scooter by dividing the Seoul into a 200m grid. we develop spatial auotcorrelation model such as spatial lag model, spatial error model, spatial durbin model, and spatial durbin error model in order to consider the characteristics of the aggregated data based on a specific space, and the spatial durbin error model is selected as the final model. As a result, demographic factor, land use factor, and transport facility factors have statistically significant impacts on usage of shared e-scooter. The result of this study will be used as basic data for suggesting efficient operation strategies considering the characteristics of weekday and weekend.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.3
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• pp.29-47
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• 2024

Many domestic and foreign studies derive factors that significantly affect the use of shared E-scooters based on performance data, but few studies have been conducted with comparative analysis models using predictive power, applying them to other regions. Therefore, by clearly establishing detailed influencing factors and scope in Gwangjin-gu and Gangnam-gu by using domestic shared E-scooter performance data, this study enhances predictive power, and the Geographically Weighted Regression model is derived through spatial autocorrelation verification. Based on the results, the direction of a construction model created from regional differences was presented, and major implications from the user's perspective are derived based on the difference between actual use and the model's prediction.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.124-140
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• 2023

The rapid growth in shared E-scooters worldwide has led to many studies on the topic. The results of these studies are still in the early stages, and the main factors affecting trips are being identified. In particular, the development of trip-generation models is very important for transportation planning, and a new transportation mode for developing the models for shared E-scooters is lacking both domestically and internationally. This study aims to develop a trip generation model for shared E-scooters using significant variables by thoroughly reviewing previous studies. The trip characteristics of major service areas and other areas may differ owing to the trip characteristics of the mode. The trip generation models were developed based on the service trip density by dividing the areas by service level. The factors affecting shared E-scooter trips in major service areas included the presence of universities, closeness centrality, and cultural areas, while factors affecting the trips in minor service areas included the presence of universities, betweenness centrality, and trip distance. The developed models provide basic information that can be used to establish transport policies for introducing shared E-scooters in cities in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.22-39
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• 2021

This study estimated mode choice models for access travel and neighborhood travel from an SP survey in metropolitan areas where shared e-scooter services are offered. Model results show that travel time and travel cost have negative effects on mode utility. It is also revealed that people are more sensitive to travel time in access travel, whereas they are more influenced by travel cost in neighborhood travel. Looking at individual and household attributes, it has a positive effect when under 40 yerars of age, owning bikes, being a public transportation user, while it has been shown a negative effect in less than 3 million won in monthly household income and owning individual cars.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.131-142
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• 2023

In this paper, we propose a new station selection algorithm to solve the illegal parking problem of shared electric scooters and improve the service quality. Recently, as a solution to the urban transportation problem, shared electric scooters are attracting attention as the first and last mile means between public transportation and final destinations. As a result, the shared electric scooter market grew rapidly, problems caused by electric scooters are becoming serious. Therefore, in this study, text data are collected to understand the nature of the problem, and the problems related to shared scooters are viewed from the perspective of pedestrians and users in 'LDA Topic Modeling', and a station extension algorithm is based on this. Some parking lots have already been installed, but the existing parking lot location is different from the actual area of tow. Therefore, in this study, we propose an algorithm that can install stations at high actual tow density using mixed clustering technology using K-means after primary clustering by DBSCAN, reflecting the 'current state of electric scooter tow in Seoul'.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.1
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• pp.19-30
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• 2023

An electric scooter(e-scooter), one popularized micro-mobility vehicle has shown rapidly increasing use in many cities. In South Korea, the use of e-scooters has greatly increased, as some companies have launched e-scooter sharing services in a few large cities, starting with Seoul in 2018. However, the use of e-scooters is still controversial because of issues such as parking and safety. Since the perception toward the means of transportation affects the mode choice, it is necessary to track the trends for electric scooters to make the use of e-scooters more active. Hence, this study aimed to analyze the trends related to e-scooters. For this purpose, we analyzed news articles related to e-scooters published from 2014 to 2020 using dynamic topic modeling to extract issues and sentiment analysis to investigate how the degree of positive and negative opinions in news articles had changed. As a result of topic modeling, it was possible to extract three different topics related to micro-mobility technologies, shared e-scooter services, and regulations for micro-mobility, and the proportion of the topic for regulations for micro-mobility increased as shared e-scooter services increased in recent years. In addition, the top positive words included quick, enjoyable, and easy, whereas the top negative words included threat, complaint, and ilegal, which implies that people satisfied with the convenience of e-scooter or e-scooter sharing services, but safety and parking issues should be addressed for micro-mobility services to become more active. In conclusion, this study was able to understand how issues and social trends related to e-scooters have changed, and to determine the issues that need to be addressed. Moreover, it is expected that the research framework using dynamic topic modeling and sentiment analysis will be helpful in determining social trends on various areas.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.1-16
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• 2022

Recently, e-scooters have been attracting attention as eco-friendly modes of transportation in cities due to an increasing interest in the environment. Accordingly, various studies on usage behavior are being conducted, but studies that reflect individual travel attitudes are insufficient. Therefore, this study surveyed commuters in Seoul and analyzed respondents' traveling attitudes through factor analysis. It also built a binary logistic regression model for the intention to use shared e-scooters to determine how individual travel behaviors are affected. In particular, the model results showed that age, the main mode of transportation (car), walking time to the bus stop, and four travel attitude variables (disutility of travel, preference to self-drive, internet/smartphone friendliness, and willingness to pay extra money for services) significantly affected the intention to use shared e-scooters. This study is expected to be used as basic data, with aspect to travel behavior, for the efficient operation and use of shared e-scooters in the future.