CN113361790A - Method for predicting running speed of vehicle on uphill road section in plateau area - Google Patents
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Abstract
The application relates to a method for predicting the running speed of a vehicle on an uphill road section in a plateau area. The method comprises the following steps: obtaining the design speed of an uphill road section in a plateau area; inputting the designed speed into a pre-constructed vehicle running speed analysis model of an uphill road section in the plateau area, and predicting the actual running speed of the vehicle on the uphill road section in the plateau area to obtain a prediction result of the actual running speed of the vehicle on the uphill road section in the plateau area; the vehicle running speed analysis model of the uphill section in the plateau area is a non-linear regression fitting model of the speeds of different vehicle types in the plateau area under different slopes according to the sensibility of the different vehicle types to different longitudinal slopes, the expected speeds of the different vehicle types in the plateau area and the ideal speed model of the uphill section, so that the vehicle running speed analysis model of the uphill section in the plateau area is obtained, and the accuracy of the running speed prediction of the vehicles in the uphill section in the plateau area can be improved by adopting the vehicle running speed analysis model for prediction.
Description
Technical Field
The application relates to the technical field of road traffic, in particular to a method for predicting the running speed of vehicles on an uphill road section in a plateau area.
Background
Road traffic occupies an irreplaceable position in a traffic system of a plateau area due to special natural geographic conditions and historical development factors of the plateau area, however, from the aspect of road safety, the road safety condition of the plateau area is relatively serious compared with other areas, and the kilometer accidents of the plateau area are far higher than those of the plateau area and the hilly area. With the ever-increasing demand of road traffic, how to travel more safely and efficiently becomes a key problem to be solved urgently by road design workers.
The main technical indexes adopted in the current specifications and design standards consider the driving safety of the vehicle, but the design speed is a fixed value, and the driving of the vehicle on the road is a dynamic change process. Therefore, the running speed can reflect the comprehensive influence of the geometric alignment of the road, the vehicle performance, the road side environment and the driver behavior on the actual driving. At present, a design speed method is adopted for road design, and an operation speed method is used for road safety evaluation, but a complete system for road design by the operation speed method is formed in part of countries in the world.
At present, an operation speed prediction model of road traffic characteristics, road conditions geometric conditions and road area environmental characteristics is established mainly according to actual operation speed observation investigation and mathematical statistics regression analysis of vehicles on a typical road. The original data source of the main research result of the prediction model is high-speed measured data with the average altitude of about 1500 meters, and the average altitude of the plateau area is more than 4000 meters, so the operation speed prediction model adopted in the specification is not suitable for the plateau area, and the problem that the operation speed prediction result of the vehicle on the uphill road section in the plateau area is inaccurate exists.
Disclosure of Invention
In view of the above, it is necessary to provide a method for predicting the operating speed of a vehicle on an uphill road in a highland, which can improve the accuracy of the result of predicting the operating speed of the vehicle on the uphill road in the highland.
A method for predicting an operating speed of a vehicle on an uphill road segment in a plateau area, the method comprising:
acquiring the design speed of an ascending road section in a plateau area;
inputting the designed speed into a pre-constructed vehicle running speed analysis model of an uphill road section in the plateau area, and predicting the actual running speed of the vehicle on the uphill road section in the plateau area to obtain a prediction result of the actual running speed of the vehicle on the uphill road section in the plateau area;
the construction mode of the vehicle running speed analysis model of the uphill road section in the plateau area comprises the following steps:
respectively analyzing the sensibility of different vehicle types to different longitudinal slope gradients by utilizing correlation coefficients and T hypothesis test in combination with the influence of plateau areas on the vehicle power;
revising the expected speed in the specification according to the measured data, and calibrating the expected speeds of different vehicle types in the plateau area;
analyzing the stress condition of the vehicle and determining an ideal speed model of the uphill road section;
and carrying out nonlinear regression fitting on the speeds of different vehicle types in the plateau area under different slopes according to the sensitivity of the different vehicle types to different longitudinal slopes, the expected speeds of the different vehicle types in the plateau area and the ideal speed model of the uphill section, so as to obtain a vehicle running speed analysis model of the uphill section in the plateau area.
In one embodiment, the step of analyzing the sensitivities of different vehicle types to different longitudinal slopes by using correlation coefficients and T hypothesis testing in combination with the influence of the plateau area on the vehicle power includes:
analyzing a Pearson correlation coefficient r, assuming that acceleration of a vehicle is a first variable and a longitudinal gradient is a second variable, and a linear correlation is assumed between the first variable and the second variable, which are random variables:
wherein r is pierceCoefficient of correlation, xiIs a sample of the first variable numbered i, yiIs a sample of the second variable numbered i,
is the average number of samples of the first variable,
is the sample average of the second variable;
using the T test method to propose hypothesis H0：
r ≠ 0, where H0Assuming for the first time that there is no significant linear relationship between said first variable and said second variable, H1Representing that alternative hypotheses have a significant linear relationship between the first variable and the second variable;
the statistical quantity t is:
where n is the number of Pearson' S correlation coefficients r, SrThe variance of the Pearson correlation coefficient r is obtained, and t is a statistic;
if t | ≧ tα/2(n-2), rejecting the original hypothesis H0Indicating that a significant linear relationship exists between the first variable and the second variable of the population, wherein alpha is a significance level and is 0.05;
if t-<tα/2(n-2), then the original hypothesis H is agreed0Indicating that there is no significant linear relationship between the first variable and the second variable of the population.
In one embodiment, the step of revising the expected speed in the specification according to the measured data and calibrating the expected speeds of different vehicle types in the plateau area includes:
taking 95% of quantile values after removing abnormal values as expected speed values by utilizing actual measurement data of a plateau area;
and calibrating the expected speeds of different vehicle types in the plateau area according to the expected speed value.
In one embodiment, the step of analyzing the stress condition of the vehicle and determining the ideal speed model of the uphill road segment includes:
obtaining the total resistance of the vehicle according to the air resistance of the vehicle on the slope, the rolling resistance of wheels and the friction resistance of a chassis mechanical structure;
determining a theoretical calculation formula of the acceleration of the vehicle on the uphill road section according to the total resistance suffered by the vehicle;
the theoretical calculation formula of the acceleration is as follows:
wherein, FdragTaking 9.7815 as the total resistance of the vehicle, j is the longitudinal slope gradient, g is the gravity acceleration, v is the actual running initial speed, P is the power of the vehicle, m is the mass of the vehicle, and a is the speed of the vehicle on the uphill road section;
constructing an ideal speed model of the uphill road section based on the theoretical calculation formula of the acceleration;
the ideal speed model of the uphill road section is as follows:
a＝s1v2+s2v+s3v-1+s4-0.09781
wherein s is1Is a first alternative parameter, s2Is a second alternative parameter, s3Is a third alternative parameter, s4Is a fourth alternative parameter.
In one embodiment, the vehicle operation speed analysis model for the uphill road section in the plateau area comprises: the analysis model comprises a vehicle running speed analysis model of an uphill road section in the plateau area when the vehicle type is a small-sized vehicle and the gradient is 5%, a vehicle running speed analysis model of an uphill road section in the plateau area when the vehicle type is a large-sized vehicle and the gradient is 2%, a vehicle running speed analysis model of an uphill road section in the plateau area when the vehicle type is a large-sized vehicle and the gradient is 3%, and a vehicle running speed analysis model of an uphill road section in the plateau area when the vehicle type is a large-sized vehicle and the gradient is 4%;
the vehicle running speed analysis model of the uphill road section in the plateau area when the vehicle type is a small-sized vehicle and the gradient is 5% is as follows: a is 0.000001v2-0.004949v-5.190423/v+0.831456；
The vehicle running speed analysis model of the uphill road section in the plateau area when the vehicle type is a large vehicle and the gradient is 2% is as follows: a-0.000076 v2+0.007176v+3.235596/v-0.737904；
The vehicle running speed analysis model of the uphill road section in the plateau area when the vehicle type is a large vehicle and the gradient is 3% is as follows: a-0.000177 v2+0.017149v+7.001850/v-0.638915；
The vehicle running speed analysis model of the uphill road section in the plateau area when the vehicle type is a large vehicle and the gradient is 4% is as follows: a-0.000369 v2+0.026807v+6.964679/v-0.306882。
The method for predicting the running speed of the vehicle on the uphill road section in the plateau area obtains the design speed of the uphill road section in the plateau area; inputting the designed speed into a pre-constructed vehicle running speed analysis model of an uphill road section in the plateau area, and predicting the actual running speed of the vehicle on the uphill road section in the plateau area to obtain a prediction result of the actual running speed of the vehicle on the uphill road section in the plateau area; the vehicle running speed analysis model of the uphill road section in the plateau area is used for carrying out nonlinear regression fitting on the speeds of different vehicle types in the plateau area under different slopes according to the sensibility of the different vehicle types to different longitudinal slopes, the expected speeds of the different vehicle types in the plateau area and the ideal speed model of the uphill road section to obtain the vehicle running speed analysis model of the uphill road section in the plateau area.
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FIG. 1 is a schematic flow chart illustrating a method for predicting the operating speed of a vehicle on an uphill road section in a plateau area according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, there is provided a method for predicting the running speed of a vehicle on an uphill road segment in a plateau area, comprising the steps of:
and step S220, acquiring the design speed of the uphill road section in the plateau area.
And S240, inputting the designed speed into a pre-constructed vehicle running speed analysis model of the uphill road section in the plateau area, and predicting the actual running speed of the vehicle on the uphill road section in the plateau area to obtain a prediction result of the actual running speed of the vehicle on the uphill road section in the plateau area.
The method for constructing the vehicle running speed analysis model of the uphill road section in the plateau area comprises the following steps: respectively analyzing the sensibility of different vehicle types to different longitudinal slope gradients by utilizing correlation coefficients and T hypothesis test in combination with the influence of plateau areas on the vehicle power; revising the expected speed in the specification according to the measured data, and calibrating the expected speeds of different vehicle types in the plateau area; analyzing the stress condition of the vehicle and determining an ideal speed model of the uphill road section; and carrying out nonlinear regression fitting on the speeds of different vehicle types in the plateau area under different slopes according to the sensitivity of the different vehicle types to different longitudinal slopes, the expected speeds of the different vehicle types in the plateau area and the ideal speed model of the uphill section, so as to obtain a vehicle running speed analysis model of the uphill section in the plateau area.
In one embodiment, the method for analyzing the sensibility of different vehicle types to different longitudinal slopes by using correlation coefficients and T hypothesis testing in combination with the influence of plateau areas on the power of the vehicle comprises the following steps:
analyzing a Pearson correlation coefficient r by taking the acceleration of the vehicle as a first variable and the longitudinal gradient as a second variable, assuming that the first variable and the second variable are linearly related, and the first variable and the second variable are random variables:
wherein r is Pearson's correlation coefficient, xiIs a sample of the first variable numbered i, yiIs a sample of the second variable numbered i,
is the average number of samples of the first variable,
is the average number of samples of the second variable.
Using the T test method to propose hypothesis H0：
r ≠ 0, where H0Assuming for the first time that there is no significant linear relationship between the first variable and the second variable, H1Representing alternative assumptions there is a significant linear relationship between the first variable and the second variable.
The statistical quantity t is:
where n is the number of Pearson' S correlation coefficients r, SrIs the variance of the pearson correlation coefficient r, and t is the statistic.
If t | ≧ tα/2(n-2), rejecting the original hypothesis H0Indicating that a significant linear relationship exists between the first variable and the second variable of the population, wherein alpha is a significance level and is 0.05; if t-<tα/2(n-2), then the original hypothesis H is agreed0Indicating that there is no significant linear relationship between the first and second variables of the population.
The pearson correlation coefficient is calculated according to the observed values of the samples (the sample of the first variable and the sample of the second variable), and the specific values of the samples are different. The range of the Pearson correlation coefficient is between 1 and +1, namely r is more than or equal to-1 and less than or equal to 1, and the closer the value of the Pearson correlation coefficient | r | is to 1, the higher the linear correlation degree is, and the closer the value is to 0, the lower the linear correlation degree is. The general criteria for judgment are: l r | <0.3 is weakly correlated; low degree of correlation is more than or equal to | r | < 0.5; 0.5 ≦ r | <0.8 is significant correlation; 0.8 ≦ r | <1 is highly or strongly correlated. However, the degree to which the pearson correlation coefficient is close to 1 is related to the number n of pearson correlation coefficients r, and when n is small, | r | → 1; when n is large, | r | is easy to be small, especially when | r |, is always 1, therefore, when the sample capacity is small, it is not good to judge that there is a close linear relationship between variables only by the large pearson correlation coefficient, so the significance of the pearson correlation coefficient still needs to be checked, and the T test method is used to propose the hypothesis:
H0：
0 ≠ 0, wherein H0Assuming for the first time that there is no significant linear relationship between the first variable and the second variable, H1Indicating that a significant linear relationship exists between the alternative hypothesis first variable and the second variable; the statistical quantity t is:
where n is the number of Pearson' S correlation coefficients r, SrThe variance of the Pearson correlation coefficient r is obtained, and t is a statistic;
if t | ≧ tα/2(n-2), rejecting the original hypothesis H0Indicating that there is a significant linear relationship between the two variables of the population; if t-<tα/2(n-2), then the original hypothesis H is agreed0Indicating that there is no significant linear relationship between the first and second variables of the population, and thus to highSensitivity of different vehicle types in the original area to different longitudinal slopes.
Specifically, the method comprises the following steps: (a) when the car type is the minicar, the sensitivity analysis of the minicar and the longitudinal slope gradient:
the result of the test of the correlation between the acceleration of the small automobile on the longitudinal slope section and the longitudinal slope is shown in table 1, the pearson correlation coefficient r is 0.061, which is close to 0, and Q is 0.532 which is much larger than 0.05, and Q is a significance coefficient, which shows that the pearson correlation coefficient is not significant on the significance of 0.05 (double-side test), and it can be inferred that no obvious linear correlation exists between the acceleration of the small automobile on the longitudinal slope section and the longitudinal slope.
TABLE 1 table of the correlation test results of the acceleration of the small car on the longitudinal slope section and the longitudinal slope
According to the descriptive statistical data, as shown in table 2, the confidence intervals of 0.95 confidence levels of the acceleration of the car for different longitudinal gradient road sections are respectively:
TABLE 2 descriptive statistics Table
Table 3 paired samples test results table
Note: q. q.skA start point vehicle travel speed indicating a longitudinal gradient, k being 1, 2, 3, 4; z is a radical ofkThe end vehicle travel speed, k, representing the longitudinal gradient, is 1, 2, 3, 4.
(b) When the vehicle type is a large automobile, the sensitivity analysis of the large automobile and the gradient of a longitudinal slope is as follows:
the result of the test of the correlation between the acceleration of the large automobile on the longitudinal slope section and the longitudinal slope is shown in table 4, the pearson correlation coefficient r is-0.250 and is close to 0, and Q is 0.001 and is far less than 0.01, which shows that the pearson correlation coefficient is significantly correlated on the 0.01 level (double-side test), and the acceleration of the large automobile on the longitudinal slope section and the longitudinal slope have an obvious linear correlation.
TABLE 4 correlation test results of acceleration and longitudinal gradient of large-sized vehicle
According to the descriptive statistical data, as shown in table 5, the confidence intervals of 0.95 confidence levels of the acceleration of the large car on the different longitudinal gradient road sections are respectively:
TABLE 5 descriptive statistics Table
TABLE 6 paired samples test results Table
Note: q. q.skA start point vehicle travel speed indicating a longitudinal gradient, k being 1, 2, 3, 4; z is a radical ofkThe end vehicle travel speed, k, representing the longitudinal gradient, is 1, 2, 3, 4.
In one embodiment, the step of revising the expected speed in the specification according to the measured data and calibrating the expected speeds of different vehicle types in the plateau area comprises the following steps: taking 95% of quantile values after removing abnormal values as expected speed values by utilizing actual measurement data of a plateau area; and calibrating the expected speeds of different vehicle types in plateau areas according to the expected speed values.
The main data sources for standardizing the given expected speed value are the measured data of the Tai old high speed, the jin jiao high speed, the jin Yang high speed, the Shaanxi copper yellow high speed and the Yunnan Yuyuan high speed of China as examples: the average altitude is about 1500 meters, and the experimental section of the present application is a section of chinese road 318 and a section of ralin road with altitudes of 3700 meters and 3800 meters, respectively, which are far higher than the standard data source, so that the expected speed needs to be corrected according to the measured data. According to the measured data, the expected speed in the specification is revised, and the method for calibrating the expected speeds of different vehicle types in the plateau area comprises the following steps: and (4) taking 95% of quantile values after the abnormal values are removed as calibrated expected speed values by utilizing the actually measured data of the plateau area, and referring to a table 7.
TABLE 7 expected velocity values calibrated by measured data
In one embodiment, the step of analyzing the force condition of the vehicle to determine the ideal speed model of the uphill road segment comprises: obtaining the total resistance of the vehicle according to the air resistance of the vehicle on the slope, the rolling resistance of wheels and the friction resistance of a chassis mechanical structure; determining an ideal speed model of the vehicle on the uphill road section according to the total resistance borne by the vehicle;
the theoretical calculation formula of the acceleration is as follows:
wherein, FdragTaking 9.7815 as the total resistance of the vehicle, j is the longitudinal slope gradient, g is the gravity acceleration, v is the actual running initial speed, P is the power of the vehicle, m is the mass of the vehicle, and a is the speed of the vehicle on the uphill road section;
constructing an ideal speed model of the uphill road section based on a theoretical calculation formula of acceleration;
the ideal speed model of the uphill road section is as follows:
a＝s1v2+s2v+s3v-1+s4-0.09781
wherein s is1Is a first alternative parameter, s2Is a second alternative parameter, s3Is a third alternative parameter, s4Is a fourth alternative parameter.
Wherein the air resistance F is received according to the uphill slope of the vehiclewRolling resistance of wheel FfFrictional resistance F with chassis mechanical structurecfObtaining the total resistance force F suffered by the vehicledrag＝Fw+Ff+FcfDue to the greater mass of the truck, FdragNot responsive to differences in vehicle weight, i.e. F due to vehicle weightdragIs relatively small, so neglecting the weight of the vehicle to the total resistance F during the uphill slope of the vehicledragIs considered to be FdragOnly with respect to vehicle speed. Meanwhile, because the gradient of the longitudinal slope is smaller, sin theta is approximately equal to the gradient j of the longitudinal slope. On the basis of the acceleration, the acceleration of the vehicle uphill road section is obtained
Wherein j is the gradient of the longitudinal slope; g is the acceleration of gravity, taken as 9.7815. The basic form of the acceleration-speed relationship model for an uphill road segment (i.e. the ideal speed model for an uphill road segment) can thus be determined: a ═ s1v2+s2v+s3v-1+s4-0.09781。
In one embodiment, the vehicle operation speed analysis model for the uphill section of the plateau region includes: the analysis model comprises a vehicle running speed analysis model of an uphill road section in the plateau area when the vehicle type is a small-sized vehicle and the gradient is 5%, a vehicle running speed analysis model of an uphill road section in the plateau area when the vehicle type is a large-sized vehicle and the gradient is 2%, a vehicle running speed analysis model of an uphill road section in the plateau area when the vehicle type is a large-sized vehicle and the gradient is 3%, and a vehicle running speed analysis model of an uphill road section in the plateau area when the vehicle type is a large-sized vehicle and the gradient is 4%;
the vehicle running speed analysis model of the uphill road section in the plateau area when the vehicle type is a small-sized vehicle and the gradient is 5% is as follows: a is 0.000001v2-0.004949v-5.190423/v+0.831456；
The vehicle running speed analysis model of the uphill road section in the plateau area when the vehicle type is a large vehicle and the gradient is 2% is as follows: a-0.000076 v2+0.007176v+3.235596/v-0.737904；
The vehicle running speed analysis model of the uphill road section in the plateau area when the vehicle type is a large vehicle and the gradient is 3% is as follows: a-0.000177 v2+0.017149v+7.001850/v-0.638915；
The vehicle running speed analysis model of the uphill road section in the plateau area when the vehicle type is a large vehicle and the gradient is 4% is as follows: a-0.000369 v2+0.026807v+6.964679/v-0.306882。
The method comprises the following steps of carrying out nonlinear regression fitting on accelerated speeds of different vehicle types in a plateau area under different longitudinal slopes to obtain a vehicle running speed analysis model of an uphill road section in the plateau area, namely:
the vehicle running speed analysis model of the upslope section of the plateau area with the model of a small automobile is as follows:
the small-sized automobile running speed change is insensitive to the longitudinal slope sections with the gradient of less than 5 percent of the longitudinal slope, so that the regression analysis is only carried out on the longitudinal slope sections with the gradient of 5 percent, the large-sized automobile running speed change is sensitive to the longitudinal slope sections with different gradients, so that the regression analysis is carried out on the sections with the gradient of 2 percent, 3 percent and 4 percent of the longitudinal slope, and a determination coefficient R is usually used in the regression analysis2The goodness of fit of the model is tested, and the closer the statistic is to 1, the higher the goodness of fit of the model is. As can be seen from Table 8, the coefficient of determination R of the regression results20.266, the goodness of fit of the regression result model was betterAnd the difference shows that the speed change rule of the small cars on the road section with the 4% longitudinal slope gradient is not in accordance with the basic form of the ideal model, and the speed change rule of the cars has certain difference from the theoretical assumed working condition of the ideal model due to the performance difference among different small cars and the driving habits of drivers.
TABLE 8 parameter estimation
The vehicle running speed analysis model of the upslope section of the plateau area with the model of a large automobile is as follows:
the acceleration of the large-scale automobile under different slopes is regressed, the regression result is shown in a table 9, the regression decision coefficients under different longitudinal slope slopes are 0.922, 0.775 and 0.767 respectively, the fitting goodness of the regression result model is good, and the speed change rule of the large-scale automobile on the road section with the longitudinal slope gradient larger than or equal to 2% is more consistent with the basic form of the ideal model.
TABLE 9 parameter estimation and variance test
Referring to table 10, the finally obtained vehicle running speed analysis model of the uphill road section in the plateau area is as follows:
TABLE 10 analysis model of vehicle running speed of uphill section in plateau area
The method for predicting the running speed of the vehicle on the uphill road section in the plateau area obtains the design speed of the uphill road section in the plateau area; inputting the designed speed into a pre-constructed vehicle running speed analysis model of an uphill road section in the plateau area, and predicting the actual running speed of the vehicle on the uphill road section in the plateau area to obtain a prediction result of the actual running speed of the vehicle on the uphill road section in the plateau area; the vehicle running speed analysis model of the uphill road section in the plateau area is used for carrying out nonlinear regression fitting on the speeds of different vehicle types in the plateau area under different slopes according to the sensibility of the different vehicle types to different longitudinal slopes, the expected speeds of the different vehicle types in the plateau area and the ideal speed model of the uphill road section to obtain the vehicle running speed analysis model of the uphill road section in the plateau area.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (5)
1. A method for predicting the running speed of a vehicle on an uphill road section in a plateau area, the method comprising:
acquiring the design speed of an ascending road section in a plateau area;
inputting the designed speed into a pre-constructed vehicle running speed analysis model of an uphill road section in the plateau area, and predicting the actual running speed of the vehicle on the uphill road section in the plateau area to obtain a prediction result of the actual running speed of the vehicle on the uphill road section in the plateau area;
the construction mode of the vehicle running speed analysis model of the uphill road section in the plateau area comprises the following steps:
respectively analyzing the sensibility of different vehicle types to different longitudinal slope gradients by utilizing correlation coefficients and T hypothesis test in combination with the influence of plateau areas on the vehicle power;
revising the expected speed in the specification according to the measured data, and calibrating the expected speeds of different vehicle types in the plateau area;
analyzing the stress condition of the vehicle and determining an ideal speed model of the uphill road section;
and carrying out nonlinear regression fitting on the speeds of different vehicle types in the plateau area under different slopes according to the sensitivity of the different vehicle types to different longitudinal slopes, the expected speeds of the different vehicle types in the plateau area and the ideal speed model of the uphill section, so as to obtain a vehicle running speed analysis model of the uphill section in the plateau area.
2. The method of claim 1, wherein the step of analyzing the sensitivity of different vehicle types to different longitudinal slopes separately using correlation coefficients and T-hypothesis testing in combination with the effect of the plateau regions on vehicle power comprises:
analyzing a Pearson correlation coefficient r, assuming that acceleration of a vehicle is a first variable and a longitudinal gradient is a second variable, and a linear correlation is assumed between the first variable and the second variable, which are random variables:
wherein r is Pearson's correlation coefficient, xiIs a sample of the first variable numbered i, yiIs a sample of the second variable numbered i,
is the average number of samples of the first variable,
is the sample average of the second variable;
using the T test method to propose hypothesis H0：
r ≠ 0, where H0Assuming for the first time that there is no significant linear relationship between said first variable and said second variable, H1Representing that alternative hypotheses have a significant linear relationship between the first variable and the second variable;
the statistical quantity t is:
where n is the number of Pearson' S correlation coefficients r, SrThe variance of the Pearson correlation coefficient r is obtained, and t is a statistic;
if t | ≧ tα/2(n-2), rejecting the original hypothesis H0Indicating that there is a significant linear relationship between the first variable and the second variable of the population, where α is the level of significance and is taken to be0.05；
If t < tα/2(n-2), then the original hypothesis H is agreed0Indicating that there is no significant linear relationship between the first variable and the second variable of the population.
3. The method of claim 1, wherein the step of revising the expected speed in the specification according to the measured data to calibrate the expected speed of different vehicle types in the plateau area comprises:
taking 95% of quantile values after removing abnormal values as expected speed values by utilizing actual measurement data of a plateau area;
and calibrating the expected speeds of different vehicle types in the plateau area according to the expected speed value.
4. The method of claim 1, wherein the step of analyzing the force conditions of the vehicle to determine an ideal model of the velocity of the uphill road segment comprises:
obtaining the total resistance of the vehicle according to the air resistance of the vehicle on the slope, the rolling resistance of wheels and the friction resistance of a chassis mechanical structure;
determining a theoretical calculation formula of the acceleration of the vehicle on the uphill road section according to the total resistance suffered by the vehicle;
the theoretical calculation formula of the acceleration is as follows:
wherein, FdragTaking 9.7815 as the total resistance of the vehicle, j as the longitudinal slope gradient, g as the gravity acceleration, v as the actual running initial speed, the power of the vehicle, m as the mass of the vehicle, and a as the speed of the vehicle on the uphill road section;
constructing an ideal speed model of the uphill road section based on the theoretical calculation formula of the acceleration;
the ideal speed model of the uphill road section is as follows:
a＝s1v2+s2v+s3v-1+s4-0.09781
wherein s is1Is a first alternative parameter, s2Is a second alternative parameter, s3Is a third alternative parameter, s4Is a fourth alternative parameter.
5. The method of claim 1, wherein the vehicle operation speed analysis model for the uphill segment of the plateau region comprises: the analysis model comprises a vehicle running speed analysis model of an uphill road section in the plateau area when the vehicle type is a small-sized vehicle and the gradient is 5%, a vehicle running speed analysis model of an uphill road section in the plateau area when the vehicle type is a large-sized vehicle and the gradient is 2%, a vehicle running speed analysis model of an uphill road section in the plateau area when the vehicle type is a large-sized vehicle and the gradient is 3%, and a vehicle running speed analysis model of an uphill road section in the plateau area when the vehicle type is a large-sized vehicle and the gradient is 4%;
the vehicle running speed analysis model of the uphill road section in the plateau area when the vehicle type is a small-sized vehicle and the gradient is 5% is as follows: a is 0.000001v2-0.004949v-5.190423/v+0.831456；
The vehicle running speed analysis model of the uphill road section in the plateau area when the vehicle type is a large vehicle and the gradient is 2% is as follows: a-0.000076 v2+0.007176v+3.235596/v-0.737904；
The vehicle running speed analysis model of the uphill road section in the plateau area when the vehicle type is a large vehicle and the gradient is 3% is as follows: a-0.000177 v2+0.017149v+7.001850/v-0.638915；
The vehicle running speed analysis model of the uphill road section in the plateau area when the vehicle type is a large vehicle and the gradient is 4% is as follows: a-0.000369 v2+0.026807v+6.964679/v-0.306882。
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