基于居住區(qū)共享停車的雙層規(guī)劃誘導(dǎo)模型
發(fā)布時(shí)間：2018-07-18 10:43
【摘要】：為了緩解因城市停車資源有限造成的停車難問(wèn)題,根據(jù)共享停車?yán)碚摵椭悄芙煌ㄕT導(dǎo)策略,提出了居住區(qū)泊位參與的共享停車誘導(dǎo)服務(wù)流程,探討了實(shí)現(xiàn)居住區(qū)共享停車的基本條件,建立了誘導(dǎo)服務(wù)的泊位協(xié)調(diào)控制雙層規(guī)劃模型.該模型上層以高峰泊位空閑指數(shù)差異均值最小為目標(biāo),作為衡量該誘導(dǎo)服務(wù)能否實(shí)現(xiàn)區(qū)域范圍停車資源均衡利用的指標(biāo);下層以駕駛員停車后平均步行距離最小為目標(biāo),作為衡量共享停車是否可行的依據(jù).根據(jù)雙層規(guī)劃模型的求解原理和粒子群算法優(yōu)化思想,設(shè)計(jì)了該模型的粒子群嵌套優(yōu)化算法,并進(jìn)行仿真實(shí)驗(yàn).結(jié)果表明:鶴祥園小區(qū)最低泊位空閑指數(shù)為0.301,最大高峰泊位對(duì)外共享率為0.093,即只利用了不足1/3的閑置泊位就緩解了遠(yuǎn)洋城的停車問(wèn)題;平均步行距離為160.59m,說(shuō)明共享方案可行.
[Abstract]:In order to alleviate the parking difficulties caused by the limited parking resources in the city, according to the shared parking theory and the intelligent traffic guidance strategy, the paper puts forward the shared parking guidance service flow in the residential area. This paper discusses the basic conditions of realizing shared parking in residential area, and establishes a bilevel programming model of berth coordination control for induction service. In the upper layer of the model, the minimum average value of peak berth idle index is taken as the target to evaluate whether the inductive service can realize the balanced utilization of parking resources in the region, and the minimum average walking distance after the driver stops is the goal of the lower layer. As a measure of the feasibility of shared parking. According to the solving principle of the bilevel programming model and the idea of particle swarm optimization, the particle swarm nested optimization algorithm of the model is designed, and the simulation experiment is carried out. The results show that the lowest berth idle index is 0.301, the maximum peak berth sharing rate is 0.093, that is, the parking problem of ocean city is alleviated only by using less than 1 / 3 idle berth, and the average walking distance is 160.59 m, which shows that the sharing scheme is feasible.
【作者單位】： 吉林大學(xué)交通學(xué)院;華北理工大學(xué)建筑工程學(xué)院;
【基金】：國(guó)家973計(jì)劃資助項(xiàng)目(2014BAG03B03) 國(guó)家自然科學(xué)基金資助項(xiàng)目(51378171,61374157)
【分類號(hào)】：U491.7
，
本文編號(hào)：2131656
[Abstract]:In order to alleviate the parking difficulties caused by the limited parking resources in the city, according to the shared parking theory and the intelligent traffic guidance strategy, the paper puts forward the shared parking guidance service flow in the residential area. This paper discusses the basic conditions of realizing shared parking in residential area, and establishes a bilevel programming model of berth coordination control for induction service. In the upper layer of the model, the minimum average value of peak berth idle index is taken as the target to evaluate whether the inductive service can realize the balanced utilization of parking resources in the region, and the minimum average walking distance after the driver stops is the goal of the lower layer. As a measure of the feasibility of shared parking. According to the solving principle of the bilevel programming model and the idea of particle swarm optimization, the particle swarm nested optimization algorithm of the model is designed, and the simulation experiment is carried out. The results show that the lowest berth idle index is 0.301, the maximum peak berth sharing rate is 0.093, that is, the parking problem of ocean city is alleviated only by using less than 1 / 3 idle berth, and the average walking distance is 160.59 m, which shows that the sharing scheme is feasible.
【作者單位】： 吉林大學(xué)交通學(xué)院;華北理工大學(xué)建筑工程學(xué)院;
【基金】：國(guó)家973計(jì)劃資助項(xiàng)目(2014BAG03B03) 國(guó)家自然科學(xué)基金資助項(xiàng)目(51378171,61374157)
【分類號(hào)】：U491.7
，
本文編號(hào)：2131656
本文鏈接：http://sikaile.net/kejilunwen/jiaotonggongchenglunwen/2131656.html
最近更新
教材專著
