Article
Engineering, Civil
Huthaifa Ashqar, Mohammed Elhenawy, Hesham A. Rakha, Leanna House
Summary: Bike sharing systems are an important part of urban mobility, and the traditional measure of service quality lacks spatial correlation and discrimination between stations. Therefore, this study proposes a new measure called Optimal Occupancy, which takes into account the temporal variations and spatial dependencies of individual stations and provides better prediction of service quality at nearby locations.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Operations Research & Management Science
Rui-Na Fan, Quan-Lin Li, Xiaole Wu, Zhe George Zhang
Summary: This paper discusses a large-scale dockless bike-sharing system (DBSS) with unusable bikes and proposes a new computational method based on the RG-factorizations of block-structured Markov processes to analyze the system's performance under two batch policies. The method sets up a nonlinear matrix equation to determine arrival rates and establishes a more general product-form solution for the closed queueing network.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Physics, Multidisciplinary
Yuhan Zhang, Yichang Shao, Hui Bi, Li Aoyong, Zhirui Ye
Summary: This paper proposes a user-based repositioning method to address the imbalance in bike inventory in bike-sharing systems. The results show that this method is remarkably effective in improving the bike inventory balance and the station's turnover rate.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Environmental Studies
Fangni Zhang, Wei Liu
Summary: The study found that bike sharing can reduce total social cost, even when the operator maximizes its profit. A more attractive metro service can increase the profit of the bike sharing operator. Coordinated operation between bike sharing and metro systems can further reduce total social cost.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2021)
Article
Computer Science, Interdisciplinary Applications
Yu Jin, Cesar Ruiz, Haitao Liao
Summary: Bike-sharing systems have gained worldwide attention for their ability to enhance quality-of-life in cities. This study proposes a simulation framework to evaluate different strategies for bike rebalancing and maintenance. An optimization model is provided as an example, using enhanced k means clustering and an Ant Colony Optimization algorithm. The framework's application extends beyond bike-sharing systems to other shared transportation systems where rebalancing and maintenance optimization are essential.
SIMULATION MODELLING PRACTICE AND THEORY
(2022)
Article
Computer Science, Artificial Intelligence
Libin Zheng, Lei Chen, Cyrus Shahabi
Summary: Bike-sharing systems have become popular due to the development of mobile networks, however, not much attention has been paid to routing algorithms for shared-bike riders. This paper studies the routing problem for multiple shared-bike riders and proposes two heuristics to allocate limited resources among competing riders. The experiments show that the greedy-based routing algorithm is effective and efficient.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Management
Chenyi Fu, Ning Zhu, Shoufeng Ma, Ronghui Liu
Summary: A bike-sharing system provides an alternative transportation mode for short trips with minimal travel speed loss. However, low usage ratio and high depreciation rate in this model pose risks to sustainable development. This study proposes an integrated model for station location and vehicle service design to maximize daily revenue. A robust optimization model is used to address demand ambiguity. Numerical studies show that the proposed algorithm efficiently obtains exact solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Information Systems
Chaher Alzaman, Tariq Aljuneidi, Zhaojun Li
Summary: This study focuses on enhancing the responsiveness of supply chain networks to bike-sharing systems by analyzing multi-factor bike-sharing data and using machine learning algorithms to predict bike usage and repair. The results demonstrate the effectiveness of combining machine learning with supply chain network design in improving operational efficiency.
Article
Economics
Bruno Albert Neumann-Saavedra, Dirk Christian Mattfeld, Mike Hewitt
Summary: This paper examines the redistribution issue in station-based bike-sharing systems and analyzes the demand variability of different types of stations. The impact of demand variability on the operational implementation of redistribution plans is evaluated through agent-based simulation.
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
(2021)
Article
Multidisciplinary Sciences
Xin Liu, Konstantinos Pelechrinis
Summary: The key to the long-term success of shared transportation systems is their availability and rebalancing. This study proposes a method to estimate excess demand and supply rates, and experiments validate its accuracy.
Article
Green & Sustainable Science & Technology
Dirk Ploos van Amstel, Lenneke Kuijer, Remko van der Lugt, Berry Eggen
Summary: Closing the loop of products and materials in Product Service Systems (PSS) is crucial, and designers can enhance this process by invoking a greater sense of ownership among users. This research developed a psychological ownership-based design tool for a bicycle sharing service, and evaluated its effectiveness through design interventions and evaluations involving project members, bicycle repairers, and users of the service.
Article
Environmental Sciences
Raeven Lynn M. Clockston, David Rojas-Rueda
Summary: This study evaluated the health risks and benefits of bike-sharing systems (BSS) in the U.S. and NYC, showing that BSS provide health benefits for cyclists, primarily through increased physical activity. The research estimated reductions in premature deaths, DALYs, and positive health economic impacts as a result of BSS usage.
ENVIRONMENTAL RESEARCH
(2021)
Article
Chemistry, Multidisciplinary
Matthias Kowald, Margarita Gutjar, Kai Roeth, Christian Schiller, Till Dannewald
Summary: This paper presents a study that aims to estimate the choice parameters of station-based bike-sharing systems (BSS) through a survey, in order to simulate the effects of modal shifts in transport demand models.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Ahmet Sakir Dokuz
Summary: Bike Sharing Systems (BSS) have become popular due to their environmentally friendly nature and the fact that they promote mobility and outdoor activities. City residents tend to use certain bike stations more frequently and prevalently than others, for reasons such as the popularity and centrality of the locations. Discovering key bike stations is essential for optimal planning for BSS, bike repositioning methods, and urban land use applications, but poses challenges due to varying user preferences, weather conditions, and the large-scale nature of BSS datasets. Two interest measures and algorithms have been proposed in this study to efficiently discover key stations among BSS big datasets, showing effectiveness in making decisions about city residents' mobility behaviors and extracting knowledge about bike user preferences.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Shengjie Zhao, Kai Zhao, Yusen Xia, Wenzhen Jia
Summary: Bike traffic prediction is crucial for operations management in bike-sharing systems. Existing studies lack solutions to complex iterative optimization problems. This paper proposes a novel hyper-clustering approach that enhances spatio-temporal deep neural networks for more accurate prediction of available bikes.
INFORMATION SCIENCES
(2022)
Article
Economics
Yannick Oskar Scherr, Bruno Albert Neumann Saavedra, Mike Hewitt, Dirk Christian Mattfeld
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2019)
Article
Economics
Bruno Albert Neumann-Saavedra, Dirk Christian Mattfeld, Mike Hewitt
Summary: This paper examines the redistribution issue in station-based bike-sharing systems and analyzes the demand variability of different types of stations. The impact of demand variability on the operational implementation of redistribution plans is evaluated through agent-based simulation.
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
(2021)
Article
Neurosciences
Theda Eichler, Wiebke Roetz, Christoph Kayser, Felix Broehl, Michael Roemer, Arne Henning Witteborg, Franz Kummert, Tobias Sandmeier, Christoph Schulte, Patricia Stolz, Katharina Meyer, Holger Sudhoff, Ingo Todt
Summary: Due to changes in indication range for cochlear implants and demographic development towards an aging society, more and more people are receiving cochlear implants. Hearing therapy rehabilitation currently requires significant personnel effort and time. In this study, an app is being developed that provides personalized hearing therapy tailored to the patient, without the need for local speech therapists. The app is able to analyze patient difficulties and provide recommendations for technical adjustments.
Article
Engineering, Manufacturing
Michael Roemer
Summary: This paper presents new mixed-integer linear programming formulations for solving multi-activity shift scheduling problems (MASSP). These formulations encode shift feasibility rules in block-based state-expanded networks. The paper proposes two exact modeling techniques to address the challenge of large network sizes and reduce the solution time. Experimental results demonstrate the effectiveness of these techniques in reducing both the size of the model instances and the solution time, enabling the resolution of previously unsolved instances on a notebook computer in less than 30 minutes.
JOURNAL OF SCHEDULING
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Till Porrmann, Michael Romer
Summary: This paper proposes a method to reduce the size of state-expanded networks for multi-activity shift scheduling problems using machine learning models, which substantially reduces the size of model instances and solution times while still obtaining optimal solutions for most instances. The results show that this approach is competitive with a state-of-the-art matheuristic for multi-activity shift scheduling problems.
INTEGRATION OF CONSTRAINT PROGRAMMING, ARTIFICIAL INTELLIGENCE, AND OPERATIONS RESEARCH
(2021)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Jakob Schulte, Michael Romer, Kevin Tierney
COMPUTATIONAL LOGISTICS, ICCL 2020
(2020)
Article
Economics
Yannick Oskar Scherr, Mike Hewitt, Bruno Albert Neumann Saavedra, Dirk Christian Mattfeld
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2020)
Proceedings Paper
Transportation
Bruno A. Neumann-Saavedra, Patrick Vogel, Dirk C. Mattfeld
18TH EURO WORKING GROUP ON TRANSPORTATION, EWGT 2015
(2015)
Proceedings Paper
Computer Science, Artificial Intelligence
Patrick Vogel, Bruno A. Neumann Saavedra, Dirk C. Mattfeld
HYBRID METAHEURISTICS, HM 2014
(2014)