【摘要】：為了提高在大規(guī)模流式數(shù)據(jù)環(huán)境下交通熱點(diǎn)區(qū)域分析的算法效率,提出了一種流式數(shù)據(jù)兩階段方法;該方法在第一階段使用基于改進(jìn)Canopy算法進(jìn)行粗聚類并產(chǎn)生宏簇,在第二階段使用K-means算法進(jìn)行細(xì)聚類;并以粗聚類產(chǎn)生的宏簇個(gè)數(shù)和類簇中心位置為指導(dǎo)產(chǎn)生更加準(zhǔn)確的微簇聚類結(jié)果。在試驗(yàn)中,使用流式數(shù)據(jù)兩階段方法對(duì)北京市出租車的定位數(shù)據(jù)進(jìn)行了聚類分析;并結(jié)合熱力圖和電子地圖對(duì)聚類結(jié)果進(jìn)行可視化表達(dá),在最終的熱力分析結(jié)果中可以直觀地發(fā)現(xiàn)出租車活動(dòng)較為頻繁的熱點(diǎn)區(qū)域和線路,且與日常出行經(jīng)驗(yàn)相符合。試驗(yàn)結(jié)果表明該算法能夠?qū)崟r(shí)地對(duì)流式數(shù)據(jù)進(jìn)行聚類分析,產(chǎn)生的數(shù)據(jù)結(jié)果可供用戶在任意時(shí)間窗口范圍進(jìn)行查詢分析,有助于為交通活動(dòng)情況實(shí)時(shí)分析、交通規(guī)劃和擁堵治理等方面提供有價(jià)值的理論參考依據(jù)。
[Abstract]:In order to improve the efficiency of traffic hot spot analysis algorithm in large-scale flow data environment, a two-stage flow data analysis method is proposed, in the first stage, rough clustering based on improved Canopy algorithm is used to generate macro clusters. In the second stage, the K-means algorithm is used for fine clustering, and the number of macro clusters generated by rough clustering and the location of cluster center are used as the guidance to produce more accurate clustering results. In the experiment, a two-stage method of flow data was used to analyze the location data of taxis in Beijing, and the results of clustering were visualized with thermal maps and electronic maps. In the final thermal analysis results, the hot spots and routes with frequent taxi activities can be found directly, and the results are in accordance with the daily travel experience. The experimental results show that the algorithm can cluster and analyze the flow data in real time, and the resulting data can be queried and analyzed in any time window, which is helpful to the real-time analysis of traffic activity. Traffic planning and congestion management provide valuable theoretical reference.
【作者單位】： 大連海事大學(xué)交通運(yùn)輸管理學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(71271034、61473053) 遼寧省教育廳科技研究項(xiàng)目(L2014203) 遼寧省社會(huì)科學(xué)規(guī)劃基金(L14BGL012) 中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金(3132016046)聯(lián)合資助
【分類號(hào)】：TP311.13;U495
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本文編號(hào)：2260104