Needs assessment and scheduling scheme of shared bicycles(PDF)
《长安大学学报（社科版）》[ISSN:1671-6248/CN:61-1391/C]
- Issue:
- 2018年02期
- Page:
- 32-41
- Research Field:
- 交通运输
- Publishing date:
Info
- Title:
- Needs assessment and scheduling scheme of shared bicycles
- Author(s):
- XIA Yun; YU Qi-tong; LIN Zi-li
- School of International Business, Jinan University, Zhuhai 519000, Guangdong,China
- Keywords:
- shared bicycle; needs assessment; route optimization; fuzzy rating model; genetic algorithm
- PACS:
- F572,F724.6
- DOI:
- -
- Abstract:
- The shared economy has developed rapidly in recent years, and one of the most popular shared economic patterns is shared ridership. However, owing to the fact that an excessive number of vehicles were launched by enterprises offering shared bicycles in the past few years to gain market share, the supply and demand of parking points is not balanced; this leads to a serious disruption of the normal social order. Based on the above thinking, this paper takes 13 counties in Xi’an city as the sample. It uses the population density, per capita disposable income, and bus station density that are oriented and dimensionless to calculate the weights of each sample index and establish a fuzzy rating model of demand by using the entropy method. The results show that the information value coefficient of the per capita disposable income is the largest, that is, disposable income makes the greatest contribution to the needs assessment of shared bicycles. Based on the results of the needs assessment, a thermodynamic diagram is made, and it is found that the demand for the shared bicycle in Xi’an can be expressed by the concentric circle model. If the corresponding ratio coefficient is given for a range of radii, the demand for the shared bicycle in each area can be calculated. The scheduling of shared bicycles is the solution of the optimization problem. By randomly generating the initial path and setting the shortest route as the objective function, the capacity of the dispatching car, the maximum driving distance, and the number of the vehicles are given; the path length, path distribution, and required adjustment can be obtained by using the genetic algorithm. On the basis of the empirical conclusions, this paper puts forward four suggestions: reasonable distribution; planning of electronic fence parking points; vehicle scheduling based on big data and space elements; and the introduction of a mixed management model.
Last Update: 2018-05-31