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Evaluation of driving behavior based on massive vehicle trajectory data 被引量:8
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作者 Sun Chao Chen Xiaohong +1 位作者 Zhang H.Michael Zhang Junfeng 《Journal of Southeast University(English Edition)》 EI CAS 2019年第4期502-508,共7页
Based on the driver surveillance video data and controller area network(CAN)data,the methods of studying commercial vehicles’driving behavior is relatively advanced.However,these methods have difficulty in covering p... Based on the driver surveillance video data and controller area network(CAN)data,the methods of studying commercial vehicles’driving behavior is relatively advanced.However,these methods have difficulty in covering private vehicles.Naturalistic driving studies have disadvantages of small sample size and high cost,one new driving behavior evaluation method using massive vehicle trajectory data is put forward.An automatic encoding machine is used to reduce the noise of raw data,and then driving dynamics and self-organizing mapping(SOM)classification are used to give thresholds or the judgement method of overspeed,rapid speed change,rapid turning and rapid lane changing.The proportion of different driving behaviors and typical dangerous driving behaviors is calculated,then the temporal and spatial distribution of drivers’driving behavior and the driving behavior characteristics on typical roads are analyzed.Driving behaviors on accident-prone road sections and normal road sections are compared.Results show that in Shenzhen,frequent lane changing and overspeed are the most common unsafe driving behaviors;16.1%drivers have relatively aggressive driving behavior;the proportion of dangerous driving behavior is higher outside the original economic special zone;dangerous driving behavior is highly correlated with traffic accident frequency. 展开更多
关键词 driving behavior global positioning system(GPS)navigating data automatic coding machine self-organizing mapping(SOM)
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GRACE time-varying gravity field solutions based on PANDA software
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作者 Xiang Guo Qile Zhao 《Geodesy and Geodynamics》 2018年第2期162-168,共7页
The conventional dynamic approach for gravity filed modelling has been implemented in the PANDA(Position and Navigation Data Analyst) software. A variant of the so-called ’two-step’ method for gravity field modellin... The conventional dynamic approach for gravity filed modelling has been implemented in the PANDA(Position and Navigation Data Analyst) software. A variant of the so-called ’two-step’ method for gravity field modelling is adopted for this purpose, where the GRACE(Gravity Recovery and Climate Experiment)orbits are derived from the GPS(Global Positioning System) data in a first step followed by a simultaneous determination of dynamic orbit and gravity filed from the GPS-derived orbits and K-band rangerate measurements in a second step. In this way, the monthly gravity field solutions complete to degree and order 96 are produced for the period Jan. 2005 to Dec. 2010. Their performance is assessed by comparing them with the official solutions, i.e., CSR RL05, GFZ RL05 a and JPL RL05. A comparison in the spectral domain in terms of geoid heights reveals that the obtained solutions present the smallest degree amplitudes at degree 30-75. A further analysis of mass changes in the spatial domain demonstrates that the main signals observed from the obtained solutions are in great agreement with those from the official solutions. Remarkably, the correlation coefficients of mass changes in large river basins from the official solutions with respect to those from the obtained solutions are all above 0.97. These results demonstrate that the obtained solutions are comparable to the official solutions. 展开更多
关键词 Time-varying gravity field PANDA(Position and navigation data Analyst) GRACE(Gravity Recovery and Climate Experiment)
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