The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
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Study on Multi-vehicle Routing Problem Using Clustering Method for Demand Responsive Transit

수요응답형 대중교통체계를 위한 클러스터링 기반의 다중차량 경로탐색 방법론 연구

  • Kim, Jihu (Dept. of Civil & Environmental Eng., KAIST) ;
  • Kim, Jeongyun (Dept. of Civil & Environmental Eng., KAIST) ;
  • Yeo, Hwasoo (Dept. of Civil & Environmental Eng., KAIST)
  • 김지후 (한국과학기술원 건설 및 환경공학과) ;
  • 김정윤 (한국과학기술원 건설 및 환경공학과) ;
  • 여화수 (한국과학기술원 건설 및 환경공학과)
  • Received : 2020.07.22
  • Accepted : 2020.10.22
  • Published : 2020.10.31
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Abstract

The Demand Responsive Transit (DRT) system is the flexible public transport service that determines the route and schedule of the service vehicles according to users' requests. With increasing importance of public transport systems in urban areas, the development of stable and fast routing algorithms for DRT has become the goal of many researches over the past decades. In this study, a new heuristic method is proposed to generate fast and efficient routes for multiple vehicles using demand clustering and destination demand priority searching method considering the imbalance of users' origin and destination demands. The proposed algorithm is tested in various demand distribution scenarios including random, concentration and directed cases. The result shows that the proposed method reduce the drop of service ratio due to an increase in demand density and save computation time compared to other algorithms. In addition, compared to other clustering-based algorithms, the walking cost of the passengers is significantly reduced, but the detour time and in-vehicle travel time of the passenger is increased due to the detour burden.

수요응답형 대중교통체계 시스템은 사용자의 요청에 따라 서비스 차량의 경로와 스케줄을 설정하는 유동적인 대중교통 서비스이다. 도시 지역에서 대중교통 시스템의 중요성이 증가함에 따라, 수요응답형 대중교통체계를 위한 안정적이고 빠른 경로탐색 방법의 개발 또한 다양하게 연구되고 있다. 본 연구에서는 빠르고 효율적인 다중차량경로 탐색을 위해, 수요 기종점들의 클러스터링 기술을 활용한 종점수요 우선탐색의 휴리스틱 방법이 제안되었다. 제안된 방법은 기종점 수요 분포가 무작위인 경우, 집중된 경우와 방향성을 가지는 경우에 대하여 테스트되었다. 제안된 알고리즘은 수요밀도의 증가로 인한 서비스 비율의 감소를 저감시키며, 계산 속도가 비교적 빠른 장점을 보인다. 또한, 다른 클러스터링 기반 알고리즘에 비해 수요밀도 증가에 따른 서비스 비율 감소율이 낮고, 차량 용량의 활용성이 개선된 반면, 차량 운행경로 길이의 증가로 승객의 차량 탑승시간은 상대적으로 증가하는 특성을 보인다.

Keywords

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