摘要
随着城市化进程的加速,道路交通拥堵问题日益严峻。文章聚焦某景区主要交叉路口车流量数据,采用K-means聚类算法,利用数学软件建立交叉路口交通流量分析模型,将交通流量数据划分为三个峰期:高峰期、平峰期、低峰期等。通过对车流量不同峰期阶段的聚类,掌握车流量在不同时间段的分布特征及变化规律,为后续的交通管理和优化提供基础数据支撑,对于缓解景区交通拥堵、提高车流速度、优化交通流量管理、提升旅客满意度等方面具有明显的优势。
The rapid pace of urbanization has led to a growing challenge of road traffic congestion in urban areas.This study focuses on the traffic flow data collected from key intersections in a scenic area.A traffic flow analysis model was developed using the K-means clustering algorithm and mathematical software tools.The traffic data was categorized into three distinct periods:peak hours,off-peak hours,and low-flow hours.Through clustering analysis across different peak periods,this approach enables the identification of temporal patterns and distribution characteristics of traffic flow.It provides foundational data for effective traffic management and optimization strategies.The proposed method demonstrates significant benefits in mitigating traffic congestion,enhancing travel efficiency,optimizing traffic operations,and improving visitor satisfaction in scenic areas.
作者
秦娟
Qin Juan(Yan'an Vocational&Technical College,Yan'an 716000,China)
出处
《办公自动化》
2025年第16期18-20,共3页
Office Informatization
基金
延安职业技术学院2025年度校级科研项目:大数据背景下人工智能与高职数学建模的融合研究与实践,项目编号:YZKY2512。
