MaaS 제공을 위한 ABATA 통행 분석 기반의 First/Last Mile 이동 수단 공급 방안 연구
A Study on the Supply of First/Last Mile Transportation Methods Based on ABATA Travel Patterns Analysis for the Provision of MaaS
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초록
오늘날 사람들이 도시에서 이용하는 이동 수단은 기존에 존재하던 대중교통, 승용차, 택시에서부터 공유 자전거, 공유 전동 킥보드까지 그 종류가 다양해졌다. 또한 모바일 플랫폼 기반의 검색, 주문, 결제 서비스가 개발되면서 교통 서비스 역시 플랫폼 기반의 통합 서비스로 변화하기 시작했다. 통합 이동 서비스로서 등장한 MaaS는 현재 전 세계적으로 연구 및 운영되고있으나 기존 대중교통의 통합 제공 수준에 머무른다. 문헌 고찰 결과, 현행 수준의 MaaS에서제기되는 First/Last Mile 문제는 새로운 이동 수단을 통합한 개선된 정책을 수립함으로써 해결될 가능성이 있다. 따라서 본 연구는 사람들의 통행에 대한 보다 높은 분석력을 갖춘 것으로평가되는 활동기반 교통분석모형인 ABATA 시스템을 기반으로 도시 인구의 통행 패턴을 분석하여 성공적인 MaaS 제공을 위한 First/Last Mile 이동 수단 공급 방안을 수립하고자 한다.
Abstract
Today, people in cities use differentthe types of transportation that rangepeople use in cities have diversified from existing public transportation, cars, taxis to shared bicycles and shared electric kickboards . In addition, with the development of mobile platform –based search, order, and payment services, and transportation services have also begun to change into platform-based integrated services. In particular, MaaS, which has emerged as an integrated mobile service and, is currently being studied and operated worldwide ., However, MaaS but remains at the level of the integrated provision of the existing public transportation . As a result of Specifically, the results of a literature review on this issue reveal that, the First/Last Mile problem raised at the current level of MaaS is likely to be solved by establishing an improved policy incorporating new means of transportation. Therefore, this study aims to establish a First/Last Mile transportation supply plan for successful MaaS provision . This establishment is realized by analyzing the traffic patterns of urban populations usingbased on the ABATA system,, an activity-based traffic analysis model withevaluated as having higher analysis power on people's traffic.
참고문헌 (40)
- Arentze, T. A., "A learning-based transportation oriented simulation system", Transportation Research Part B: Methodological, 38(7), pp.613~633 (2004)
- Babicheva, T., "Empty Vehicle Redistribution with Time Windows in Autonomous Taxi Systems", ACM Transactions on Data Science, 2(1), pp.1~22 (2021)
- Baek, K., "Electric scooter sharing: How do people value it as a last-mile transportation mode?", Transportation Research Part D: Transport and Environment, 90, pp.102642~ (2021)
- Bellemans, T., "Implementation framework and development trajectory of FEATHERS activity-based simulation platform", Transportation Research Record, 2175(1), pp.111~119 (2010)
- Bhat, C. R., "Comprehensive econometric microsimulator for daily activity-travel patterns", Transportation Research Record, 1894(1), pp.57~66 (2004)
- Caggiani, L., "A dynamic clustering method for relocation process in free-floating vehicle sharing systems", Transportation Research Procedia, 27, pp.278~285 (2017)
- 조창현, "활동기반모형을 이용한 유연근무제의 교통부문 영향 연구", 대한교통학회지, 35(6), pp.511~524 (2017), 대한교통학회[DOI]
- 최현주, "TRANSUMS의 국내 활용화 방안에 관한 연구", 대한토목학회 논문집D, 24(2004), pp.593~600 (2004), 대한토목학회
- 엄진기, "시․공간 활동인구 추정에 의한 통행수요 예측", 대한교통학회지, 26(5), pp.163~174 (2008), 대한교통학회
- Eom, J. K., "Development and application of the Activity-BAsed Traveler Analyzer (ABATA) system", Future Generation Computer Systems, 106, pp.135~153 (2020)
- Eom, J. K., "Activity-Based Traveler Analyzer Using Mobile and Socioeconomic Bigdata: Case Study of Seoul in Korea", International Conference on Big Data, pp.312~318 (2018), Springer
- Guan, Y., "Cumulative prospect theory based dynamic pricing for shared mobility on demand services", 2019 IEEE 58th Conference on Decision and Control(CDC), pp.2239~2244 (2019), IEEE
- Heikkilä, S., "Mobility as a service-a proposal for action for the public administration", ~ (2014)
- Hellebuyck, A., "Investigating dynamic pricing to solve the fleet rebalancing problem in bike sharing systems", Master, pp.2019
- Horni, A., "Introducing matsim", pp.3~7 (2016), Ubiquity Press
- Large-scale simulation-based evaluation of fleet repositioning strategies for dynamic rideshare in New York City, "Jung, J. Y.", SAE, ~, pp.SAE Technical Paper, No. 2019-01-0924 (2019)
- 김경옥, "공유자전거 시스템의 이용 예측을 위한K-Means 기반의 군집 알고리즘", 정보처리학회 논문지, 10(5), pp.169~178 (2021), 한국정보처리학회
- Kumar, P., "An algorithm for integrating peer-to-peer ridesharing and schedule-based transit system for first mile", last mile access, 2021, pp.122, 102891~
- Lahoorpoor, B., "Spatial cluster-based model for static rebalancing bike sharing problem", Sustainability, 11(11), pp.3205~ (2019)
- 이은탁, "이용수요 기반의 서울시 공공자전거 재배치전략 도출", 대한교통학회지, 37(1), pp.27~38 (2019), 대한교통학회[DOI]
- Lei, C., "Path-based dynamic pricing for vehicle allocation in ridesharing systems with fully compliant drivers", Transportation Research Procedia, 38, pp.77~97 (2019)
- Liu, C., "META: A City-Wide Taxi Repositioning Framework Based on Multi-Agent Reinforcement Learning", IEEE Transactions on Intelligent Transportation Systems (2021)
- Liu, Z., "Context-Aware Taxi Dispatching at City-Scale Using Deep Reinforcement Learning", IEEE Transactions on Intelligent Transportation Systems (2020)
- Lv, C., "A hybrid algorithm for the static bike-sharing re-positioning problem based on an effective clustering strategy", Transportation Research Part B: Methodological, 140, pp.1~21 (2020)
- Ma, G., "Rebalancing stochastic demands for bike-sharing networks with multi-scenario characteristics", Information Sciences, 554, pp.177~197 (2021)
- Ma, T. Y., "A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers", Transportation Research Part E:Logistics and Transportation Review, 128, pp.417~442 (2019)
- Mohiuddin, H., "Planning for the First and Last Mile: A Review of Practices at Selected Transit Agencies in the United States", Sustainability, 13(4), pp.2222~ (2021)
- Niu, K., "Real-time taxi-passenger prediction with L-CNN", IEEE Transactions on Vehicular Technology, 68(5), pp.4122~4129 (2018)
- Pan, L., "A deep reinforcement learning framework for rebalancing dockless bike sharing systems", Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), pp.1393~1400 (2019)
- Park, K., "First-", last-mile experience matters: The influence of the built environment on satisfaction and loyalty among public transit riders, 2021, pp.112, 32~42
- Qiu, H., "Dynamic pricing in shared mobility on demand service", arXiv preprint arXiv:1802.03559, ~ (2018)
- Mobility and the Sharing Economy: Potential to Overcome First-and Last-Mile Public Transit Connections, "Shaheen, S.", Transportation Sustainability Research Center, ~ (2016)
- Shiv, A., "Analysis of last mile transport pilot: Implementation of the model and its adaptation among local citizens", Master, pp.2018
- Sochor, J., "A topological approach to Mobility as a Service: A proposed tool for understanding requirements and effects, and for aiding the integration of societal goals", Research in Transportation Business & Management, 27, pp.3~14 (2018)
- Stiglic, M., "Enhancing urban mobility:Integrating ride-sharing and public transit", Computers & Operations Research, 90, pp.12~21 (2018)
- Tolomei, L., "Relocation strategies for e-scooter system optimization", Doctor, pp.2021
- Yan, C., "Dynamic pricing and matching in ride hailing platforms", Naval Research Logistics(NRL), 67(8), pp.705~724 (2020)
- Introduction of MaaS(Mobility as a Service) Seoul, "Yoon, H.", The Seoul Institute, ~, pp.Policy Report, no. 283 (2019)
- Zhang, J., "A dynamic pricing scheme with negative prices in dockless bike sharing systems", Transportation Research Part B: Methodological, 127, pp.201~224 (2019)
- Zhao, B., "Preference-aware task assignment in on-demand taxi dispatching: An online stable matching approach", Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), pp.2245~2252 (2019)