CN114399900B - Smart city traffic management system and method based on remote sensing technology - Google Patents
Smart city traffic management system and method based on remote sensing technology Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G—PHYSICS
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- 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
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- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
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- G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
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Abstract
The invention provides a smart urban traffic management system based on a remote sensing technology, which comprises remote sensing equipment, a GPS data receiving module, a control center and traffic lights. According to the intelligent urban traffic management system and method based on the remote sensing technology, the remote sensing technology based on the high-resolution satellite is combined with the GPS positioning technology based on the map navigation APP, the time duration of the red light and the green light of the traffic signal lamp of the congested road section can be controlled and adjusted according to the real-time road condition, so that smooth roads intersected with the congested road section can share certain traffic pressure, the long-time serious traffic congestion is avoided, and the overall efficiency of urban traffic operation is improved.
Description
Technical Field
The invention belongs to the technical field of intelligent traffic systems, and particularly relates to a smart city traffic management system and method based on a remote sensing technology.
Background
In recent years, with the development of urbanization in China, population is continuously gathered to cities, so that more and more cities are faced with the problem of traffic congestion, public trip, large energy consumption and environmental pollution are influenced, and the normal performance and sustainable development of urban functions are also influenced. An Intelligent Transportation System (ITS) has been produced as an effective tool for solving the problems of traffic safety, efficiency and congestion.
The inventor finds that at intersections of many traffic congestion road sections, the traffic flow in a certain direction (such as the east-west direction) tends to travel very slowly, but the traffic flow in a direction perpendicular to the direction (such as the north-south direction) tends to be relatively smooth. This is mainly due to the fact that the red and green durations of the traffic lights currently used in cities are fixed, and the real-time adjustment cannot be carried out, so that the traffic flow pressure in the congestion direction cannot be shared by roads in the unobstructed direction.
Disclosure of Invention
In view of the above defects in the prior art, the present invention aims to provide a smart urban traffic management system and method based on a remote sensing technology, which can control and adjust the time lengths of the red light and the green light of traffic signal lamps in congested road sections in real time according to road conditions based on a high resolution satellite remote sensing technology and in combination with a GPS positioning technology, so that unobstructed roads intersected with the congested roads can share certain traffic pressure, thereby avoiding long-time serious traffic congestion and improving the overall efficiency of urban traffic operation.
In order to achieve the above object, in one aspect, the present invention provides a smart city traffic management system based on remote sensing technology, which includes a remote sensing device, a GPS data receiving module, a control center and a traffic signal lamp; wherein the content of the first and second substances,
the remote sensing equipment is used for acquiring a high-resolution satellite remote sensing image of the urban road and sending the remote sensing image to the control center;
the GPS data receiving module is used for acquiring congestion road section information on the map navigation APP in real time and sending the information to the control center;
the control center is used for judging the congestion degree of the urban road according to the received congestion road section information and the remote sensing image and sending a control instruction to the corresponding traffic signal lamp according to the congestion degree;
the traffic signal lamp can receive a control instruction from the control center in real time, and adjust the duration of the red light and the green light according to the received control instruction.
Furthermore, the remote sensing images are panchromatic band images and multispectral band images, wherein the ground resolution of the panchromatic band images is better than 0.61m, and the ground resolution of the multispectral band images is better than 2.44m.
Further, the GPS data receiving module may be connected to a smart computer device (e.g., a smart phone, a tablet computer, etc.) equipped with a map navigation APP (e.g., a Baidu map, a Gade map, a Google map, etc.) to obtain real-time congestion road segment information.
Furthermore, the GPS data receiving module may also be directly connected to a server of a map navigation APP provider (e.g., a Baidu map, a Gade map, a Google map, etc.) to obtain information of congested road segments and/or GPS data of vehicles on urban roads in real time, and accordingly determine traffic information on the congested road segments. Of course, such a connection generally requires obtaining consent from the map navigation APP provider and its users.
Furthermore, the control center and the traffic signal lamp send and receive control commands through a cellular network module (such as a 3G, 4G and 5G communication module).
On the other hand, the invention provides a smart urban traffic management method based on a remote sensing technology, which is used for reducing the occurrence of traffic jam on urban roads and comprises the following steps:
step S1: the GPS data receiving module acquires information of a congested road section on a map navigation APP in real time and sends the information to the control center;
step S2: the control center judges whether the congested road section is provided with a traffic signal lamp or not, if the traffic signal lamp exists, the traffic signal lamp is positioned at an intersection or a T-shaped intersection, namely, a road intersecting (generally vertical) the congested road section exists, and the road is called as an intersected road section; the control center segments the jammed road sections and the intersected road sections according to traffic signal lamps;
and step S3: the remote sensing equipment acquires high-resolution satellite remote sensing images of the congested road section and the intersected road section, and judges the traffic flow conditions of the congested road section and the intersected road section according to the remote sensing images;
and step S4: if the traffic flow condition of the intersected road section is obviously superior to that of the congested road section, the control center sends a control instruction to a traffic signal lamp of the congested road section, so that the green light time of the congested road section is prolonged, and/or the red light time of the congested road section is shortened, and the green light time of the corresponding intersected road section is correspondingly shortened, and/or the red light time of the intersected road section is prolonged.
Further, the congested link information in step S1 includes a congested link location and a traffic flow length.
Further, regarding the determination of the traffic flow conditions of the congested road segment and the intersected road segment according to the remote sensing image in the step S3, there are a lot of technical solutions available in the prior art for determining the traffic flow conditions on the urban road according to the high-resolution satellite remote sensing image. For example, the chinese patent application CN102855759A realizes automatic acquisition of high-resolution satellite remote sensing traffic flow information, which can calculate data such as single vehicle driving speed, road section traffic flow speed, traffic flow density, traffic flow, road space occupancy, vehicle head distance, and the like.
Further, the traffic situation in step S3 is determined by at least one index of the traffic length, the link traffic flow speed, the traffic flow density, the traffic flow, and the road space occupancy.
Further, the traffic flow conditions of the congested road section and the intersected road section in the step S3 adopt a sectional statistical mode, and a section is formed between two adjacent traffic signal lamps on the same road.
Further, the fact that the traffic flow conditions of the intersection road sections in step S4 are significantly better than those of the congested road sections means that at least one of the intersection road sections and the congested road sections has a difference of more than 60%, or at least two of the intersection road sections and the congested road sections have a difference of more than 50%, or at least three of the intersection road sections and the congested road sections have a difference of more than 40%; or at least four of the four parts have a difference of more than 30 percent, or five parts have a difference of more than 20 percent. For example, if only the index of the traffic flow density k is considered, when (k 1-k 2)/k 2>60% is satisfied between the traffic flow density k1 of the congested road segment and the traffic flow density k2 of the intersected road segment, it is considered that the traffic flow condition of the intersected road segment is obviously better than that of the congested road segment, and at this time, the green time of the traffic signal lamp of the congested road segment needs to be prolonged, and the green time of the traffic signal lamp of the corresponding intersected road segment needs to be correspondingly shortened. Wherein k represents the density of the traffic flow, namely the number of all vehicles on a road with a certain unit length, and the unit is veh/km.
Further, the relationship between the time t for which the red light or green light is extended or shortened and the traffic flow density k1 of the congested road section and the traffic flow density k2 of the intersected road section is:
t＝f(k1-k2)/k2
wherein f is a proportionality coefficient, and the optimal value of f is different due to different road conditions of the position of each traffic signal lamp; t is in seconds and rounded to an integer. For example, t =3s, means that the green time of a congested road segment is lengthened by 3s and the green time of a corresponding intersecting road segment is shortened by 3s.
Furthermore, through simulation tests of the inventor, the optimal value of f is approximately between 2.7 and 7.3, and specific numerical values are different according to factors such as the length and the width of a congested road section and an intersected road section where a traffic signal lamp is located.
Further, for each traffic light, the optimal value of f can be obtained through long-term observation of data (e.g., t, f, (k 1-k 2)/k 2) of the intersection and through machine learning. For example, in a certain congestion situation at the intersection, the control center tries to take the value of f to 2, and then observes the change of (k 1-k 2)/k 2 with the time T after adjusting the red light and green light time, for example, how long (k 1-k 2)/k 2 can be reduced to below 0.5. In this manner, through multiple attempts and long-term data observations, the optimal scaling factor for the traffic signal can be found.
According to the intelligent urban traffic management system and method based on the remote sensing technology, the remote sensing technology based on the high-resolution satellite is combined with the GPS positioning technology based on the map navigation APP, the time duration of the red light and the green light of the traffic signal lamp of the congested road section can be controlled and adjusted according to the real-time road condition, so that smooth roads intersected with the congested road section can share certain traffic pressure, the long-time serious traffic congestion is avoided, and the overall efficiency of urban traffic operation is improved.
Drawings
FIG. 1 is a schematic diagram of a high resolution satellite remote sensing image of a smart city traffic management system based on remote sensing technology in accordance with a preferred embodiment of the present invention;
fig. 2 is a flow chart illustrating a smart city traffic management method based on remote sensing technology according to a preferred embodiment of the present invention.
Detailed Description
The following examples are given to illustrate the present invention in detail, and the following examples are given to illustrate the detailed embodiments and specific procedures of the present invention, but the scope of the present invention is not limited to the following examples.
In a preferred embodiment, the smart urban traffic management system based on the remote sensing technology comprises a remote sensing device, a GPS data receiving module, a control center and a traffic signal lamp; the remote sensing equipment is used for acquiring a high-resolution satellite remote sensing image (shown in figure 1) of an urban road and sending the remote sensing image to the control center; the GPS data receiving module is used for acquiring congestion road section information on the map navigation APP in real time and sending the information to the control center; the control center is used for judging the congestion degree of the urban road according to the received congestion road section information and the remote sensing image and sending a control instruction to the corresponding traffic signal lamp according to the congestion degree; the traffic signal lamp can receive a control instruction from the control center in real time, and adjust the duration of the red light and the green light according to the received control instruction.
The remote sensing image is a panchromatic waveband image and a multispectral waveband image, wherein the ground resolution of the panchromatic waveband image is better than 0.61m, and the ground resolution of the multispectral waveband image is better than 2.44m.
The GPS data receiving module may be connected to an intelligent computer device (e.g., a smart phone, a tablet computer, etc.) equipped with a map navigation APP (e.g., a Baidu map, a Gauder map, a Google map, etc.) to acquire real-time congestion road segment information.
The GPS data receiving module can also be directly connected with a server of a map navigation APP provider (such as a Baidu map, a Gade map, a Google map and the like) to acquire the information of the congested road sections and/or the GPS data of vehicles on the urban roads in real time, and accordingly, the traffic information on the congested road sections is judged. Of course, such a connection generally requires obtaining consent from the map navigation APP provider and its users.
The control center and the traffic signal lamp send and receive control instructions through a cellular network module (such as a 3G, 4G and 5G communication module).
As shown in fig. 2, the present embodiment further provides a smart city traffic management method based on remote sensing technology to reduce the occurrence of traffic congestion on urban roads, including the following steps:
step S1: the GPS data receiving module acquires information of a congested road section on a map navigation APP in real time and sends the information to the control center; the congestion section information includes a congestion section position and a traffic flow length.
Step S2: the control center determines whether there is a traffic light on the congested road segment (east-west road segment in fig. 1), and the coordinate points marked in fig. 1 are crossroads or t-junctions having traffic lights, such as (0, 0), (1, 0), (2, 0), (-1, 0), (-2, 0), (-3, 0), (-4, 0), (0, 1), (0, -1), (0, -2), etc. In fig. 1, the north-south road segment perpendicular to the congested road segment is the intersection road segment of the congested road segment.
And step S3: the remote sensing equipment acquires high-resolution satellite remote sensing images of the congested road section and the intersected road section, and judges the traffic flow conditions of the congested road section and the intersected road section according to the remote sensing images; the traffic flow condition is judged by at least one index of the traffic flow length (obtained by map navigation APP or remote sensing images), the road section traffic flow speed, the traffic flow density, the traffic flow and the road space occupancy, and is preferably the road section traffic flow speed or the traffic flow density. The traffic flow conditions of the congested road section and the intersection road section adopt a sectional statistical mode, and a section is arranged between two adjacent traffic signal lamps on the same road, for example, a section is arranged between (0, 0) and (1, 0), and another section is arranged between (0, 0) and (-1, 0). In this embodiment, (0, 0) is set as the origin of coordinates because the (0, 0) to (1, 0) sections and (0, 0) to (-1, 0) sections are the sections with the minimum traffic flow speed, that is, the most congested sections among the congested sections.
And step S4: if the traffic flow condition of the intersected road section is obviously superior to that of the congested road section, the control center sends a control instruction to a traffic signal lamp of the congested road section, so that the green light time of the congested road section is prolonged, and/or the red light time of the congested road section is shortened, and the green light time of the corresponding intersected road section is correspondingly shortened, and/or the red light time of the intersected road section is prolonged. In the embodiment, the fact that the traffic flow condition of the intersection road section is obviously better than that of the congestion road section means that the difference between the intersection road section and the congestion road section in two indexes of traffic flow speed or traffic flow density is more than 60%, or the difference between the intersection road section and the congestion road section in the two indexes is more than 50%. The relationship between the time t for which the red light or the green light is extended or shortened and the traffic flow density k1 of the congested road section and the traffic flow density k2 of the intersected road section is as follows:
t＝f(k1-k2)/k2
wherein f is a proportionality coefficient, and the optimal value of f is different due to different road conditions of the position of each traffic signal lamp; t is in seconds and rounded off to give an integer. For example, in this embodiment, t =3s means that the green light time of the congested road segment is prolonged by 3s and the green light time of the corresponding intersection road segment is shortened by 3s, so that the vehicles on the congested road segment can have more time to pass through, while the vehicles on the intersection road segment sacrifice some passing time, but the overall passing efficiency is greatly improved, that is, k1+ k2 is significantly reduced compared with that before adjustment. Through simulation tests of the inventor, the optimal value of f is approximately between 2.7 and 7.3.
For each traffic light, the best value for f can be obtained through long-term observation of the intersection's data and through machine learning. For example, in this embodiment, the initial value of (k 1-k 2)/k 2 is 1.0, the control center tries to take the value of f as 2, and then prolongs the green time of the congested road segment by 2s and shortens the green time of the corresponding intersection road segment by 2s; after 20min, (k 1-k 2)/k 2 decreased from 1.0 to below 0.5. Thus, through multiple attempts in a congestion situation and long-term data observation, the optimal scaling factor for the traffic signal lamp, namely the scaling factor when (k 1-k 2)/k 2 decreases fastest can be found, and k1+ k2 is obviously reduced compared with that before adjustment.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (9)
1. A method for carrying out traffic management by a smart urban traffic management system based on a remote sensing technology is used for reducing the occurrence of traffic jam on urban roads and is characterized in that the smart urban traffic management system based on the remote sensing technology comprises a remote sensing device, a GPS data receiving module, a control center and traffic lights; wherein the content of the first and second substances,
the remote sensing equipment is used for acquiring a high-resolution satellite remote sensing image of the urban road and sending the remote sensing image to the control center;
the GPS data receiving module is used for acquiring congestion road section information on the map navigation APP in real time and sending the information to the control center;
the control center is used for judging the congestion degree of the congested road section according to the received congestion road section information and the corresponding remote sensing image, and sending a control instruction to the corresponding traffic signal lamp according to the congestion degree;
the traffic signal lamp can receive a control instruction from the control center in real time and adjust the duration of the red light and the green light according to the received control instruction;
the method comprises the following steps:
step S1: the GPS data receiving module acquires information of a congested road section on a map navigation APP in real time and sends the information to the control center;
step S2: the control center judges whether a traffic signal lamp is arranged on the congested road section, if the traffic signal lamp exists, an intersected road section intersected with the congested road section exists; the control center segments the congested road sections and the intersected road sections according to traffic signal lamps;
and step S3: the remote sensing equipment acquires high-resolution satellite remote sensing images of the congested road section and the intersected road section, and judges the traffic flow conditions of the congested road section and the intersected road section according to the remote sensing images;
and step S4: if the traffic flow condition of the intersected road section is obviously superior to that of the congested road section, the control center sends a control instruction to a traffic signal lamp of the congested road section, so that the green light time of the congested road section is prolonged, and/or the red light time of the congested road section is shortened, and the green light time of the corresponding intersected road section is correspondingly shortened, and/or the red light time of the intersected road section is prolonged; wherein the content of the first and second substances,
judging the traffic flow condition in the step S3 through the traffic flow density k; the relationship between the time t for which the red light or the green light is extended or shortened and the traffic flow density k1 of the congested road section and the traffic flow density k2 of the intersected road section is as follows:
t＝f(k1-k2)/k2
wherein, f is a proportionality coefficient, and the optimal value of the proportionality coefficient is different for different traffic signal lamps; for a specific traffic signal lamp, the optimal value of f is obtained by long-term observation of data of corresponding (k 1-k 2)/k 2 decreasing rate of the specific traffic signal lamp when the f is different in value and machine learning; t is in seconds and rounded off to give an integer.
2. The method for traffic management of a smart city traffic management system based on remote sensing technology as claimed in claim 1, wherein the remote sensing images are panchromatic band images and multispectral band images, wherein the ground resolution of the panchromatic band images is better than 0.61m, and the ground resolution of the multispectral band images is better than 2.44m.
3. The method for traffic management of the smart city traffic management system based on remote sensing technology as claimed in claim 1, wherein the GPS data receiving module is connected with an intelligent computer device installed with a map navigation APP to obtain real-time information of congested road sections.
4. The method for traffic management of the smart city traffic management system based on remote sensing technology as claimed in claim 1, wherein the GPS data receiving module is directly connected with a server of a map navigation APP provider to obtain information of congested road sections and/or GPS data of vehicles on urban roads in real time, and accordingly, to determine traffic information on congested road sections.
5. The method for traffic management of a smart city traffic management system based on remote sensing technology as claimed in claim 1, wherein the control center and the traffic signal lamp transmit and receive control commands through a cellular network module.
6. The method for traffic management by a smart city traffic management system based on remote sensing technology as claimed in claim 1, wherein the information of congested road section in step S1 includes location of congested road section and length of traffic stream.
7. The method for traffic management of a smart city traffic management system based on remote sensing technology as claimed in claim 1, wherein the traffic flow situation in step S3 can be determined by at least one index of the length of the traffic flow, the speed of the road section traffic flow, the traffic flow and the road space occupancy in addition to the determination by the traffic flow density.
8. The method for traffic management by using a smart city traffic management system based on remote sensing technology as claimed in claim 7, wherein the fact that the traffic flow condition of the intersection section in step S4 is significantly better than that of the congested section means that at least one of five indexes of the intersection section and the congested section, including the length of the traffic flow, the traffic flow speed of the section, the traffic flow density, the traffic flow and the road space occupancy, differs by more than 60%; or at least two of them are different by more than 50%; or at least three of the components have a difference of more than 40 percent; or at least four of the components have a difference of more than 30%; or more than 20% of each five.
9. The method for traffic management of the intelligent city traffic management system based on remote sensing technology as claimed in claim 1, wherein f has a value between 2.7-7.3.
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| CN115273493B (en) * | 2022-05-16 | 2024-03-19 | 广西交科集团有限公司 | Interactive urban traffic intelligent control system and method |
| CN115482664B (en) * | 2022-09-13 | 2023-04-07 | 上海智能交通有限公司 | Road network traffic flow space-time prediction method for intelligent traffic |
| CN116631192B (en) * | 2023-06-12 | 2024-03-29 | 上海优景智能科技股份有限公司 | Comprehensive management method and system for smart city |
| CN117198058B (en) * | 2023-11-01 | 2024-01-30 | 合肥师范学院 | Road traffic intelligent supervision system based on remote sensing image |
| CN117809460B (en) * | 2024-03-01 | 2024-05-14 | 电子科技大学 | Intelligent traffic regulation and control method and system |
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