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

Zhang, Jie; Meng, Meng; Wang, David Z.W.; Du, Bo

doi:10.1080/15568318.2020.1849471

Cited by 14 publications

(6 citation statements)

References 30 publications

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“…In future work, the proposed model can be extended by incorporating cargo routing under stochastic demand, impacts of the devanning process on inventory control, and multi-type containers such as reefer containers. The CECR model will be further explored for application to other allocation or repositioning problems in car-sharing and bikesharing systems (28)(29)(30). Moreover, the competition between different shipping companies was ignored in this paper.…”

Section: Discussionmentioning

confidence: 99%

A Multi-Stage Approach for Empty Container Repositioning Under Coordination Among Liner Carriers

Zhang

et al. 2021

Transportation Research Record

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This paper studies the empty container repositioning (ECR) problem considering the exchange of slots and empty containers among liner shipping companies. It is common for an individual shipping company to seek an optimal solution for ECR and cargo routing to maximize its own benefits. To achieve cooperation among shipping companies, a multi-stage solution strategy is proposed. With the inverse optimization technique, the guide leasing prices of slots and empty containers among shipping companies are derived considering the schedule of vessels and cargo routing. Based on the guide leasing price, a cooperative model is formulated to minimize the total cost, which includes the transportation cost for laden containers, the inventory holding cost, the container leasing cost, and the repositioning cost. All the involved shipping companies are expected to follow the best solution of ECR and cargo routing to achieve a cooperative and stable optimum. A real-world shipping network operated by three liner shipping companies is used as a case study with promising numerical results.

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Section: Discussionmentioning

confidence: 99%

A Multi-Stage Approach for Empty Container Repositioning Under Coordination Among Liner Carriers

Zhang

et al. 2021

Transportation Research Record

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“…Community structure methodologies will be dealt with in detail in Section 3. Zhang et al [35] applied the community structure methodology to the transportation field. They presented a methodology for covering the maximum service area with the minimum number of shared bikes and allocating bikes so that the maximum number of users can use them.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The F-N algorithm is a bottom-up algorithm that combines similar nodes and communities [33]. It has the advantage of fast calculation time, but there is the disadvantage that the weight of the link cannot be reflected [35]. The greedy algorithm also improves the calculation time, but it has weaknesses in performance optimization, such as creating a super-community [42].…”

Section: Community Structurementioning

confidence: 99%

Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method

et al. 2022

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The shared e-scooter is a popular and user-convenient mode of transportation, owing to the free-floating manner of its service. The free-floating service has the advantage of offering pick-up and drop-off anywhere, but has the disadvantage of being unavailable at the desired time and place because it is spread across the service area. To improve the level of service, relocation strategies for shared e-scooters are needed, and it is important to predict the demand for their use within a given area. Therefore, this study aimed to develop a demand prediction model for the use of shared e-scooters. The temporal scope was selected as October 2020, when the demand for e-scooter use was the highest in 2020, and the spatial scope was selected as Seocho and Gangnam, where shared e-scooter services were first introduced and most frequently used in Seoul, Korea. The spatial unit for the analysis was set as a 200 m square grid, and the hourly demand for each grid was aggregated based on e-scooter trip data. Prior to predicting the demand, the spatial area was clustered into five communities using the community structure method. The demand prediction model was developed based on long short-term memory (LSTM) and the prediction results according to the activation function were compared. As a result, the model employing the exponential linear unit (ELU) and the hyperbolic tangent (tanh) as the activation function produced good predictions regarding peak time demands and off-peak demands, respectively. This study presents a methodology for the efficient analysis of the wider spatial area of e-scooters.

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“…Furthermore, they consider several measures such as traffic flow, recharging vehicle battery demand, and network capacity constraints to attract more vehicles to the charging facilities, maximize their revenue, and minimize public social costs. Besides all these, some studies formulate techniques and models to distribute bikes or repairable service parts and their facility locations through networks providing proper allocation strategies and spatial distribution for servicing the maximum area with minimum resources [ 30 – 32 ]. Furthermore, many other techniques are commonly used for location analyses.…”

Section: Introductionmentioning

confidence: 99%

Weiszfeld, tree-seed, and whale optimization algorithms comparison via locating transportation facilities with weightings considering the vulnerability and uncertainty

Demir

2022

PLoS ONE

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Searching for an optimum transportation facility location with emergency equipment and staff is essential for a specific region or a country. In this direction, this study addresses the following problems. First, the performances of the Weiszfeld, tree–seed, and whale optimization algorithms are compared, which is the first of its kind in the literature. Second, a new approach that tests the importance parameters’ effectiveness in searching for an optimum transportation facility location with emergency equipment and staff is proposed. The Weiszfeld algorithm finds viable solutions with compact data, but it may not handle big data. In contrast, the flexibility of the tree–seed and whale optimization algorithm is literally an advantage when the number of parameters and variables increases. Therefore, there is a notable need to directly compare those algorithms’ performances. If we do, the significance of extending the number of parameters with multiple weightings is appraised. According to the results, the Weiszfeld algorithm can be an almost flexible technique in continuous networks; however, it has reasonable drawbacks with discrete networks, while the tree–seed and whale optimization algorithms fit such conditions. On the other hand, these three methods do not show a fluctuating performance compared to one another based on the locating transportation facilities, and thus they deliver similar performance. Besides, although the value of accuracy is high with the application of the conventional technique Weiszfeld algorithm, it does not provide a significant performance accuracy advantage over the meta-heuristic methods.

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

Cited by 14 publications

References 30 publications

A Multi-Stage Approach for Empty Container Repositioning Under Coordination Among Liner Carriers

A Multi-Stage Approach for Empty Container Repositioning Under Coordination Among Liner Carriers

Predicting Demand for Shared E-Scooter Using Community Structure and Deep Learning Method

Weiszfeld, tree-seed, and whale optimization algorithms comparison via locating transportation facilities with weightings considering the vulnerability and uncertainty

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