Customer Incentive Rebalancing Plan in Free-Float Bike-Sharing System with Limited Information

Wu, Ruijing; Liu, Shaoxuan; Shi, Zhenyang

doi:10.3390/su11113088

Cited by 18 publications

(9 citation statements)

References 62 publications

(70 reference statements)

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“…For optimising operation strategies, the fleet management was the main approach of bike-sharing systems, focusing on distributing and repositioning bikes between stations, i.e., rebalancing the fleet within the system [72,82,101,150,175,179,182,191,192]. Wu et al [179] sought to elaborate an incentive plan for users to perform the repositioning activities, Fan et al [182] sought to determine the fleet size, while Zhang et al [82], Cao and Xu [192] and Zhou et al [101] elaborated repositioning plans, including routes, scheduling of the operation [82] and number of bicycles [192]. The study of Chang et al [84] distinguishes itself by incorporating damaged bicycles into the model.…”

Section: A Planning and Policy-makingmentioning

confidence: 99%

A Comprehensive Survey and Future Directions on Optimising Sustainable Urban Mobility

Borchers,

Wittowsky,

Augusto Souza Fernandes

2024

IEEE Access

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Climate change is currently the biggest environmental threat, being the cities responsible for a significant impact on greenhouse gas emissions. In this sense, the transport sector is among the main causes for both emissions and the depletion of non-renewable resources. Considering this scenario, there is an appeal to build more accessible, smart, sustainable and energy-efficient cities, promoting energy transition in urban systems and increasing the quality of urban life. This review aims to analyse transport and urban mobility studies that employ optimisation techniques to achieve environmental, sustainability or climate change mitigation goals. After an overview regarding modelling aspects of how such goals were addressed, the nature of the objective functions, the perspectives considered, and the network application, such studies were classified into five areas and further detailed. The areas comprise: (i) planning and policy-making; (ii) environmental variables; (iii) demand and traffic management; (iv) technology and energy; and (v) nonspatial measures. In this sense, future research directions should include optimisation models that consider the social aspects of transport and the interests of passengers, operators and the community simultaneously, improve the modelling of environmental impacts to increase its robustness, and deal with large network problems.INDEX TERMS Climate change, Greenhouse gas (GHG) emissions, Optimisation, Passenger urban transport, Sustainable mobility.

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Section: A Planning and Policy-makingmentioning

confidence: 99%

A Comprehensive Survey and Future Directions on Optimising Sustainable Urban Mobility

Borchers,

Wittowsky,

Augusto Souza Fernandes

2024

IEEE Access

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“…A stochastic simulation optimization model, which adopted a rank and search methodology, was used to maintain expected satisfactory service levels while maximizing total expected daily profit. Their findings suggested that an optimal incentive-based rebalancing plan could achieve higher bike utilization [33].…”

Section: Related Workmentioning

confidence: 99%

A Feasible Solution for Rebalancing Large-Scale Bike Sharing Systems

et al. 2021

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City bikes and bike-sharing systems (BSSs) are one solution to the last mile problem. BSSs guarantee equity by presenting affordable alternative transportation means for low-income households. These systems feature a multitude of bike stations scattered around a city. Numerous stations mean users can borrow a bike from one location and return it there or to a different location. However, this may create an unbalanced system, where some stations have excess bikes and others have limited bikes. In this paper, we propose a solution to balance BSS stations to satisfy the expected demand. Moreover, this paper represents a direct extension of the deferred acceptance algorithm-based heuristic previously proposed by the authors. We develop an algorithm that provides a delivery truck with a near-optimal route (i.e., finding the shortest Hamiltonian cycle) as an NP-hard problem. Results provide good solution quality and computational time performance, making the algorithm a viable candidate for real-time use by BSS operators. Our suggested approach is best suited for low-Q problems. Moreover, the mean running times for the largest instance are 143.6, 130.32, and 51.85 s for Q = 30, 20, and 10, respectively, which makes the proposed algorithm a real-time rebalancing algorithm.

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“…Reiss and Bosenberger [18] studied a user-based approach to a FFBS system and discussed the advantages and applicable scenarios for both operator-based and user-based relocation strategies. Wu et al [19] employed user-based tactics by incentivizing users to perform repositioning activities and constructed a more detailed quantitative model which can derive the optimal incentive scheme for the FFBS system. Some studies considered both user-based and operatorbased strategies.…”

Section: E User-based Strategiesmentioning

confidence: 99%

A Balanced Strategy for the FFBS Operator Integrating Dispatch Area, Route, and Depot Based on Multimodel Technologies

Zhou

Wong

2021

Journal of Advanced Transportation

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Bicycle scheduling is the essential strategy for balancing the demand for the public bicycle system (PBS). Existing literature pays more attention to bike scheduling models and their solutions, but seldom discusses the dispatch area and depot center. Reasonable dockless public bicycle dispatch area and optimal dockless bike dispatch depot location in the service area were discussed from the existing shared bicycle operation data in this paper. We proposed a feasible framework including bike trip network segmentation, mean-shift clustering based on the point position, VRP model, genetic algorithm, and TOPSIS evaluation method. The effectiveness and superiority of the division of the dispatch area are verified. The main evidence for this article is (1) although the cycling networks of bicycles are different at different times of the day, the results of community division are relatively stable and have great similarities. (2) The plan of the dispatch area has impacted on the operation efficiency of the PBS. For a scheduling area, the target value of the optimal scheduling strategy corresponding to different dispatch centers is obviously different. Therefore, the location of the dispatch center has a great impact on the quality of the scheduling strategy. The dispatch area determined by bike trip OD community detection has stable characteristics of scheduling costs. (3) This work is an attempt to combine big data and model technology to assist city management. We build a feasible framework to serve a balanced strategy for FFBS which can provide reasonable dispatch area, optimal dispatch depot location, dispatch truck’s route length, load action, and time window. Our proposed framework provides new ideas for regional traffic dispatching for the traffic management department and FFBS operator, which has certain practical reference significance.

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Customer Incentive Rebalancing Plan in Free-Float Bike-Sharing System with Limited Information

Cited by 18 publications

References 62 publications

A Comprehensive Survey and Future Directions on Optimising Sustainable Urban Mobility

A Comprehensive Survey and Future Directions on Optimising Sustainable Urban Mobility

A Feasible Solution for Rebalancing Large-Scale Bike Sharing Systems

A Balanced Strategy for the FFBS Operator Integrating Dispatch Area, Route, and Depot Based on Multimodel Technologies

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