Complex traffic scene travel time reliability measuring method

Abstract

The invention belongs to the technical field of traffic transportation, and particularly discloses a complex traffic scene travel time reliability measurement method, which comprises the steps of determining factors influencing travel time reliability and scenes corresponding to the factors; calculating a speed adjustment coefficient of the complex traffic scene; collecting basic information and traffic operation data of a road section; according to the speed adjustment coefficient of the complex traffic scene, the basic information of the road section and the traffic operation data, simulating travel time data of the road section under different traffic scenes and traffic flows by using VISSIM software; fitting a probability distribution function for the travel time data, estimating parameters of the fitting probability distribution function, and checking goodness of fit; and calculating a travel time reliability index according to the probability distribution function to realize measurement of the travel time reliability of the road section. The problems that in the prior art, due to the fact that the multi-factor dynamic coupling effect is not fully considered, scene coverage is incomplete, and travel time data of complex traffic scenes are difficult to obtain are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of transportation, and in particular relates to a method for measuring travel time reliability in complex traffic scenarios. Background Art

[0002] With the increasing complexity of urban traffic systems, travel time reliability assessment is becoming an increasingly important basis for traffic management and planning. Current research focuses on the independent analysis of a single influencing factor, such as only considering the impact of traffic flow fluctuations or a single weather condition. There is a lack of systematic modeling of the dynamic superposition effects of multi-dimensional interference factors, making it difficult to reflect the combination rules of heterogeneous scenarios in real traffic environments. Existing methods face significant challenges in dealing with the coupling of multiple factors: traditional static modeling methods are unable to effectively integrate the synergistic effects of dynamic interference factors such as sudden weather changes (such as heavy rain, ice and snow), temporary construction conditions, and sudden accidents; and simulation analysis based on a single data source has problems such as incomplete scenario coverage and insufficient real data, making it difficult to support refined decision-making needs. Summary of the Invention

[0003] The purpose of the present invention is to propose a method for measuring the reliability of travel time in complex traffic scenarios in order to solve the problems in the prior art of incomplete scene coverage and difficulty in obtaining travel time data for complex traffic scenarios due to insufficient consideration of the dynamic coupling effect of multiple factors.

[0004] The technical solution of the present invention is: a method for measuring travel time reliability in complex traffic scenarios, comprising the following steps:

[0005] Identify the factors that affect travel time reliability and the scenarios corresponding to each factor;

[0006] Determine the speed adjustment coefficient for each scenario, and calculate the speed adjustment coefficient for complex traffic scenarios based on the speed adjustment coefficient for each scenario;

[0007] Collect basic information of road sections and traffic operation data;

[0008] Based on the speed adjustment coefficient of complex traffic scenarios, basic information of road sections and traffic operation data, VISSIM software is used to simulate the travel time data of road sections under different traffic scenarios and traffic flows;

[0009] Fitting a probability distribution function to the travel time data, estimating the parameters of the fitted probability distribution function and testing the goodness of fit, to obtain an effectively fitted probability distribution function;

[0010] According to the effectively fitted probability distribution function, the travel time reliability index is calculated to achieve the measurement of the travel time reliability of the road section.

[0011] Preferably, the factors affecting travel time reliability include weather conditions, construction conditions and emergencies;

[0012] The weather conditions include moderate rain, heavy rain, light snow, moderate snow, heavy snow and severe cold.

[0013] The construction conditions correspond to the following scenarios: shoulder closure, single lane closure, double lane closure, and three+ lane closures;

[0014] The scenarios corresponding to the sudden accidents include minor accidents, general accidents, major accidents and extremely serious accidents.

[0015] Preferably, the calculation formula of the speed adjustment coefficient of the complex traffic scene is:

[0016]

[0017] Among them, α m represents the speed adjustment coefficient of the complex traffic scene m, with a value range of 0 to 1. The smaller the value, the lower the adjusted speed in the complex traffic scene. V represents the number of influencing factor categories that exist simultaneously in the complex traffic scene, with a value range of 0 to 3. α kl It represents the speed adjustment coefficient of the lth scenario under the kth type of influencing factors, and its value range is 0 to 1.

[0018] Preferably, the basic information of the road section includes the road section type, the road section length, the number of one-way lanes in the road section and the design speed of the road section; wherein the road section type includes urban expressway, urban main road, urban secondary road and urban branch road;

[0019] The traffic operation data includes the hourly traffic volume of the road section and the free flow speed of the road section.

[0020] Preferably, the travel time data of the road section under different traffic scenarios and traffic flows are simulated using VISSIM software based on the speed adjustment coefficient of the complex traffic scenario, basic information of the road section and traffic operation data, specifically:

[0021] Establish a road section geometry model based on the road section type, road section length, and number of one-way lanes on the road section, input the hourly traffic volume of the road section, and set travel time detectors at both ends of the road section;

[0022] Set the expected speed distribution of the road section under complex traffic scenarios based on the road section design speed and the speed adjustment coefficient of the complex traffic scenario, and set the number of vehicles in each speed distribution interval according to the preset ratio;

[0023] Set simulation duration and simulation interval;

[0024] Run the simulation model and obtain travel time data for road sections under different traffic scenarios and traffic flows based on the travel time detector.

[0025] Preferably, the calculation formula for the expected speed distribution of the road section under the complex traffic scenario is:

[0026] f(v m )=α m f(v d )

[0027] Among them, f(v m ) represents the expected speed distribution of the road section under complex traffic scenarios, α m Indicates the speed adjustment coefficient for complex traffic scenarios, f(v d ) represents the original expected speed distribution of the road section.

[0028] As an option, a probability distribution function is fitted to the travel time data, specifically:

[0029] The probability distribution function curve is fitted to the travel time data based on the log-normal distribution. The specific formula is:

[0030]

[0031] Where F(t) represents the probability density distribution function of the travel time of the road segment, t represents the observed travel time data of the road segment, σ and μ are fitting parameters, σ represents the logarithmic standard deviation of the probability density distribution function of the travel time of the road segment, μ represents the logarithmic mean of the probability density distribution function of the travel time of the road segment, π represents pi, exp represents the natural exponential function, and ln represents the logarithmic function with the natural base.

[0032] Preferably, estimating the parameters of the fitted probability distribution function and testing the goodness of fit are specifically as follows:

[0033] The maximum expectation algorithm in the maximum likelihood estimation was used to estimate the fitting parameters μ and σ, and the goodness of fit was judged based on the KS test. When the test statistic D < 0.15, the probability distribution function fitting was valid.

[0034] Preferably, the calculated travel time reliability index is specifically:

[0035] The free-flow travel time of a section is calculated based on the section length and the free-flow speed of the section. 1.05 times the free-flow travel time of the section is defined as the travel time reliability threshold. A quantitative model of the travel time reliability of the section is established based on the probability distribution function:

[0036]

[0037] Where R(T0) represents the reliability index of the road section travel time, P represents the probability function symbol, T0 represents the travel time reliability threshold, which is 1.05 times the free flow travel time of the road section, μf represents the free-flow travel time of the segment, that is, the segment length divided by the free-flow speed of the segment, d represents the differential sign, F(t) represents the probability density distribution function of the travel time of the segment, t represents the observed travel time data of the segment, σ and μ are fitting parameters, σ represents the logarithmic standard deviation of the probability density distribution function of the travel time of the segment, μ represents the logarithmic mean of the probability density distribution function of the travel time of the segment, π represents pi, exp represents the natural exponential function, and ln represents the logarithmic function with a natural base.

[0038] The beneficial effects of the present invention are:

[0039] The present invention comprehensively considers three factors that affect travel time reliability: weather conditions, construction conditions, and sudden accidents. It generates 175 complex traffic scenarios covering levels 0-3 and calculates speed adjustment coefficients for different traffic scenarios. This overcomes the limitations of traditional methods that only consider a single scenario or a fixed combination, and can improve the comprehensiveness of travel time reliability assessments in complex traffic scenarios. In addition, the present invention uses simulation to obtain road section travel time data, solving the problem of the difficulty in obtaining actual travel time data in complex traffic scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 The figure shows a flow chart of a method for measuring travel time reliability in a complex traffic scenario provided in Example 1 of the present invention.

[0041] Figure 2 Shown is a frequency histogram of road section travel time data provided in Example 2 of the present invention.

[0042] Figure 3 The figure shows a probability distribution function curve of the road section travel time data provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0043] The exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the embodiments shown and described in the accompanying drawings are merely exemplary and are intended to illustrate the principles and spirit of the present invention, rather than to limit the scope of the present invention.

[0044] Example 1:

[0045] like Figure 1 As shown in FIG, a method for measuring travel time reliability in complex traffic scenarios includes the following steps:

[0046] S1. Identify the factors that affect travel time reliability and the scenarios corresponding to each factor;

[0047] Specifically, factors affecting travel time reliability include weather conditions, construction conditions, and emergencies. The scenarios covered by each influencing factor are as follows:

[0048] Weather conditions = {moderate rain, heavy rain, light snow, moderate snow, heavy snow, severe cold};

[0049] Construction Status = {Shoulder Closed, One Lane Closed, Two Lane Closed, Three+ Lane Closed};

[0050] Sudden accident = {minor accident, general accident, major accident, major accident}.

[0051] S2. Determine the speed adjustment coefficient for each scenario, and calculate the speed adjustment coefficient for complex traffic scenarios based on the speed adjustment coefficient for each scenario;

[0052] Specifically, the speed adjustment coefficients for each scenario are shown in Tables 1, 2, and 3. The speed adjustment coefficient calculation formula for complex traffic scenarios is:

[0053]

[0054] Among them, α m represents the speed adjustment coefficient of the complex traffic scene m, with a value range of 0 to 1. The smaller the value, the lower the adjusted speed in the complex traffic scene. V represents the number of influencing factor categories that exist simultaneously in the complex traffic scene, with a value range of 0 to 3. α kl It represents the speed adjustment coefficient of the lth scenario under the kth type of influencing factors, with a value range of 0 to 1. The speed adjustment coefficients of some complex traffic scenarios are shown in Table 4;

[0055] Table 1 Speed adjustment coefficients for various weather conditions

[0056] Weather conditions moderate rain heavy rain light snow moderate snow heavy snow severe cold <![CDATA[Speed adjustment coefficient (α1)]]> 0.94 0.92 0.89 0.87 0.85 0.94

[0057] Table 2 Speed adjustment coefficients for various construction scenarios

[0058] Construction status Shoulder closure Cycle lane closures Two lanes closed Three+ lane closures <![CDATA[Speed adjustment coefficient (α2)]]> 0.91 0.87 0.69 0.61

[0059] Table 3 Speed adjustment coefficients for various accident scenarios

[0060] Sudden Accident Minor accident General accidents major accident Major accident <![CDATA[Speed adjustment coefficient (α3)]]> 0.85 0.70 0.50 0.30

[0061] Table 4 Speed adjustment coefficients for some complex traffic scenarios

[0062]

[0063] S3. Collect basic road section information and traffic operation data;

[0064] Specifically, the basic information of the road section includes the road section type, road section length, number of one-way lanes on the road section, and the road section design speed; the traffic operation data includes the hourly traffic volume of the road section and the road section free flow speed. Among them, the road section types include urban expressways, urban main roads, urban secondary roads, and urban branch roads, and the corresponding road section free flow speed v f See Table 5.

[0065] Table 5 Free flow speed corresponding to different road section types

[0066] Road segment type <![CDATA[v f (km / h)]]> Urban Expressway 65 city main roads 40 Urban secondary roads 30 Urban branch road 20

[0067] S4. Use VISSIM software to simulate travel time data for road sections under different traffic scenarios and traffic flows, based on speed adjustment coefficients for complex traffic scenarios, basic road section information, and traffic operation data.

[0068] Specifically, a road section geometry model is established based on the road section type, road section length, and number of one-way lanes on the road section, the hourly traffic volume of the road section is input, and travel time detectors are set at both ends of the road section;

[0069] According to the design speed of the road section and the speed adjustment coefficient of the complex traffic scene, the expected speed distribution f(v m ), according to the expected speed distribution f(v m ) Input the vehicle composition (g) and set the number of vehicles in each speed distribution interval (speed from low to high) in the ratio of 1:2:4:2:1; set the simulation duration to 3600 seconds and the simulation interval to 1 second.

[0070] Among them, the expected speed distribution f(v m ) is calculated as:

[0071] f(v m )=α m f(v d )

[0072] Among them, f(v d ) represents the original expected speed distribution of the road section, as shown in Table 6;

[0073] Run the simulation model and obtain travel time data for different traffic scenarios and traffic flows based on the travel time detector.

[0074] Table 6 Original expected speed distribution corresponding to different design speeds

[0075]

[0076]

[0077]

[0078] S5. Fitting a probability distribution function to the travel time data, estimating parameters of the fitted probability distribution function and testing the goodness of fit to obtain a valid fitted probability distribution function;

[0079] Specifically, the collected travel time data was cleaned using Python to remove data points with obvious errors or anomalies, such as abnormally short or long travel time records caused by detector failures. The travel time data was then fitted with a probability distribution function curve based on the log-normal distribution. The specific formula is:

[0080]

[0081] Where F(t) represents the probability density distribution function of the travel time of the road segment, t represents the observed travel time data of the road segment, σ represents the logarithmic standard deviation (s) of the probability density distribution function of the travel time of the road segment, μ represents the logarithmic mean (s) of the probability density distribution function of the travel time of the road segment, π represents pi, exp represents the natural exponential function, and ln represents the logarithmic function with the natural base as the base.

[0082] Estimate the parameters of the fitted probability distribution function and test the goodness of fit;

[0083] Specifically, the maximum expectation algorithm in the maximum likelihood estimation was used to estimate the fitting parameters μ and σ, and the goodness of fit was judged based on the KS test. The significance level a was set to 0.5, and when the test statistic D < 0.15, the null hypothesis was accepted and the distribution model was deemed valid.

[0084] S6. Calculate the travel time reliability index based on the effectively fitted probability distribution function to measure the travel time reliability of the road section;

[0085] Specifically, the free-flow travel time of a section is calculated based on the section length and the free-flow speed of the section. 1.05 times the free-flow travel time of the section is defined as the travel time reliability threshold. A quantitative model of the travel time reliability of the section is established based on the probability distribution function of the travel time data:

[0086]

[0087] Where R(T0) represents the reliability index of the road section travel time, P represents the probability function symbol, T0 represents the travel time reliability threshold (s), which is 1.05 times the free flow travel time of the road section, T0 = 94.12s, μ f represents the free flow travel time of the road section (s), which is the length of the road section divided by the free flow speed of the road section, μ f=89.64s, d represents the differential sign; the results show that under the condition of “moderate rain + single lane closure”, the travel time reliability R(T0) of this section is 0.5579.

[0088] The value of R(T0) ranges from 0 to 1, and its value is strictly monotonically positively correlated with the reliability level. That is, when R(T0)→1, it indicates that in a complex traffic scenario, the probability that the travel time of a road section exceeds the threshold T0 approaches zero, and the travel time reliability reaches the optimal state.

[0089] This invention addresses the limitations of existing technologies by generating a composite scenario space covering multiple levels of heterogeneous scenarios; establishing a nonlinear speed influence coefficient, providing a method for characterizing speed decay due to the coupling of multiple factors; and combining VISSIM microsimulation with statistical distribution fitting to generate travel time data for complex traffic scenarios without relying on real-world data, achieving a closed-loop analysis from scenario modeling to reliability quantification. Compared to traditional single-factor analysis methods, this solution overcomes the difficulty of data acquisition in complex traffic scenarios through dynamic scenario combination and statistical fitting, providing a scalable method for analyzing the effects of multiple-factor coupling.

[0090] The present invention can be applied to multiple fields and has wide applicability and practicality. In the dynamic management and control of urban traffic, complex traffic scenarios with heavy rain and construction conditions are rehearsed, the travel time reliability attenuation trend of the main roads is evaluated based on the travel time reliability threshold, and the signal timing and diversion schemes are optimized. In severe weather emergency dispatch, the travel time reliability index under historical traffic scenarios is called in combination with meteorological warning data to formulate graded speed limits and road closure strategies. In the impact assessment of construction conditions, the coupling effect of single / double lane closures and traffic flow is simulated, the speed attenuation loss is quantified, and the detour route and temporary traffic facility layout are optimized. In the development of intelligent transportation systems, the dynamic complex traffic scenario generation module and the travel time reliability calculation engine are integrated to provide the travel time prediction platform with multi-factor interference modeling capabilities to support real-time risk assessment and decision optimization.

[0091] Example 2:

[0092] Based on Example 1, the present invention uses an urban trunk road with a design speed of 60 km / h as an example to describe in detail the travel time reliability measurement method for complex traffic scenarios proposed by the present invention. The specific process is as follows:

[0093] The factors affecting the reliability of travel time on this road section are weather conditions and construction status, corresponding to the scenarios of moderate rain and single lane closure, respectively.

[0094] According to Tables 1 and 2, the speed adjustment coefficient for moderate rain is 0.94, the speed adjustment coefficient for single lane closure is 0.87, and the speed impact coefficient for the traffic scenario "moderate rain + single lane closure" is 0.82.

[0095] The section type T′, section length d, number of one-way lanes n, and hourly traffic volume Q of the section are collected, as shown in Table 7. The basic data of the section are then imported based on the “Link and Connectors-Type / No.of Lines / Link Length” function of the VISSIM software.

[0096] Table 7 Modeling data of urban trunk road sections with a design speed of 60 km / h

[0097]

[0098] According to Table 6, the original expected speed distribution of the road section f(60km / h) is obtained, and then the original expected speed distribution is adjusted to obtain the speed distribution of the road section f(v 中雨+单车道封闭 ), as shown in Table 8. Based on the “Base Data-Distributions-Desired Speed Distributions” function of VISSIM software, the speed distribution is calculated according to f(v 中雨+单车道封闭 ) Set the expected speed distribution of the road section, and at the same time set the number of vehicles in each speed distribution interval (speed from low to high) according to 1:2:4:2:1 based on the "Traffic-Compositions" function of the VISSIM software.

[0099] Table 8 Adjusted speed distribution of urban trunk roads with a design speed of 60 km / h

[0100]

[0101] The travel time detector was set using the “Travel time measurement: select link” function of the VISSIM software. The starting point detector was set at 0 m of the section using the “From Section” function, and the end point detector was set at 996.634 m of the section using the “To Section” function. The travel time simulation duration was set to 3600 s and the simulation interval was set to 1 s using the “Evaluation-Files-Travel time-Configuration-Time from 0s to 3600s, Interval 1s” function in the VISSIM software. The travel time data results of each vehicle on the section were obtained using the “Simulation continuous” function in the VISSIM software.

[0102] Based on Python's Matplotlib and NumPy libraries, a frequency histogram is drawn for the travel time data output by the simulation, such as Figure 2 As shown, the probability distribution function and curve of the lognormal distribution are fitted based on the travel time data. The probability distribution curve is shown in Figure 3 As shown, the probability distribution function is shown as follows:

[0103]

[0104] Where F(t) represents the probability density distribution function of the travel time of the road segment, t represents the observed travel time data of the road segment, σ represents the logarithmic standard deviation (s) of the probability density distribution function of the travel time of the road segment, μ represents the logarithmic mean (s) of the probability density distribution function of the travel time of the road segment, π represents pi, exp represents the natural exponential function, and ln represents the logarithmic function with the natural base as the base.

[0105] The maximum expectation algorithm in the maximum likelihood estimation was used to estimate the fitting parameters, and the logarithmic mean μ = 104.86s and the logarithmic standard deviation σ = 54.63s were obtained. The KS test statistic D = 0.1047, D ≤ D α =0.15, the distribution model is considered valid.

[0106] The calculated travel time reliability threshold for the road section under the "moderate rain + single lane closure" scenario is 94.12s. A travel time reliability quantification model for the road section under the "moderate rain + single lane closure" scenario is established based on the travel time data probability distribution function:

[0107]

[0108] Where R(T0) represents the reliability index of the road section travel time, P represents the probability function symbol, T0 represents the travel time reliability threshold (s), which is 1.05 times the free flow travel time of the road section, T0 = 94.12s, μ f represents the free flow travel time of the road section (s), which is the length of the road section divided by the free flow speed of the road section, μ f =89.64s, d represents the differential sign; the results show that under the condition of “moderate rain + single lane closure”, the travel time reliability R(T0) of this section is 0.5579.

[0109] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A method for measuring travel time reliability in complex traffic scenarios, characterized by: The following steps are involved: Identify the factors that affect travel time reliability and the scenarios corresponding to each factor; Determine the speed adjustment coefficient for each scenario, and calculate the speed adjustment coefficient for complex traffic scenarios based on the speed adjustment coefficient for each scenario; Collect basic information of road sections and traffic operation data; Based on the speed adjustment coefficient of complex traffic scenarios, basic information of road sections and traffic operation data, VISSIM software is used to simulate the travel time data of road sections under different traffic scenarios and traffic flows; Fitting a probability distribution function to the travel time data, estimating the parameters of the fitted probability distribution function and testing the goodness of fit, to obtain an effectively fitted probability distribution function; According to the effectively fitted probability distribution function, the travel time reliability index is calculated to achieve the measurement of the travel time reliability of the road section.

2. The method for measuring travel time reliability in complex traffic scenarios according to claim 1 is characterized in that: The factors that affect travel time reliability include weather conditions, construction conditions and emergencies; The weather conditions include moderate rain, heavy rain, light snow, moderate snow, heavy snow and severe cold. The construction conditions correspond to the following scenarios: shoulder closure, single lane closure, double lane closure, and three+ lane closures; The scenarios corresponding to the sudden accidents include minor accidents, general accidents, major accidents and extremely serious accidents.

3. The method for measuring travel time reliability in complex traffic scenarios according to claim 1, characterized in that: The calculation formula of the speed adjustment coefficient of the complex traffic scene is: Among them, α m represents the speed adjustment coefficient of the complex traffic scene m, with a value range of 0 to 1. The smaller the value, the lower the adjusted speed in the complex traffic scene. V represents the number of influencing factor categories that exist simultaneously in the complex traffic scene, with a value range of 0 to 3. α kl It represents the speed adjustment coefficient of the lth scenario under the kth type of influencing factors, and its value range is 0 to 1.

4. The method for measuring travel time reliability in complex traffic scenarios according to claim 1, characterized in that: The basic information of the road section includes the road section type, road section length, number of one-way lanes in the road section and the design speed of the road section; wherein the road section type includes urban expressway, urban main road, urban secondary road and urban branch road; The traffic operation data includes the hourly traffic volume of the road section and the free flow speed of the road section.

5. The method for measuring travel time reliability in complex traffic scenarios according to claim 4, characterized in that: The speed adjustment coefficient of the complex traffic scene, basic information of the road section and traffic operation data are used to simulate the travel time data of the road section under different traffic scenes and traffic flows using VISSIM software, specifically: Establish a road section geometry model based on the road section type, road section length, and number of one-way lanes on the road section, input the hourly traffic volume of the road section, and set travel time detectors at both ends of the road section; Set the expected speed distribution of the road section under complex traffic scenarios based on the road section design speed and the speed adjustment coefficient of the complex traffic scenario, and set the number of vehicles in each speed distribution interval according to the preset ratio; Set simulation duration and simulation interval; Run the simulation model and obtain travel time data for road sections under different traffic scenarios and traffic flows based on the travel time detector.

6. The method for measuring travel time reliability in complex traffic scenarios according to claim 5, characterized in that: The calculation formula for the expected speed distribution of the road section under the complex traffic scenario is: f(v m )=α m f(v d ) Among them, f(v m ) represents the expected speed distribution of the road section under complex traffic scenarios, α m Indicates the speed adjustment coefficient for complex traffic scenarios, f(v d ) represents the original expected speed distribution of the road section.

7. The method for measuring travel time reliability in complex traffic scenarios according to claim 1, characterized in that: Fit a probability distribution function to the travel time data, specifically: The probability distribution function curve is fitted to the travel time data based on the log-normal distribution. The specific formula is: Where F(t) represents the probability density distribution function of the travel time of the road segment, t represents the observed travel time data of the road segment, σ and μ are fitting parameters, σ represents the logarithmic standard deviation of the probability density distribution function of the travel time of the road segment, μ represents the logarithmic mean of the probability density distribution function of the travel time of the road segment, π represents pi, exp represents the natural exponential function, and ln represents the logarithmic function with the natural base.

8. The method for measuring travel time reliability in complex traffic scenarios according to claim 7, characterized in that: The method of estimating the parameters of the fitted probability distribution function and testing the goodness of fit is as follows: The maximum expectation algorithm in the maximum likelihood estimation was used to estimate the fitting parameters μ and σ, and the goodness of fit was judged based on the KS test. When the test statistic D < 0.15, the probability distribution function fitting was valid.

9. The method for measuring travel time reliability in complex traffic scenarios according to claim 1, characterized in that: The calculated travel time reliability index is specifically: The free-flow travel time of a section is calculated based on the section length and the free-flow speed of the section. 1.05 times the free-flow travel time of the section is defined as the travel time reliability threshold. A quantitative model of the travel time reliability of the section is established based on the probability distribution function: Where R(T0) represents the reliability index of the road section travel time, P represents the probability function symbol, T0 represents the travel time reliability threshold, which is 1.05 times the free flow travel time of the road section, μ f represents the free-flow travel time of the segment, that is, the segment length divided by the free-flow speed of the segment, d represents the differential sign, F(t) represents the probability density distribution function of the travel time of the segment, t represents the observed travel time data of the segment, σ and μ are fitting parameters, σ represents the logarithmic standard deviation of the probability density distribution function of the travel time of the segment, μ represents the logarithmic mean of the probability density distribution function of the travel time of the segment, π represents pi, exp represents the natural exponential function, and ln represents the logarithmic function with a natural base.

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