Research On Parking Distance Based On Factors Outside The Cave Environment And Driving Conditions

This article combines the National Natural Science Foundation of China "based on the space-time environment and driving factors of highway tunnel tunnel brightness calculation method research" project,based on the existing parking line of sight research,investigating the road tunnel tunnel traffic environment,from the cave environment and driving Starting with the conditional factors,through field tests and indoor simulation tests,the system studied the relationship between parking sight distance and the landscape environment outside the cave under different driving conditions.The main research contents and results of this article are as follows:(1)On the basis of literature research,the landscape type outside the highway tunnel and the reaction time of the driver are introduced.The influence of different landscape environments outside the cave on the brightness outside the cave is analyzed,and the literature survey is used to quantitatively analyze the influence of the landscape environment on the driver’s reaction time using the brightness outside the cave.(2)Considering the increase in braking force during vehicle braking,the vehicle braking process is divided into four stages:driver reaction,vehicle brake system lag,vehicle braking force increase,and full braking.Based on the principle of kinematics,a parking sight distance correction model is obtained.The calculated value of the model is greater than the specification value,and the higher the speed,the greater the difference.(3)Taking multiple tunnels in Chongqing as the research object,five measuring points at equal intervals are arranged near the tunnel section,and the vehicle speed and traffic flow of each measuring point are measured.It is found that the distribution of traffic flow has spatiotemporal characteristics.From 9 o’clock to 17 o’clock in the afternoon,the traffic flow decreases first and then increases;the speed of vehicles in the approaching section of the tunnel presents a law of decreasing first and then increasing.According to the traffic flow and speed data software,the flow-speed model is obtained by fitting,and the parking line-of-sight model based on the traffic flow and speed is established.(4)The VB programming software was used to write the reaction time measurement program,and a simulation device for the external environment of the highway tunnel under dynamic driving environment was developed,which provided a test platform for the research of parking sight distance outside the tunnel based on the external environment and driving conditions..(5)Using the environment simulation device outside the highway tunnel,the red light spot randomly appearing near the tunnel entrance is used to simulate the front brake taillights.The test obtains the driver’s reaction time at the tunnel entrance and finds the form of the gate and the vegetation outside the cave.And the pavement outside the cave has no significant effect on the reaction time.The change rule of the reaction time at different times of the day and at each measuring point outside the tunnel is studied,the reaction time model is fitted,and the parking line-of-sight model based on the reaction time is established.(6)Contrast and analyze the theory and test of the parking sight distance model.The design speed and response time are selected in the specification to calculate the parking sight distance.In the test,the effect of the landscape outside the cave on the reaction time,as well as the traffic flow and speed are studied.The impact on parking sight distance is found that the reaction time under actual driving conditions is significantly lower than the response time setting of 2.5s.The change in traffic flow will affect the vehicle speed.The design speed in the specification does not fully reflect the impact of driving conditions on parking sight distance.relationship.In addition,the driver is influenced by the factors outside the cave landscape and driving conditions,and establishes a parking sight distance correlation model based on the outside landscape environment and driving conditions.