인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Free-floating sharing with e-mopeds and e-scooters is an upcoming mobility trend. The main problem in shared micromobility is uneven fleet distribution. One option to correct this asymmetry is user-based redistribution. This is especially challenging in free-floating, as the business area must be subdivided. While much research exists for car sharing and station-based bike sharing, the formation and evaluation of sub-areas in free-floating micromobility has barely been analyzed. In this work, we investigate the generation of sub-areas by irregular forms using the Voronoi diagram. The booking data of a free-floating e-moped sharing service in Stuttgart, Germany, serve as basis. Four different generation methods are compared: k-means, density-based spatial clustering of applications with noise (DBSCAN), grid-based clustering, and the use of the underlying road network. The results of the tessellation are evaluated on the basis of geometric properties, data distribution, and adaptability. The k-means algorithm efficiently generates cells in a uniform way, while DBSCAN can adapt well to different demand patterns based on density distribution. Grid-based clustering allows for the consideration of the radius. Metrics for the clusters and polygons were used to compare the methods. It can be seen that k-means (k = 122) performs better in both comparisons. Grid-based ended up in the middle and delivers polygons similar in shape to k-means, while DBSCAN had difficulty adapting to the data. The polygons from the street network generate many polygons, the number and characteristics of which are difficult to influence compared to the other methods.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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