Fine calibration method of simplified car-following model based on multi-source data

Abstract

The invention discloses a refined calibration method of a simplified car-following model based on multi-source data, and belongs to the technical field of intelligent traffic. In order to improve the accuracy of a simulation result of a simplified car-following model, the method comprises the following steps: acquiring road network data, geomagnetic detection equipment data, geomagnetic detection data and floating car GPS data; road grades are divided, and the roads are broken and combined; carrying out data processing, and removing missing data or abnormal data; calibrating the average vehicle length of different types of vehicles; calibrating an average headstock distance when different types of vehicles are static through intersection investigation; calculating the average effective vehicle length when different types of vehicles are static; calibrating free flow velocities of different grades of roads; on the basis of the processed geomagnetic detection data, calibrating the traffic capacity of different grades of roads and the corresponding average effective vehicle length; and calculating driver response time coefficients of different grades of roads. According to the invention, the accuracy of the simulation result of the simplified car-following model is improved.

Description

Technical Field

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

[0002] Car Following (CF) behavior is the most basic micro-driving behavior, describing the interaction between two adjacent vehicles in a convoy on a one-way road with restricted overtaking. The Car Following model uses dynamic methods to study the corresponding behavior of the Following Vehicle (FV) caused by the change in the motion state of the Leading Vehicle (LV). By analyzing the way in which each vehicle follows one by one, the characteristics of single-lane traffic flow can be understood, thus building a bridge between the driver's micro-behavior and the macro-phenomenon of traffic.

[0003] The simplified car-following model is mainly used for large-scale lane-level mesoscopic traffic model simulation. The model assumes that the vehicle has a constant acceleration in a short time interval. There are few public parameter calibration methods for the simplified car-following model. The parameters of the simplified car-following model are usually set based on experience or reference to other car-following model parameter calibration methods, resulting in insufficient refinement of parameter calibration, which affects the 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 to 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: A refined calibration method for a simplified car-following model based on multi-source data comprises the following steps: S1. Obtain road network data, geomagnetic detection equipment data, geomagnetic detection data, 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. The geomagnetic detection data obtained in step S1, the floating vehicle GPS The 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; S4. Calibrate the average vehicle length of different types of vehicles based on the processed geomagnetic detection data obtained in step S3; S5. Determine the average headway distance between different types of vehicles at rest through intersection surveys; 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 of different types of vehicles at rest obtained in step S5; S7. The processed floating vehicle obtained in step S3 GPS Data, calibrate the free flow speed of different levels of roads; S8. Based on the processed geomagnetic detection data obtained in step S3, calibrate the capacity of different levels of roads and the corresponding average effective vehicle length; S9. Calculate the driver reaction time coefficient for different levels of roads.

[0006] Further, 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.

[0007] Furthermore, the specific implementation method of step S2 includes the following steps: S2.1. Set road grades, including expressways, first-class highways, expressways, interchange ramps, second-class highways, trunk roads, secondary trunk roads, and branch roads; further divide expressways and expressways into expressways-tunnels, expressways-non-tunnels, expressways-tunnels, and expressways-non-tunnels; S2.2. Section 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.

[0008] Furthermore, the specific implementation method of step S4 includes the following steps: S4.1. Match the geomagnetic detection equipment data with the road network data using TransCAD software; S4.2. Associating geomagnetic detection equipment data with geomagnetic detection data; S4.3. The geomagnetic detection data is divided into sections LINK The roads are grouped into different levels, and the data of each group is arranged in ascending order according to the length of the vehicles, and the data of the smallest 5% and the largest 5% of the vehicles are eliminated; 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: ; Among them, represents the vehicle length of the type th vehicle in the vehicle model i , N represents the total number of vehicles in the vehicle model type .

[0009] Furthermore, the specific implementation method of step S5 includes the following steps: S5.1. Select multiple intersections where large, medium, and small vehicles pass through, and shoot the vehicle queuing video at the intersection approach from the vertical approach direction; S5.2. Select the road markings at the vehicle parking position from the vehicle queuing video at the intersection approach, and measure the length of the road markings L , as well as the pixel length corresponding to the road markings in the video l , and calculate the length corresponding to one pixel in the video ; S5.3. Measure the pixel length type corresponding to the headway between the i th vehicle of the vehicle model when it stops in the vehicle queuing video at the intersection approach, and then calculate the headway that the vehicle maintains with the vehicle in front when it is stationary; S5.4. Group the data based on all headway data according to vehicle types, and take the average value of each group of data as the headway type that the vehicle model maintains with the vehicle in front when it is stationary, and the formula is as follows ; Among them, represents the headway type that the i th vehicle of the vehicle model

[0010] Furthermore, the calculation formula of step S6 is as follows: ; Among them, is the average effective vehicle length of the vehicle model type when it is stationary.

[0011] Furthermore, the calculation formula of step S7 is as follows: S7.1. Screen the processed floating vehicle GPS data obtained in step S3 with the time being from 1 to 5 am and the vehicle status being passenger-carrying, and associate the floating vehicle GPS data points with the road segments based on the location LINK ; S7.2. Group the floating GPS data by vehicle ID and then sort it in ascending order of time sequence; S7.3. Traverse the vehicle IDs, calculate the distance and time difference between two adjacent data, and calculate the average vehicle speed; S7.4. Group the different speed data according to the road grade of the corresponding section LINK class and a 5-minute time interval, and calculate the average vehicle speed of the grouped class by road grade group . The calculation formula is as follows: ; Among them, represents the average speed data of the class th vehicle in the grouping by road grade group ; i S7.5. For each road grade, sort the groups in ascending order of average speed, and select the data with a speed at the 95th percentile as the free flow speed .

[0012] Furthermore, the calculation formula of step S8 is as follows: S8.1. For the geomagnetic detection data related to the road network sections and geomagnetic detection equipment information obtained in step S4, group them according to the road grade of the section where they are located LINK and a 5-minute interval, and count the traffic volume and vehicle type composition of each road grade grouping, and calculate the average vehicle speed; LINK S8.2. For each road grade, sort the road grade groupings in ascending order of traffic volume, and select the data with a traffic volume at the 90%-100% percentile and an average operating speed greater than 80% of the free flow speed of this road grade; S8.3. Calculate the average value of the traffic volume of each road grade as the vehicle passing capacity of a single lane of this grade of road for 5 minutes , and count the corresponding vehicle type composition. The expression is: ; Among them, class represents the traffic volume of the k th data in the road grade M ; class represents the data volume of the road grade class ; S8.4. Convert the vehicle passing capacity of a single lane of each road grade for 5 minutes to the vehicle passing capacity of a single lane for the peak hour , and count the vehicle type composition. The calculation formula is as follows: ; 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 vehicle types , the calculation formula is as follows: ; in, It indicates the average effective vehicle length of vehicles of type type on the road of class. is the proportion of vehicles of type type on roads of class class.

[0013] Furthermore, the calculation formula of step S9 is as follows: S9.1. Setting the driver reaction time ResponseTime 1.25s; S9.2. Calculate the traffic capacity, average effective vehicle length, free flow speed, and driver reaction time of different road classes. class Driver reaction time coefficient , the calculation formula is as follows: .

[0014] Beneficial effects of the present invention: The present invention discloses 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, avoid the problem of overly rough or overly simplified parameter settings in traditional methods, significantly improve the accuracy of parameter calibration and the reliability of traffic flow simulation results, and provide more accurate data support for traffic management and optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of a refined calibration method of a simplified car-following model based on multi-source data according to the present invention; Figure 2 It is a structural block diagram of a refined calibration method of a simplified car-following model based on multi-source data according to the present invention; Figure 3 This is a road grade block diagram of the present invention. DETAILED DESCRIPTION

[0016] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the specific embodiments described are only a part of the embodiments of the present invention, rather than all of the specific embodiments. The components of the specific embodiments of the present invention usually described and shown in the drawings here can be arranged and designed in various different configurations, and the present invention can also have other embodiments.

[0017] Therefore, the following detailed description of the specific embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents the 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 creative efforts fall within the scope of protection of the present invention.

[0018] To further understand the content, features and effects of the present invention, the following specific embodiments are exemplified and combined with the attached Figure 1 - attached Figure 3 The details are as follows:

[0019] Example 1: A refined calibration method for a simplified car-following model based on multi-source data, comprising the following steps: S1. Obtain road network data, geomagnetic detection device data, geomagnetic detection data, and floating car GPS data; Further, 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 a geomagnetic device number, longitude of the installation location, latitude of the installation location, and lane number field; the geomagnetic detection data includes a geomagnetic device number, timestamp, vehicle type, speed, and vehicle length; the floating car GPS data includes vehicle ID , timestamp, longitude, latitude, speed, direction, and status information; S2. Based on the road network data obtained in step S1, divide the road grades and perform operations of breaking and merging the roads; Further, the specific implementation method of step S2 includes the following steps: S2.1. Set the road grades, including expressways, first-class highways, expressways, interchange ramps, second-class highways, arterial roads, sub-arterial roads, and branch roads; further divide the expressways and expressways into expressway-tunnel, expressway-non-tunnel, expressway-tunnel, and expressway-non-tunnel; S2.2. Section 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 metres; S3. The geomagnetic detection data obtained in step S1, the floating vehicle GPS The 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; Furthermore, the specific implementation method of step S3. includes the following steps: S3.1. Eliminate unreasonable geomagnetic detection data that meets the following conditions: missing any field data in the geomagnetic device number, timestamp, vehicle type, speed, and vehicle length; data with a speed greater than 120; if the amount of data eliminated from a geomagnetic device exceeds 5%, the device data will no longer be used for parameter calibration; 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; S4. Calibrate the average vehicle length of different types of vehicles based on the processed geomagnetic detection data obtained in step S3; Furthermore, the specific implementation method of step S4 includes the following steps: S4.1. Match the geomagnetic detection equipment data with the road network data using TransCAD software; S4.2. Associating geomagnetic detection equipment data with geomagnetic detection data; S4.3. The geomagnetic detection data is divided into sections LINK The roads are grouped into different levels, and the data of each group is arranged in ascending order according to the length of the vehicles, and the data of the smallest 5% and the largest 5% of the vehicles are eliminated; 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: ; in, Indicates vehicle model type Middle i The vehicle length of the vehicle, N Indicates vehicle model type Total number of vehicles; S5. Calibrate the average headway when different types of vehicles are stationary through intersection surveys; Further, the specific implementation method of step S5 includes the following steps: S5.1. Select multiple intersections where large vehicles, medium-sized vehicles, and small vehicles pass through, and shoot the vehicle queuing video of the intersection approach from the vertical approach direction; S5.2. Select the road markings at the vehicle parking positions from the vehicle queuing video of the intersection approach, and measure the length of the road markings L , as well as the pixel length corresponding to the road markings in the video l , and calculate the length corresponding to one pixel in the video ; S5.3. Measure the pixel length corresponding to the headway when the type th i vehicle of the vehicle type stops in the vehicle queuing video of the intersection approach, and then calculate the headway maintained by the vehicle when stationary from the preceding vehicle ; ; S5.4. Group the data based on all headway data according to vehicle types, and take the average value of each group of data as the headway maintained by the vehicle type type when stationary from the preceding vehicle , and the formula is as follows ; Among them, represents the headway maintained by the type th i vehicle of the vehicle type when stationary from the preceding vehicle.

[0020] S6. Based on the average vehicle length of different types of vehicles obtained in step S4 and the average headway when different types of vehicles are stationary obtained in step S5, calculate the average effective vehicle length when different types of vehicles are stationary; Further, the calculation formula of step S6 is as follows: ; Among them, is the average effective vehicle length when the vehicle type type is stationary; S7. Based on the processed floating vehicle GPS data obtained in step S3, calibrate the free flow speed of different grades of roads; Further, the calculation formula of step S7 is as follows: S7.1. Screen the processed floating vehicle GPS data obtained in step S3 with the time being from 1 to 5 o'clock in the morning and the vehicle status being carrying passengers, and associate the floating vehicle GPS data points with the road sections based on the location LINK ; S7.2. Group the floating GPS data by vehicle ID and then sort it in ascending order of time sequence; S7.3. Traverse the vehicle IDs, calculate the distance and time difference between two adjacent data, and calculate the average vehicle speed; S7.4. Group the different speed data according to the road grade of the corresponding road section LINK class and a 5-minute time interval, and calculate the average vehicle speed of the grouped class by road grade group . The calculation formula is as follows: ; where represents the average speed data of the class th vehicle in the grouping by road grade group ; i S7.5. For each road grade, sort the groups in ascending order of average speed, and select the data with a speed at the 95th percentile as the free flow speed .

[0021] S8. Based on the processed geomagnetic detection data obtained in step S3, calibrate the traffic capacity and corresponding average effective vehicle length of different grade roads; Furthermore, the calculation formula of step S8 is as follows: S8.1. For the geomagnetic detection data associated with the road network sections LINK and the geomagnetic detection device information obtained in step S4, group them according to the road grade of the section where they are located LINK and a 5-minute interval, and count the traffic volume and vehicle type composition of each road grade grouping, and calculate the average vehicle speed; S8.2. For each road grade, sort the road grade groupings in ascending order of traffic volume, and select the data with a traffic volume at the 90%-100% percentile and an average running speed greater than 80% of the free flow speed of this road grade; S8.3. Calculate the average value of the traffic volume of each road grade as the vehicle traffic capacity of a single lane of this grade road for 5 minutes , and count the corresponding vehicle type composition. The expression is: ; where represents the traffic volume of the class th data in the road grade k ; M represents the data volume of the road grade class ; ​​S8.4. Convert the vehicle passing capacity of each road grade in a single lane within 5 minutes into the vehicle passing capacity of a single lane during the peak hour , and count the vehicle type composition. The calculation formula is as follows: ;

[0022] Calculate the average effective vehicle length of different road grades when the road traffic volume is close to the passing capacity according to the vehicle type composition and the average effective vehicle length of different vehicle types , and the calculation formula is as follows: ; Among them, represents the average effective vehicle length calibrated by the vehicle data of type type on the road of class class, is the proportion of vehicles of type type on the road of class class.

[0023] S9. Calculate the driver reaction time coefficient of different grades of roads.

[0024] Furthermore, the calculation formula of step S9 is as follows: S9.1. Set the driver reaction time ResponseTime to be 1.25 s; S9.2. Calculate the driver reaction time coefficient of different road grades class based on the passing capacity, average effective vehicle length, free flow speed, and driver reaction time of different road grades , and the calculation formula is as follows: .

[0025] It should be noted that relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0026] Although the present application has been described above with reference to specific embodiments, various modifications can be made thereto and components therein can be replaced with equivalents without departing from the scope of the present application. In particular, as long as there is no structural conflict, the features in the specific embodiments disclosed in the present application can be combined with each other in any way, and the exhaustive description of these combinations is not given in this specification only for the sake of saving space and resources. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling 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, It includes the following steps: S1. Obtain road network data, geomagnetic detection device data, geomagnetic detection data, and floating vehicle GPS data; S2. Based on the road network data obtained in step S1, divide the road grades and perform operations of breaking and merging on the roads; S3. Process the geomagnetic detection data and floating vehicle data obtained in step S1, remove missing data or abnormal data, and obtain the processed geomagnetic detection data and processed floating vehicle data; GPS Process the geomagnetic detection data and floating vehicle data obtained in step S1, remove missing data or abnormal data, and obtain the processed geomagnetic detection data and processed floating vehicle data; GPS data; S4. Calibrate the average vehicle lengths of different types of vehicles based on the processed geomagnetic detection data obtained in step S3; S5. Calibrate the average headway when different types of vehicles are stationary through intersection surveys; S6. Calculate the average effective vehicle lengths when different types of vehicles are stationary based on the average vehicle lengths of different types of vehicles obtained in step S4 and the average headway when different types of vehicles are stationary obtained in step S5; S7. Based on the processed floating car GPS data obtained in step S3, calibrate the free flow speeds of different levels of roads; S8. Based on the processed geomagnetic detection data obtained in step S3, calibrate the traffic capacities of different grades of roads and the corresponding average effective vehicle lengths; S9. Calculate the driver response time coefficients of different grades 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 a geomagnetic device number, longitude of the installation location, latitude of the installation location, and lane number field; the geomagnetic detection data includes a geomagnetic device number, timestamp, vehicle type, speed, and vehicle length; the floating car GPS data includes the 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. Set road grades, including expressways, first-class highways, expressways, interchange ramps, second-class highways, arterial roads, sub-arterial roads, and branch roads; further divide expressways and expressways into expressway - tunnels, expressway - non-tunnels, expressway - tunnels, and expressway - non-tunnels; S2.

2. Road Section Interruption and Merging: Set the road sections of interchange ramps, secondary highways, arterial roads, sub-arterial roads, and branch roads LINK with a length not exceeding 350 meters, and set the road sections of expressways, first-class highways, and expressways LINK not exceeding 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. Match the geomagnetic detection equipment data with the road network data through TransCAD software; S4.

2. Associate the geomagnetic detection equipment data with the geomagnetic detection data; S4.

3. Group the geomagnetic detection data according to the road grades of the road sections where they are located LINK Group the data of each group in ascending order of vehicle length, and exclude the data of the smallest 5% and the largest 5% of the vehicle lengths; Take the average value of the grouped data of different types of vehicles as the average vehicle length of the vehicle model type , and the formula is as follows: , as follows: ； Among them, represents the vehicle length of the type th vehicle in the i vehicle type, and N represents the total number of vehicles in the type vehicle 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 specific implementation method of step S5 includes the following steps: S5.

1. Select multiple intersections where large vehicles, medium-sized vehicles, and small vehicles pass through, and take videos of the vehicle queues at the intersection approach roads from the vertical approach road direction; S5.

2. Select the road markings at the vehicle parking position from the vehicle queue video at the approach of the intersection, and measure the length of the road markings L , as well as the pixel length corresponding to the road markings in the video l , and calculate the length corresponding to one pixel in the video ; S5.

3. Measuring the vehicle types in the vehicle queue video at the approach of the intersection type of the i pixel length corresponding to the headway between the front ends of the vehicles when the th vehicle stops, and then calculating the headway maintained with the preceding vehicle when the vehicle is stationary ; S5.

4. Group the data of all headway distances by vehicle type, and take the average value of each group of data as the vehicle model type The headway distance maintained with the vehicle in front when stationary , and the formula is as follows ； Among them, represents the vehicle model type in the i headway distance maintained between the front of the vehicle and the vehicle in front when the 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 S6 is as follows: ； Among them, is the vehicle model type average effective vehicle length at rest.

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 S7 is as follows: S7.

1. Screen the processed floating cars obtained in step S3 with the screening time being from 1 to 5 o'clock in the early morning and the vehicle status being carrying passengers GPS data, and associate the floating car GPS data points with road segments based on the location LINK ; S7.

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

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

4. Group the different speed data according to the road grade of the corresponding road section LINK class , and group them at 5-minute intervals to calculate the average vehicle speed of the groups class of the road grade group . The calculation formula is as follows: ​ ； Among them, represents the grouping of class road grades group and the average speed data of the i vehicle in the S7.

5. For each road class, sort each group in ascending order of average speed, and filter out the data with speeds at the 95th percentile as the free-flow speed .

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 S8 is as follows: S8.

1. For the associated road network sections obtained in step S4 LINK and the geomagnetic detection data of the geomagnetic detection devices, group them according to the road grade of the section where they are located LINK and the 5-minute interval, and count the traffic volume and vehicle type composition of each road grade group, and calculate the average vehicle speed; S8.

2. For each road grade, sort the grouped data of each road grade in ascending order according to traffic flow, and filter out the data with traffic flow in the 90%-100% percentile and an average running speed greater than 80% of the free flow speed of this road grade; S8.

3. Calculate the average value of the traffic flow of each road class as the vehicle passing capacity of a single lane of this class of road in 5 minutes , and count the corresponding vehicle type composition. The expression is as follows: ； Among them, represents the road grade class in the k traffic volume of the th data; M represents the road grade class data volume; S8.

4. Convert the vehicle passing capacity of each road grade in a single lane for 5 minutes into the vehicle passing capacity of a single lane during the peak hour , and count the vehicle type composition. The calculation formula is as follows: ； Calculate the average effective vehicle length of different road grades when the road traffic volume approaches the capacity according to the vehicle type composition and the average effective vehicle length of different vehicle models. , and the calculation formula is as follows: ； Among them, represents the average effective vehicle length calibrated for vehicle data of type type on roads of class class, is the proportion of vehicles of type type on roads of class class.

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

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

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

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