CN111882867A - City wisdom traffic early warning system based on thing networking - Google Patents
City wisdom traffic early warning system based on thing networking Download PDFInfo
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- CN111882867A CN111882867A CN202010638099.3A CN202010638099A CN111882867A CN 111882867 A CN111882867 A CN 111882867A CN 202010638099 A CN202010638099 A CN 202010638099A CN 111882867 A CN111882867 A CN 111882867A
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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
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
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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
Abstract
The invention provides an urban intelligent traffic early warning system based on the Internet of things, which comprises the following components: the road monitoring and collecting nodes are distributed in all roads of a city, are used for collecting traffic flow information of the roads in real time and sending the traffic flow information to the traffic prediction module; the vehicle-mounted terminal acquisition node is arranged in a vehicle running on a road, and is used for acquiring path planning information of the vehicle and sending the path planning information to the traffic prediction module; the traffic prediction module is respectively in communication connection with the road monitoring acquisition node and the vehicle-mounted terminal acquisition node, and is used for predicting the traffic flow condition of the urban road according to the traffic flow information of the road and the path planning information of the vehicles and sending out corresponding early warning information according to the prediction result. The system is beneficial to improving the foresight of urban road traffic condition analysis and meeting the requirements of modern urban intelligent traffic systems.
Description
Technical Field
The invention relates to the technical field of urban intelligent traffic, in particular to an urban intelligent traffic early warning system based on the Internet of things.
Background
Along with the development direction of smart cities being proposed, the development of smart traffic in cities is more and more emphasized by people.
In the prior art, road monitoring of urban traffic can only reflect real-time traffic conditions (such as traffic flow, congestion degree and the like) of a road, and when the road is congested, the traffic conditions of the road are reflected through a radio station, a map APP and the like. But can not predict the traffic condition of the urban road in advance and make corresponding early warning information, and can not meet the requirement of urban intelligent traffic.
Disclosure of Invention
Aiming at the problems, the invention aims to provide an urban intelligent traffic early warning system based on the Internet of things.
The purpose of the invention is realized by adopting the following technical scheme:
the utility model provides a city wisdom traffic early warning system based on thing networking, includes:
the road monitoring and collecting nodes are distributed in all roads of a city, are used for collecting traffic flow information of the roads in real time and sending the traffic flow information to the traffic prediction module;
the vehicle-mounted terminal acquisition node is arranged in a vehicle running on a road, and is used for acquiring path planning information of the vehicle and sending the path planning information to the traffic prediction module;
the traffic prediction module is respectively in communication connection with the road monitoring acquisition node and the vehicle-mounted terminal acquisition node, and is used for predicting the traffic flow condition of the urban road according to the traffic flow information of the road and the path planning information of the vehicles and sending out corresponding early warning information according to the prediction result.
In one embodiment, the path planning information of the vehicle includes destination information, route road information, and required time.
In an implementation manner, the vehicle-mounted terminal acquisition node may be disposed in a vehicle-mounted navigation system or a user mobile phone APP, and is configured to acquire path planning information of a vehicle according to navigation information set by a user and upload the path planning information to a traffic prediction module.
In one embodiment, the traffic prediction module is disposed in a cloud computing platform.
In one embodiment, each road monitoring and collecting node corresponds to one road, and the road monitoring and collecting nodes are used for collecting traffic flow information of the corresponding road.
In one embodiment, the system further comprises a map database module, wherein urban road map information is prestored in the map database module and comprises communication information between roads.
In one embodiment, the traffic prediction module predicts traffic flow conditions of an urban road according to traffic flow information of the road and path planning information of vehicles, and includes:
predicting traffic flow information of each road after a time period, and marking the road with the predicted traffic flow larger than a set threshold as a congestion early warning road;
the traffic flow information of the road after a time period delta t is predicted, wherein the adopted traffic flow prediction model is as follows:
N(t+Δt)＝N(t)+β1×Δt×N+β2×EN
wherein t represents the current time, N (t + Δ t) represents the predicted traffic flow of the road in a time period Δ t, N (t) represents the current road traffic flow acquired by the road monitoring acquisition node, and N represents the traffic flow change rate acquired according to the traffic flow of the road in 1 or more time periods before the current time; EN represents the number of vehicles which are estimated to arrive at the road after a time period and are acquired according to the vehicle path planning information acquired by the vehicle-mounted terminal acquisition node;
and when the predicted traffic flow N (t + delta t) of the road is judged to be larger than the set threshold value, marking the road as a congestion early warning road.
The invention has the beneficial effects that:
the system of the invention comprehensively predicts the traffic flow of the urban road by combining the real-time traffic flow information acquired from the urban road and the path planning information of the vehicles and sends out the corresponding road congestion early warning information according to the prediction result, thereby being beneficial to improving the foresight of the traffic condition analysis of the urban road, improving the performance of the urban intelligent traffic early warning system and meeting the requirements of the modern urban intelligent traffic system.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a frame structure diagram of the present invention.
Reference numerals:
road monitoring collection node 100, vehicle-mounted terminal collection node 200 and traffic prediction module 300
Detailed Description
The invention is further described in connection with the following application scenarios.
Referring to fig. 1, it shows an urban intelligent traffic early warning system based on internet of things, which is characterized by comprising:
the road monitoring and collecting nodes 100 are distributed in each road of the city, and are used for collecting traffic flow information of the road in real time and sending the traffic flow information to the traffic prediction module 300;
the system comprises a vehicle-mounted terminal acquisition node 200, a traffic prediction module 300 and a vehicle-mounted terminal acquisition node 200, wherein the vehicle-mounted terminal acquisition node 200 is arranged in a vehicle running on a road, and is used for acquiring path planning information of the vehicle and sending the path planning information to the traffic prediction module 300;
the traffic prediction module 300 is in communication connection with the road monitoring collection node 100 and the vehicle-mounted terminal collection node 200, and is configured to predict traffic flow conditions of an urban road according to traffic flow information of the road and path planning information of vehicles, and send out corresponding early warning information according to a prediction result.
The urban road is divided into a one-way lane as one road, namely, a two-way lane, which is set to include two roads in the application.
Meanwhile, in order to improve the accuracy and adaptability of the system, the road traffic flow information acquired by the road monitoring acquisition node 100 should be the average traffic flow of each lane in the road; for a one-way four-lane road, for example, the road monitoring collection node 100 records that the traffic flow of the high road should be 4 divided by the total traffic flow of the road.
In one embodiment, the path planning information of the vehicle includes destination information, route road information, and required time.
In an embodiment, the vehicle-mounted terminal acquisition node 200 may be disposed in a vehicle-mounted navigation system or a user mobile phone APP, and configured to acquire path planning information of a vehicle according to navigation information set by a user and upload the path planning information to the traffic prediction module 300.
In one embodiment, the traffic prediction module 300 is disposed in a cloud computing platform.
When the vehicle built-in navigation system or the user mobile phone adopts APP navigation, the vehicle built-in navigation system or the user mobile phone is connected with the cloud computing platform where the traffic prediction module 300 is located through wireless communication, and real-time data interaction is performed. When the traffic prediction module 300 predicts a congestion early warning road, feeding back early warning information to a built-in navigation system or a user mobile phone APP for displaying in real time; the method is beneficial to the user to adjust the path planning scheme in time.
According to the traffic flow prediction scheme, the change condition of the road traffic flow can be predicted in advance, particularly, gathering activities are carried out on people with major activities or traffic condition prediction is carried out on places where people flow is likely to gather, such as important places (such as airports and railway stations), the early warning effect can be achieved, early response measures can be made by management departments or the masses, and the management effect of urban intelligent traffic is improved.
In one embodiment, each road monitoring and collecting node 100 corresponds to one road, and the road monitoring and collecting node 100 is configured to collect traffic flow information of the corresponding road.
In one embodiment, the system further comprises a map database module, wherein urban road map information is prestored in the map database module and comprises communication information between roads.
In one embodiment, the traffic prediction module 300 predicts the traffic flow situation of the urban road according to the traffic flow information of the road and the path planning information of the vehicle, and includes:
predicting traffic flow information of each road after a time period, and marking the road with the predicted traffic flow larger than a set threshold as a congestion early warning road;
the traffic flow information of the road after a time period delta t is predicted, wherein the adopted traffic flow prediction model is as follows:
N(t+Δt)＝N(t)+β1×Δt×N+β2×EN
wherein t represents the current time, N (t + Δ t) represents the predicted traffic flow of the road after a time period Δ t, N (t) represents the current road traffic flow acquired by the road monitoring acquisition node, and N represents the traffic flow change rate acquired according to the traffic flow of the road in 1 or more time periods before the current time; EN represents the number of vehicles which are estimated to arrive at the road after a time period and are acquired according to the vehicle path planning information acquired by the vehicle-mounted terminal acquisition node; beta is a1And beta2Respectively, a trend influence factor, wherein1+β2＝1，β1、β2＞0
And when the predicted traffic flow N (t + delta t) of the road is judged to be larger than the set threshold value, marking the road as a congestion early warning road.
In one scenario, according to the traffic flow prediction results of the roads, when the traffic flow prediction results of a plurality of roads in one area are all larger than a set threshold value, the area is marked as a congestion early warning area.
In the embodiment, a technical scheme for predicting the traffic flow of an actual urban road is provided, the traffic flow is predicted on the road by the method, the road congestion condition which may occur at a certain time point can be accurately predicted, meanwhile, the condition of a route or a vehicle in the route with the road as a destination is considered in the prediction process, the accuracy of traffic condition prediction is improved, and meanwhile, the prediction result is combined to perform early warning prompt. The use effect of the early warning system is improved.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be analyzed by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (7)
1. The utility model provides a city wisdom traffic early warning system based on thing networking which characterized in that includes:
the road monitoring and collecting nodes are distributed in each road of a city, are used for collecting traffic flow information of the road in real time and sending the traffic flow information to the traffic prediction module;
the vehicle-mounted terminal acquisition node is arranged in a vehicle running on a road, is used for acquiring path planning information of the vehicle and sending the path planning information to the traffic prediction module;
and the traffic prediction module is respectively in communication connection with the road monitoring acquisition node and the vehicle-mounted terminal acquisition node, and is used for predicting the traffic flow condition of the urban road according to the traffic flow information of the road and the path planning information of the vehicles and sending corresponding early warning information according to the prediction result.
2. The system of claim 1, wherein the route planning information of the vehicle comprises destination information, route road information and required time.
3. The urban intelligent traffic early warning system based on the internet of things according to claim 1, wherein the vehicle-mounted terminal acquisition node can be arranged in a vehicle built-in navigation system or a user mobile phone APP and used for acquiring path planning information of a vehicle according to navigation information set by a user and uploading the path planning information to the traffic prediction module.
4. The internet of things-based urban intelligent traffic early warning system according to claim 1, wherein the traffic prediction module is disposed in a cloud computing platform.
5. The urban intelligent traffic early warning system based on the internet of things as claimed in claim 1, wherein each road monitoring and collecting node corresponds to a road, and the road monitoring and collecting nodes are used for collecting traffic flow information of the corresponding road.
6. The urban intelligent traffic early warning system based on the Internet of things as claimed in claim 1, further comprising a map database module, wherein urban road map information is prestored in the map database module and comprises communication information between roads.
7. The system of claim 5, wherein the traffic prediction module predicts the traffic flow of the urban road according to the traffic flow information of the road and the path planning information of the vehicle, and comprises:
predicting traffic flow information of each road after a time period, and marking the road with the predicted traffic flow larger than a set threshold as a congestion early warning road;
the traffic flow information of the road after a time period delta t is predicted, wherein the adopted traffic flow prediction model is as follows:
N(t+Δt)＝N(t)+β1×Δt×N+β2×EN
wherein t represents the current time, N (t + Δ t) represents the predicted traffic flow of the road after a time period Δ t, N (t) represents the current road traffic flow acquired by the road monitoring acquisition node, and N represents the traffic flow change rate acquired according to the traffic flow of the road in 1 or more time periods before the current time; EN represents the number of vehicles which are estimated to arrive at the road after a time period and are acquired according to the vehicle path planning information acquired by the vehicle-mounted terminal acquisition node;
and when the predicted traffic flow N (t + delta t) of the road is judged to be larger than the set threshold value, marking the road as a congestion early warning road.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202010638099.3A CN111882867A (en) | 2020-07-03 | 2020-07-03 | City wisdom traffic early warning system based on thing networking |
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| CN202010638099.3A CN111882867A (en) | 2020-07-03 | 2020-07-03 | City wisdom traffic early warning system based on thing networking |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112289063A (en) * | 2020-11-20 | 2021-01-29 | 烟台职业学院 | Smart city data migration and storage management system based on Internet of things |
| CN112330074A (en) * | 2020-12-02 | 2021-02-05 | 公安部交通管理科学研究所 | Bayonet traffic early warning method based on mobile police service |
| CN113409579A (en) * | 2021-06-28 | 2021-09-17 | 鄂尔多斯市龙腾捷通科技有限公司 | Intelligent city traffic control system based on AI internet of things technology |
| CN113670317A (en) * | 2021-06-21 | 2021-11-19 | 福建睿思特科技股份有限公司 | Smart city security protection intelligent lamp pole control method |
| CN113808387A (en) * | 2021-07-30 | 2021-12-17 | 张承梅 | Highway wisdom traffic diversion system |
| CN114792471A (en) * | 2022-04-25 | 2022-07-26 | 南京泛在地理信息产业研究院有限公司 | Intelligent city traffic management early warning system based on GIS platform |
| CN116453360A (en) * | 2023-05-08 | 2023-07-18 | 广东骏思信息科技有限公司 | Traffic management system based on big data |
-
2020
- 2020-07-03 CN CN202010638099.3A patent/CN111882867A/en not_active Withdrawn
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112289063A (en) * | 2020-11-20 | 2021-01-29 | 烟台职业学院 | Smart city data migration and storage management system based on Internet of things |
| CN112330074A (en) * | 2020-12-02 | 2021-02-05 | 公安部交通管理科学研究所 | Bayonet traffic early warning method based on mobile police service |
| CN113670317A (en) * | 2021-06-21 | 2021-11-19 | 福建睿思特科技股份有限公司 | Smart city security protection intelligent lamp pole control method |
| CN113670317B (en) * | 2021-06-21 | 2024-02-02 | 福建睿思特科技股份有限公司 | Smart city security intelligent lamp post control method |
| CN113409579A (en) * | 2021-06-28 | 2021-09-17 | 鄂尔多斯市龙腾捷通科技有限公司 | Intelligent city traffic control system based on AI internet of things technology |
| CN113808387A (en) * | 2021-07-30 | 2021-12-17 | 张承梅 | Highway wisdom traffic diversion system |
| CN114792471A (en) * | 2022-04-25 | 2022-07-26 | 南京泛在地理信息产业研究院有限公司 | Intelligent city traffic management early warning system based on GIS platform |
| CN116453360A (en) * | 2023-05-08 | 2023-07-18 | 广东骏思信息科技有限公司 | Traffic management system based on big data |
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Application publication date: 20201103 |