交通运输研究2024，Vol.10Issue(1)：10-17,27,9.DOI:10.16503/j.cnki.2095-9931.2024.01.002
基于机器学习空间聚类的出租车停靠站点布局规划
Taxi Stand Layout Planning Using Machine Learning-Based Spatial Clustering
摘要
Abstract
The arbitrary stopping of taxis has caused a certain negative effect on urban traffic.In order to regulate the order of taxi operation,improve the conditions of taxi operation and residents'riding,a taxi stand layout planning method which combined the spatial information of taxi trips with machine learning algorithms was proposed.Firstly,the GPS trajectory data of taxis was used to extract the origins of taxi trips.Then,the HDBSCAN clustering method was used to perform spatial density clustering on the ori-gins of taxi trips,the clusters were formed and their centers were used as alternative locations for the layout of taxi stands.Finally,to verify the feasibility and efficiency of the proposed method,a typical area with rich land use types and high population density in the central urban area of Chongqing was selected as an example for case analysis.The results showed that the 107 alternative locations were mainly located in commercial centers and residential areas,which was basically consistent with the spatial distribution of areas with high taxi demand.The 300-meter coverage rate of taxi stands in the layout reached 76.0%,and the uncovered areas were mainly urban green spaces and water bodies.Re-search has shown that machine learning algorithm can achieve efficient layout planning of taxi stands,but in the planning and implementation stages,the setting of parking space should also be comprehen-sively considered in conjunction with the characteristics of the built environment in adjacent areas.关键词
城市交通/布局规划/空间聚类/出租车停靠站点/轨迹数据/机器学习算法/HDBSCANKey words
urban traffic/layout planning/spatial clustering/taxi stand/trajectory data/machine learning algorithm/HDBSCAN(Hierarchical Density-Based Spatial Clustering of Applications with Noise)分类
交通工程引用本文复制引用
年光跃,黄建云,潘海啸..基于机器学习空间聚类的出租车停靠站点布局规划[J].交通运输研究,2024,10(1):10-17,27,9.基金项目
国家自然科学基金区域创新发展联合基金项目(U20A20330) （U20A20330）
