A refined calibration method for a simplified car-following model based on multi-source data

Abstract

A refined calibration method for a simplified car-following model based on multi-source data belongs to the field of intelligent transportation technology. In order to improve the accuracy of the simulation results of the simplified car-following model, the present invention includes obtaining road network data, geomagnetic detection equipment data, geomagnetic detection data, floating vehicle GPS Data is collected; roads are divided into different grades and roads are interrupted and merged; data is processed to remove missing or abnormal data; the average vehicle length of different types of vehicles is calibrated; the average headway between different types of vehicles at rest is calibrated through intersection surveys; the average effective vehicle length of different types of vehicles at rest is calculated; the free flow speed of different grades of roads is calibrated; the capacity of different grades of roads and the corresponding average effective vehicle length are calibrated based on processed geomagnetic detection data; and the driver reaction time coefficient of different grades of roads is calculated. This invention improves the accuracy of the simulation results of the simplified car-following model.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent transportation technology, and in particular relates to a refined calibration method for a simplified car-following model based on multi-source data. Background Art

[0002] Car following (CF) is the most fundamental microscopic driving behavior, describing the interaction between adjacent vehicles in a platoon on a one-way street with overtaking restrictions. The car following model uses dynamics to study the corresponding behavior of the following vehicle (FV) caused by changes in the leading vehicle's (LV) motion state. By analyzing the individual car following patterns, we understand the characteristics of single-lane traffic flow, thereby building a bridge between driver microscopic behavior and macroscopic traffic phenomena.

[0003] The simplified car-following model is primarily used for simulating large-scale, lane-level, mesoscopic traffic models. The model assumes that vehicles have constant acceleration within short time intervals. Publicly available parameter calibration methods for the simplified car-following model are limited, and parameters are typically set based on experience or by reference to other car-following model calibration methods. This results in insufficiently refined parameter calibration, impacting simulation results. Summary of the Invention

[0004] The problem to be solved by the present invention is to improve the accuracy of simulation results of a simplified car-following model, and propose a refined calibration method of the simplified car-following model based on multi-source data.

[0005] To achieve the above object, the present invention is implemented through the following technical solutions:

[0006] A refined calibration method for a simplified car-following model based on multi-source data includes the following steps:

[0007] S1. Obtain road network data, geomagnetic detection equipment data, geomagnetic detection data, floating vehicle GPS data;

[0008] S2 based on the road network data obtained in step S1, divide the road level, and interrupt and merge the road operation;

[0009] S3. The geomagnetic detection data obtained in step S1, the floating car GPS Data is processed to remove missing or abnormal data, and the processed geomagnetic detection data and the processed floating vehicle data are obtained. GPS data;

[0010] S4 based on the processed geomagnetic detection data obtained in step S3 to calibrate the average vehicle length of different types of vehicles;

[0011] S5. Calibrate the average headway between different types of vehicles at rest through intersection surveys;

[0012] S6. Based on the average vehicle length of different types of vehicles obtained in step S4 and the average headway between different types of vehicles at rest obtained in step S5, calculate the average effective vehicle length of different types of vehicles at rest;

[0013] S7. The floating vehicle after processing based on step S3 GPS Data, calibrate the free flow speed of different levels of roads;

[0014] S8 based on the processed geomagnetic detection data obtained in step S3, calibrate the different levels of road capacity and the corresponding average effective vehicle length;

[0015] S9. Calculate the driver reaction time coefficient for different levels of roads.

[0016] Furthermore, the road network data in step S1 includes a unique number, length, direction, road grade, number of lanes, and free flow speed; the geomagnetic detection device data includes the geomagnetic device number, longitude of the installation location, latitude of the installation location, and lane number field; the geomagnetic detection data includes the geomagnetic device number, timestamp, vehicle type, speed, and vehicle length; the floating vehicle GPS Data includes vehicles ID , timestamp, longitude, latitude, speed, direction, status information.

[0017] Furthermore, the specific implementation method of step S2 includes the following steps:

[0018] S2.1. Establish road classifications, including expressways, first-class highways, expressways, interchange ramps, second-class highways, trunk roads, secondary trunk roads, and branch roads. Expressways and expressways are further categorized as expressway-tunnel, expressway-non-tunnel, expressway-tunnel, and expressway-non-tunnel.

[0019] S2.2. Road segment interruption and merging: setting up sections of interchange ramps, secondary roads, main roads, secondary roads, and branch roads LINK The length does not exceed 350 meters, and the sections of expressways, first-class highways and expressways are set up LINK Not more than 550 meters.

[0020] Furthermore, the specific implementation method of step S4 includes the following steps:

[0021] S4.1. Use TransCAD software to match geomagnetic detection equipment data with road network data.

[0022] S4.2. Correlate geomagnetic detection device data with geomagnetic detection data;

[0023] S4.3. The geomagnetic detection data is divided into sections according to the road LINK The roads are grouped into different levels, and the data of each group is arranged in ascending order of vehicle length, and the data of the smallest 5% and largest 5% of vehicle length are eliminated;

[0024] Take the average of the grouped data of different types of vehicles as the vehicle type type Average vehicle length , the formula is as follows:

[0025] ;

[0026] in, Indicates vehicle model type Middle i The vehicle length of the vehicle, N Indicates vehicle model type The total number of vehicles.

[0027] Furthermore, the specific implementation method of step S5 includes the following steps:

[0028] S5.1. Select multiple intersections with large, medium, and small vehicles and capture video of the vehicle queues at the intersection entrances from a perpendicular direction.

[0029] S5.2. Select the road marking at the vehicle parking location from the video of the vehicle queue at the intersection entrance and measure the length of the road marking. L , and the pixel length of the road markings in the video l , calculate the length corresponding to one pixel in the video ;

[0030] S5.3. Measure the vehicle types in the video of the vehicle queue at the intersection entrance type No. i The pixel length corresponding to the distance between the heads of two vehicles when the vehicle is parked , and then calculate the headway between the vehicle and the vehicle in front when the vehicle is stationary ;

[0031] S5.4. Group all headway data by vehicle type and take the average of each group as the vehicle type. type The headway distance to the vehicle ahead when stationary , the formula is as follows

[0032] ;

[0033] in, Indicates vehicle model type Middle i The headway distance between a vehicle and the vehicle ahead when the vehicle is stationary.

[0034] Furthermore, the calculation formula of step S6 is as follows:

[0035] ;

[0036] in, For car models type Average effective vehicle length at rest.

[0037] Furthermore, the calculation formula of step S7 is as follows:

[0038] S7.1. The floating vehicles obtained in step S3 are filtered for vehicles with a time of 1-5 am and a vehicle status of carrying passengers. GPS Data, based on location association of floating car GPS data points with road sections LINK ;

[0039] S7.2. Group the floating GPS data by vehicle ID and sort them in ascending chronological order.

[0040] S7.3. Traverse the vehicle IDs, calculate the distance and time difference between two adjacent data points, and calculate the average speed of the vehicles.

[0041] S7.4. Different speed data are divided into different categories according to the road level of the corresponding road section LINK. class , group by 5-minute time intervals and calculate road grade class Grouping group Average vehicle speed , the calculation formula is as follows:

[0042] ;

[0043] in, Indicates road grade class Grouping group Middle i Average speed data of the vehicle;

[0044] S7.5. For each road class, sort the groups in ascending order of average speed and select the data at the 95th percentile as the free flow speed. .

[0045] Furthermore, the calculation formula of step S8 is as follows:

[0046] S8.1. For the associated road network segments obtained in step S4 LINK The geomagnetic detection data of the geomagnetic detection equipment information is based on the road section LINK The traffic volume and vehicle type composition of each road grade group are counted and the average vehicle speed is calculated.

[0047] S8.2. For each road class, group the data by traffic volume, sorting them in ascending order. Select data with traffic volume between the 90th and 100th percentiles and an average operating speed greater than 80% of the free flow speed for that road class.

[0048] S8.3. Calculate the average flow rate for each road class as the 5-minute vehicle capacity for a single lane of that road class. , and count the corresponding vehicle types, the expression is:

[0049] ;

[0050] in, Indicates road grade class Middle k The flow of data; M Indicates road grade class The amount of data;

[0051] S8.4. Convert the 5-minute vehicle capacity of a single lane for each road class to the peak hour vehicle capacity of a single lane. , and count the vehicle type composition, the calculation formula is as follows:

[0052] ;

[0053] Calculate the average effective vehicle length of different road grades when the road traffic volume is close to the traffic capacity based on the vehicle type composition and the average effective vehicle length of different models , the calculation formula is as follows:

[0054] ;

[0055] in, Indicates the average effective vehicle length of the vehicle type on the road of class. is the proportion of vehicles of type type on roads of class.

[0056] Furthermore, the calculation formula of step S9 is as follows:

[0057] S9.1. Setting the driver reaction time ResponseTime 1.25s;

[0058] S9.2. Calculate the traffic flow rate of different road classes based on their capacity, average effective vehicle length, free flow speed, and driver reaction time. class Driver reaction time coefficient , the calculation formula is as follows:

[0059] .

[0060] Beneficial effects of the present invention:

[0061] The present invention describes a refined calibration method for a simplified car-following model based on multi-source data. Through the fusion and analysis of multi-source data, the method can more comprehensively consider actual traffic conditions and complex road environments, avoiding the problem of overly rough or oversimplified parameter settings in traditional methods, significantly improving the accuracy of parameter calibration and the reliability of traffic flow simulation results, and providing more accurate data support for traffic management and optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a flowchart of a refined calibration method for a simplified car-following model based on multi-source data according to the present invention;

[0063] Figure 2 This is a structural block diagram of a refined calibration method for a simplified car-following model based on multi-source data according to the present invention;

[0064] Figure 3 This is a road grade block diagram of the present invention. DETAILED DESCRIPTION

[0065] In order to make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present invention and are not intended to limit the present invention. That is, the specific embodiments described herein are only some embodiments of the present invention, not all embodiments. Generally, the components of the specific embodiments of the present invention described and illustrated in the drawings herein can be arranged and designed in various different configurations, and the present invention can also have other embodiments.

[0066] Therefore, the following detailed description of the specific embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but is merely representative of selected specific embodiments of the present invention. All other specific embodiments obtained by those skilled in the art based on the specific embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0067] In order to further understand the content, features and effects of the present invention, the following specific embodiments are given as examples, and the attached Figure 1 -Attached Figure 3 The detailed instructions are as follows:

[0068] Example 1:

[0069] A refined calibration method for a simplified car-following model based on multi-source data includes the following steps:

[0070] S1. Obtain road network data, geomagnetic detection equipment data, geomagnetic detection data, floating vehicle GPS data;

[0071] Furthermore, the road network data in step S1 includes a unique number, length, direction, road grade, number of lanes, and free flow speed; the geomagnetic detection device data includes the geomagnetic device number, longitude of the installation location, latitude of the installation location, and lane number field; the geomagnetic detection data includes the geomagnetic device number, timestamp, vehicle type, speed, and vehicle length; the floating vehicle GPS Data includes vehicles ID , timestamp, longitude, latitude, speed, direction, status information;

[0072] S2 based on the road network data obtained in step S1, divide the road level, and interrupt and merge the road operation;

[0073] Furthermore, the specific implementation method of step S2 includes the following steps:

[0074] S2.1. Establish road classifications, including expressways, first-class highways, expressways, interchange ramps, second-class highways, trunk roads, secondary trunk roads, and branch roads. Expressways and expressways are further categorized as expressway-tunnel, expressway-non-tunnel, expressway-tunnel, and expressway-non-tunnel.

[0075] S2.2. Road segment interruption and merging: setting up sections of interchange ramps, secondary roads, main roads, secondary roads, and branch roads LINK The length does not exceed 350 meters, and the sections of expressways, first-class highways and expressways are set up LINK not exceeding 550 metres;

[0076] S3. The geomagnetic detection data obtained in step S1, the floating car GPS Data is processed to remove missing or abnormal data, and the processed geomagnetic detection data and the processed floating vehicle data are obtained. GPS data;

[0077] Furthermore, the specific implementation method of step S3 includes the following steps:

[0078] S3.1. Eliminate unreasonable geomagnetic detection data that meets the following conditions: missing data in any of the following fields: geomagnetic device number, timestamp, vehicle type, speed, or vehicle length; or data with a speed greater than 120. If the amount of excluded data for a geomagnetic device exceeds 5%, the device data will no longer be used for parameter calibration.

[0079] S3.2. Eliminate unreasonable floating vehicles that meet the following conditions GPS Data: Missing Vehicles ID , timestamp, longitude, latitude, speed, direction angle, any field data in status information; data with speed greater than 120; data with direction angle field value less than 0 or greater than 360;

[0080] S4 based on the processed geomagnetic detection data obtained in step S3 to calibrate the average vehicle length of different types of vehicles;

[0081] Furthermore, the specific implementation method of step S4 includes the following steps:

[0082] S4.1. Use TransCAD software to match geomagnetic detection equipment data with road network data.

[0083] S4.2. Correlate geomagnetic detection device data with geomagnetic detection data;

[0084] S4.3. The geomagnetic detection data is divided into sections according to the road LINK The roads are grouped into different levels, and the data of each group is arranged in ascending order of vehicle length, and the data of the smallest 5% and largest 5% of vehicle lengths are eliminated;

[0085] Take the average of the grouped data of different types of vehicles as the vehicle type type Average vehicle length , the formula is as follows:

[0086] ;

[0087] in, Indicates vehicle model type Middle i The vehicle length of the vehicle, N Indicates vehicle model type The total number of vehicles;

[0088] S5. Calibrate the average headway between different types of vehicles at rest through intersection surveys;

[0089] Furthermore, the specific implementation method of step S5 includes the following steps:

[0090] S5.1. Select multiple intersections with large, medium, and small vehicles and capture video of the vehicle queues at the intersection entrances from a perpendicular direction.

[0091] S5.2. Select the road marking at the vehicle parking location from the video of the vehicle queue at the intersection entrance and measure the length of the road marking. L , and the pixel length of the road markings in the video l, calculate the length corresponding to one pixel in the video ;

[0092] S5.3. Measure the vehicle types in the video of the vehicle queue at the intersection entrance type No. i The pixel length corresponding to the distance between the heads of two vehicles when the vehicle is parked , and then calculate the headway between the vehicle and the vehicle in front when the vehicle is stationary ;

[0093] S5.4. Group all headway data by vehicle type and take the average of each group as the vehicle type. type The headway distance to the vehicle ahead when stationary , the formula is as follows

[0094] ;

[0095] in, Indicates vehicle model type Middle i The headway distance between a vehicle and the vehicle ahead when the vehicle is stationary.

[0096] S6. Based on the average vehicle length of different types of vehicles obtained in step S4 and the average headway between different types of vehicles at rest obtained in step S5, calculate the average effective vehicle length of different types of vehicles at rest;

[0097] The calculation formula of the further step S6 is as follows:

[0098] ;

[0099] in, For car models type Average effective vehicle length at rest;

[0100] S7. The floating vehicle after processing based on step S3 GPS Data, calibrate the free flow speed of different levels of roads;

[0101] Furthermore, the calculation formula of step S7 is as follows:

[0102] S7.1. The floating vehicles obtained in step S3 are filtered for vehicles with a time of 1-5 am and a vehicle status of carrying passengers. GPS Data, based on location association of floating car GPS data points with road sections LINK ;

[0103] S7.2. Group the floating GPS data by vehicle ID and sort them in ascending chronological order.

[0104] S7.3. Traverse the vehicle IDs, calculate the distance and time difference between two adjacent data points, and calculate the average speed of the vehicles.

[0105] S7.4. Different speed data are divided into different categories according to the road level of the corresponding road section LINK. class , group by 5-minute time intervals and calculate road grade class Grouping group Average vehicle speed , the calculation formula is as follows:

[0106] ;

[0107] in, Indicates road grade class Grouping group Middle i Average speed data of the vehicle;

[0108] S7.5. For each road class, sort the groups in ascending order of average speed and select the data at the 95th percentile as the free flow speed. .

[0109] S8 based on the processed geomagnetic detection data obtained in step S3, calibrate the different levels of road capacity and the corresponding average effective vehicle length;

[0110] Furthermore, the calculation formula of step S8 is as follows:

[0111] S8.1. For the associated road network segments obtained in step S4 LINK The geomagnetic detection data of the geomagnetic detection equipment information is based on the road section LINK The traffic volume and vehicle type composition of each road grade group are counted and the average vehicle speed is calculated.

[0112] S8.2. For each road class, group the data by traffic volume, sorting them in ascending order. Select data with traffic volume between the 90th and 100th percentiles and an average operating speed greater than 80% of the free flow speed for that road class.

[0113] S8.3. Calculate the average flow rate for each road class as the 5-minute vehicle capacity for a single lane of that road class. , and count the corresponding vehicle types, the expression is:

[0114] ;

[0115] in, Indicates road grade class Middle kThe flow of data; M Indicates road grade class The amount of data;

[0116] S8.4. Convert the 5-minute vehicle capacity of a single lane for each road class to the peak hour vehicle capacity of a single lane. , and count the vehicle type composition, the calculation formula is as follows:

[0117] ;

[0118] Calculate the average effective vehicle length of different road grades when the road traffic volume is close to the traffic capacity based on the vehicle type composition and the average effective vehicle length of different models , the calculation formula is as follows:

[0119] ;

[0120] in, Indicates the average effective vehicle length of the vehicle type on the road of class. is the proportion of vehicles of type type on roads of class.

[0121] S9. Calculate the driver reaction time coefficient for different levels of roads.

[0122] Furthermore, the calculation formula of step S9 is as follows:

[0123] S9.1. Setting the driver reaction time ResponseTime 1.25s;

[0124] S9.2. Calculate the traffic flow rate of different road classes based on their capacity, average effective vehicle length, free flow speed, and driver reaction time. class Driver reaction time coefficient , the calculation formula is as follows:

[0125] .

[0126] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0127] Although the present application has been described above with reference to specific embodiments, various modifications may be made thereto and components may be substituted with equivalents without departing from the scope of the present application. In particular, as long as there are no structural conflicts, the various features of the embodiments disclosed herein may be combined with each other in any manner, and the omission of an exhaustive description of these combinations in this specification is solely for the sake of space and resource conservation. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions within the scope of the claims.

Claims

1. A refined calibration method for a simplified car-following model based on multi-source data, characterized in that: The steps include: S1. Obtain road network data, geomagnetic detection equipment data, geomagnetic detection data, and floating vehicle GPS data; S2. Based on the road network data obtained in step S1, the road level is divided and the roads are interrupted and merged; S3 step S1 obtained geomagnetic detection data, floating car GPS data is processed, eliminating missing data or abnormal data to obtain processed geomagnetic detection data, processed floating car GPS data; S4. Calibrate the average vehicle length of different types of vehicles based on the processed geomagnetic detection data obtained in step S3; S5. Calibrate the average headway between different types of vehicles at rest through intersection surveys; The specific implementation method of step S5 includes the following steps: S5.

1. Select several intersections that are used by large, medium, and small vehicles, and film the vehicle queues at the intersection entrances from a perpendicular direction. S5.

2. Select the road marking at the vehicle parking location from the video of the vehicle queue at the intersection entrance, measure the length L of the road marking, and the pixel length l corresponding to the road marking in the video, and calculate the length corresponding to one pixel in the video. S5.

3. Measure the pixel length corresponding to the headway distance when the i-th vehicle of type type stops in the video of the vehicle queue at the intersection entrance. Then calculate the headway between the vehicle and the vehicle in front when the vehicle is stationary S5.

4. Group all headway distance data by vehicle type and take the average of the grouped data as the headway distance d maintained between the vehicle type and the preceding vehicle when the vehicle type is stationary. type , the formula is as follows in, Indicates the headway between the i-th vehicle and the preceding vehicle when the vehicle is stationary. S6. Calculate the average effective vehicle length of different types of vehicles at rest based on the average vehicle length of different types of vehicles obtained in step S4 and the average headway between different types of vehicles at rest obtained in step S5; S7. Based on the processed floating car GPS data obtained in step S3, the free flow speed of different levels of roads is calibrated; S8. Based on the processed geomagnetic detection data obtained in step S3, the traffic capacity of different levels of roads and the corresponding average effective vehicle length are calibrated; S9. Calculate the driver reaction time coefficient for different levels of roads.

2. The refined calibration method of a simplified car-following model based on multi-source data according to claim 1, characterized in that: In step S1, the road network data includes a unique number, length, direction, road grade, number of lanes, and free flow speed; the geomagnetic detection device data includes the geomagnetic device number, the longitude of the installation location, the latitude of the installation location, and the lane number field; the geomagnetic detection data includes the geomagnetic device number, timestamp, vehicle type, speed, and vehicle length; the floating vehicle GPS data includes vehicle ID, timestamp, longitude, latitude, speed, direction, and status information.

3. The refined calibration method of a simplified car-following model based on multi-source data according to claim 1 or 2, characterized in that: The specific implementation method of step S2 includes the following steps: S2.

1. Establish road classifications, including expressways, first-class highways, expressways, interchange ramps, second-class highways, trunk roads, secondary trunk roads, and branch roads. Expressways and expressways are further categorized as expressway-tunnel, expressway-non-tunnel, expressway-tunnel, and expressway-non-tunnel. S2.

2. Road section interruption and merging: The LINK length of a road section with interchange ramps, secondary highways, main roads, secondary roads, and branch roads shall not exceed 350 meters; the LINK length of a road section with expressways, primary highways, and fast roads shall not exceed 550 meters.

4. The refined calibration method of a simplified car-following model based on multi-source data according to claim 3, characterized in that: The specific implementation method of step S4 includes the following steps: S4.

1. Use TransCAD software to match geomagnetic detection equipment data with road network data. S4.

2. Associating geomagnetic detection equipment data with geomagnetic detection data; S4.

3. Group the geomagnetic detection data by road level according to the LINK section. Arrange the data in each group in ascending order of vehicle length, and remove the data with the smallest 5% and largest 5% vehicle lengths. Take the average value of the grouped data of different types of vehicles as the average vehicle length l of vehicle type type , the formula is as follows: in, represents the length of the i-th vehicle in model type, and N represents the total number of vehicles of model type.

5. The refined calibration method of a simplified car-following model based on multi-source data according to claim 4, characterized in that: The calculation formula of step S6 is as follows: L type =d type +l type Among them, L type The average effective vehicle length of vehicle type type when stationary.

6. The refined calibration method of a simplified car-following model based on multi-source data according to claim 5, characterized in that: The calculation formula of step S7 is as follows: S7.1 screening time 1-5 am, the vehicle status is the passenger step S3 obtained after processing the floating car GPS data, based on the location of the associated floating car GPS data points and road sections LINK; S7.

2. Group the floating vehicle GPS data by vehicle ID and then sort them in ascending chronological order; S7.

3. Traverse the vehicle IDs, calculate the distance and time difference between two adjacent data points, and calculate the average speed of the vehicles; S7.

4. Group the speed data by the road class and 5-minute time interval of the corresponding road segment LINK, and calculate the average vehicle speed of the group of road class. The calculation formula is as follows: in, Represents the average speed data of the i-th vehicle in the group of road class class; S7.

5. For each road class, sort the groups in ascending order of average speed and select the data with the speed at the 95th percentile as the free flow speed fspeed. class .

7. The refined calibration method of a simplified car-following model based on multi-source data according to claim 6, characterized in that: The calculation formula of step S8 is as follows: S8.

1. The geomagnetic detection data of the associated road network segment LINK and the geomagnetic detection equipment information obtained in step S4 are grouped according to the road grade of the road segment LINK and the 5-minute interval, and the traffic volume and vehicle type composition of each road grade group are statistically analyzed to calculate the average vehicle speed; S8.

2. For each road class, group the data by traffic volume, sorting them in ascending order. Select data with traffic volume between the 90th and 100th percentiles and an average operating speed greater than 80% of the free flow speed for that road class. S8.

3. Calculate the average flow rate of each road class as the 5-minute vehicle capacity of a single lane of that class of road, q class , and count the corresponding vehicle types, the expression is: in, represents the flow of the kth data in the road class; M represents the amount of data in the road class; S8.

4. Convert the 5-minute vehicle capacity of a single lane of each road grade into the peak hour vehicle capacity of a single lane. class , and count the vehicle type composition, the calculation formula is as follows: cap class =q class ×12 Calculate the average effective vehicle length of different road grades when the road traffic volume is close to the traffic capacity based on the vehicle type composition and the average effective vehicle length of different models The calculation formula is as follows: in, Indicates the average effective vehicle length of the vehicle type on the road of class. is the proportion of vehicles of type type on roads of class.

8. The refined calibration method of a simplified car-following model based on multi-source data according to claim 7, characterized in that: The calculation formula of step S9 is as follows: S9.

1. Set the driver reaction time ResponseTime to 1.25s. S9.

2. Calculate the driver reaction time factor TimeFactor for different road classes based on the capacity, average effective vehicle length, free flow speed, and driver reaction time of different road classes. class , the calculation formula is as follows:

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