Non-signalized intersection model calibration method based on aerial photography data of unmanned aerial vehicle

Abstract

The invention discloses a non-signalized intersection model calibration method based on unmanned aerial vehicle aerial photography data, and belongs to the technical field of intelligent traffic. In order to improve a simulation result of a two-parameter gap acceptance model, the method comprises the following steps: acquiring road network data, and acquiring vehicle trajectory data and an intersection aerial photo through an unmanned aerial vehicle; processing the obtained road network data, merging road network unidirectional lines, and then classifying intersections with to-be-calibrated parameters; processing the obtained vehicle trajectory data, and removing missing or abnormal data to obtain processed vehicle trajectory data; associating the obtained processed vehicle track data with the corresponding intersection turning direction; calibrating expected speeds of different types of intersections and different turning directions; and calibrating critical gaps and critical waiting time of different types of intersections. According to the method, the simulation result of the two-parameter gap acceptance model is optimized.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent transportation technology, and in particular relates to a method for calibrating a signalless intersection model based on drone aerial photography data. Background Art

[0002] There is a common problem in many meso-micro simulation software. No matter how long the vehicles wait at the intersection in the traffic flow simulation, the minimum acceptable gap is a fixed value, which does not match the actual situation. Generally speaking, motor vehicle drivers will become impatient when the waiting time increases, and are willing to accept a smaller gap in order to pass the intersection faster.

[0003] For example, a two-parameter gap acceptance model can simulate the effect of driver impatience when they are unable to enter conflicting traffic flows. At the intersection conflict point, the decision of whether a low-priority vehicle will take precedence over a high-priority vehicle is based on two variables: the available gap and and relative waiting time , these two variables and the critical gap G , critical waiting time W Parameters are used together to calculate the probability that a vehicle with a lower priority will precede a vehicle with a higher priority (i.e., the priority probability P ). The model form is as follows: ; Currently, there are few parameter calibration methods for this type of dual-parameter gap acceptance model. Usually, the parameters are set based on experience or default parameters are used, which results in insufficient refinement of parameter calibration and affects the simulation results. Summary of the invention

[0004] The problem to be solved by the present invention is to improve the simulation results of a dual-parameter gap acceptance model and propose a method for calibrating a signal-free intersection model based on drone aerial photography data.

[0005] To achieve the above object, the present invention is implemented through the following technical solutions: A method for calibrating a model of an unsignalized intersection based on drone aerial photography data comprises the following steps: S1. Obtain road network data and collect vehicle trajectory data and aerial photos of intersections through drones; S2. Process the road network data obtained in step S1, merge the one-way lines of the road network, and then classify the intersections to be calibrated parameters; S3. The vehicle trajectory data obtained in step S1 is processed to remove missing or abnormal data to obtain processed vehicle trajectory data; S4. Associating the processed vehicle trajectory data obtained in step S3 with the corresponding intersection turn; S5. Calibrate the expected speeds for different types of intersections and different turning directions; S6. Calibrate the critical gaps and critical waiting times for different types of intersections.

[0006] Furthermore, the road network data in step S1 includes attributes such as the unique section number, length, direction, road grade, and number of lanes; the vehicle trajectory data is extracted from the aerial video taken by the drone, with a time accuracy of 0.1 s, including the vehicle unique number, timestamp, lane number, longitude, latitude, and speed; the aerial view of the intersection includes the intersection area and the information of signs and markings.

[0007] Furthermore, the specific implementation method of step S2 includes the following steps: S2.1. Merge one-way lines of the road network: Merge two single lines representing two directions of the same road into a single line representing two directions of the road; S2.2. Classify the intersections with parameters to be calibrated, and classify the un-signalized intersections to be calibrated into cross intersections, T-shaped intersections, roundabouts, and confluence points.

[0008] Furthermore, the specific implementation method of step S3 includes the following steps: S3.1. Eliminate the data missing any one of the fields of vehicle unique number, timestamp, lane number, longitude, latitude, and speed; S3.2. Eliminate the data with a speed greater than 120; S3.3. If the proportion of the eliminated data volume of a certain vehicle trajectory data exceeds 5%, then delete the vehicle trajectory data.

[0009] Furthermore, the specific implementation method of step S4 includes the following steps: S4.1. For the processed vehicle trajectory data obtained in step S3, filter out the vehicle trajectory data within 50 meters of the center point of each intersection based on the position; S4.2. Group the vehicle trajectory data obtained in step S4.1 based on the vehicle ID, and sort the vehicle trajectory data in chronological order; Based on the fact that the same vehicle may pass through an intersection multiple times and the turning directions when passing through the intersection at different times are different, set a grouping flag between adjacent trajectory points with a time difference greater than 600 s, and further group the vehicle trajectory data, and set the grouping number PERIOD_ID respectively; S4.3. Traverse each group of vehicle trajectory data based on vehicle ID and PERIOD_ID, select the first and last trajectory points of the group, associate the road segment number LINK_ID corresponding to the trajectory points through location, the vehicle number corresponding to the first trajectory point, and the incoming road segment FROM_LINK of the vehicle passing through the intersection. The last trajectory point corresponds to the outgoing road segment TO_LINK, so as to determine the turning number MOVEMENT_ID of the vehicle at the intersection, and establish a one-to-one correspondence between the vehicle trajectory and the intersection turning.

[0010] Furthermore, the specific implementation method of step S5 includes the following steps: S5.1. On the basis of step S4, screen the vehicle trajectory data with time from 10:00 to 16:00 for calibrating the turning speed; S5.2. Group the vehicle trajectory data according to the intersection turning class and a 5-minute time interval, and calculate the average vehicle speed of each group group , and the calculation formula is as follows: ; ; where, represents the class th average vehicle speed data in the group group of the intersection turning i , N represents the data volume of the group class of the intersection turning group ; S5.3. Sort the average vehicle speeds of each group in ascending order of speed, and take the data with the speed at the 50% quantile as the expected speed of the intersection turning.

[0011] Furthermore, the specific implementation method of step S6 includes the following steps: S6.1. Set the turning type of the parameter to be calibrated and the corresponding high-priority turning according to the intersection type: Set the turning type of the parameter to be calibrated at the cross intersection as left turn on the approach road. Straight ahead on the oncoming approach road is a high-priority turning relative to the left turn on the approach road, and the left turn on the approach road is a low-priority turning relative to the straight ahead on the oncoming approach road; Set the turning type of the parameter to be calibrated at the roundabout as the roundabout entrance. Straight ahead in the outermost lane of the roundabout road is a high-priority turning relative to the roundabout entrance, and the roundabout entrance is a low-priority turning relative to the straight ahead in the outermost lane of the roundabout road; Set the turning type of the parameter to be calibrated at the confluence as the ramp entrance. Straight ahead on the main line is a high-priority turning relative to the ramp entrance, and the ramp entrance is a low-priority turning relative to the straight ahead on the main line; S6.2. Calibrate the critical gap and critical waiting time parameters for low-priority turns in S6.1: S6.2.1. Traverse the intersections with parameters to be calibrated and obtain the vehicle trajectory point data within 50 meters of the center points of the intersections with parameters to be calibrated; S6.2.2. Identify the parking positions of vehicles turning in different directions: Divide the import road section into m sections at 5-meter intervals, assign the vehicle trajectory point data obtained in step S6.2.1 to the corresponding sections according to the coordinates, and count the number of trajectory points with a speed < 5 km / h in each section. The section with the largest number of points is the straight or left-turn parking position section ; S6.2.3. Sort the vehicle trajectory points: Filter the trajectory points of low-priority turning vehicles and sort the vehicles based on the time of the first trajectory point of the vehicle; filter the trajectory points of high-priority turning vehicles and sort the vehicles based on the time of the first trajectory point of the vehicle; let the set of available gap data of vehicles passing through the intersection be denoted as , and each data in the set is in the form of . For high-priority turning vehicles and low-priority turning vehicles in conflict at the intersection, let be the time difference between the time when the high-priority turning vehicle arrives at the parking position and the time when the low-priority turning vehicle arrives at the parking position. If the low-priority vehicle passes through the intersection first, let , and add the data to . If the high-priority vehicle passes through the intersection first, let , and add the data to ; The set of relative waiting time data of vehicles passing through the intersection is denoted as , and each data in the set is in the form of . Let be the difference between the waiting duration of the low-priority vehicle at the parking position and the waiting duration of the high-priority vehicle at the parking position. If the low-priority vehicle passes through the intersection, let , and add the data to . If the high-priority vehicle passes through the intersection first, let , and add the data to ; S6.2.4. Identify the moments when vehicles arrive at and leave the parking positions: Traverse the trajectory points of the low-priority vehicles , find the points entering the straight or left-turn parking position section and the points for leaving the straight or left-turn parking position section of , and are the moments for entering and leaving the straight or left-turn parking position section respectively; traverse the trajectory points of high-priority vehicles to find the point for entering the straight or left-turn parking position section and the point for leaving the straight or left-turn parking position section of , and are the moments for entering and leaving the straight or left-turn parking position section respectively; S6.2.5. Identify the precedence relationship of vehicles passing through the intersection: S6.2.5.1. Traverse low-priority vehicles ; S6.2.5.2. Traverse the trajectory points of low-priority vehicles , the time corresponding to this point is denoted as t , and the horizontal and vertical coordinates are denoted as , respectively. Let the next point be , where = t +0.5, and the horizontal and vertical coordinates are denoted as , respectively; S6.2.5.3. Traverse high-priority vehicles ; S6.2.5.4. Traverse the trajectory points of high-priority vehicles , the time corresponding to this point is denoted as u , and the horizontal and vertical coordinates are denoted as , respectively. Let the next point be , where = u +0.5, and the horizontal and vertical coordinates are denoted as , respectively; S6.2.5.5. Identify whether the line segments and conflict in time and space: If the following formula holds, then the line segments and conflict in time and space, enter S6.2.5.6, otherwise return to S6.2.5.4 and continue to traverse the next high-priority vehicle trajectory point. The formula is: ; ; ; S6.2.5.6. Identify the passing order of conflicting vehicles through the intersection: If , it indicates that vehicle passes through the intersection before vehicle . Add the available gap data and the relative waiting time data to and respectively, enter S6.2.5.1, and continue to traverse the next low-priority vehicle; If , it indicates that vehicle passes through the intersection before vehicle . Add the available gap data and the relative waiting time data to and respectively, enter S6.2.5.3, and continue to traverse the next high-priority vehicle; S6.2.6. Filter the set of available gap data of vehicles passing through the intersection 's data, denoted as , and statistically count the frequency of , at 0.5s time intervals respectively, where is the total number of data in that satisfy , is the total number of data in that satisfy ; Calculate the frequency of , and the expression is: ; Obtain the minimum value and of that satisfy ; Obtain the maximum value and of that satisfy ; According to the two-parameter gap acceptance model, let , , where G is the critical gap; Calibrate the final critical gap G through the following formula, and the expression is: ; S6.2.7. Screening the relative waiting time data set in the data, denoted as , group and statistically count the frequency at 0.5s time intervals respectively , where is the total number of data in that meet the condition, is the total number of data in that meet the condition; calculate the frequency , and the expression is: ; Obtain the minimum value and of that meet the conditions; ; Obtain the maximum value and of that meet the conditions; ; According to the two-parameter gap acceptance model, let , , where W is the critical waiting time; Calculate the critical waiting time W through the following formula, and the expression is: .

[0012] Advantages of the present invention: The method for calibrating the signal-free intersection model based on UAV aerial photography data of the present invention can quickly and comprehensively obtain high-precision vehicle trajectory data of the intersection by using UAV aerial photography data, thereby calibrating the parameters of the two-parameter gap acceptance model. Compared with the traditional empirical setting and manual measurement methods, it greatly reduces the manual measurement error and workload, and greatly improves the work efficiency and the accuracy of the calibration results. In addition, the results of the present invention are applied to the traffic simulation of signal-free intersections, which can better reflect the actual traffic conditions of signal-free intersections, help to optimize traffic planning and design, and have good economic benefits. Description of the drawings

[0013] Figure 1 is the flowchart of the method for calibrating the signal-free intersection model based on UAV aerial photography data of the present invention; Figure 2 is the structural block diagram of the method for calibrating the signal-free intersection model based on UAV aerial photography data of the present invention; Figure 3 This is the classification diagram of intersections for the parameters to be calibrated in the present invention. Specific implementation manners

[0014] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners. It should be understood that the specific implementation manners described herein are only used to explain the present invention and are not used to limit the present invention, that is, the specific implementation manners described are only a part of the implementation manners of the present invention, rather than all the specific implementation manners. Generally, the components of the specific implementation manners of the present invention described and shown in the accompanying drawings herein can be arranged and designed in various different configurations, and the present invention can also have other implementation manners.

[0015] Therefore, the detailed description of the specific implementation manners of the present invention provided in the accompanying drawings below is not intended to limit the scope of the claimed invention, but only represents the selected specific implementation manners of the present invention. Based on the specific implementation manners of the present invention, all other specific implementation manners obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0016] To further understand the content, features and effects of the present invention, the following specific implementation manners are exemplified and are described in detail in conjunction with the attached Figure 1 - Attached Figure 3 as follows:

[0017] Embodiment 1: A method for calibrating a signal-free intersection model based on UAV aerial photography data, comprising the following steps: S1. Obtain road network data, and collect vehicle trajectory data and intersection aerial photography maps through a UAV; Furthermore, the road network data in step S1 includes attributes such as the unique number, length, direction, road grade, and number of lanes of the road section; the vehicle trajectory data is extracted from the UAV aerial photography video with a time accuracy of 0.1 s, and includes the unique number of the vehicle, time stamp, lane number, longitude, latitude, and speed; the intersection aerial photography map includes the intersection area and sign and marking information.

[0018] S2. Process the road network data obtained in step S1, merge the one-way lines of the road network, and then classify the intersections for the parameters to be calibrated; Furthermore, the specific implementation method of step S2 includes the following steps: S2.1. Merge the one-way lines of the road network: Merge two single lines representing two directions of the same road into a single line representing the two directions of the road; S2.2. Classification of parameter intersections to be calibrated: divide the unsignalized intersections to be calibrated into cross intersections, T-intersections, roundabouts, and merges.

[0019] S3. The vehicle trajectory data obtained in step S1 is processed to remove missing or abnormal data to obtain processed vehicle trajectory data; Furthermore, the specific implementation method of step S3 includes the following steps: S3.1. Eliminate data that is missing any of the vehicle unique number, timestamp, lane number, longitude, latitude, and speed fields; S3.2. Eliminate data with a speed greater than 120; S3.3. If the proportion of the excluded data of a vehicle trajectory data exceeds 5%, the vehicle trajectory data shall be deleted.

[0020] S4. Associating the processed vehicle trajectory data obtained in step S3 with the corresponding intersection turn; Furthermore, the specific implementation method of step S4 includes the following steps: S4.1. The processed vehicle trajectory data obtained in step S3 is filtered out based on the location of the vehicle trajectory data within 50 meters of the center point of each intersection; S4.2. The vehicle trajectory data obtained in step S4.1 is grouped based on the vehicle ID, and the vehicle trajectory data is sorted in chronological order; Since the same vehicle may pass through an intersection multiple times and the turns taken at different times are different, a grouping flag is set between adjacent trajectory points with a time difference greater than 600s, and the vehicle trajectory data is further grouped, and the group numbers PERIOD_ID are set respectively; S4.3. Traverse each group of vehicle trajectory data based on vehicle ID and PERIOD_ID, select the first and last trajectory points of the group, and associate the trajectory points with the link number LINK_ID through the position. The first trajectory point corresponds to the vehicle number and the entrance link FROM_LINK of the intersection through which the vehicle passes, and the last trajectory point corresponds to the exit link TO_LINK, thereby determining the turning number MOVEMENT_ID of the vehicle at the intersection and establishing a one-to-one correspondence between the vehicle trajectory and the intersection turn.

[0021] S5. Calibrate the expected speeds for different types of intersections and different turns; Furthermore, the specific implementation method of step S5 includes the following steps: S5.1. Based on step S4, the vehicle trajectory data of 10-16 points is selected for calibrating the steering speed; S5.2. Turn vehicle trajectory data according to intersectionclass , group by 5 - minute time intervals and calculate the average vehicle speed for each group . The calculation formula is as follows: , and the formula is as follows: ; Wherein, represents the class average vehicle speed data of the th vehicle in the group corresponding to the intersection turning i , and N represents the data volume of the group corresponding to the intersection turning class ; S5.3. Sort the average vehicle speeds of each group in ascending order of speed, and take the data at the 50% quantile of the speed as the expected speed of intersection turning.

[0022] S6. Calibrate the critical gap and critical waiting time of different types of intersections; Furthermore, the specific implementation method of step S6 includes the following steps: S6.1. Set the turning type of the parameter to be calibrated and the corresponding high - priority turning according to the intersection type: Set the turning type of the parameter to be calibrated at a cross - intersection as left - turn at the approach lane. Straight - ahead at the oncoming approach lane is a high - priority turning relative to the left - turn at the approach lane, and left - turn at the approach lane is a low - priority turning relative to straight - ahead at the oncoming approach lane; Set the turning type of the parameter to be calibrated at a roundabout as entry to the roundabout. Straight - ahead in the outermost lane of the roundabout road is a high - priority turning relative to entry to the roundabout, and entry to the roundabout is a low - priority turning relative to straight - ahead in the outermost lane of the roundabout road; Set the turning type of the parameter to be calibrated at a merge intersection as ramp entry. Straight - ahead on the main line is a high - priority turning relative to ramp entry, and ramp entry is a low - priority turning relative to straight - ahead on the main line; S6.2. Calibrate the critical gap and critical waiting time parameters of each low - priority turning in step S6.1: S6.2.1. Traverse the intersections with parameters to be calibrated and obtain the vehicle trajectory point data within 50 meters of the center point of the intersections with parameters to be calibrated; S6.2.2. Identify the parking positions of vehicles turning in different directions: Divide the approach road section into m sections at 5 - meter intervals, distribute the vehicle trajectory point data obtained in step S6.2.1 to the corresponding sections according to the coordinates, and count the number of trajectory points with a speed < 5 km / h in each section. The section with the largest number of points is the straight - ahead or left - turn parking position section ; S6.2.3. Sort the vehicle trajectory points: Screen the trajectory points of low-priority turning vehicles and sort the vehicles based on the time of the first trajectory point of the vehicle; screen the trajectory points of high-priority turning vehicles and sort the vehicles based on the time of the first trajectory point of the vehicle; let the set of available gap data of the vehicles passing through the intersection be denoted as , and each data in the set is in the form of . For the high-priority turning vehicles and low-priority turning vehicles with conflicts at the intersection, let be the time difference between the time when the high-priority turning vehicle arrives at the stop position and the time when the low-priority turning vehicle arrives at the stop position. If the low-priority vehicle passes through the intersection first, let , and add the data to . If the high-priority vehicle passes through the intersection first, let , and add the data to ; let the set of relative waiting time data of the vehicles passing through the intersection be denoted as , and each data in the set is in the form of . Let be the time difference between the waiting duration of the low-priority vehicle at the stop position and the waiting duration of the high-priority vehicle at the stop position. If the low-priority vehicle passes through the intersection, let , and add the data to . If the high-priority vehicle passes through the intersection first, let , and add the data to ; S6.2.4. Identify the moments when the vehicle arrives at and leaves the stop position: Traverse the trajectory points of the low-priority vehicle , find the point that enters the straight or left-turn stop position segment and the point that leaves the straight or left-turn stop position segment . and are the moments when entering and leaving the straight or left-turn stop position segment respectively; traverse the trajectory points of the high-priority vehicle , find the point that enters the straight or left-turn stop position segment and the point that leaves the straight or left-turn stop position segment . and are the moments when entering and leaving the straight or left-turn stop position segment respectively; S6.2.5. Identify the passing sequence of vehicles at intersections: S6.2.5.1. Traverse low-priority vehicles ; S6.2.5.2. Traverse the trajectory points of low-priority vehicles , and record the corresponding time of this point as t , and record the horizontal and vertical coordinates as 、 , and let the next point be , where = t + 0.5, and record the horizontal and vertical coordinates as 、 ; S6.2.5.3. Traverse high-priority vehicles ; S6.2.5.4. Traverse the trajectory points of high-priority vehicles , and record the corresponding time of this point as u , and record the horizontal and vertical coordinates as 、 , and let the next point be , where = u + 0.5, and record the horizontal and vertical coordinates as 、 ; S6.2.5.5. Identify whether the line segments and conflict in time and space: If the following formula holds, then the line segments and conflict in time and space, enter S6.2.5.6, otherwise return to S6.2.5.4 and continue to traverse the next trajectory point of the high-priority vehicle. The formula is: ; ; ; S6.2.5.6. Identify the passing sequence of conflicting vehicles at intersections: If , it indicates that vehicle passes through the intersection before vehicle . Add the available gap data and the relative waiting time data to and respectively, and enter S6.2.5.1 to continue traversing the next low-priority vehicle; If , it indicates that vehicle passes through the intersection before vehicle Previously through the intersection, the available gap data and the relative waiting time data are respectively added to and Then enter S6.2.5.3 to continue traversing the next high-priority vehicle; S6.2.6. Filter the set of available gap data of vehicles passing through the intersection in The data is denoted as and grouped and statistically counted at 0.5s time intervals respectively The frequency of , where is in the data that satisfies The total number of data, is in the data that satisfies The total number of data; calculate The frequency of , and the expression is: ; Obtain the minimum value of that satisfies and ; Obtain the maximum value of that satisfies and ; According to the two-parameter gap acceptance model, let , , where G is the critical gap; Calibrate the final critical gap G through the following formula, and the expression is: ; S6.2.7. Filter the set of relative waiting time data in The data is denoted as and grouped and statistically counted at 0.5s time intervals respectively The frequency of , , where is in the data that satisfies The total number of data, is in the data that satisfies The total number of data; calculate The frequency of , and the expression is:​​ ; Obtain the minimum value of and that ; ; Obtain the maximum value of and that ; ; According to the two-parameter gap acceptance model, let , , where W is the critical waiting time; Calculate the critical waiting time through the following formula W , and the expression is: .

[0023] 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 actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also 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 existence of additional identical elements in the process, method, article or device comprising the said element.

[0024] Although the present application has been described above with reference to specific embodiments, various improvements can be made thereto and components thereof 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 various features in the specific embodiments disclosed in the present application can be combined with each other in any way, and the fact that the combinations of these are not exhaustively described in this specification is 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 calibration method for a signal - free intersection model based on UAV aerial photography data, characterized in that The steps include: S1. Obtain road network data and collect vehicle trajectory data and aerial photos of intersections through drones; S2. Process the road network data obtained in step S1, merge the one-way lines of the road network, and then classify the intersections to be calibrated parameters; S3. The vehicle trajectory data obtained in step S1 is processed to remove missing or abnormal data to obtain processed vehicle trajectory data; S4. Associating the processed vehicle trajectory data obtained in step S3 with the corresponding intersection turn; S5. Calibrate the expected speeds for different types of intersections and different turns; S6. Determine the critical gap and critical waiting time for different types of intersections.

2. The calibration method of the signal-free intersection model based on UAV aerial photography data according to claim 1, wherein The road network data in step S1 includes the unique number of the road section, length, direction, road grade, and number of lanes; the vehicle trajectory data is extracted through drone aerial video with a time accuracy of 0.1s, including the unique vehicle number, timestamp, lane number, longitude, latitude, and speed; the aerial view of the intersection includes the intersection area and sign and marking information.

3. A method for calibrating a signal-free intersection model based on UAV aerial photography data according to claim 1 or 2, characterized in that The specific implementation method of step S2 includes the following steps: S2.

1. Merge one-way lines in road network: Merge two single lines representing two directions of the same road into a single line representing the two directions of the road; S2.

2. Classification of parameter intersections to be calibrated: divide the unsignalized intersections to be calibrated into cross intersections, T-intersections, roundabouts, and merges.

4. A method for calibrating a signal - free intersection model based on UAV aerial photography data according to claim 3, characterized in that, The specific implementation method of step S3 includes the following steps: S3.

1. Eliminate data that is missing any of the vehicle unique number, timestamp, lane number, longitude, latitude, and speed fields; S3.

2. Eliminate data with a speed greater than 120; S3.

3. If the proportion of the excluded data of a vehicle trajectory data exceeds 5%, the vehicle trajectory data shall be deleted.

5. A method for calibrating a signal-free intersection model based on UAV aerial photography data according to claim 4, characterized in that The specific implementation method of step S4 includes the following steps: S4.

1. The processed vehicle trajectory data obtained in step S3 is filtered out based on the location of the vehicle trajectory data within 50 meters of the center point of each intersection; S4.

2. The vehicle trajectory data obtained in step S4.1 is grouped based on the vehicle ID, and the vehicle trajectory data is sorted in chronological order; Since the same vehicle may pass through an intersection multiple times and the turns taken at different times are different, a grouping flag is set between adjacent trajectory points with a time difference greater than 600s, and the vehicle trajectory data is further grouped, and the group numbers PERIOD_ID are set respectively; S4.

3. Traverse each group of vehicle trajectory data based on vehicle ID and PERIOD_ID, select the first and last trajectory points of the group, and associate the trajectory points with the link number LINK_ID through the position. The first trajectory point corresponds to the vehicle number and the entrance link FROM_LINK of the intersection through which the vehicle passes, and the last trajectory point corresponds to the exit link TO_LINK, thereby determining the turning number MOVEMENT_ID of the vehicle at the intersection and establishing a one-to-one correspondence between the vehicle trajectory and the intersection turn.

6. The calibration method of the signal-free intersection model based on UAV aerial photography data according to claim 5, characterized in that The specific implementation method of step S5 includes the following steps: S5.

1. Based on step S4, filter the vehicle trajectory data from 10:00 to 16:00 for calibrating the steering speed; S5.

2. Group the vehicle trajectory data according to intersection turns class and 5-minute time intervals, and calculate the average vehicle speed of each group group . The calculation formula is as follows: ​ ； Among them, represents the grouping of intersection turns class and group the i th average vehicle speed data in the group, N while class represents the data volume of the grouping of intersection turns group . S5.

3. Sort the average speeds of each group of vehicles in ascending order of speed, and take the data at the 50% quantile of the speed as the expected intersection steering speed.

7. A method for calibrating a signal-free intersection model based on UAV aerial photography data according to claim 6, characterized in that, The specific implementation method of step S6 includes the following steps: S6.

1. Set the steering type of the parameter to be calibrated and the corresponding high-priority steering according to the intersection type: Set the steering type of the parameter to be calibrated at the cross intersection as a left turn on the approach road. The straight-ahead movement on the oncoming approach road is a high-priority steering relative to the left turn on the approach road, and the left turn on the approach road is a low-priority steering relative to the straight-ahead movement on the oncoming approach road; Set the steering type of the parameter to be calibrated at the roundabout as a roundabout entrance. The straight-ahead movement in the outermost lane of the roundabout road is a high-priority steering relative to the roundabout entrance, and the roundabout entrance is a low-priority steering relative to the straight-ahead movement in the outermost lane of the roundabout road; Set the steering type of the parameter to be calibrated at the merge intersection as a ramp entrance. The straight-ahead movement on the main line is a high-priority steering relative to the ramp entrance, and the ramp entrance is a low-priority steering relative to the straight-ahead movement on the main line; S6.

2. Calibrate the critical gap and critical waiting time parameters of the low-priority steering in step S6.1: S6.2.

1. Traverse the intersections of the parameters to be calibrated and obtain the data of each vehicle trajectory point within 50 meters of the center point of the intersections of the parameters to be calibrated; S6.2.

2. Identify the parking positions of vehicles with different steering directions: Divide the imported road section into m sections at intervals of 5 meters. According to the coordinates, distribute the vehicle trajectory point data obtained in step S6.2.1 to the corresponding sections, and count the number of trajectory points with a speed < 5 km / h in each section. The section with the largest number of points is the straight or left-turn parking position section ; S6.2.

3. Sort the vehicle trajectory points: Filter the vehicle trajectory points of the low-priority steering and sort the vehicles based on the time of the first trajectory point of the vehicle; filter the vehicle trajectory points of the high-priority steering and sort the vehicles based on the time of the first trajectory point of the vehicle; Let the set of available gap data of vehicles passing through the intersection be denoted as , and each data in the set is in the form of . For the high-priority turning vehicle and the low-priority turning vehicle with conflicts at the intersection, let be the difference between the time when the high-priority turning vehicle arrives at the stop position and the time when the low-priority turning vehicle arrives at the stop position. If the low-priority vehicle passes through the intersection first, let , and add the data to . If the high-priority vehicle passes through the intersection first, let , and add the data to ; Let the set of relative waiting time data of vehicles passing through the intersection be denoted as , and each data in the set is in the form of . Let be the difference between the waiting time of the low-priority vehicle at the stop position and the waiting time of the high-priority vehicle at the stop position. If the low-priority vehicle passes through the intersection, let , and add the data to . If the high-priority vehicle passes through the intersection first, let , and add the data to ; S6.2.

4. Identify the moments when the vehicle arrives at and leaves the parking position: Traverse the low-priority vehicles 's trajectory points , and find the points entering the straight or left-turn parking position section and the points leaving the straight or left-turn parking position section , and are the moments of entering and leaving the straight or left-turn parking position section respectively; Traverse the high-priority vehicles 's trajectory points , and find the points entering the straight or left-turn parking position section and the points leaving the straight or left-turn parking position section , and are the moments of entering and leaving the straight or left-turn parking position section respectively; S6.2.

5. Identify the passing sequence relationship of vehicles at the intersection: S6.2.5.

1. Traverse low-priority vehicles ; S6.2.5.

2. Traverse the low-priority vehicle trajectory points , the corresponding time of this point is recorded as t , the horizontal and vertical coordinates are respectively recorded as 、 , let the next point be , where = t +0.5, the horizontal and vertical coordinates are respectively recorded as 、 ; S6.2.5.

3. Traverse high-priority vehicles ; S6.2.5.

4. Traverse the high-priority vehicle trajectory points , and record the corresponding time of this point as u , and record the horizontal and vertical coordinates as 、 , let the next point be , where = u + 0.5, and record the horizontal and vertical coordinates as 、 ; S6.2.5.

5. Identify line segments and whether there are conflicts in time and space: If the following formula holds, then the line segments and conflict in time and space, enter S6.2.5.6, otherwise return to S6.2.5.4 and continue to traverse the next high-priority vehicle trajectory point. The formula is: ； ； ； S6.2.5.

6. Identify the passing order of conflicting vehicles through the intersection: If , it indicates that vehicle passes through the intersection before vehicle . Add the available gap data and the relative waiting time data to and respectively, enter S6.2.5.1, and continue to traverse the next low-priority vehicle; If , it indicates that vehicle passes through the intersection before vehicle . Add the available gap data and the relative waiting time data to and respectively, enter S6.2.5.3, and continue to traverse the next high-priority vehicle; S6.2.

6. Screen the set of available gap data of vehicles passing through the intersection of the data, denoted as , and group and count them at 0.5s time intervals respectively for the frequency of , , where is the total number of data in that meet is the total number of data in that meet ; calculate the frequency of , and the expression is: ； Obtain the and of minimum value ; Obtain the and that satisfy maximum value , According to the two-parameter gap acceptance model, let , , where G is the critical gap; Calibrate the final critical gap through the following formula G , and the expression is: ； S6.2.

7. Screening the relative waiting time data set in the data, denoted as , group and count the frequencies at 0.5s time intervals respectively , where is the total number of data in that meet the condition, and is the total number of data in that meet the condition; calculate the frequency of , and the expression is: ； Obtain the and of the minimum value ; Obtain those satisfying and of the maximum value ; According to the two-parameter gap acceptance model, let , , where W is the critical waiting time; Calculate the critical waiting time using the following formula W , and the expression is: 。

Images