• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.45-56
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• 2022

Recently, bicycle sharing system is introduced and the usage of shared bicycles is increasing in Suwon city. Despite the need to expand the bicycle road infrastructure, this is not the case. Therefore, this research attempts to propose a method for bicycle lane installation in Suwon city. For this, this research conducted location analysis based on the shared bicycle usage data and trip inducing facility data. Using location analysis results, appropriate routes for bicycle lanes are selected. As a result, two routes are selected. These routes have advantages that it is easy to connect with the existing bicycle roads or traffic inducing facilities and to install using the existing bicycle roads. However, these routes also have disadvantage that traffic congestion may occur due to the occupancy of the existing road space. It is expected that this research may contribute to expansion and maintenance of bicycle lane infrastructure, the bicycle and PM sharing service usage, implementation of sustainable urban transportation systems in Suwon city.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.3
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• pp.3-9
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• 2013

As interest in green growth picks up, the importance of bicycles which are an environment friendly means of communication has been magnified. However, bicycle routes which are the base of bicycles are designed without considering topographic elements and thus many users are experiencing inconvenience in using bicycles. The present study presents a routing technique to select optimal routes when selecting routes in commuting to school utilizing bicycles. To this end, a formula for optimum route calculation considering slope and intersections was drawn and a method to select optimum routes by applying modified Dijkstra Algorithms was studied. According to the results, the bicycle routes for commuting to school should be selected by the shortest time rather than the shortest distances to the destination, because it required reach the destination faster. Therefore when selecting the routes, it must be based on the shortest time considering waiting time due to crosswalks or crossroads and speed variations due to slopes.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.4
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• pp.182-190
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• 2009

To solve the problems of traffic congestion, air pollution and energy derived from increased car consumption, people are in recognition of the importance of green mode, bicycle. Bicycle use rate in Busan is lower due to the terrain and limited public transportation accessibility. Therefore, geographical conditions and use activation should be initially considered in the bicycle route planning. We calculated a weight using AHP(Analytic Hierarchy Process), make a database using GIS tool and deduced the routes applying calculated weight in this study. The result of this study, We could get reliable data as inspecting consistency of the research. Routes are deducted in the place where using demand is higher than arbitrarily chosen routes. Therefore, the route planning through AHP is expected to be utilized in area-specialty-reflected route planning or bicycle road alternatives testing.

• Journal of information and communication convergence engineering
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• v.17 no.2
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• pp.142-148
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• 2019

Relatively few countries provide separate bicycle lanes for cyclists. Hence, tools for suggesting cycling routes are essential for a safe and pleasant cycling experience. This study aims to develop a mobile application to build cycling routes based on user preferences, specifically location, search radius, ride distance, and number of optimal routes. Our application calls the Strava API to retrieve Strava cycling segments crowdsourced from the cycling community. Then, it creates a graph consisting of the start and end points of these segments. Beginning from a user-specified location, the depth-first search algorithm (DFS) is applied to find routes that conform to the user's preferences. Next, a set of optimal routes is obtained by computing a trade-off ratio for every discovered route. This ratio is calculated from the lengths of all segments and the lengths of all connecting paths. The connected routes can be displayed on a map on an Android device or exported as a GPX file to a bike computer. Future work must be performed to improve the design of the user interface and user experience.

• Journal of Environmental Health Sciences
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• v.35 no.6
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• pp.447-453
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• 2009

Cycling has been lately recommended as an alternative commuting mode because it is believed to be good for health and the environment. However, the exposure to environmental pollutants, such as fine particulates, could be a potential problem for cycling in urban environments. In this study, we compared commuters' $PM_{10}$ exposure using the different transportation modes of bicycle and bus. When a bicycle was used as a commuting mode, the additional $PM_{10}$ exposure due to transportation was about 3.5 times higher than that when using a bus. The difference of additional $PM_{10}$ exposures by cycling and bus was statistically significant (p<0.01). The $PM_{10}$ exposure during cycling was significantly correlated with atmospheric $PM_{10}$ concentration (r=0.98, p<0.01) and its correlation coefficient was higher than that of bus (r=0.55, p<0.05). The results of this study demonstrated that the main reasons of higher $PM_{10}$ exposure when using the bicycle as the mode of transport were its vicinity to road traffic and routes that were unavoidably close to road traffic. Bicycle commuting along the road side may not be good for health. Exclusive bicycle lanes away from road traffic are recommended.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.5
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• pp.425-433
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• 2012

Dijkstra's algorithm is one of well-known methods to find shortest paths over a network. However, more research on $A^*$ algorithm is necessary to discover the shortest route to a goal point with the heuristic information rather than Dijkstra's algorithm which aims to find a path considering only the shortest distance to any point for an optimal path search. Therefore, in this paper, we compared Dijkstra's algorithm and $A^*$ algorithm for bicycle route selection. For this purpose, the horizontal distance according to slope angle and average speed were calculated based on factors which influence bicycle route selection. And bicycle routes were selected considering the shortest distance or time-dependent shortest path using Dijkstra's or $A^*$ algorithm. The result indicated that the $A^*$ algorithm performs faster than Dijkstra's algorithm on processing time in large study areas. For the future, optimal path selection algorithm can be used for bicycle route plan or a real-time mobile services.

• The Journal of Bigdata
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• v.6 no.2
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• pp.139-149
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• 2021

Ddareungi, a public bicycle service in Seoul, establishes itself as a means of daily transportation for citizens in Seoul. We speculated that the pattern of using Ddareungi may have changed since COVID-19. This study explores changes in using Ddareungi after COVID-19 with descriptive statistical analysis and network analysis. The analysis results are summarized as follows. The average traveling distance and average traveling speed have decreased over the entire time in a day since COVID-19. The round trip rate has increased at dawn and morning and has decreased in the evening and night. The average weighted degree and average clustering coefficient have decreased, and the modularity has increased. The clusters, located north of the Han River in Seoul, had a similar geographic distribution before and after COVID-19. However, the clusters, located south of the Han River, had different geographic distributions after COVID-19. Traveling routes added to the top 5 traffic rankings after COVID-19 had an average traveling distance of fewer than 1,000 meters. We expect that the results of this study will help improve the public bicycle service in Seoul.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.27-32
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• 2022

As the need for eco-friendly transportation increases due to the deepening climate crisis, many local governments in Korea are introducing shared bicycles. Due to anxiety about public transportation after COVID-19, bicycles have firmly established themselves as the axis of daily transportation. The use of shared bicycles is spread, and the demand for bicycles is increasing by rental offices, but there are operational and management difficulties because the demand is managed under a limited budget. And unfortunately, user behavior results in a spatial imbalance of the bike inventory over time. So, in order to easily operate the maintenance of shared bicycles in Seoul, bicycles should be prepared in large quantities at a time of high demand and withdrawn at a low time. Therefore, in this study, by using machine learning, the linear regression algorithm and MS Azure ML are used to predict and analyze when demand is high. As a result of the analysis, the demand for bicycles in 2018 is on the rise compared to 2017, and the demand is lower in winter than in spring, summer, and fall. It can be judged that this linear regression-based prediction can reduce maintenance and management costs in a shared society and increase user convenience. In a further study, we will focus on shared bike routes by using GPS tracking systems. Through the data found, the route used by most people will be analyzed to derive the optimal route when installing a bicycle-only road.