The extensive use of mobile phones has produced a massive amount of trajectory data and provided the possibility to conduct travel behaviour analyses. In this study, a method of travel behaviour analysis is proposed based on extensive trajectory data obtained from Didi Chuxing, China. The travel time regularity and traffic hot spots are analysed from spatial perspectives for three modes: workday, weekend, and the double 11 shopping festival modes. Then a network analysis of the travel hot spots is conducted to study the regularity of travel behaviours. In addition, the travel regularity has been studied and origin/destination prediction of the hot spots has been conducted based on the multivariate $\delta $-long short-term memory model. The results indicate that the distribution of the travel time during peak hours and at the travel hot spots can effectively reflect the temporal travel regularity of residents. Additionally, the network can reflect the spatial travel regularity of residents. The results provide reference information for improving urban traffic control.
Analysis of road travel behaviour based on big trajectory data
IET Intelligent Transport Systems ; 14 , 12 ; 1691-1703
2020-10-13
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
extensive trajectory data , reference information , data handling , mobile phones , travel hot spots , traffic information systems , intelligent transportation systems , temporal travel regularity , δ-long short-term memory model , China , spatial travel regularity , double shopping festival modes , travel time regularity , road traffic control , travel behaviour analysis , traffic hot spots , urban traffic control , network analysis , mobile radio , origin-destination prediction , road travel behaviour , big trajectory data , Didi Chuxing , recurrent neural nets
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