PURP: A Scalable System for Predicting Short-Term Urban TrafficFlow Based on License Plate Recognition Data

作     者：Shan Zhang Qinkai Jiang Hao Li Bin Cao Jing Fan 

作者机构：School of Computer Science and TechnologyZhejiang University of TechnologyHangzhou 310000China 

出 版 物：《Big Data Mining and Analytics》 (大数据挖掘与分析（英文）)

年 卷 期：2024年第7卷第1期

页      面：171-187页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 082303[工学-交通运输规划与管理] 0835[工学-软件工程] 082302[工学-交通信息工程及控制] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0823[工学-交通运输工程] 

基　　金：This work was supported by the National Natural Science Foundation of China(Nos.62072405 and 62276233) the Key Research Project of Zhejiang Province(No.2023C01048) 

主　　题：traffic flow prediction k-Nearest Neighbor(KNN) License Plate Recognition(LPR)data spatio-temporalcontext 

摘      要：Accurate and efficient urban traffic flow prediction can help drivers identify road traffic conditions in real-time,consequently helping them avoid congestion and accidents to a certain ***,the existing methods for real-time urban traffic flow prediction focus on improving the model prediction accuracy or efficiency while ignoring the training efficiency,which results in a prediction system that lacks the scalability to integrate real-time traffic flow into the training *** conduct accurate and real-time urban traffic flow prediction while considering the latest historical data and avoiding time-consuming online retraining,herein,we propose a scalable system for Predicting short-term URban traffic flow in real-time based on license Plate recognition data(PURP).First,to ensure prediction accuracy,PURP constructs the spatio-temporal contexts of traffic flow prediction from License Plate Recognition(LPR)data as effective ***,to utilize the recent data without retraining the model online,PURP uses the nonparametric method k-Nearest Neighbor(namely KNN)as the prediction framework because the KNN can efficiently identify the top-k most similar spatio-temporal contexts and make predictions based on these contexts without time-consuming model retraining *** experimental results show that PURP retains strong prediction efficiency as the prediction period increases.