CN117409572B - Road traffic flow data management method and system based on signal processing - Google Patents
Road traffic flow data management method and system based on signal processing Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
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Abstract
The invention discloses a road traffic flow data management method and system based on signal processing, wherein a road comprises a first lane and a second lane, and the method comprises the following steps: s101: acquiring the road acoustic waveform, and acquiring the first vehicle flow and the second vehicle flow according to the acoustic waveform; s102: acquiring the number of net-linked vehicles in the road, the number of traditional vehicles and the ratio of net-linked vehicles in a first vehicle road; s103: and obtaining a driving strategy of the network-connected vehicles according to the number of the network-connected vehicles in the road and the traffic flow condition of the road so as to improve the traffic capacity of the road. The technical problem that current multi-lane traffic's traffic capacity is low can be solved to this scheme.
Description
Technical Field
The invention belongs to the field of intelligent network traffic management and control, and particularly relates to a road traffic flow data management method and system based on signal processing.
Background
With the development of social economy and transportation industry, the automobile conservation amount is continuously increased, and the transportation mode of the transportation system is expanded from a single transportation system to a comprehensive transportation system. In recent years, the increasing number of motor vehicles on roads causes the ever-increasing traffic demands of people to be mismatched with the traffic capacity of road sections, and a series of problems such as traffic jam and the like are generated, and the problems also draw a great deal of attention from government departments, scholars and technicians of various countries.
As a future development form of automobiles, the occupancy of intelligent network automobiles is gradually increased in traffic composition. Under the mixed traffic scene, a special lane is arranged for the intelligent network-connected automobile, so that the collision among vehicles with different intelligent degrees can be reduced, and the traffic efficiency is improved. However, for multilane, such as a two-lane road, the intelligent network vehicle-connected lane cannot affect the traditional lane, and the efficiency of road traffic is also low, so how to improve the traffic capacity of the multilane road is needed to be solved.
Disclosure of Invention
The invention provides a road traffic flow data management method and system based on signal processing, which aim to solve the technical problem that the current traffic capacity of multi-lane traffic is low.
First aspect
The invention provides a road traffic flow data management method based on signal processing, wherein a road comprises a first lane and a second lane, and the method comprises the following steps:
s101: acquiring the road acoustic waveform, and acquiring the first vehicle flow and the second vehicle flow according to the waveform;
S102: acquiring the number of net-linked vehicles in the road, the number of traditional vehicles and the ratio of net-linked vehicles in a first vehicle road;
S103: and obtaining a driving strategy of the network-connected vehicles according to the number of the network-connected vehicles in the road and the traffic flow condition of the road so as to improve the traffic capacity of the road.
Further, S101 specifically includes:
S1011: acquiring the road acoustic waveform, wherein the acoustic waveform comprises a first acoustic waveform of a first lane and a second acoustic waveform of a second lane;
S1012: filtering and smoothing the first acoustic waveform and the second acoustic waveform to obtain a first processed waveform and a second processed waveform;
s1023: acquiring a first waveform and a second waveform, wherein the peak value of the first processing waveform and the second processing waveform is larger than a preset peak value;
S1024: acquiring the first waveform and the second waveform, wherein the rising edge rate of the waveforms is greater than the first waveform number and the second waveform number of a preset rate;
s1025: and obtaining the first vehicle flow and the second vehicle flow according to the first waveform quantity and the second waveform quantity.
Further, the obtaining the number of network vehicles and the number of traditional vehicles in the road specifically includes:
acquiring the number of the network-connected network signals and the number of the network-connected network signals in the first lane;
and obtaining the number of the traditional vehicles in the first lane according to the first lane flow and the number of the network signals of the network-in-first-lane vehicles.
Further, the obtaining the driving strategy of the internet-connected vehicle specifically includes:
First strategy: the internet-connected vehicle only runs on the first lane, and the traditional vehicle runs on the first lane and the second lane; or alternatively, the first and second heat exchangers may be,
Second strategy: the internet-connected vehicle only runs on the first lane, and the traditional vehicle only runs on the second lane; or alternatively, the first and second heat exchangers may be,
Third strategy: the internet-enabled vehicle travels in the first lane and the second lane, and the conventional vehicle travels in the first lane and the second lane.
Further, the step S103 specifically includes:
When the number of network-connected vehicles in the road is smaller than the target number and the traffic flow condition of the road is larger than the maximum traffic flow condition of the second strategy, the running strategy of the network-connected vehicles selects the first strategy;
when the number of the network-connected vehicles in the road is smaller than the target number and the traffic flow condition of the road is smaller than the maximum traffic flow condition of the second strategy, the running strategy of the network-connected vehicles selects the second strategy or the third strategy;
when the number of the network-connected vehicles in the road is larger than the target number and the traffic flow condition of the road is larger than the maximum traffic flow condition of the second strategy, the driving strategy of the network-connected vehicles selects the third strategy;
And when the number of the network-connected vehicles in the road is larger than the target number and the traffic flow condition of the road is smaller than the maximum traffic flow condition of the third strategy, selecting the second strategy or the first strategy by the driving strategy of the network-connected vehicles.
Further, the maximum traffic flow condition of the first strategy adopts the formula:
Wherein, Traffic flow for the first lane and the second lane under a first strategy; c 0 is the traffic capacity of the traditional vehicle when the first lane only runs, and p is the ratio of the network-in-road vehicle to the total vehicles in the road; Alpha is the ratio of the average critical spacing between the net-linked vehicles and the average critical spacing between the traditional vehicles, beta is the ratio of the average critical spacing between the net-linked vehicles and the average critical spacing between the traditional vehicles, gamma is the ratio of the average critical spacing between the traditional vehicles and the average critical spacing between the traditional vehicles, lambda is the ratio of the net-linked vehicles to the total vehicles in the first lane.
Further, the maximum traffic flow condition of the second strategy adopts the formula:
Wherein, Traffic flow for the first lane and the second lane under a second strategy; c 0 is the traffic capacity of the traditional vehicle when the first lane only runs, and p is the ratio of the network-in-road vehicle to the total vehicles in the road; Alpha is the ratio of the average critical spacing between the net-linked vehicles and the average critical spacing between the traditional vehicles, beta is the ratio of the average critical spacing between the net-linked vehicles and the average critical spacing between the traditional vehicles, gamma is the ratio of the average critical spacing between the traditional vehicles and the average critical spacing between the traditional vehicles, lambda is the ratio of the net-linked vehicles to the total vehicles in the first lane.
Further, the maximum traffic flow condition of the third strategy adopts the formula:
Wherein, Traffic flow for the first lane and the second lane under a third strategy; c 0 is the traffic capacity of the traditional vehicle when the first lane only runs, and p is the ratio of the network-in-road vehicle to the total vehicles in the road;
Alpha is the ratio of the average critical distance between the net-linked vehicles and the average critical distance between the traditional vehicles, beta is the ratio of the average critical distance between the net-linked vehicles and the average critical distance between the traditional vehicles, gamma is the ratio of the average critical distance between the traditional vehicles and the average critical distance between the traditional vehicles, lambda 1 is the ratio of the net-linked vehicles to the total vehicles in the first lane, lambda 2 is the ratio of the net-linked vehicles to the total vehicles in the second lane.
Further, the target number of network links in the road satisfies the formula:
Wherein, Alpha is the ratio of the average critical spacing between the net-linked vehicles and the average critical spacing between the traditional vehicles, beta is the ratio of the average critical spacing between the net-linked vehicles and the average critical spacing between the traditional vehicles, gamma is the ratio of the average critical spacing between the traditional vehicles and the average critical spacing between the traditional vehicles, lambda is the ratio of the net-linked vehicles to the total vehicles in the first lane.
Second aspect
The invention provides a road traffic flow data management system based on signal processing, a road comprises a first lane and a second lane, comprising
The first acquisition module is used for acquiring the road acoustic waveform, and acquiring the first vehicle flow and the second vehicle flow according to the waveform;
the second acquisition module is used for acquiring the number of net-linked vehicles in the road, the number of traditional vehicles and the ratio of net-linked vehicles in the first road;
The obtaining module is used for obtaining the running strategy of the network-connected vehicles according to the number of the network-connected vehicles in the road and the traffic flow condition of the road so as to improve the traffic capacity of the road.
Compared with the prior art, the invention has at least the following beneficial effects:
In the invention, the proper driving strategy, namely the lanes on which the network vehicles can drive and the lanes on which the traditional vehicles can drive, is selected for the current road by acquiring the number of the network vehicles in the current road and the traffic flow condition of the road, so that the traffic capacity of the road is improved.
Drawings
The above features, technical features, advantages and implementation of the present invention will be further described in the following description of preferred embodiments with reference to the accompanying drawings in a clear and easily understood manner.
FIG. 1 is a schematic flow chart of a road traffic flow data management method based on signal processing;
Fig. 2 is a schematic structural diagram of a road traffic flow data management system based on signal processing according to the present invention.
Detailed Description
In one embodiment, referring to fig. 1 of the specification, the present invention provides a flow chart of a road traffic flow data management method based on signal processing.
The invention provides a road traffic flow data management method based on signal processing, wherein a road comprises a first lane and a second lane, and the method comprises the following steps:
s101: and acquiring the road acoustic waveform, and acquiring the first vehicle flow and the second vehicle flow according to the waveform.
Further, the step S101 specifically includes:
s1011: and acquiring the road acoustic waveform, wherein the acoustic waveform comprises a first acoustic waveform of a first lane and a second acoustic waveform of a second lane.
S1012: and filtering and smoothing the first acoustic waveform and the second acoustic waveform to obtain a first processed waveform and a second processed waveform.
Specifically, the first acoustic waveform and the second acoustic waveform are subjected to filtering processing, so that noise and non-vehicle acoustic waveforms in the acoustic waveforms can be filtered. The first acoustic waveform and the second acoustic waveform are subjected to smoothing processing, so that some burr waveforms in the waveforms can be reduced, and some noise waveforms can be filtered more carefully.
S1023: and acquiring a first waveform and a second waveform, wherein the peak value of the first processing waveform and the second processing waveform is larger than a preset peak value.
Specifically, the acquisition of a waveform having a peak value Yu Yushe larger than that of the waveform is desirable to obtain a sound wave emitted from a vehicle, and to reduce the formation of a sound wave due to sounds emitted from other objects, such as a pedestrian or other objects.
S1024: and acquiring the first waveform and the second waveform, wherein the rising edge rate of the waveforms is larger than the first waveform quantity and the second waveform quantity of the preset rate.
Specifically, waveforms generated by the vehicle during travel have similar sonic rising edge rates. The number of traveling vehicles can be obtained by acquiring the number of acoustic waveform rising edge rates that are greater than the preset rate.
S1025: and obtaining the first vehicle flow and the second vehicle flow according to the first waveform quantity and the second waveform quantity.
Specifically, since the first waveform number and the second waveform number can represent the total number of vehicles in the first lane and the total number of vehicles in the second lane. In unit time, the first vehicle flow can be obtained according to the total number of vehicles in the first lane; and obtaining the second vehicle flow according to the total number of the vehicles in the second lane.
S102: and acquiring the number of the net-linked vehicles in the road, the number of the traditional vehicles and the ratio of the net-linked vehicles in the first vehicle road.
Further, the obtaining the number of network vehicles and the number of traditional vehicles in the road specifically includes:
and acquiring the number of the network-in-road network signals and the number of the network-in-first-lane network signals.
Specifically, the network-connected vehicles have respective network signals, so that the number of the network signals of the network-connected vehicles in the road and the number of the network signals of the network-connected vehicles in the first lane can be obtained in the process of receiving and transmitting the signals of the network-connected vehicles.
And obtaining the number of the traditional vehicles in the first lane according to the first lane flow and the number of the network signals of the network-in-first-lane vehicles.
Specifically, the first lane flow is the total number of vehicles in unit time, the total number of vehicles consists of the number of network-connected vehicles and the number of traditional vehicles, the number of network signals of the network-connected vehicles in the first lane can represent the number of network-connected vehicles, and the number of network-connected vehicles is subtracted from the total number of vehicles, so that the number of traditional vehicles of the first lane can be obtained.
S103: and obtaining a driving strategy of the network-connected vehicles according to the number of the network-connected vehicles in the road and the traffic flow condition of the road so as to improve the traffic capacity of the road.
Further, the obtaining the driving strategy of the internet-connected vehicle specifically includes:
First strategy: the internet-connected vehicle only runs on the first lane, and the traditional vehicle runs on the first lane and the second lane; or alternatively, the first and second heat exchangers may be,
Second strategy: the internet-connected vehicle only runs on the first lane, and the traditional vehicle only runs on the second lane; or alternatively, the first and second heat exchangers may be,
Third strategy: the internet-enabled vehicle travels in the first lane and the second lane, and the conventional vehicle travels in the first lane and the second lane.
Further, the step S103 specifically includes:
When the number of network-connected vehicles in the road is smaller than the target number and the traffic flow condition of the road is larger than the maximum traffic flow condition of the second strategy, the running strategy of the network-connected vehicles selects the first strategy;
when the number of the network-connected vehicles in the road is smaller than the target number and the traffic flow condition of the road is smaller than the maximum traffic flow condition of the second strategy, the running strategy of the network-connected vehicles selects the second strategy or the third strategy;
when the number of the network-connected vehicles in the road is larger than the target number and the traffic flow condition of the road is larger than the maximum traffic flow condition of the second strategy, the driving strategy of the network-connected vehicles selects the third strategy;
And when the number of the network-connected vehicles in the road is larger than the target number and the traffic flow condition of the road is smaller than the maximum traffic flow condition of the third strategy, selecting the second strategy or the first strategy by the driving strategy of the network-connected vehicles.
Further, the maximum traffic flow condition of the first strategy adopts the formula:
Wherein, Traffic flow for the first lane and the second lane under a first strategy; c 0 is the traffic capacity of the traditional vehicle when the first lane only runs, and p is the ratio of the network-in-road vehicle to the total vehicles in the road; Alpha is the ratio of the average critical spacing between the net-linked vehicles and the average critical spacing between the traditional vehicles, beta is the ratio of the average critical spacing between the net-linked vehicles and the average critical spacing between the traditional vehicles, gamma is the ratio of the average critical spacing between the traditional vehicles and the average critical spacing between the traditional vehicles, lambda is the ratio of the net-linked vehicles to the total vehicles in the first lane.
Specifically, λ is the ratio of the net vehicle in the first lane to the total vehicle in the first lane, and therefore, for no net vehicle in the second lane, λ 2 =0.
Wherein, Q 1 is the traffic flow of the two lanes under the first strategy, Q 1 is the traffic flow of the first lane, Q 2 is the traffic flow of the second lane, p is the ratio of the net-linked vehicle in the road to the total vehicle in the road, and λ 1 is the ratio of the net-linked vehicle in the first lane to the total vehicle in the first lane.
The traffic capacity of the two lanes is denoted as C 1,C0, respectively, then,
At λ 1 =1, there is a maximum traffic flow case:
Wherein, Traffic flow for the first lane and the second lane under a first strategy; c 0 is the traffic capacity of the traditional vehicle when the first lane only runs, and p is the ratio of the network-in-road vehicle to the total vehicles in the road; Alpha is the ratio of the average critical spacing between the net-linked vehicles and the average critical spacing between the traditional vehicles, beta is the ratio of the average critical spacing between the net-linked vehicles and the average critical spacing between the traditional vehicles, gamma is the ratio of the average critical spacing between the traditional vehicles and the average critical spacing between the traditional vehicles, lambda is the ratio of the net-linked vehicles to the total vehicles in the first lane.
Further, the maximum traffic flow condition of the second strategy adopts the formula:
Wherein, Traffic flow for the first lane and the second lane under a second strategy; c 0 is the traffic capacity of the traditional vehicle when the first lane only runs, and p is the ratio of the network-in-road vehicle to the total vehicles in the road; Alpha is the ratio of the average critical spacing between the net-linked vehicles and the average critical spacing between the traditional vehicles, beta is the ratio of the average critical spacing between the net-linked vehicles and the average critical spacing between the traditional vehicles, gamma is the ratio of the average critical spacing between the traditional vehicles and the average critical spacing between the traditional vehicles, lambda is the ratio of the net-linked vehicles to the total vehicles in the first lane.
In particular, the method comprises the steps of,
In the case where the first lane and the second lane flows reach the maximum value at the same time, there is a maximum traffic flow condition:
Wherein, Traffic flow for the first lane and the second lane under a second strategy; c 0 is the traffic capacity of the traditional vehicle when the first lane only runs, and p is the ratio of the network-in-road vehicle to the total vehicles in the road; Alpha is the ratio of the average critical spacing between the net-linked vehicles and the average critical spacing between the traditional vehicles, beta is the ratio of the average critical spacing between the net-linked vehicles and the average critical spacing between the traditional vehicles, gamma is the ratio of the average critical spacing between the traditional vehicles and the average critical spacing between the traditional vehicles, lambda is the ratio of the net-linked vehicles to the total vehicles in the first lane.
Further, the maximum traffic flow condition of the third strategy adopts the formula:
Wherein, Traffic flow for the first lane and the second lane under a third strategy; c 0 is the traffic capacity of the traditional vehicle when the first lane only runs, and p is the ratio of the network-in-road vehicle to the total vehicles in the road;
Alpha is the ratio of the average critical distance between the net-linked vehicles and the average critical distance between the traditional vehicles, beta is the ratio of the average critical distance between the net-linked vehicles and the average critical distance between the traditional vehicles, gamma is the ratio of the average critical distance between the traditional vehicles and the average critical distance between the traditional vehicles, lambda 1 is the ratio of the net-linked vehicles to the total vehicles in the first lane, lambda 2 is the ratio of the net-linked vehicles to the total vehicles in the second lane.
In particular, the method comprises the steps of,
When p=1, the second policy has a maximum traffic flow condition, and the maximum traffic flow condition employs the formula:
Wherein, Traffic flow for the first lane and the second lane under a third strategy; c 0 is the traffic capacity of the traditional vehicle when the first lane only runs, and p is the ratio of the network-in-road vehicle to the total vehicles in the road;
Alpha is the ratio of the average critical distance between the net-linked vehicles and the average critical distance between the traditional vehicles, beta is the ratio of the average critical distance between the net-linked vehicles and the average critical distance between the traditional vehicles, gamma is the ratio of the average critical distance between the traditional vehicles and the average critical distance between the traditional vehicles, lambda 1 is the ratio of the net-linked vehicles to the total vehicles in the first lane, lambda 2 is the ratio of the net-linked vehicles to the total vehicles in the second lane.
Further, the target number of network links in the road satisfies the formula:
Wherein, Alpha is the ratio of the average critical spacing between the net-linked vehicles and the average critical spacing between the traditional vehicles, beta is the ratio of the average critical spacing between the net-linked vehicles and the average critical spacing between the traditional vehicles, gamma is the ratio of the average critical spacing between the traditional vehicles and the average critical spacing between the traditional vehicles, lambda is the ratio of the net-linked vehicles to the total vehicles in the first lane.
In particular, inIn the case of/>That is, in the case where the ratio of the network-in-road linkage to the total vehicles in the road is the specific value, the three strategies are selected so that the maximum road traffic capacity can be achieved.
Compared with the prior art, the invention has at least the following beneficial effects:
In the invention, the proper driving strategy, namely the lanes on which the network vehicles can drive and the lanes on which the traditional vehicles can drive, is selected for the current road by acquiring the number of the network vehicles in the current road and the traffic flow condition of the road, so that the traffic capacity of the road is improved.
Example 2
In one embodiment, referring to fig. 2 of the drawings, the present invention provides a schematic structural diagram of a road traffic flow data management system 30 based on signal processing.
The invention provides a road traffic flow data management system based on signal processing, a road comprises a first lane and a second lane, and the system comprises:
A first obtaining module 301, configured to obtain the road acoustic waveform, and obtain the first vehicle flow and the second vehicle flow according to the waveform;
A second obtaining module 302, configured to obtain the number of network-connected vehicles in the road and the number of traditional vehicles, and the ratio of network-connected vehicles in the first road;
and the obtaining module 303 is configured to obtain a driving strategy of the internet-connected vehicles according to the number of the internet-connected vehicles in the road and the traffic flow condition of the road, so as to improve the traffic capacity of the road.
Compared with the prior art, the invention has at least the following beneficial effects:
In the invention, the proper driving strategy, namely the lanes on which the network vehicles can drive and the lanes on which the traditional vehicles can drive, is selected for the current road by acquiring the number of the network vehicles in the current road and the traffic flow condition of the road, so that the traffic capacity of the road is improved.
Claims (5)
1. A method of managing road traffic flow data based on signal processing, wherein a road includes a first lane and a second lane, comprising:
s101: acquiring the road acoustic waveform, and acquiring the first vehicle flow and the second vehicle flow according to the acoustic waveform;
s102: acquiring the number of network-connected vehicles and the number of traditional vehicles in the road and the ratio of the network-connected vehicles in the first lane;
S103: obtaining a driving strategy of the network-connected vehicles according to the number of the network-connected vehicles in the road and the traffic flow condition of the road so as to improve the traffic capacity of the road;
the running strategy of the internet-connected vehicle specifically comprises the following steps:
First strategy: the internet-connected vehicle only runs on the first lane, and the traditional vehicle runs on the first lane and the second lane; or alternatively, the first and second heat exchangers may be,
Second strategy: the internet-connected vehicle only runs on the first lane, and the traditional vehicle only runs on the second lane; or alternatively, the first and second heat exchangers may be,
Third strategy: the internet-connected vehicle runs in the first lane and the second lane, and the traditional vehicle runs in the first lane and the second lane;
the step S103 specifically includes:
When the number of the network-connected vehicles in the road is smaller than the target number and the traffic flow condition of the road is larger than the maximum traffic flow condition of the second strategy, the running strategy of the network-connected vehicles selects the first strategy;
when the number of the network-connected vehicles in the road is smaller than the target number and the traffic flow condition of the road is smaller than the maximum traffic flow condition of the second strategy, the running strategy of the network-connected vehicles selects the second strategy or the third strategy;
when the number of the network-connected vehicles in the road is larger than the target number and the traffic flow condition of the road is larger than the maximum traffic flow condition of the second strategy, the driving strategy of the network-connected vehicles selects the third strategy;
When the number of the network-connected vehicles in the road is larger than the target number and the traffic flow condition of the road is smaller than the maximum traffic flow condition of the third strategy, the running strategy of the network-connected vehicles selects the second strategy or the first strategy;
The maximum traffic flow condition of the second strategy adopts the formula:
；
Wherein,
Traffic flow for the first lane and the second lane under a second strategy;
driving only the legacy vehicle traffic capacity for the first lane;
；
For the ratio of the average critical spacing between the internet-connected vehicles following the traditional vehicles to the average critical spacing between the traditional vehicles following the traditional vehicles,/> For the ratio of the average critical spacing between the net vehicles followed by the net vehicles to the average critical spacing between the traditional vehicles followed by the traditional vehicles,/>For the ratio of the average critical spacing between the conventional vehicles following the net train to the average critical spacing between the conventional vehicles following the conventional vehicles,/>A ratio of the net vehicle in the first lane to a total vehicle in the first lane;
the maximum traffic flow condition of the third strategy adopts the formula:
；
Wherein,
Traffic flow for the first lane and the second lane under a third strategy;
driving only the legacy vehicle traffic capacity for the first lane;
；
；
For the ratio of the average critical spacing between the internet-connected vehicles following the traditional vehicles to the average critical spacing between the traditional vehicles following the traditional vehicles,/> For the ratio of the average critical spacing between the net vehicles followed by the net vehicles to the average critical spacing between the traditional vehicles followed by the traditional vehicles,/>For the ratio of the average critical spacing between the conventional vehicles following the net train to the average critical spacing between the conventional vehicles following the conventional vehicles,/>For the ratio of the net car in the first lane to the total car in the first lane,/>And the ratio of the net-connected vehicle in the second lane to the total vehicle in the second lane is set.
2. The method for managing road traffic flow data according to claim 1, wherein said S101 specifically comprises:
s1011: acquiring the road acoustic waveform, wherein the acoustic waveform comprises a first acoustic waveform of the first lane and a second acoustic waveform of the second lane;
S1012: filtering and smoothing the first acoustic waveform and the second acoustic waveform to obtain a first processed waveform and a second processed waveform;
s1023: acquiring a first waveform and a second waveform, wherein the peak value of the first processing waveform and the second processing waveform is larger than a preset peak value;
S1024: acquiring the first waveform and the second waveform, wherein the rising edge rate of the waveforms is greater than the first waveform number and the second waveform number of a preset rate;
s1025: and obtaining the first vehicle flow and the second vehicle flow according to the first waveform quantity and the second waveform quantity.
3. The method for managing road traffic flow data according to claim 1, wherein the obtaining the number of network-connected vehicles and the number of conventional vehicles in the road specifically comprises:
Acquiring the number of the network-connected network signals in the road and the number of the network-connected network signals in the first lane;
and obtaining the number of the traditional vehicles in the first lane according to the first lane flow and the number of the network signals of the internet-connected vehicles in the first lane.
4. The method for managing road traffic flow data according to claim 1, characterized in that the target number of network links in the road satisfies the formula:
；
Wherein,
The target quantity of the network-connected vehicles is;
，/> For the ratio of the average critical spacing between the internet-connected vehicles following the traditional vehicles to the average critical spacing between the traditional vehicles following the traditional vehicles,/> For the ratio of the average critical spacing between the net vehicles followed by the net vehicles to the average critical spacing between the traditional vehicles followed by the traditional vehicles,/>For the ratio of the average critical spacing between the conventional vehicles following the net train to the average critical spacing between the conventional vehicles following the conventional vehicles,/>And the ratio of the net-connected vehicle in the first lane to the total vehicle in the first lane is set.
5. A signal processing based road traffic flow data management system applied to the method of any one of claims 1 to 4, characterized in that the road comprises a first lane and a second lane, comprising
The first acquisition module is used for acquiring the road acoustic waveform, and acquiring the first vehicle flow and the second vehicle flow according to the waveform;
the second acquisition module is used for acquiring the number of net-linked vehicles in the road, the number of traditional vehicles and the ratio of net-linked vehicles in the first road;
The obtaining module is used for obtaining the running strategy of the network-connected vehicles according to the number of the network-connected vehicles in the road and the traffic flow condition of the road so as to improve the traffic capacity of the road.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005190028A (en) * | 2003-12-24 | 2005-07-14 | Honda Electronic Co Ltd | Traffic counting method and device |
| CN104821080A (en) * | 2015-03-02 | 2015-08-05 | 北京理工大学 | Intelligent vehicle traveling speed and time predication method based on macro city traffic flow |
| CN109785632A (en) * | 2019-03-14 | 2019-05-21 | 济南浪潮高新科技投资发展有限公司 | A kind of magnitude of traffic flow statistical method and device |
| CN113345268A (en) * | 2021-07-16 | 2021-09-03 | 长沙理工大学 | CAV lane change decision-making method for expressway down-ramp diversion area based on automatic driving special lane deployment scene |
| KR102305267B1 (en) * | 2021-03-12 | 2021-09-27 | 주식회사 대경이앤씨 | Sensitive traffic signal control system using continuous image detection |
| CN114360266A (en) * | 2021-12-20 | 2022-04-15 | 东南大学 | Intersection reinforcement learning signal control method for sensing detection state of internet connected vehicle |
| CN114897306A (en) * | 2022-04-12 | 2022-08-12 | 重庆大学 | Method for calculating road section impedance of mixed traffic flow considering automatic driving special lane |
| CN115619012A (en) * | 2022-10-08 | 2023-01-17 | 河北工业大学 | Method for calculating traffic capacity of confluence area of mixed traffic flow highway with special lane |
| CN116013075A (en) * | 2023-01-05 | 2023-04-25 | 云控智行(上海)汽车科技有限公司 | Highway vehicle conversion coefficient dynamic calculation algorithm and system based on multisource perception data |
-
2023
- 2023-09-04 CN CN202311127891.2A patent/CN117409572B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005190028A (en) * | 2003-12-24 | 2005-07-14 | Honda Electronic Co Ltd | Traffic counting method and device |
| CN104821080A (en) * | 2015-03-02 | 2015-08-05 | 北京理工大学 | Intelligent vehicle traveling speed and time predication method based on macro city traffic flow |
| CN109785632A (en) * | 2019-03-14 | 2019-05-21 | 济南浪潮高新科技投资发展有限公司 | A kind of magnitude of traffic flow statistical method and device |
| KR102305267B1 (en) * | 2021-03-12 | 2021-09-27 | 주식회사 대경이앤씨 | Sensitive traffic signal control system using continuous image detection |
| CN113345268A (en) * | 2021-07-16 | 2021-09-03 | 长沙理工大学 | CAV lane change decision-making method for expressway down-ramp diversion area based on automatic driving special lane deployment scene |
| CN114360266A (en) * | 2021-12-20 | 2022-04-15 | 东南大学 | Intersection reinforcement learning signal control method for sensing detection state of internet connected vehicle |
| CN114897306A (en) * | 2022-04-12 | 2022-08-12 | 重庆大学 | Method for calculating road section impedance of mixed traffic flow considering automatic driving special lane |
| CN115619012A (en) * | 2022-10-08 | 2023-01-17 | 河北工业大学 | Method for calculating traffic capacity of confluence area of mixed traffic flow highway with special lane |
| CN116013075A (en) * | 2023-01-05 | 2023-04-25 | 云控智行(上海)汽车科技有限公司 | Highway vehicle conversion coefficient dynamic calculation algorithm and system based on multisource perception data |
Non-Patent Citations (1)
| Title |
|---|
| 北京理工大学学报2011年总目次(第31卷);北京理工大学学报;20111215(第12期);全文 * |
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