Large-Scale Trip Planning for Bike-Sharing Systems

2017 ◽  
Author(s):  
Zhi Li ◽  
Jianhui Zhang ◽  
Jiayu Gan ◽  
Pengqian Lu ◽  
Fei Lin
Keyword(s):  
Large Scale ◽  
Trip Planning ◽  
Bike Sharing

Large-scale trip planning for bike-sharing systems

2019 ◽  
Vol 54 ◽  
pp. 16-28 ◽  
Author(s):  
Zhi Li ◽  
Jianhui Zhang ◽  
Jiayu Gan ◽  
Pengqian Lu ◽  
Zhigang Gao ◽  
...  
Keyword(s):  
Large Scale ◽  
Trip Planning ◽  
Bike Sharing

scholarly journals Maximum Closeness Centrality k-Clubs: A Study of Dock-Less Bike Sharing

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Ali Rahim Taleqani ◽  
Chrysafis Vogiatzis ◽  
Jill Hough
Keyword(s):  
Computational Complexity ◽  
Large Scale ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Closeness Centrality ◽  
Computational Time ◽  
Greedy Heuristic ◽  
New Paradigm ◽  
Coordination Costs ◽  
Bike Sharing

In this work, we investigate a new paradigm for dock-less bike sharing. Recently, it has become essential to accommodate connected and free-floating bicycles in modern bike-sharing operations. This change comes with an increase in the coordination cost, as bicycles are no longer checked in and out from bike-sharing stations that are fully equipped to handle the volume of requests; instead, bicycles can be checked in and out from virtually anywhere. In this paper, we propose a new framework for combining traditional bike stations with locations that can serve as free-floating bike-sharing stations. The framework we propose here focuses on identifying highly centralized k-clubs (i.e., connected subgraphs of restricted diameter). The restricted diameter reduces coordination costs as dock-less bicycles can only be found in specific locations. In addition, we use closeness centrality as this metric allows for quick access to dock-less bike sharing while, at the same time, optimizing the reach of service to bikers/customers. For the proposed problem, we first derive its computational complexity and show that it is NP-hard (by reduction from the 3-SATISFIABILITY problem), and then provide an integer programming formulation. Due to its computational complexity, the problem cannot be solved exactly in a large-scale setting, as is such of an urban area. Hence, we provide a greedy heuristic approach that is shown to run in reasonable computational time. We also provide the presentation and analysis of a case study in two cities of the state of North Dakota: Casselton and Fargo. Our work concludes with the cost-benefit analysis of both models (docked vs. dockless) to suggest the potential advantages of the proposed model.

Towards Optimal Free-of-Charge Trip Planning in Bike-Sharing Systems

2017 ◽  
Author(s):  
Jian Xu ◽  
Jianliang Xu ◽  
Guanjie Cao ◽  
Haitao Xu ◽  
Ming Xu ◽  
...  
Keyword(s):  
Trip Planning ◽  
Bike Sharing

Simulation optimization for a large-scale bike-sharing system

2016 ◽  
Author(s):  
Nanjing Jian ◽  
Daniel Freund ◽  
Holly M. Wiberg ◽  
Shane G. Henderson
Keyword(s):  
Large Scale ◽  
Simulation Optimization ◽  
Bike Sharing

Large-Scale Needfinding: Methods of Increasing User-Generated Needs From Large Populations

2015 ◽  
Vol 137 (7) ◽  
Author(s):  
Cory R. Schaffhausen ◽  
Timothy M. Kowalewski
Keyword(s):  
Product Development ◽  
Large Scale ◽  
Early Stage ◽  
User Needs ◽  
Critical Component ◽  
Trip Planning ◽  
Final Study ◽  
Large Populations ◽  
User Access ◽  
Shared Context

Understanding user needs and preferences is increasingly recognized as a critical component of early stage product development. The large-scale needfinding methods in this series of studies attempt to overcome shortcomings with existing methods, particularly in environments with limited user access. The three studies evaluated three specific types of stimuli to help users describe higher quantities of needs. Users were trained on need statements and then asked to enter as many need statements and optional background stories as possible. One or more stimulus types were presented, including prompts (a type of thought exercise), shared needs, and shared context images. Topics used were general household areas including cooking, cleaning, and trip planning. The results show that users can articulate a large number of needs unaided, and users consistently increased need quantity after viewing a stimulus. A final study collected 1735 needs statements and 1246 stories from 402 individuals in 24 hr. Shared needs and images significantly increased need quantity over other types. User experience (and not expertise) was a significant factor for increasing quantity, but may not warrant exclusive use of high-experience users in practice.

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