摘要
随着成都市中心区域人口的快速增加,在特定时段的拥堵现象已经成为限制城市发展的顽疾之一,通过准确的预测未来短时路面的交通信息,能有效的提高特定时段的车的通行效率。本文中根据采集到的相关指标进行数据训练,建立决策树分类模型。使用该模型可根据道路的时段、天气、平均速度等指标预测出短时内该路面的交通流情况,随后相关监管部门可以利用当今社会发达的新媒体进行推送,让市民合理规划出行。
With the rapid increase in the population in the central area of Chengdu,congestion in a certain period has become one of the stubborn diseases that restrict the development of the city.By accurately predicting the short-term road traffic information in the future,it can effectively improve the traffic efficiency of vehicles in a specific period.In this paper,data training is carried out according to the collected relevant indicators,and a decision tree classification model is established.The model can be used to predict the traffic flow on the road in a short period of time based on the time of the road,weather,average speed,and other indicators,and then the relevant regulatory authorities can use the advanced new media today to push,so that the citizens can plan their trips reasonably.
作者
杨京典
Jingdian Yang(Chengdu College of UESTC,610097,China)
关键词
中文交通拥堵
机器学习
决策树
预测
Traffic Jam
Machine Learning
Decision Tree
Prediction
