Research On Variable Speed Limit Strategy Of Connected And Autonomous Vehicles On Expressways In Adverse Weather

Adverse weather will have a significant impact on expressway traffic safety.For traditional human-driving vehicles,adverse weather will greatly affect the driver’s psychological and physiological performance,vehicle performance and road performance due to its suddenness,locality and commonness,thus affecting the safety level of traffic flow.With the application of new generation information technology such as artificial intelligence and mobile internet,vehicle cruise system and advanced driving assistance system are gradually applied.Connected and autonomous vehicles(CAVs)that can realize vehicle to vehicle(V2V)communication and vehicle to infrastructure(V2I)communication have gradually appeared on the road.The safe driving of CAVs in adverse weather deserves our attention.The paper adopts relevant data at home and abroad,applies the data analysis and simulation methods,and takes the micro behavior of vehicles as the starting point do the research.The paper constructs the car-following model of CAVs to investigates the influence of CAVs on the micro behavior and macro fundamental diagram;proposes a safety surrogate measure which is suitable for risk perception and evaluation of CAVs;explores the key problems of variable speed limit control strategy of expressway CAVs in adverse weather.This study provides technical support for improving the level of traffic safety in adverse weather and builds a basis for traffic safety assessment and vehicle management control for the mixed traffic in the near future.From the perspective of engineering application,the research of this paper will help to improve the traffic safety management level of expressway in the future connected and autonomous environment and reduce the potential risk of expressway in adverse weather,which has an important engineering application value.The main research contents and results of this paper are as follows:Firstly,the data of car-following behavior collected in Norway and China in adverse weather is analyzed to explore the difference of driving behavior caused by adverse weather.According to the traffic flow and weather data collected in Norway,the changes of driving behavior such as speed,distance headway and time headway are compared and analyzed in clear/rainy/snowy weather with different road conditions.Then,the impact of clear/rainy/foggy weather from natural driving data of Shanghai on the speed,distance headway and time headway are analyzed.By comparing the results of the two datasets,universal conclusions are acquired.Secondly,according to the different mechanism of car-following behavior between CAVs and traditional human-driving vehicles,a car-following model for CAVs in line with its behavior law is constructed.In V2V environment,the behavior of CAVs will be affected by multiple preceding vehicles,thus the car-following model of CAVs is build based on the traditional car-following model and considers the speed difference between multiple preceding vehicles and the ego vehicle,the acceleration of multiple preceding vehicles and the different reaction time.The car-following model of humandriving vehicles is calibrated with the vehicle trajectories and the stability of the model is explored.The impact of CAVs on traffic flow is evaluated from the perspectives of safety,efficiency and energy consumption to acquire the framework for obtaining the optimal value of model parameters and the methof to acquire the maximum platoon size.The mixed traffic flow is simulated to analyze the impact of different number of communication vehicles and platon size.Thirdly,based on the car-following model,the impact of CAVs on mixed macro traffic flow fundamental diagram of human-friving vehicles and CAVs,namely the relationhisp among the traffic flow,density and speed.and heterogeneous traffic flow fundamental diagram are analyzed respectively.The degradation of CAVs when following a human-driving vehicle is considered.The deterministic modeling for the traffic flow fundamental diagram is carried out to analyze the influence of CACC penetration rate and platoon intensity on the fundamental diagram.The sensitivity of basic parameters is investigated.Considering the heterogeneity of driver’s reaction time,the stochastic modeling of traffic flow fundamental diagram is carried out,and the number of simulation vehicles required to obtain a stable fundamental diagram is simulated and analyzed.At the same time,the effects of penetration rate and platoon intensity on the fundamental diagram are also explored.Fourthly,for the vehicle trajectories of different vehicle type combinations in the traffic flow,taking the distance headway and time headway as examples,explore the variation law of the indicator with the vehicle speed,find the optimal fitting distribution in different speed ranges and obtain the influence mechanism of different vehicle type combinations on the car-following behavior.Then,according to the risk homeostasis theory,the stable car-following fragments are screened,the drivers’ preference for risk selection of different vehicle combinations is explored from the perspective of safety surrogate measures,and the car-following heterogeneity caused by vehicle combinations is verified from the perspective of safety surrogate measures and the index of group aggressiveness of drivers.Further,by comparing the performance of different safety surrogate measures,the threshold of a SSM in different vehicle combinations is determined,which can be applied to the early risk warning of CAVs.Based on the risk homeostasis theory and field theory,a safety surrogate measures based on risk perception is proposed.The new indicator can more accurately match the driver’s perception and behavior of risk,and has higher risk prediction accuracy,timeliness,robustness and so on.Fifth,for potential traffic risk caused by heavy rain,considering the affected visibility and ground friction coefficient,different variable speed limit control methods are designed for human-driving vehicles and CAVs respectively,so as to reduce the risk level of traffic flow.The traffic flow models in clear and adverse weather are calibrated respectively to build the simulation platform to describe the drivers’ behavior in adverse weather.According to the relationship between driver’s sight distance affected by adverse weather and space distance,considering the vehicle braking performance,different driving styles are introduced into the calculation method of variable speed limit,and the reasonable speed limit values corresponding to different driving styles are obtained.The control effects of different speed limit strategies on traffic safety and traffic efficiency of human-driving vehicles and CAVs with different driving styles in adverse weather are compared.