摘要
游客在景点旅游时的时空信息和行为信息都被蕴含在所获取的多源异构旅游数据中,为了更好地对景点车流量大数据进行监测分析及预测,本文首先对比元旦假期前后车牌数据,进行去重处理后即可对来往车辆是否为自驾游车辆进行判别,然后提出了基于HDFS的景点交通流数据存储,并利用已知模型表示景区内交通流量和密度的关系并提出相关建议。最后,采用K近邻非参数回归等算法来预测短时交通流。
The tourists’ time-space information and behavior information in scenic spots are contained in the obtained multi-source heterogeneous tourism data. In order to better monitor, analyze and predict the big data of traffic flow, this paper first compares the license plate data before and after the New Year’s Day holiday, and can judge whether the passing vehicles are self-driving vehicles after De-duplication processing, then the data storage of scenic spot traffic flow based on HDFS is proposed, and the relationship between traffic flow and density and some relevant suggestions are put forward. Finally, K-nearest Neighbor Nonparametric regression algorithm is used to predict short-term traffic flow.
出处
《运筹与模糊学》
2022年第2期198-203,共6页
Operations Research and Fuzziology
