基于實測微觀駕駛狀態(tài)的交通安全風險分析及模型校正
[Abstract]:With the development of economy, more and more vehicles enter ordinary families. With the popularity of vehicles, traffic safety problems are becoming more and more serious. Unsafe traffic environment will not only bring loss of property, but also threaten the safety of human life. In the traffic safety problem, how to take measures to avoid the danger is more important than remedying after the accident. Firstly, based on the NGSIM data, the traffic state and risk are analyzed. Then according to the Fuzzy C-means clustering method (FCM) clustering method, the measured data are divided into different clusters, and the Helly model is used to correct the different clustering, and the different characteristics of the formation of different risks are analyzed. Finally, the risk index is introduced into the Helly model to avoid the high traffic safety risk. The simulation results show that the driver can adjust his driving behavior according to his driving state to avoid the potential risk by introducing the risk index. Secondly, several vehicle groups are extracted from the NGSIM data, and the influence of the driving state and risk of the first vehicle in the vehicle group on the follower vehicle is analyzed, as well as the interaction between the two adjacent front and rear vehicles in the vehicle group. The measured data show that the velocity and velocity difference of the head car have obvious correlation with the speed and the distance of the vehicle, and the risk of the first car can affect the fifth car. Therefore, according to the speed and speed difference of the vehicle, the speed and the distance of the vehicle are further divided into clusters. The results show that with the increasing of the following distance, the risk of classification 1 does not change much, the risk of category 2 decreases gradually, and the risk of category 3 increases gradually. Overall, Category 2 has the highest risk. Finally, we calibrate the models of different vehicle groups and analyze the risk of vehicles in different positions. It is found that for vehicles in different positions, special attention should be paid to the variation of speed and speed to reduce or avoid risks. Finally, we analyze the difference between macro and micro states and risks by using the experimental data from the suburb of Hefei, China, and find that the microscopic state can better reflect the instantaneous change of driving state. The clustering results show that classification 3 has a small speed difference and a large vehicle spacing, which is a low risk state; The velocity difference and spacing of classification 1 are small, but the velocity difference and spacing of classification 2 are large. Therefore, compared with classification 3, both have a certain degree of risk. Then, we compare the NGSIM data with the experimental data to analyze the traffic state and risk difference of different data sources. In terms of traffic state, the velocity and density distribution range of NGSIM data is much smaller than that of car-following experiment data, and the risk is slightly smaller than that of car-following experiment. In the aspect of vehicle group status and risk correlation, the first car speed has a stronger correlation with the following vehicle, but the risk correlation between the vehicle group is less. Under different classification, the range of distance distribution of NGSIM data is larger, and the range of velocity difference distribution of NGSIM data is larger, so the risk of corresponding NGSIM data is lower.
【學位授予單位】:北京交通大學
【學位級別】:碩士
【學位授予年份】:2017
【分類號】:U491
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