Allocation optimization of bicycle-sharing stations at scenic spots

Guo, Tangyi; Zhang, Ping; Shao, Fang; Liu, Yingshun

doi:10.1007/s11771-014-2314-8

Cited by 15 publications

(16 citation statements)

References 13 publications

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“…In the literature, different location optimization models for station-based vehicle sharing systems have been presented [29][30][31][32][33][34][35][36]. We propose an optimization model for the definition of the number of docks/stalls of each station and the total number of EVs, assuming that the number of the stations ns and their positions are known a priori.…”

Section: First Stage: E-bikes Sharing System Network Designmentioning

confidence: 99%

A Novel Distributed System of e-Vehicle Charging Stations Based on Pumps as Turbine to Support Sustainable Micromobility

et al. 2021

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Recent statistics shows that CO2 emissions from road transport have been increasing. In addition, the paradigm of “more electric vehicles” does not seem to be positive from the environmental point of view. In fact, assuming the current energy mix system, studies focusing on Life Cycle Assessment and Well-To-Wheels analysis demonstrate that electric vehicles are less eco-friendly than traditional internal combustion-based engines. Then, it is mandatory to shift toward renewable energy sources to produce electricity with less CO2 emission. In this work, it is proposed to use a new e-vehicles charging system based on Pumps used as Turbine (PATs). This system uses the pressure in excess that could be available in a water distribution network (WDN). Such an excess of pressure is usually destroyed by pressure-reducing valves with the aim to reduce water leaks. PATs are also able to reduce water pressure and produce electrical energy that can be supplied to e-vehicles charging stations. Then, a bi-level methodology to design and optimize the e-charging stations system for (individual or shared) e-bikes and/or e-scooters is proposed. The method allows determining the optimal number of e-vehicles, charging stations docks, and PATs on the study area according to the WDN layout and hydraulic properties as well as the road network characteristics and demand of e-vehicles. The potential of the methodology is shown by an application to a real case study.

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Section: First Stage: E-bikes Sharing System Network Designmentioning

confidence: 99%

A Novel Distributed System of e-Vehicle Charging Stations Based on Pumps as Turbine to Support Sustainable Micromobility

et al. 2021

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“…Equation ( 4) is to optimize the construction of the number of bike-sharing parking spots, to avoid the situation where the number of parking spots is too small or too much, resulting in inefficient use of shared bike systems or resource redundancy; equation (5) is the target area i within the service area of the optimized parking spot j' s scope of services of binary variable, that is, to ensure that the user can find the parking spot within the maximum tolerable walking distance to complete the borrowing and returning of the bike within the given target area; equation (6) provides a bike borrowing and returning service for at least one bike-sharing parking spot in any target area; equation ( 7) serves at least one target area for a shared bike parking spot; equations (8) and ( 9) restrict users to borrow and return bikes at only optimized parking spots; equation (10) constrains the number of borrowed bikes at any one parking spot to be equal to the sum of the number of returned bikes from any parking spot to each parking spot; equation (11) constrains the number of bikes returned at any one parking spot to be equal to the sum of the number of vehicles returned from each parking spot to any one of the parking spots; equation (12) constrains the number of vehicles demanded from each target area to travel to other areas; equation ( 13) is the binary variable to optimize the parking spot; and equation (14) ensures that the number of vehicles in the target area meets the demand in the target area.…”

Section: Layout Optimization Modelmentioning

confidence: 99%

“…Due to the close connection between the parking spots of bike sharing and the surrounding infrastructure, some scholars have considered the influence of the surrounding environment on the layout of shared bikes. In view of the optimization of the layout of shared bikes parking spots in scenic areas, Guo et al [11] considered the distribution of subway stations and bus stations around the scenic area, by proposing an optimization model based on clustering and greedy algorithms, and solved the problem with an optimization coverage rate of 89.2%. In establishing the layout optimization model of the shared bikes parking spot, we can also consider the multiobjective optimization model such as travel cost and construction cost [12].…”

Section: Introductionmentioning

confidence: 99%

Layout Optimization of Campus Bike-Sharing Parking Spots

Guo

Yang

Liu

et al. 2020

Journal of Advanced Transportation

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The rapid development of bike sharing has posed some challenges to the traffic management on campus. The bike sharing on campus has problems such as messy parking, and some buildings in the peak hours have no bikes to borrow. Thus, alternative parking spots are proposed based on the layout principle of parking spots for bicycles. An optimization model of the layout of campus bike-sharing parking spots with travel time and construction cost as the optimization goal is established, and the branch and bound algorithm is used to solve the model. Finally, the study analysis is carried out by optimizing the layout of the bike-sharing parking spot of Nanjing University of Science and Technology. The results show that, after optimizing the layout of parking spots, the average travel time of users is reduced by 6.0%, and the total construction cost is reduced by 27.3%. While being convenient for campus bike-sharing users, it also provides scientific decision-making support for the campus traffic management.

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“…The development of bikesharing worldwide can be categorized into four generations: white bikes, coin deposit systems, IT-based systems, and demand responsive, multimodal systems (11).…”

Section: Bikesharing Worldwidementioning

confidence: 99%

Understanding the Utilization Characteristics of Bicycle-Sharing Systems in Underdeveloped Cities: A Case Study in Xuchang City, China

Yang

Tao

et al. 2017

Transportation Research Record: Journal of the Transportation R

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In recent years, a growing number of cities in China have successively rolled out bicycle-sharing systems to facilitate bicycle use, including not only metropolises but also some underdeveloped cities with populations of less than 1 million. One of those underdeveloped cities, Xuchang, launched its bicycle-sharing system in 2014. This service provides a convenient way for members to cycle for some of their short trips. Interest in the bicycle-sharing systems of metropolises is growing rapidly; however, studies on underdeveloped cities are still limited. This study investigated the factors influencing the adoption of a bicycle-sharing system in Xuchang, by analyzing massive smart card data from July 2014 to mid-April 2015 and 500 intercept survey questionnaires in April 2015. Different questions were ready for members and nonmembers in the questionnaires and the statistical results show the characteristics of users of the Xuchang bicycle-sharing system, including demographic characteristics, travel habits, and degree of satisfaction. Moreover, the space–time distribution characteristics of the Xuchang bicycle-sharing system were analyzed by dividing a massive data set into three groups: weekdays, weekends, and holidays. Results showed that compared with the clearly defined role of “resolve the last-kilometer problem” in a metropolis, bicycle-sharing in underdeveloped cities acts as an alternative way of transportation rather than a transfer traffic mode. Results also showed that bicycle-sharing systems gained more popularity in underdeveloped cities than in metropolises because of the smaller extent of egression, resident travel habits, the traffic environment, and so on.

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Allocation optimization of bicycle-sharing stations at scenic spots

Cited by 15 publications

References 13 publications

A Novel Distributed System of e-Vehicle Charging Stations Based on Pumps as Turbine to Support Sustainable Micromobility

A Novel Distributed System of e-Vehicle Charging Stations Based on Pumps as Turbine to Support Sustainable Micromobility

Layout Optimization of Campus Bike-Sharing Parking Spots

Understanding the Utilization Characteristics of Bicycle-Sharing Systems in Underdeveloped Cities: A Case Study in Xuchang City, China

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