基于熵权与模糊聚类算法的城市停车分区研究

Urban Parking Zoning Based on Entropy Weight and Fuzzy Clustering Algorithm

停车分区调控是交通需求管理中的一项重要措施,现有的城市停车分区方法一般是结合停车供需状况以及行政区界限进行划分,具有主观性较强、考虑因素单一等局限性。从区位属性、社会经济发展水平、道路供给水平、公交供给水平和停车需求水平五个方面出发建立停车分区指标综合评价体系,引入信息论中的熵值理论来确定各个指标的权重,然后采用无监督分类中的模糊聚类算法进行停车分区聚类,获得停车分区划分结果,以达到对传统停车分区方法进行改进的效果。最后以常德市作为实例进行应用,验证了该停车分区方法的可操作性,可为城市停车分区研究和停车管理政策的制定提供参考。

Parking zoning regulation is an important measure in traffic demand management. The existing urban parking zoning method is generally based on the parking supply and demand situation and administrative boundaries, which has the limitations of strong subjectivity and single consideration. To improve the traditional parking zoning method, a comprehensive evaluation system based on the location, social and economic development, road supply, bus supply and parking demand for parking zone indicators is established. The entropy method in information theory is introduced to determine the weight of each index, and then the fuzzy clustering algorithm in unsupervised classification is adopted to cluster parking zones. Finally, a case study of Changde City is carried out to validate the feasibility of this parking zoning method, which can provide reference for urban parking zoning and management.