This study focuses on the break-taking behaviour pattern of long-distance freight vehicles, providing a new perspective on the study of behaviour patterns and simultaneously providing a reference for transport management departments and related enterprises. On the basis of global positioning system trajectory data, the authors select stopping points as break-taking sites of long-distance freight vehicles and then classify the stopping points into three different classes based on the break-taking duration. They then explore the relationship of the distribution of the break-taking frequency between the three single classifications and their combinations, on the basis of the break-taking duration distribution. They find that the combination is a Gaussian distribution when each of the three individual classes is a Gaussian distribution, contrasting with the power-law distribution of the break-taking duration. Then, they do experimental analysis to the distribution of the break-taking durations and frequencies, and find that, for the durations, the three single classifications can be fitted individually by an exponential distribution and together by a power-law distribution, for the frequencies, both the three single classifications and together can be fitted by a Gaussian distribution, so that it can validate the above theoretical analysis.
Break-taking behaviour pattern of long-distance freight vehicles based on GPS trajectory data
IET Intelligent Transport Systems ; 11 , 6 ; 340-348
2017-08-01
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
break-taking behaviour pattern , break-taking frequency , break-taking duration distribution , Global Positioning System , exponential distribution , traffic engineering computing , power-law distribution , long-distance freight vehicles , GPS trajectory data , Gaussian distribution , classifications , pattern classification
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