Developing a supervised learning-based simulation method as a decision support tool for rebalancing problems in bike-sharing systems

Maleki, Abolfazl; Nejati, Erfan; Aghsami, Amir; Jolai, Fariborz

doi:10.1016/j.eswa.2023.120983

Cited by 6 publications

(1 citation statement)

References 37 publications

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“…At the same time, to impose some additional constraints on the rebalancing process, it may be necessary to design multi-objective optimization algorithms to encapsulate the impact of these constraints [34,42]. In recent years, with the continuous development of machine learning technology, data-driven machine learning methods have been gradually applied to the optimization and solution process of rebalancing models [43,44]. However, the rebalancing process has many problems and challenges waiting to be solved [45].…”

Section: Literature Reviewmentioning

confidence: 99%

Can Docked Bike-Sharing Systems Reach Their Dual Sustainability in Terms of Environmental Benefits and Financial Operations? A Comparative Study from Nanjing, 2017 and 2023

Liu,

Zhao

et al. 2023

Sustainability

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In this paper, we investigate the sustainability of docked bike-sharing in Nanjing in terms of environmental benefits and financial operations by comparing the data of March 2017 and March 2023 in Nanjing. We modify a community detection method, give and prove dynamic boundary conditions for the objective function of the heuristic algorithm, and realize the estimation of the rebalancing coefficients for this mega-system, thus obtaining more accurate emission factors. We find that there are significant differences in the results obtained from environmental benefit assessments over time. Further, there are also significant differences at the national level. This may signify that the assessment data of one country’s system cannot give a direct reference for another country’s system. Second, we considered the economic basis required for the environmental benefits of docked bike-sharing systems. We have calculated the sustainability of the system’s financial operations by considering its revenues over the next nine years, including the cost of facility inputs, facility upgrades, dispatching costs, labor costs, maintenance costs, and the time value of money. The results show a 4.6-fold difference in emission factors between 2017 and 2023; comparing 2017 to 2023 (when demand loss has been severe), the investment in 2017 will be recouped 2 years later than in 2023. Switching distribution vehicles from fuel vehicles to electric trikes would severely deteriorate the operator’s key financial metrics while only reducing the emission factor value by 8.64 gCO2 eq/km, leading to an unsustainable system. This signals the potential for the financial unsustainability, or even bankruptcy, of operators if the requirements for sustained emissions reductions from the bike-sharing system are divorced from the form of the economy on which it is sustainably operated. Finally, we consider the geographical patterns between environmental benefits and financial operations. We find that financial sustainability varies across geographic locations. Under financial sustainability, we gave emission factors under the mix distribution vehicle scenario.

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