| With the continuous development of social economy,the number of vehicles in China has been greatly increased.The increasing vehicle flow increases the complexity of mixed traffic flow and intensifies conflicts,resulting in traffic congestion of varying degrees,discharge of a large number of harmful substances and environmental pollution to a certain extent.At present,automatic driving has achieved rapid development,has achieved a certain degree of car following and speed control and other functions,automatic driving technology is constantly improving.At the same time,the research on the impact of automatic driving on the mixed traffic flow（Mixed traffic flow composed of automatic vehicles,manual vehicles and non-motor vehicles）should be improved.In the study of mixed traffic flow,the role of autonomous driving is not completely clear,and there are few literatures on the influence of autonomous driving on indicators.In the traffic benefit research,the improvement effect of autonomous driving on traffic benefit is not completely clear,there are few traffic benefit evaluation methods based on autonomous driving,and some evaluation methods use subjective data.In general,the application of automatic driving is a good means to improve the operation state of mixed traffic flow,which has certain practical significance to alleviate congestion and improve traffic efficiency.Therefore,it is necessary to study the impact of automatic driving.In the research of the impact of automatic vehicles on mixed traffic flow indicators,based on the typical cross intersection in the city,the traffic volume data are obtained through field research.The road network is constructed with the help of SUMO simulation software,and the selection and parameter calibration of the car-following models of manual and autonomous vehicles are completed.The simulation is carried out under different permeability,and the influence curve of autonomous driving on mixed traffic flow related indicators of different demand modes is obtained.The influence of non-motor vehicle flow and flow direction is studied;Based on the simulation data of automatic vehicles,a more objective and reasonable PCA principal component analysis method is applied to establish a comprehensive evaluation process of traffic efficiency under different penetration rates of automatic vehicles,and the more important influencing factors are extracted.The changing laws of traffic efficiency in different demand modes under the framework of automatic vehicles,and moderate results were analyzed;in the research on traffic efficiency improvement,a road network autonomous driving permeability control system based on V2I technology and a secondary road crossing guidance system based on non-vehicle identification technology are proposed.with the help of Synchro7 software,the road network construction and timing scheme were optimized.The indicators have been improved to varying degrees,and the optimization plan has certain feasibility.The study found that:①Autonomous driving plays an obvious optimization role in mixed traffic flow,and has a significant impact on high traffic flow,but not on middle and low traffic flow.② In the low permeability range（0-0.2）and the high permeability range（0.8-1）,the influence of autopilot on simulation output index and traffic benefit is relatively gentle,and the influence is most significant in the middle and high permeability range（0.3-0.7）.③ The increasing number of non-motor vehicles has a certain limit.When the number of non-motor vehicles reaches 800 vehicles/hour,the influence on the average speed of right-turning vehicles tends to be stable,and the change of the average loss time is the least.④When the non-motor vehicle flow is 800 vehicles/hour,the second crossing has the best lifting effect. |
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