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Dockless bike sharing is an emerging paradigm. Like many other technologies, it brings advantages and disadvantages to communities. Further investigation into public opinion will shed light on the impact of this technology on communities and provide input to city authorities for transportation planning. Transportation planning processes can be enhanced by engaging the community through social media technologies. Social media like Twitter, Facebook, and other microblogging media have been used for planning, but have not been extensively evaluated for that purpose. This study examined approximately 32,000 posts on Twitter to assess public opinion on dockless bike-sharing systems. Using a mix of text mining and statistical techniques, we examined relevant posts to determine the sentiment polarity of tweets, the underlying topics in the tweets, and the extent of engagement and impact on the decision-making process. Results given by two different sentiment algorithms show that there is more positive than negative polarity across the algorithms. Also, the findings show that the underlying topics in tweets include electric scooters, private e-hailing companies, and blockage of sidewalks, among others. The results indicate that the dockless shared mobility models are potentially useful in generating participation, but faced substantial technical, analytical, and communication barriers to influencing decision-making.

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.

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