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Detecting Pattern Changes in Individual Travel Behavior from Vehicle GPS/GNSS Data

機(jī)譯:從車輛GPS / GNSS數(shù)據(jù)檢測(cè)個(gè)人出行行為的模式變化

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摘要

Although stable in the short term, individual travel behavior generally tends to change over the long term. The ability to detect such changes is important for product and service providers in continuously changing environments. The aim of this paper is to develop a methodology that detects changes in the patterns of individual travel behavior from vehicle global positioning system (GPS)/global navigation satellite system (GNSS) data. For this purpose, we first define individual travel behavior patterns in two dimensions: a spatial pattern and a frequency pattern. Then, we develop a method that can detect such patterns from GPS/GNSS data using a clustering algorithm. Finally, we define three basic pattern-change scenarios for individual travel behavior and introduce a pattern-matching metric for detecting these changes. The proposed methodology is tested using GPS datasets from three randomly selected anonymous users, collected by a Chinese automotive manufacturer. The results show that our methodology can successfully identify significant changes in individual travel behavior patterns.
機(jī)譯:盡管短期內(nèi)穩(wěn)定,但個(gè)人旅行行為通常會(huì)長(zhǎng)期發(fā)生變化。對(duì)于不斷變化的環(huán)境中的產(chǎn)品和服務(wù)提供商而言,檢測(cè)到此類更改的能力很重要。本文的目的是開發(fā)一種方法,該方法可從車輛全球定位系統(tǒng)(GPS)/全球?qū)Ш叫l(wèi)星系統(tǒng)(GNSS)數(shù)據(jù)中檢測(cè)出個(gè)人旅行行為的模式變化。為此,我們首先在兩個(gè)維度上定義各個(gè)旅行行為模式:空間模式和頻率模式。然后,我們開發(fā)了一種可以使用聚類算法從GPS / GNSS數(shù)據(jù)檢測(cè)此類模式的方法。最后,我們?yōu)閭€(gè)人的出行行為定義了三種基本的模式變化方案,并引入了一種模式匹配度量來(lái)檢測(cè)這些變化。該方法由中國(guó)一家汽車制造商收集,使用來(lái)自三個(gè)隨機(jī)選擇的匿名用戶的GPS數(shù)據(jù)集進(jìn)行了測(cè)試。結(jié)果表明,我們的方法可以成功地識(shí)別出個(gè)人出行行為模式的重大變化。

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