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

Abstract

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.

WEA: A Node Importance Algorithm in Weighted Networks

“…Seven algorithms were selected for conducting the comparison with WEA on those attached datasets, including the classical weighted version of the BT [26], CL [27], EC [30], WC [28], SC [29], the improved algorithm (HI) [25] developed by Lue et al and the WD [24]. To validate the experiment, we chose two mainstream tests to measure the node importance sorting: Connectivity test and SIR test.…”

Allocation strategies in a dockless bike sharing system: a community structure-based approach

“…Cheng et al (2019) developed a site selection method to solve the inadequate utilisation and unreasonable distribution of the geographical resource in dockless bike sharing system. Rahim Taleqani et al (2020) discussed a new paradigm to identify candidate locations that could be geo-fenced in dockless bike sharing. The above studies show dockless bike sharing has different unique characteristics than stationbased bike sharing (Xu et al, 2019;Yang et al, 2019).…”

Micromobility Station Placement Optimization for a Rural Setting

“…Additional motivations for deployments include the promotion of physical exercise, congestion reduction, pollution reduction, and support for multimodal transportation connections. Organizations have also deployed dock-less BSSs, but issues such as sidewalk clutter, interference with pedestrian traffic, and increased coordination costs tampered their adoption in many cities [4]. Even with docked BSS, there are numerous challenges to integrating them into communities and the transportation network.…”