Proceedings of the 2011 International Workshop on Trajectory Data Mining and Analysis 2011
DOI: 10.1145/2030080.2030086
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Urban mobility study using taxi traces

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Cited by 92 publications
(57 citation statements)
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“…They also identify vehicle stop positions and discover single trip length distribution deviates from an exponential and favors a power law gradually when trip length becomes longer. In summary, the results of urban mobility in [13] and [11] are consistent with ours. Thus, it can be conjectured that the phenomenon may not happen accidentally and exist in urban areas of cities widely.…”
Section: Displacementsupporting
confidence: 91%
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“…They also identify vehicle stop positions and discover single trip length distribution deviates from an exponential and favors a power law gradually when trip length becomes longer. In summary, the results of urban mobility in [13] and [11] are consistent with ours. Thus, it can be conjectured that the phenomenon may not happen accidentally and exist in urban areas of cities widely.…”
Section: Displacementsupporting
confidence: 91%
“…Similar results are also discovered from GPS trackings [9,2,10,11,12,13], wireless network traces [14], check-ins from locationbased services [15,16] and even movements of banking notes [17].…”
Section: Introductionsupporting
confidence: 74%
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“…According to references [5][6][7][8][9][10][11][12][13][14][15][16][17][18], the distribution of travelled distance is exponential or follows Lévy-distribution. About travel time distribution, the literature says almost nothing, so we tried to fit various types of distributions by using MATLAB, Wolfram Mathematica and Easy Fit StatAssist [19] (this latter allows calculations for datasets with less than 250000 points, so for the large dataset, it was not used).…”
Section: Fitting Of Distribution Functionsmentioning
confidence: 99%
“…The transition probabilities (i.e. the matrix elements) were extracted from the raw GPS data in the following way: every car got an index of 1 if it was moving in the investigated time interval (based on similar investigations found in the literature [5][6][7][8][9][10][11][12][13][14][15][16][17][18]) a car was considered to be moving if its speed was between 1m/s and 34m/s: values out of this range were rejected) and an index of 0, if it was parking. By counting the 0 → 0, 0 → 1 1 → 0 and 1 → 1 transitions, we could determine the matrix elements for the given time interval.…”
Section: Transition Probabilitiesmentioning
confidence: 99%