CA3210895A1 - A practical method to collect and measure traffic flow data through ip cameras for origin-destination survey studies and for other uses - Google Patents

Abstract

To conduct traffic studies, it might be essential to have reliable, accurate, and up-to-date traffic flow data. However, traffic flow data seem to be generally unavailable, not up to date, inaccurate, and difficult to access.
Nevertheless, one of the most recognized methods for collecting traffic data appears to be through Origin-Destination surveys (0/D). These can, for example, help to estimate the effects of telework on traffic following the COVID-19 pandemic.
The Federal Highway Administration (FHWA) recommends a variety of approaches for carrying out O/D surveys, but these have significant limitations. It seems necessary to collect data from thousands of households and drivers through surveys, questionnaires, and interviews, and it appears that these surveys are very expensive and can be conducted only every five to ten years. Traffic data can therefore quickly become obsolete, be distorted by bias or poor sampling, and might require complex statistical methods. In addition, O/D surveys do not seem to consider short-term variations, and they might generate inaccurate traffic patterns.
My invention aims to obtain more accurate Origin-Destination surveys that could make data more conveniently accessible. It also aims for the gathered data to be more conveniently accessed by cities, transport agencies, public bodies, and others.
My method can provide for the installation of a series of Internet Protocol (IP) cameras to measure traffic flows simultaneously or quasi-simultaneously on different sections of a road or highway network around an urban agglomeration. It can make it possible to measure movements quickly and conveniently from one part of a city to another.

It appears to provide access to reliable data, without the need to collect large-scale sampling or to use data disseminated by various suppliers, such as mobile device and GPS suppliers. It can also avoid the installation of expensive devices that have to be integrated into the IT systems of cities, agencies, and public bodies.
My invention appears to be a simple, accurate, practical, and economic method to improve the results of 0/D surveys and for other uses that can extend to the permanent and automated management of traffic and mobility in real time. It can transform static traffic signal systems into dynamic ones without the need to implement major changes to current systems.
SPECIFICATION
TITLE: A PRACTICAL METHOD TO COLLECT AND MEASURE TRAFFIC
FLOW DATA THROUGH IP CAMERAS FOR ORIGIN-DESTINATION
SURVEY STUDIES AND FOR OTHER USES
BACKGROUND OF THE INVENTION
The present invention belongs to the field of Transportation and Traffic research as well as the methodologies behind them.
EXISTING CURRENT SYSTEMS
To conduct traffic studies, it might be essential to have reliable, accurate, and up-to-date data on traffic flows. One of the most recognized and commonly used methods might be to collect travel data through Origin-Destination or travel surveys.
However, Origin-Destination surveys may have significant limitations such as high costs, data that can quickly become obsolete as travel habits change over time, and a lack of precision due to low response rates and potential biases.
Besides, data from mobile devices and GPS navigation systems may not be practical and economic solutions for conducting Origin-Destination surveys because they appear to be disseminated among several providers, and there might be legal, security, and privacy issues to consider.
Other approaches, like installing devices permanently along roads, can also have disadvantages due to the wide range of equipment on the market, the costs of purchase, installation, and maintenance, and the complexity of data processing.
Even if there seems to be a multitude of approaches in image recognition and artificial vision, one might consider problems with lighting, visibility, and temperature. Also, it might be unthinkable to track all vehicles from their departure points to their arrival destinations. In addition, large-scale routes between various parts of a geographic region might need to be inferred, which is a very complex task.
The purpose of my invention aims to collect and process accurate traffic flow data to estimate the movements of a group of vehicles without necessarily identifying all of them in a very precise way.
My method could improve the accuracy of data collected from Origin-Destination surveys and measure traffic flows at any time of day or night, heading toward or away from city centers or between various parts of a city.
In addition, my method can be used for other purposes, such as automated management of traffic systems in real-time. For example, traffic data collected with my invention can be used to adapt traffic light systems in real-time from a Traffic Management Center.
Furthermore, it appears that the two widely deployed Traffic Light Systems (TLS) are the Static-TLS and the Adaptive TLS. The Static-TLS appears to be programmed for a given period, meaning that the duration of cycles and phases cannot change during that entire period, regardless of traffic flow variations. These periods could be morning rush hour, afternoon rush hour, noon, evening, night, weekends, and so on.
On the other hand, Adaptive TLS appears to partially respond to demand.
They generally work with detection loops installed under the pavement and connected to a controller installed near the traffic light system.
The main limitations of Static TLS appear to be that they do not respond efficiently to demand variations. Another flaw is that most of them do not seem to be updated frequently enough. Regarding Adaptive TLS, they appear to be too expensive to buy, install, and maintain. Also, they seem to be complicated to operate, and many experts doubt their effectiveness.
Regarding these systems, it is a concept that has been around for almost a hundred years (since the late 1920s), particularly static systems.
Nevertheless, it seems that there are now almost 400,000 Traffic Light Systems in North America. Modifying them would be too expensive, much less replacing them.
My invention might transform current Traffic Light Systems to be more effective, without modifying them from top to bottom and without installing complex and expensive accessories whose effectiveness is unknown.

BENEFITS
The availability of comprehensive, accurate, and easily accessible traffic flows appears to be the essential ingredient to enable traffic engineers, experts, consultants, staff of cities, and government agencies to assess traffic problems.
Origin-Destination surveys seem to be a recognized method that is used in most major cities and regions in North America and elsewhere in the world. These surveys likely involve collecting a huge amount of data from interviews, online and paper questionnaires, and field interviews. For example, the next Origin-Destination survey in the Capitale-Nationale region of Quebec in Canada appears to involve polling 40,000 people.
My approach could take a different perspective by estimating Origin to Destination trips from the distribution of traffic flow on the network. And this objective can be achieved with my method by using IP cameras judiciously distributed on the road network, as shown in Figure 1: 101, 102, 103, 104, 105, 106.
Since traffic volume can give clues to the distribution of trips, my method aims to reconstruct these trips based on variations in traffic volume at different locations captured by IP cameras. For example, it seems possible to deduce the journeys based on the rate of vehicles entering or leaving the network and estimate how the traffic flow can be distributed over the network thereafter.
Moreover, my invention might permit collecting short-term variations in traffic flow, anonymizing data, and gathering them in a convenient and coherent system. Moreover, my method can be adapted according to available resources and the desired data accuracy.

It could, for example, allow engineers and transport experts to model movements at any time of the day, night, week, month, or year and to study traffic phenomena by micro-simulation with a computer. A
microscopic simulator or traffic micro-simulator reproduces the individual movements of vehicles, pedestrians, cyclists, or any other moving object on a computer screen.
In addition, my method appears to provide an essential tool that can enhance Traffic Control Systems and Traffic Light Systems. It appears to be possible to know the traffic flow rate at any time of the day. Since traffic flow variation seems to be the essential ingredient for programming Traffic Light Systems adequately, my method appears to be able to dynamically modify traffic light sequences in real-time, using, for example, Webster's method.
Another advantage of my invention is that it could permit transforming Static TLS to Adaptive TLS without implementing major changes to road infrastructure and IT systems.
It also can avoid changing internal methods in force in cities, agencies, and public bodies. Furthermore, all these systems could be centralized and operated by a Traffic Management Center 34 as shown in Figure 1.

Description

BRIEF DESCRIPTION OF DRAWING FIGURES
FIG.1 appears to be a complete view showing all the elements of the method.
FIG.2 appears to be an Origin-Destination matrix between different geographical areas of an urban agglomeration.
DETAILED DESCRIPTION OF THE DRAWING FIGURES
In the following description and in the accompanying drawings, the numeral numbers may refer to identical parts in the various figures. FIG.1 shows all the elements of the method 20 which include:
- The figure represents a network 500 of roads or highways which surround, for example, an urban agglomeration.
- The geographical sectors 200, 2001, 20011, 300, 3001, 300" represent different parts of the agglomeration. An Origin-Destination survey seeks to measure the number of vehicles leaving one area 200, 2001, 200" to go to another area 300, 3001, 300", during the morning peak periods, for example.
- The goal could be to estimate the routes from IP cameras 100, 101, 102, 103, 104, 105, 106.
- These IP cameras take pictures of road sections 100', 101', 1021 , 1031, 1041, 1051, 1061 .
- My method may infer the paths to compare the circulation flow distribution on the network. For example, traffic flows of sections 1001, 101', 102 merge at section 103' and then separate at sections 1041, 1051, 1061. It could therefore possibly deduce the traffic flow Date Recue/Date Received 2023-09-01 distribution based on the percentage of vehicles that might come from one place to another place. For example, vehicles can come from 200, 2001, 20011, merge into section 1031, and separate further into 300, 3001, 300.
- It can be possible to measure these flows and compare each percentage relative to adjacent sections (e.g., to compare flow distribution from 200, 2001, 200" and to compare flow distribution going to 300, 3001, 300).
- The traffic flow in each section can be measured with IP cameras 100, 102, 103, 104, 105, 106.
- Vehicles 1 to 18, as well as other vehicles not numbered in the figure, which circulate on the network 500, appear to be cars, trucks, buses, motorcycles, bicycles, or any object, person, or animal which circulates there by any means of transport whatsoever.
- In Figure 1, seven IP cameras 100, 101, 102, 103, 104, 105, 106 appear to be installed along road sections; IP cameras take video images of road sections 100', 101', 1021, 1031, 1041, 1051, 1061 , preferably in a synchronized way, but not necessarily.
- In section 1001, camera 100 captures movements of vehicles 5, 6.
- In section 101', camera 101 captures movements of vehicles 7.
- In section 1021, camera 102 captures movements of vehicles 8, 9.
- In section 1031, camera 103 captures movements of vehicles 10, 11, 12, 13.
- In section 1041, camera 104 captures movements of vehicles 14.
- In section 1051, camera 105 captures movements of vehicles 15, 16.

Date Recue/Date Received 2023-09-01 - In section 1061, camera 106 captures movements of vehicles 17, 18.
- A wireless data transmission system 26, 26' could be connected to each camera and can receive data via transmission channels 10011 , 101", 102, 103, 104, 105, 106.
- The wireless data transmission system 26, 26' can transfer data 32, 32' to a Center of Analysis and Treatment 30.
- The Center of Analysis and Treatment 30 can process data to potentially estimate traffic flow on each road section 100', 101', 1021, 1031, 1041, 1051, 1061 .
- The Center of Analysis and Treatment 30 can validate the results and can use the method of its choice, such as monitoring and tracking a sample of vehicles by artificial vision algorithm.
- Data could be sent 50, 50' to the cloud 28, 28' where they can be easily accessible in an appropriate format. This could be an automated way to get real-time traffic data for cities, agencies, and public bodies.
- The Center of Analysis and Treatment 30 transmits data 51 to the Traffic Management Center 34 by fast and secure communication means.
- Traffic Management Center 34 can be represented by a road viewing station, using screens and computers 35, where decisions can be made in real-time to manage traffic by operators, technicians, and engineers.

Date Recue/Date Received 2023-09-01 - Traffic Management Center 34 intervenes dynamically in real-time and remotely to improve circulation movements.
- Center of Analysis and Treatment 30 estimates the variation of traffic flow on section 104' and transmits information to the Traffic Management Center 34, which intervenes remotely to change the duration of the Traffic Light System 48.
- Link 58 between the Traffic Management Center 34 and the Traffic Lights System 48 can be a fast, secure and efficient means of transmission like a dedicated internet link, a fiber optic network, or any fast, secure and efficient communication means.
- Traffic flow can be calculated or estimated in real-time on section 106' and this information can be transmitted to the Center of Analysis and Treatment 30, and the latter can then transmit and display messages on a Variable Message Sign 60.
- Center of Analysis and Treatment 30 transmits information to the Traffic Management Center 34, which can then inform drivers to adjust their speed or change lanes to reduce the risk of congestion.
- Links 59 between the Traffic Management Center 34 and a Variable Message Sign 60 can be a secure means of transmission like a dedicated internet link, a fiber optic network, or any fast, secure and efficient communication means.
- Pictures, videos, and data can be transmitted 44, 44' to the cloud 28, 28' where they can be accessible to cities, agencies, public bodies, and consultants according to their needs.
Date Recue/Date Received 2023-09-01 - Cities, agencies, and public bodies can operate and optimize Traffic Light Systems 48 more frequently with the data obtained 52 and previously processed by the Center of Analysis and Treatment 30.
They can also use data for other purposes such as travel planning, public transport studies, urban studies, geometric layouts, and so on.
- Cities, agencies, and public bodies can transform a Static Traffic Light System 48 into a permanent Adaptive Traffic Light System 48' by by automating the transfer process between the Center of Analysis and Treatment 30, the Traffic Management Center 34 and the Traffic Light System 48, which then can becomes an Adaptive Traffic Light System 481 .
- Link 51 can be a secure means of transmission like a dedicated internet link, a direct communication network, a fiber optic network, or any secure and fast communication means.
FIG.2 shows an example of an Origin-Destination Matrix 600. This is an example of results that can be calculated and compared to traditional O/D
surveys.
The first column of the table appears to be the Origin sectors 200, 2001 , 200"; the first row of the table appears to be the Destination sectors 300, 3001, 300.
The intersection of columns and rows could show the flow measured on any day and at any time. For example, the center cell shows the flow calculated from the starting point Origin 200' to the ending point Destination 3001 .

Date Recue/Date Received 2023-09-01 The result would therefore appear in the table 600 as in a conventional Origin-Destination survey, which seems to be carried out every five to ten years using questionnaires, interviews, and sampling.
For example, if 20% of the traffic comes from 200' and 50% of the 103' section goes towards 3001, then 10% of 200' theoretically goes towards 3001. This could be an example of an approach for measuring traffic distribution, but any other effective method can be used. This approach can be replicated on a larger network.
SUMMARY OF THE INVENTION
The present invention appears to be a method 20 for measuring and processing real-time traffic data and accessing it via cloud computing.
The method can measure traffic flows at different locations on a road or highway network and can permit the estimation of traffic movements between different parts of a geographical region.
Since vehicles entering a section of road must somehow exit it, our method estimates traffic flow on different paths by comparing traffic flows on each road section 100', 101', 1021, 1031, 1041, 1051, 1061. By comparing the distribution of traffic flows, it may be possible to infer movements from an origin 200, 2001, 200" to a destination 300, 3001, 30011 .
IP cameras could be installed in suitable places to take pictures of road sections 101' to 106' where moving objects appear, like vehicles, pedestrians, cyclists, animals, or any other object. The pictures can be taken simultaneously, preferably, but not necessarily, to improve the accuracy of the results. Therefore, it may make it possible to infer traffic movements between different parts over a whole network surrounding an urban area.

Date Recue/Date Received 2023-09-01 The accuracy of the estimated traffic flow from an Origin to a Destination might depend on the number of IP cameras installed along the network.
For example, if IP cameras are installed at all entry and exit points of the network as in Figure 1, the accuracy of the data will be better.
It also makes it possible to use data for other purposes, such as to adjust Traffic Light Systems 48 according to the variation of traffic flow in road section 1041. Rather than scheduling the traffic lights statically for an entire period like the morning rush hour, it may be possible to dynamically change the traffic light schedule based on information transmitted by the Center of Analysis and Treatment 30 to the Traffic Management Center 34, which then applies remote changes to Traffic Light System 48.
It should be noted that most Traffic Light Systems appear to be static systems, meaning that the duration of cycles and phases cannot vary during each period (AM or PM peak hour). Our method may allow for dynamic intervention in these systems through the Traffic Management Center 34. It appears that most major cities are already equipped with a Traffic Management Center 34. Therefore, there seems to be no need to significantly modify the current Traffic Light Systems 48.
When the Traffic Management Center 34 transmits a signal 58 to the Static Traffic Light Systems 48 to modify the traffic light sequences, the latter automatically becomes a Dynamic Traffic Light System 48' during the required period.
However, it might be possible to transform these Static Traffic Light Systems 48 into permanent Adaptive Traffic Light Systems 48' by automating the interventions of the Traffic Management Center 34 according to the information transmitted by the Center of Analysis and Treatment 30.

Date Recue/Date Received 2023-09-01 Using my invention, it then appears that a city, agency, or public body can transform current Static Traffic Light Systems 48 into Dynamic Traffic Light Systems 48' without significant changes in infrastructure, IT systems, internal processes, and at low cost.
Moreover, the present invention makes it possible to gather data in one place and to quickly access it at any time via the cloud.
The method utilizes IP cameras 100 to 106 connected to transmission accessory channels 26, 261, like 5G or any effective and secure communication link.
Our method might permit the comparison of traffic density and average speed on various road sections on the network, to predict congestion situations, and to intervene in real or near-real time.
Our method might not be limited to a particular artificial vision algorithm, but it can be applied with any efficient algorithm. In order to identify vehicles, pedestrians, objects, or other moving objects in different lighting and visibility conditions, such as during the night and in various climatic conditions, lidar sensors and infrared cameras can be used in a similar fashion.
These data will be processed in the right format and accessible in the cloud to reduce data collection costs, field trips to collect data, and for updating Traffic Light Systems more frequently at a lower cost.
It also might allow the transfer of data to a micro-simulator or any kind of simulator in order to study traffic phenomena and to help develop more effective solutions in real-time, which can be adapted to driving habits and local culture.

Date Recue/Date Received 2023-09-01 Data obtained after the treatment of the pictures can be sent 50, 50' to the cloud 28, 28' and appear to be available 52, 52' to the Traffic Management Center 34 of cities, agencies, or public bodies for different purposes; management of traffic lights, management of traffic control systems, studies related to transport and urban planning.
The Traffic Management Center 34 can receive real-time interventions or suggestions from the Center of Analysis and Treatment 30 to improve traffic mobility.
According to a preferred embodiment of the invention a method for collecting and measuring real-time traffic data and permitting, among other things is provided, to estimate traffic flow movements from an Origin to a Destination of a road network, comprising at least one of the following steps:
Choose a series of Internet Protocol (IP) cameras (100, 101, 102, 103, 104, 105, 106), and/or Install said IP cameras at different road sections (100', 1011, 1021, 1031 , 1041, 1051, 1061) in order to capture pictures of moving vehicles (5 to 18), pedestrians, cyclists, animals, and moving objects at said road sections, and/or Preferably, but not necessarily, synchronize all said IP cameras so that they take series of said pictures every fraction of a second during the same approximate laps of time from said road sections (1001, 101', 1021 , 1031, 1041, 1051, 1061), and/or Connect said IP cameras to a wireless data transmission system (26, 261) in order to transfer said pictures by secure, efficient, and rapid means to a Center of Analysis and Treatment (30), and/or Date Recue/Date Received 2023-09-01 Access and process said pictures at said Center of Analysis and Treatment (30) in order to obtain real-time traffic data and to estimate average traffic flow from said road sections (100', 101', 1021, 1031, 1041, 1051, 1061), and/or Process said real-time traffic data collected by said IP cameras (100, 101, 102, 103, 104, 105, 106) at said Center of Analysis and Treatment (30) in order to propose intervention scenarios to a Traffic Management Center (34) to enable it to intervene remotely, dynamically, and in real-time on Traffic Light System (48), in order to improve traffic management and mobility, and/or To measure the traffic flow, the IP camera 104 can be used, which captures video images of section 1041. These images can then be transmitted to the Center of Analysis and Treatment 30, by fast, secure and efficient means of communication 26 'and 32, such as a dedicated fast Internet line or a dedicated optical fiber, and/or The images can then be analyzed and processed in the Center of Analysis and Treatment 30 using an efficient computer vision algorithm to measure the traffic flow on section 1041, for example by counting the number of vehicles and their average speed, and/or Knowing the traffic flow in real time, the Center of Analysis and Treatment can use Webster's method to revise the optimal phase and cycle 25 duration of the Static Traffic Light System 48, and/or This new sequence can then be considered as an intervention scenario that the Center of Analysis and Treatment 30 can transmit to the Traffic Date Recue/Date Received 2023-09-01 Management Center 34 through a fast, secure and efficient transmission channel 51, and/or The Traffic Management Center 34 operated by operators, technicians or engineers can modify the real-time sequences of the Static Traffic Light System 48 in a fast, secure and efficient way 58, and/or It is also possible to make the previous process automatic to permanently transform a Static Traffic Light System 48 into an Adaptive Traffic Light System 48' without significantly modifying the existing infrastructures and the internal IT systems of the city, the agency or of the public body, and/or The variable messages displayed on the panel 60 may follow a similar process through the transmission channel 59 and the Center of Analysis and Treatment 30 might use the Fundamental Diagram to estimate the optimum speed based on the flow rate measured on Section 103 and the Traffic Management Center 34 may display the recommended speed, and/or Process said real-time traffic data collected by said IP cameras (100, 101, 102, 103, 104, 105, 106) at said Center of Analysis and Treatment (30) in order to propose intervention scenarios to a Traffic Management Center (34) to enable it to intervene remotely, dynamically, and in real-time on Variable Message Sign (60), and/or Send said real-time traffic data to the cloud (28, 281) and make it securely accessible and in an appropriate format to agencies, cities, public bodies, and others for different uses according to their needs.

Date Recue/Date Received 2023-09-01 PARTS
1 to 18 Vehicles 20 Method to collect and measure average traffic flow data through IP
cameras for travel survey studies and other uses 26, 26' Wireless data transmission system 28, 28' The cloud 30 Center of Analysis and Treatment 32, 32' Signal between the wireless data transmission system and the Center of Analysis and Treatment 30 34 Traffic Management Center 35 Screens, computers, software, equipment to monitor and manage traffic systems 44, 44' Signal between the wireless data transmission system and the cloud 48 Static Traffic Light System (STLS) 48' Adaptive Traffic Light System (ATLS) 50, 50' Signal between the Center of Analysis and Treatment 30 and the cloud 28, 28' 51 Real-time intervention transmission 52, 52' Signal between the cloud and the city, agencies, public bodies 34 58 Signal between the Traffic Management Center 34 and the Traffic Light System 48 Date Recue/Date Received 2023-09-01 59 Signal between the Traffic Management Center 34 and Variable Message Sign 60 60 Variable Message Sign 100 to 106 IP cameras 100' to 106' Road sections 100" to 106" Transmission channels 200, 2001, 200" Geographical sectors of a city or of an agglomeration 300, 3001, 300" Geographical sectors of a city or of an agglomeration 500 Network of roads or highways 600 Origin-Destination Matrix Date Recue/Date Received 2023-09-01

Claims (16)

IN THE CLAIMS:
The embodiments of the invention in which exclusive property or privilege is claimed are defined as follows:

1. A method for collecting and measuring real-time traffic data and permitting, among other things, to estimate traffic flow movements from an Origin to a Destination of a road network, comprising the following steps:
(a) Choose a series of Internet Protocol (IP) cameras (100, 101, 102, 103, 104, 105, 106), (b) Install said IP cameras at different road sections (1001, 1011, 1021 , 1031, 1041, 1051, 1061) in order to capture pictures of moving vehicles (5 to 18), pedestrians, cyclists, animals, and moving objects at said road sections, (c) Preferably, but not necessarily, synchronize all said IP cameras so that they take series of said pictures every fraction of a second during the same approximate laps of time from said road sections (100', 101', 1021, 1031, 1041, 1051, 1061), (d) Connect said IP cameras to a wireless data transmission system (26, 261) in order to transfer said pictures by secure, efficient, and rapid means to a Center of Analysis and Treatment (30), (e) Access and process said pictures at said Center of Analysis and Treatment (30) in order to obtain real-time traffic data and to estimate average traffic flow from said road sections (1001, 1011 , 1021, 1031, 1041, 1051, 1061), (f) Process said real-time traffic data collected by said IP cameras (100, 101, 102, 103, 104, 105, 106) at said Center of Analysis and Treatment (30) in order to propose intervention scenarios to a Traffic Management Center (34) to enable it to intervene remotely, dynamically, and in real-time on Traffic Light System (48), in order to improve traffic management and mobility, (g) Process said real-time traffic data collected by said IP cameras (100, 101, 102, 103, 104, 105, 106) at said Center of Analysis and Treatment (30) in order to propose intervention scenarios to a Traffic Management Center (34) to enable it to intervene remotely, dynamically, and in real-time on Variable Message Sign (60), (h) Send said real-time traffic data to the cloud (28, 281) and make it securely accessible and in an appropriate format to agencies, cities, public bodies, and others for different uses according to their needs.

2. The method of claim 1 wherein said Center of Analysis and Treatment (30) processes said pictures of said road sections in order to estimate the average traffic flow from an Origin sector (200, 2001 , 20011) to a Destination sector, similarly to an Origin-Destination Matrix (600).

3. The method of claim 2 wherein said Center of Analysis and Treatment (30) uses any accurate approach to obtain accurate said average traffic flow from an Origin sector (200, 2001, 20011) to a Destination sector (300, 3001, 30011).

4. The method of claim 2 wherein said Center of Analysis and Treatment (30) uses any appropriate artificial intelligence approaches to process said pictures in order to obtain accurate said real-time traffic data.

5. The method of claim 1 wherein said pictures can be processed directly by each said IP camera (100, 101, 102, 103, 104, 105, 106) in order to transfer already partially processed data to the Center of Analysis and Treatment (30).

6. The method of claim 1 wherein said Traffic Management Center (34) is connected to said Traffic Light System (48) by a fast and secure communication system (58) for commanding remotely said Traffic Light System in real-time.

7. The method of claim 1 is used to process traffic information data in said Center of Analysis and Treatment (30), to transmit said processed traffic information data to said Traffic Management Center (34) for commanding remotely Variable Message Sign (60), and to transmit messages to drivers by appropriate means (59).

8. The method of claim 1 wherein said cities is connected to said cloud (28, 28') for easy accessing said traffic data.

9. The method of claim 1 wherein different lighting and visibility conditions on the roadside said IP cameras are used with lidar sensors.

10. The method of claim 1 wherein different lighting and visibility conditions on the roadside said IP cameras are used with infrared IP
ca meras.

11. The method of claim 1 wherein said wireless data transmission system (26, 26') is an accurate and secure transmission system.

12. The method of claim 1 wherein said real time data can be transferred into a micro-simulator in order to study traffic phenomena and to intervene, if necessary, in real time via the Traffic Management Center (34).

13. The method of claim 1 wherein said cities, agencies, public bodies and consultants can manage transport, mobility and urban planning by the mean of said real-time traffic data.

14. The method of claim 1 wherein said real-time traffic data can be anonymized to respect legal and privacy issues.

15. The method of claim 1 to 14 wherein said real-time traffic data can be used to transform Static Traffic-Light-Systems (48) to Adaptive Traffic-Light-Systems (48').

16. The method of claim 1 to 15 wherein said Static Traffic-Light-Systems can be transform to said Adaptive Traffic-Light-Systems without implemented costly and fundamental changes in current infrastructure, into computer systems and into IT systems currently in force in cities, agencies and public bodies.