Driving behavior is heterogeneous for various drivers due to the different influencing factors as reaction time,gender,driving years and so on.Some existing works tried to reproduce some of the complex characteristics...Driving behavior is heterogeneous for various drivers due to the different influencing factors as reaction time,gender,driving years and so on.Some existing works tried to reproduce some of the complex characteristics of real traffic flow by taking into account the heterogeneous driving behavior,and the drivers are generally divided into two classes(including aggressive drivers and careful drivers)or three classes(including aggressive drivers,normal drivers and careful drivers).Nevertheless,the classification approaches have not been verified,and the rationality of the classifications has not been confirmed as well.In this study,the trajectory data of drivers is extracted from the NGSIM datasets.By combining the K-Means method and Silhouette measure index,the drivers are classified into four clusters(named as clusters A,B,C and D,respectively)in accordance with the acceleration and time headway.The two-dimensional approach is applied to analyze the characteristics of different clusters.Here,one dimension consists of“Cautious”and“Aggressive”behaviors in terms of velocity and acceleration,and the other dimension consists of“Sensitive”and“Insensitive”behaviors in terms of reaction time.Finally,the fuel consumption and emissions for different clusters are calculated by using the VT-Micro model.A surprising result indicates that overly“cautious”and“sensitive”behaviors may result in more fuel consumption and emissions.Therefore,it is necessary to find the balance between the driving characteristics.展开更多
基金partially supported by the National Natural Science Foundation of China(Grant Nos.71621001,71671014 and 71631007)the National Key R&D Program of China(Grant No.2018YFB1601200)+1 种基金the Central Public-interest Scientific Institution Basal Research Fund(Grant No.20196104)the Strategic planning policy of the Ministry of transport(Grant No.2019-17-4).
文摘Driving behavior is heterogeneous for various drivers due to the different influencing factors as reaction time,gender,driving years and so on.Some existing works tried to reproduce some of the complex characteristics of real traffic flow by taking into account the heterogeneous driving behavior,and the drivers are generally divided into two classes(including aggressive drivers and careful drivers)or three classes(including aggressive drivers,normal drivers and careful drivers).Nevertheless,the classification approaches have not been verified,and the rationality of the classifications has not been confirmed as well.In this study,the trajectory data of drivers is extracted from the NGSIM datasets.By combining the K-Means method and Silhouette measure index,the drivers are classified into four clusters(named as clusters A,B,C and D,respectively)in accordance with the acceleration and time headway.The two-dimensional approach is applied to analyze the characteristics of different clusters.Here,one dimension consists of“Cautious”and“Aggressive”behaviors in terms of velocity and acceleration,and the other dimension consists of“Sensitive”and“Insensitive”behaviors in terms of reaction time.Finally,the fuel consumption and emissions for different clusters are calculated by using the VT-Micro model.A surprising result indicates that overly“cautious”and“sensitive”behaviors may result in more fuel consumption and emissions.Therefore,it is necessary to find the balance between the driving characteristics.
