کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5102507 | 1480088 | 2017 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Discovering urban mobility patterns with PageRank based traffic modeling and prediction
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
فیزیک ریاضی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Discovering urban mobility patterns with PageRank based traffic modeling and prediction Discovering urban mobility patterns with PageRank based traffic modeling and prediction](/preview/png/5102507.png)
چکیده انگلیسی
Urban transportation system can be viewed as complex network with time-varying traffic flows as links to connect adjacent regions as networked nodes. By computing urban traffic evolution on such temporal complex network with PageRank, it is found that for most regions, there exists a linear relation between the traffic congestion measure at present time and the PageRank value of the last time. Since the PageRank measure of a region does result from the mutual interactions of the whole network, it implies that the traffic state of a local region does not evolve independently but is affected by the evolution of the whole network. As a result, the PageRank values can act as signatures in predicting upcoming traffic congestions. We observe the aforementioned laws experimentally based on the trajectory data of 12000 taxies in Beijing city for one month.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Physica A: Statistical Mechanics and its Applications - Volume 485, 1 November 2017, Pages 23-34
Journal: Physica A: Statistical Mechanics and its Applications - Volume 485, 1 November 2017, Pages 23-34
نویسندگان
Minjie Wang, Su Yang, Yi Sun, Jun Gao,