A data processing system for obtaining vehicle flow

Abstract

The present invention provides a data processing system for obtaining traffic flow, comprising: a preset time slice list, a lane information list corresponding to the preset time slice list, a processor, and a memory storing a computer program. When the computer program is executed by the processor, the following steps are implemented: obtaining the preset traffic flow list; inputting the lane information list and the preset traffic flow list into a preset simulation model to obtain a first vehicle speed list corresponding to the lane information list; and obtaining a target traffic flow list corresponding to the lane information list based on the first vehicle speed list. It can be seen that the present invention inputs traffic flow and lane information into the simulation model to obtain vehicle speed, analyzes and processes the vehicle speed, continuously updates the traffic flow input into the simulation model, and determines the optimal traffic flow through the simulation model, so that the road traffic predicted based on the optimal traffic flow and vehicle information is closer to the real-world effect, thereby improving the accuracy of the predicted road traffic.

Description

Technical Field

[0001] The present invention relates to the field of smart transportation, and in particular to a data processing system for obtaining vehicle flow. Background Art

[0002] With the rapid development of cities, urban road networks are constantly expanding, and the number of vehicles on the roads is growing rapidly. Accurately simulating road traffic and analyzing and optimizing the simulated road traffic can provide effective guidance for alleviating traffic pressure. Most of the existing road traffic simulation data are set by manual experience or collected through actual collection equipment. The simulation data is directly input into the simulation model to predict road traffic.

[0003] However, the above method also has the following technical problems:

[0004] Because the simulation model is greatly simplified compared to the real world, the data set by artificial experience or collected by actual collection equipment is directly input into the simulation model as raw data. The simulated road network is not detailed enough compared to the real road network, which can easily cause deviations that are inconsistent with the actual situation. In addition, the data set by artificial experience or collected by actual collection equipment is limited, and the area of ​​the simulated road network is small. Therefore, after inputting the raw data into the simulation model, it cannot achieve the effect of real-world application, thereby reducing the accuracy of predicting road traffic. Summary of the Invention

[0005] In view of the above technical problems, the technical solution adopted by the present invention is:

[0006] A data processing system for obtaining traffic flow, comprising: a preset time slice list A = {A1, ..., A i ,……,A m}、Lane information list B corresponding to A={B1,……,B i ,……,B m}, a processor and a memory storing a computer program, wherein A i is the i-th preset time slice at the current time point, i=1...m, m is the number of preset time slices at the current time point, B i ={B i1 ,……,B ij ,……,B in(i)}, B ij ={B 1 ij ,……,B r ij ,……,B s(ij) ij}, B r ij For A iLane information of the rth lane of the jth green wave section, r = 1...s(ij), s(ij) is the lane information of the rth lane of the jth green wave section in A i The number of lanes in the jth green wave section, j = 1...n(i), n(i) is the number of lanes in A i The number of medium green wave road sections, when the computer program is executed by the processor, implements the following steps:

[0007] S100, according to B, obtain the preset traffic flow list C corresponding to B = {C1, ..., C i ,……,C m}, C i ={C i1 ,……,C ij ,……,C in(i)}, C ij ={C 1 ij ,……,C r ij ,……,C s(ij) ij}, C r ij For B r ij The corresponding preset traffic flow.

[0008] S200, input B and C into the preset simulation model, obtain the first vehicle speed list E corresponding to B = {E1, ..., E i ,……,E m}, E i ={E i1 ,……,E ij ,……,E in(i)}, E ij ={E 1 ij ,……,E r ij ,……,E s(ij) ij}, E r ij For B r ij The corresponding first vehicle speed.

[0009] S300, according to E, obtain the target traffic flow list F corresponding to B = {F1, ..., F i ,……,F m}, F i ={F i1 ,……,F ij ,……,F in(i)}, F ij ={F 1 ij,……,F r ij ,……,F s(ij) ij}, F r ij For B r ij The corresponding target traffic flow, wherein the step S300 includes the following steps to obtain F r ij :

[0010] S301, if E i All E r ij ∈[E0, E1], then determine C r ij F r ij Otherwise, execute step S303, wherein E0 is the lower limit value of the preset vehicle speed range, and E1 is the upper limit value of the preset vehicle speed range.

[0011] S303, to E r ij Process and obtain F r ij .

[0012] The present invention has at least the following beneficial effects:

[0013] The present invention provides a data processing system for obtaining traffic flow, comprising: a preset time slice list, a lane information list corresponding to the preset time slice list, a processor, and a memory storing a computer program. When the computer program is executed by the processor, the following steps are implemented: obtaining the preset traffic flow list; inputting the lane information list and the preset traffic flow list into a preset simulation model to obtain a first vehicle speed list corresponding to the lane information list; and obtaining a target traffic flow list corresponding to the lane information list based on the first vehicle speed list. It can be seen that the present invention inputs traffic flow and lane information into the simulation model to obtain vehicle speed, analyzes and processes the vehicle speed, continuously updates the traffic flow input into the simulation model, and determines the optimal traffic flow through the simulation model, so that the road traffic predicted based on the optimal traffic flow and vehicle information is closer to the real-world effect, thereby improving the accuracy of the predicted road traffic. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 A flowchart of a computer program executed by a data processing system for obtaining vehicle flow provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0017] The present invention provides a data processing system for obtaining vehicle flow, comprising: a preset time slice list A = {A1, ..., A i ,……,A m}、Lane information list B corresponding to A={B1,……,B i ,……,B m}, a processor and a memory storing a computer program, wherein A i is the i-th preset time slice at the current time point, i=1...m, m is the number of preset time slices at the current time point, B i ={B i1 ,……,B ij ,……,B in(i)}, B ij ={B 1 ij ,……,B r ij ,……,B s (ij) ij}, B r ij For A i Lane information of the rth lane of the jth green wave section, r = 1...s(ij), s(ij) is the lane information of the rth lane of the jth green wave section in A i The number of lanes in the jth green wave section, j = 1...n(i), n(i) is the number of lanes in A i The number of green wave sections, when the computer program is executed by the processor, implements the following steps, such as Figure 1 As shown:

[0018] S100, according to B, obtain the preset traffic flow list C corresponding to B = {C1, ..., C i ,……,C m}, C i ={C i1 ,……,C ij ,……,C in(i)}, C ij ={C1 ij ,……,C r ij ,……,C s(ij) ij}, C r ij For B r ij The corresponding preset traffic flow, wherein those skilled in the art know that the preset traffic flow is set by those skilled in the art according to actual needs.

[0019] Specifically, the value of the current time point is one day.

[0020] Specifically, the value range of the preset time slice is 5 minutes to 10 minutes, and those skilled in the art can set the value of the preset time slice according to actual needs.

[0021] Specifically, the lane information includes: lane length, a first road intersection ID corresponding to the lane, a second road intersection ID corresponding to the lane, a lane type, and a green light start time and a green light end time of a traffic light corresponding to the lane.

[0022] Specifically, the first road intersection ID is an identification of the first road intersection.

[0023] Specifically, the second road intersection ID is an identification of the second road intersection.

[0024] Furthermore, the first road intersection is a road intersection of an entry lane.

[0025] Furthermore, the second road intersection is a road intersection of an exit lane.

[0026] Furthermore, the traffic direction of the lane is from the first road intersection corresponding to the first road intersection ID to the second road intersection corresponding to the second road intersection ID.

[0027] Furthermore, the lane types include at least: a left-turn lane, a right-turn lane, a straight-ahead lane, a lane for both left-turn and straight-ahead driving, a lane for both right-turn and straight-ahead driving, and a lane for both U-turn and left-turn driving.

[0028] S200, input B and C into the preset simulation model, obtain the first vehicle speed list E corresponding to B = {E1, ..., E i ,……,E m}, E i ={E i1 ,……,E ij ,……,E in(i)}, E ij ={E 1ij ,……,E r ij ,……,E s(ij) ij}, E r ij For B r ij The corresponding first vehicle speed.

[0029] Specifically, the preset simulation model is a traffic simulation model. Any traffic simulation model in the prior art falls within the protection scope of the present invention and will not be described in detail here.

[0030] Specifically, step S200 includes the following steps:

[0031] S201, obtain B r ij Corresponding vehicle position coordinate list G r ij ={G r1 ij ,……,G rx ij ,……,G rp ij}, G rx ij ={G rx1 ij ,……,G rxy ij ,……,G rxq ij}, G rxy ij For A i The corresponding time B is at the yth second r ij The vehicle position coordinates corresponding to the xth vehicle in the lane, x = 1...p, p is the number of vehicles in the lane, y = 1...q, q is A i The corresponding number of seconds.

[0032] Specifically, in step S201, p=C r ij ×q, q=t×60, t is the length of the preset time slice.

[0033] Specifically, a preset following model and a preset merging model are set in the preset simulation model, which are used to determine the vehicle position coordinates of each vehicle in each lane in each second of a preset time slice based on lane information and traffic flow. Those skilled in the art know that any following model and merging model in the prior art and the method for obtaining vehicle position coordinates based on the following model and merging model all fall within the scope of protection of the present invention.

[0034] Specifically, the vehicle position coordinates can be understood as: the coordinate point of the vehicle in a preset coordinate system, where the preset coordinate system takes the center of a map composed of lanes as the origin, the east-west direction of the map as the x-axis and the east direction as the positive direction of the x-axis, and the north-south direction of the map as the y-axis and the north direction as the positive direction of the y-axis.

[0035] S203, according to G rxy ij , get H rxy ij , H rxy ij G rxy ij With G rx(y-1) ij The distance between vehicles, where when y=1, H rxy ij =0, the vehicle distance is the distance between the two vehicle position coordinates of the same vehicle in the lane. It can be understood that when the vehicle does not change lanes, the vehicle distance is the straight-line distance between the two vehicle position coordinates. When the vehicle changes lanes, the vehicle distance is not the straight-line distance between the two vehicle position coordinates, but the distance the vehicle travels from one vehicle position coordinate to another vehicle position coordinate along the lane according to the lane's traffic direction.

[0036] S205, according to H rxy ij , get E r ij , E r ij Meet the following conditions:

[0037]

[0038] As described above, the traffic flow and lane information are input into the simulation model, and the vehicle position coordinates of each vehicle in each lane in each second of a preset time slice are obtained. Based on the vehicle position coordinates, the first vehicle speed of the lane is obtained, and the first vehicle speed is analyzed and processed. The traffic flow input into the simulation model is continuously updated. The optimal traffic flow can be determined through the simulation model, so that the road traffic predicted based on the optimal traffic flow and vehicle information is closer to the real-world effect, thereby improving the accuracy of predicting road traffic.

[0039] S300, according to E, obtain the target traffic flow list F corresponding to B = {F1, ..., F i ,……,F m}, F i ={F i1 ,……,F ij ,……,F in(i)}, Fij ={F 1 ij ,……,F r ij ,……,F s(ij) ij}, F r ij For B r ij The corresponding target traffic flow.

[0040] Specifically, the step S300 includes the following steps to obtain F r ij :

[0041] S301, if E i All E r ij ∈[E0, E1], then determine C r ij F r ij Otherwise, execute step S303, wherein E0 is the lower limit value of the preset vehicle speed range, and E1 is the upper limit value of the preset vehicle speed range. Those skilled in the art can set the lower limit value of the preset vehicle speed range and the upper limit value of the preset vehicle speed range according to actual needs.

[0042] S303, to E r ij Process and obtain F r ij .

[0043] Specifically, step S303 includes the following steps:

[0044] S3031, according to E r ij , get the updated C i ={C i1 ,……,C ij ,……,C in(i)}, C ij ={C 1 ij ,……,C r ij ,……,C s(ij) ij}.

[0045] Specifically, step S3031 includes the following steps:

[0046] S10, if E r ij <E0, then use the first processing rule to obtain the updated C r ij, where the first processing rule meets the following conditions: C r ij =C r ij -1 / 2×C r ij .

[0047] S20, if E r ij > E1, then use the second processing rule to obtain the updated C r ij , where the second processing rule meets the following conditions: C r ij =C r ij +1 / 2×C r ij .

[0048] S30, if E r ij ∈[E0, E1], get the updated C r ij , where the updated C r ij Compared with C before the update r ij The same can be understood as keeping C r ij constant.

[0049] S3033, B i With the updated C i Input into the preset simulation model to obtain the updated E i ={E i1 ,……,E ij ,……,E in(i)}, E ij ={E 1 ij ,……,E r ij ,……,E s(ij) ij}.

[0050] S3035, if the updated E i All E r ij ∈[E0, E1], then determine the updated C r ij F r ij , otherwise execute step S303.

[0051] As described above, by analyzing and processing the first vehicle speed and continuously updating the traffic flow input into the simulation model, the optimal traffic flow can be determined through the simulation model, so that the road traffic predicted based on the optimal traffic flow and vehicle information is closer to the real-world effect, thereby improving the accuracy of predicting road traffic.

[0052] The present invention provides a data processing system for obtaining traffic flow, comprising: a preset time slice list, a lane information list corresponding to the preset time slice list, a processor, and a memory storing a computer program. When the computer program is executed by the processor, the following steps are implemented: obtaining the preset traffic flow list; inputting the lane information list and the preset traffic flow list into a preset simulation model to obtain a first vehicle speed list corresponding to the lane information list; and obtaining a target traffic flow list corresponding to the lane information list based on the first vehicle speed list. It can be seen that the present invention inputs traffic flow and lane information into the simulation model to obtain vehicle speed, analyzes and processes the vehicle speed, continuously updates the traffic flow input into the simulation model, and determines the optimal traffic flow through the simulation model, so that the road traffic predicted based on the optimal traffic flow and vehicle information is closer to the real-world effect, thereby improving the accuracy of the predicted road traffic.

[0053] Although some specific embodiments of the present invention have been described in detail by way of example, it will be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It will also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A data processing system for obtaining vehicle flow, characterized in that: The system includes: a preset time slice list A={A1, ..., A i ,……,A m }、The lane information list B corresponding to A={B1,...,B i ,……,B m }, a processor and a memory storing a computer program, wherein A i is the i-th preset time slice at the current time point, i=1…m, m is the number of preset time slices at the current time point, B i ={B i1 ,……,B ij ,……,B in（i） }, B ij ={B 1 ij ,……,B r ij ,……,B s（ij） ij }, B r ij For A i Lane information of the rth lane of the jth green wave section, r=1……s(ij), s(ij) is the lane information of the rth lane of the jth green wave section in A i The number of lanes in the jth green wave section, j = 1...n(i), n(i) is the number of lanes in A i The number of medium green wave road sections, when the computer program is executed by the processor, implements the following steps: S100, according to B, obtain the preset traffic flow list C corresponding to B = {C1, ..., C i ,……,C m }, C i ={C i1 ,……,C ij ,……,C in（i） }, C ij ={C 1 ij ,……,C r ij ,……,C s（ij） ij }, C r ij For B r ij Corresponding preset traffic flow; S200, input B and C into the preset simulation model, obtain the first vehicle speed list E corresponding to B = {E1, ..., E i ,……,E m }, E i ={E i1 ,……,E ij ,……,E in（i） }, E ij ={E 1 ij ,……,E r ij ,……,E s（ij） ij }, E r ij For B r ij a corresponding first vehicle speed; and setting a preset following model and a preset merging model in a preset simulation model for determining the vehicle position coordinates of each vehicle in each lane in each second of a preset time slice based on lane information and traffic flow; S300, according to E, obtain the target traffic flow list F corresponding to B = {F1, ..., F i ,……,F m }, F i ={F i1 ,……,F ij ,……,F in（i） }, F ij ={F 1 ij ,……,F r ij ,……,F s（ij） ij }, F r ij For B r ij The corresponding target traffic flow, wherein the step S300 includes the following steps to obtain F r ij : S301, if E i All E r ij [E0, E1], then determine C r ij F r ij Otherwise, execute step S303, wherein E0 is the lower limit of the preset speed range, and E1 is the upper limit of the preset speed range; S303, to E r ij Process and obtain F r ij ; Step S303 includes the following steps: S3031, according to E r ij , get the updated C i ={C i1 ,……,C ij ,……,C in（i） }, C ij ={C 1 ij ,……,C r ij ,……,C s（ij） ij }; Among them, if E r ij <E0, then use the first processing rule to obtain the updated C r ij , the first processing rule meets the following conditions: C r ij =C r ij -1 / 2×C r ij If E r ij > E1, then use the second processing rule to obtain the updated C r ij , the second processing rule meets the following conditions: C r ij =C r ij +1 / 2×C r ij If E r ij [E0, E1], get the updated C r ij , the updated C r ij Compared with C before the update r ij same; S3033, B i With the updated C i Input into the preset simulation model to obtain the updated E i ={E i1 ,……,E ij ,……,E in（i） }, E ij ={E 1 ij ,……,E r ij ,……,E s（ij） ij }; S3035, if the updated E i All E r ij [E0, E1], then determine the updated C r ij F r ij , otherwise execute step S303.

2. The data processing system for obtaining vehicle flow according to claim 1, characterized in that: The following steps are included in step S200: S201, obtain B r ij Corresponding vehicle position coordinate list G r ij ={G r1 ij ,……,G rx ij ,……,G rp ij }, G rx ij ={G rx1 ij ,……,G rxy ij ,……,G rxq ij }, G rxy ij For A i The corresponding time B is at the yth second r ij The vehicle position coordinates corresponding to the xth vehicle in the lane, x=1…p, p is the number of vehicles in the lane, y=1…q, q is A i The corresponding number of seconds; S203, according to G rxy ij , get H rxy ij , H rxy ij G rxy ij With G rx（y-1） ij The distance between vehicles, where when y=1, H rxy ij =0, the vehicle distance is the distance between the two vehicle position coordinates of the same vehicle in the lane; S205, according to H rxy ij , get E r ij , E r ij Meet the following conditions: 。 3. The data processing system for obtaining vehicle flow according to claim 1, characterized in that: The value of the current time point is one day.

4. The data processing system for obtaining vehicle flow according to claim 1, characterized in that: The lane information includes: lane length, a first road intersection ID corresponding to the lane, a second road intersection ID corresponding to the lane, a lane type, and a green light start time and a green light end time of a traffic light corresponding to the lane.

5. The data processing system for obtaining vehicle flow according to claim 2, characterized in that: In step S201, q meets the following conditions: q=t×60, where t is the length of the preset time slice.

6. The data processing system for obtaining vehicle flow according to claim 5, characterized in that: In step S201, p meets the following conditions: p=C r ij ×q。

Images