Wei Shen, Y. Kamarianakis, L. Wynter, Jingrui He, Qing He, Richard D. Lawrence, G. Swirszcz
{"title":"基于GPS数据的交通速度预测:IEEE ICDM竞赛任务3报告","authors":"Wei Shen, Y. Kamarianakis, L. Wynter, Jingrui He, Qing He, Richard D. Lawrence, G. Swirszcz","doi":"10.1109/ICDMW.2010.52","DOIUrl":null,"url":null,"abstract":"This report summarizes the methodologies and techniques we developed and applied for tackling task 3 of the IEEE ICDM Contest on predicting traffic velocity based on GPS data. The major components of our solution include 1) A pre-processing procedure to map GPS data to the network, 2) A K-nearest neighbor approach for identifying the most similar training hours for every test hour, and 3) A heuristic evaluation framework for optimizing parameters and avoiding over-fitting. Our solution finished Second in the final evaluation.","PeriodicalId":170201,"journal":{"name":"2010 IEEE International Conference on Data Mining Workshops","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Traffic Velocity Prediction Using GPS Data: IEEE ICDM Contest Task 3 Report\",\"authors\":\"Wei Shen, Y. Kamarianakis, L. Wynter, Jingrui He, Qing He, Richard D. Lawrence, G. Swirszcz\",\"doi\":\"10.1109/ICDMW.2010.52\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This report summarizes the methodologies and techniques we developed and applied for tackling task 3 of the IEEE ICDM Contest on predicting traffic velocity based on GPS data. The major components of our solution include 1) A pre-processing procedure to map GPS data to the network, 2) A K-nearest neighbor approach for identifying the most similar training hours for every test hour, and 3) A heuristic evaluation framework for optimizing parameters and avoiding over-fitting. Our solution finished Second in the final evaluation.\",\"PeriodicalId\":170201,\"journal\":{\"name\":\"2010 IEEE International Conference on Data Mining Workshops\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE International Conference on Data Mining Workshops\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDMW.2010.52\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Conference on Data Mining Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMW.2010.52","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This report summarizes the methodologies and techniques we developed and applied for tackling task 3 of the IEEE ICDM Contest on predicting traffic velocity based on GPS data. The major components of our solution include 1) A pre-processing procedure to map GPS data to the network, 2) A K-nearest neighbor approach for identifying the most similar training hours for every test hour, and 3) A heuristic evaluation framework for optimizing parameters and avoiding over-fitting. Our solution finished Second in the final evaluation.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
