商业区公共停车场需求预测模型研究

Research on public parking demand forecast model in commercial zone

目前各大城市普遍存在停车难的问题,特别是在商业中心区,人、车流量大,以往停车需求预测方法通常采用停车生成率模型,利用土地用地性质及面积进行预测,文中在停车生成率模型研究的基础上,使用PLS方法对传统停车生成率模型进行改进,模型考虑混合用地之间的相互关系,能够更合理地预测停车需求。以哈尔滨香坊区为例,通过现场调查获得8个综合商业区数据,预测了2019年、2020年的工作日与周末停车需求,并以2个代表性商业区进行验证,实验结果表明,相对原始停车生成率模型,基于PLS改进的停车需求预测模型预测更准确,相对误差更低。

Parking is a common difficult in metropolises at present. The passenger flow and traffic flow are large,especially in CBDs(central business districts). In the previous parking demand forecast methods,the previous parking generation rate model is usually used to predict the demand according to the nature and area of land use. On the basis of the research on parking generation rate model,the PLS(partial least squares regression)method is used to improve the traditional model. In the model,the relationship among the mixed-use lands are taken into account,so the parking demand can be predicted more reasonably. Xiangfang District in Harbin is taken as an example in the research,data of 8 comprehensive commercial districts are obtained by on-the-spot investigation to predict the parking demand on working days and weekends in 2019 and 2020. Two representative commercial districts are used for verification. The experimental results show that,in comparison with the original parking generation rate model,the PLS improved parking demand forecast model is of more accuracy and lower relative error.