Bi-level Programming Model for Shared Parking Considering Residential Parking Resources
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摘要: 为了缓解因城市停车资源有限造成的停车难问题,根据共享停车理论和智能交通诱导策略,提出了居住区泊位参与的共享停车诱导服务流程,探讨了实现居住区共享停车的基本条件,建立了诱导服务的泊位协调控制双层规划模型.该模型上层以高峰泊位空闲指数差异均值最小为目标,作为衡量该诱导服务能否实现区域范围停车资源均衡利用的指标;下层以驾驶员停车后平均步行距离最小为目标,作为衡量共享停车是否可行的依据.根据双层规划模型的求解原理和粒子群算法优化思想,设计了该模型的粒子群嵌套优化算法,并进行仿真实验.结果表明:鹤祥园小区最低泊位空闲指数为0.301,最大高峰泊位对外共享率为0.093,即只利用了不足1/3的闲置泊位就缓解了远洋城的停车问题;平均步行距离为160.59m,说明共享方案可行.Abstract: In order to relieve the parking problem caused by limited parking resources, a shared parking guidance and service process with the participation of residential parking spaces was proposed, which was based on the sharing parking theory and intelligent traffic guidance strategy. First, the basic conditions of sharing parking with residential areas were discussed. Then, a bi-level programming model of sharing parking guidance was established. In the model, the goal of the upper-level programming is to minimize the mean value of unoccupied parking difference index (MUPDI), which is the index to measure whether the parking resources are available for balanced use; while the goal of the lower-level is to minimize the average walking distance after parking, which is the basis to evaluate the feasibility of a shared parking. Finally, based on the solving principle of the bi-level programming model and the particle swarm optimization algorithm, a nested optimization algorithm was proposed to solve the programming model, and a simulation-based case study was made to verify their validity. The simulation shows that although the minimum real value of the parking unoccupied rate is 0.301 in the Hexiangyuan residential area, the maximum ratio of the residential parking lots that need to be shared for balanced use in peak time is only 0.093. This means that using the sharing scheme, the parking problem of the Ocean City can be relieved by using only 1/3 the unoccupied parking lots in the residential area. In addition, the average walking distance of 160.59 m shows the feasibility of the sharing scheme.
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Key words:
- parking /
- guidance /
- bi-level design /
- nested optimization algorithm /
- urban traffic
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