RU2772622C2 - Predictive traffic management system - Google Patents

Abstract

FIELD: automotive industry.

SUBSTANCE: inventions group relates to a system and a method for optimizing the movement of vehicles in a certain area. The traffic control system contains: monitoring mechanisms, traffic control/navigation systems mechanisms, sets of analytical machines, sets of evaluation machines, control mechanism. Monitoring mechanisms provide real-time data on traffic flow characteristics throughout a given area. The traffic management/traffic information navigation systems have a defined interface for signaling a device with a set of capabilities located at a known location in a particular area. The analytical engines are configured to predict and/or detect traffic congestion or traffic delays based on real-time data received from each of the monitoring mechanisms. Evaluation vehicles determine the functions to be performed by the traffic control mechanisms/traffic information navigation systems in a given area in order to prevent or minimize travel delays. The control mechanism controls the interface of the selected traffic control mechanisms/navigation systems and causes them to perform certain functions.

EFFECT: increased efficiency of traffic flow management for a certain area.

22 cl, 8 dwg

Description

FIELD OF TECHNOLOGY

The present invention relates to a traffic control system. In particular, the invention relates to the optimization of traffic flow within a certain area.

BACKGROUND OF THE INVENTION

Almost all modern cities and urban centers face significant traffic congestion problems. These problems lead to huge losses in the form of lost productive time due to delays in moving from one point to another. One estimate of the financial impact of this phenomenon comes from a report published in The Economist in an article titled "The Cost of Traffic Jams", published in November 2014, which cites data provided by a London-based consulting company and INRIX (a data analytics firm). on traffic), which estimated the impact of such delays on the economies of the UK, France, Germany and America at $200 billion. Other data presented in an article (“Routing Multiple Vehicles Cooperatively: Minimizing Road Network Breakdown Probability”) published in IEEE Transactions on Emerging Topics in Computational Intelligence in February 2017 estimates financial costs in developed countries around the world at 2,200 billion .USD per year. In another article titled "Traffic Congestion Costs Americans $124 Billion a Year, Report Says", published on 14 October 2014 in Forbes magazine, similar data on large economic costs are given. These assessments clearly indicate the widespread recognition of the magnitude of the harmful effects of the problem.

In addition to the financial impact, there is a significant adverse impact on human health caused by physical and emotional stress. The escalation of the situation can lead to road violence, which can pose a serious threat to life and property. Pollution-related health problems can create additional negative consequences for the quality of life of people who directly use vehicles, as well as people living near busy roads. A working paper titled "New research finds link between Air Pollution and Traffic Accidents", published by the Grantham Research Institute for Climate Change and the Environment on October 3rd, 2016 on the London School of Economics website, contains scientific data on this issue. Similarly, an article titled "Traffic Congestion and Infant Health: Evidence from E-ZPass" by Janet Currie and Reed Walker, published in the American Economic Journal in January 2011, provides quantitative evidence for health impact problems from Air pollution caused by traffic jams.

Traffic management is designed to minimize traffic congestion and is carried out mainly by regulating traffic at intersections. Previously, the systems were manually controlled, but they have been almost completely replaced by automatic traffic lights operating by switching at fixed intervals. Such "Automated traffic control systems" have been in use for a long period of time and have eased the burden of manual traffic control. Enhancements have been made to these systems by shifting the phases of traffic synchronization signals sequentially along the main route in a staggered fashion to create a "green wave" of continuously moving traffic. The data of the "Step by Step" Traffic Control System have also been used for a long time. Both of these systems do not take into account the actual traffic conditions that exist at the intersection, and are programmed to work depending on the "expected" conditions calculated from traffic observations received over a certain period of time. One way or another, the rapid growth of traffic volumes has shown the imperfection of both systems and even their counterproductiveness in certain situations.

The shortcomings of Automatic and Staged Traffic Control systems have led to the development and implementation of "Adaptive Traffic Control Systems" capable of quickly detecting traffic conditions in the immediate vicinity of an intersection and synchronizing traffic lights accordingly. These systems underlie the existing technological platforms currently used in most modern cities. A variety of sensors are used, the most common being "inductive loop" motion sensors embedded in the roadbed in front of an intersection to detect the presence and movement of vehicles. Other detection devices used are video camera, radar and ultrasonic sensors.

Some adaptive traffic control systems use a "centralized" control method in which signals from a number of road intersections are routed to a central control center that sets the signal switching sequence for each intersection. Such systems are generally referred to as Vehicle-Actuated Coordinated Junctions (VACJ) systems. The control function is implemented at all or several intersections as part of a central or hierarchical structure. One example of such a control method is the SCOOT (Split, Cycle and Offset Optimization Technique) system, which is currently widely used and uses binary signals from built-in inductive loop sensors at intersections from a central computer system. Another such system, SCATS (Sydney Co-ordinated Adaptive Traffic System), on the other hand, is a hierarchical and distributive system that uses incremental feedback from inductive loop sensors located in front of the stop bar at each intersection. Similarly, UTOPIA ( U rban Traffic OP timization by Integrated Automation) is another hierarchical VACJ -type system using decentralized control that uses loop detectors that are located immediately after the previous intersection to obtain information about the vehicle. This allows neighboring intersections to be taken into account to provide dynamic coordinated control. The system uses a 3-level hierarchy - Low level to monitor a specific intersection, District level which controls a group of intersections, and City level which integrates UTOPIA with other systems such as bus timetables to create a macroscopic model. RHODES (Real-time Hierarchical Optimized Distributed and Effective System) is another system that uses a 3-level hierarchy similar to UTOPIA, but based on predicted arrival patterns of a batch/series (a group of vehicles moving in the same direction) at an intersection for determining the mode of traffic signaling. Prodyn is another decentralized control system that also uses detector information from neighboring intersections, and the information is passed from intersection to intersection with decreasing importance.

On the other hand, drive-by-vehicle-actuated (VAIJ) systems such as MOVA (Microprocessor Optimized Vehicle Actuated) focus solely on individual interchanges to provide even more targeted control. Individual intersections operate as separate islands that communicate with the traffic controllers of neighboring intersections, but function independently of each other. As of 2011; about 3,000 MOVA systems were used in the UK. SURTRAC is another similar system, details of which are published in the article "SURTRAC: Scalable Urban Traffic Control" presented at the Carnegie Mellon University Research Showcase of the School of Computer Science in January 2013. Similar systems are proposed in the article "Multiagent Based Decentralized Traffic Light Control for Large Urban Transportation System", published in the journal Mathematical Problems in Engineering under ID number 104349, as well as in technical report 06-039 "Look-Ahead Traffic Adaptive Control of a Single Intersection - A Taxonomy and a new Hybrid Algorithm, published by Delft University of Technology. Such systems are considered groups of "Intelligent Agents" that control individual traffic intersections.

In all the above systems, regardless of their type, all control activities are carried out near the intersection, and all the attention of the above solutions is "concentrated" on the intersections. The behavior of traffic at points far from the intersection is ignored and it is expected that the traffic congestion phenomena at such places will be eliminated by actions performed at the intersections. This fundamental approach defines the main limitations of all such systems.

The growing intensity and global impact of traffic congestion has necessitated significant research in this area, and many academic teams, organizations and companies are actively involved in research and development of improved solutions. Through these studies it has been established that the traffic conditions at any given location are often the result of a much wider distribution of traffic to several other connected locations. The size of this "neighbourhood" can vary widely and dynamically, and may even cover an area of several square kilometers where many traffic intersections are located. The behavior of traffic over large spaces between intersections has a very marked effect on the overall traffic congestion phenomenon. Therefore, it is essential to have both a general idea and to obtain local information by monitoring traffic conditions throughout the transport network. To achieve this goal, "evolving" technologies are becoming available.

Evolving traffic control solutions address the monitoring problem in one of two ways. The first way is to deploy the Floating Car Data (FCD) traffic detection system. In the book "Introduction to intelligent Systems in Traffic and Transportation" by Ana L.C. Bazzan and Franziska Klugl, and published by Morgan & Claypool Publishers, describes systems such as an on-board sensor system on vehicles that “move” in traffic and provide traffic information by wireless transmission of traffic parameters at appropriate locations in real time. In some implementations, these are special vehicles, such as taxis or trucks, designed for the delivery of goods and other commercial purposes. This FCD system has so far been found to be of limited use because (i) it does not cover the entire network and (ii) the system is not fully representative due to their relatively low speeds. For example, approximately 700 taxi cars in Stuttgart (data from 2003) are equipped with GPS devices that transmit information about their location and speed to the control center approximately every 30 seconds. Similarly, in the scientific article: "The Use of Adaptive Traffic Signal Systems Based on Floating Car Data" by Vittorio Astarita, Vincenzo Pasquale Giofre, Giuseppe Guido and Alessandro Vitale, published in Wireless Communications and Mobile Computing, Volume 2017 (ID : 4617451) provides valuable information about this emerging technology.

The second method achieves the same result more efficiently by using the same data collected by crowdsourcing through the ubiquitous mobile phones of drivers and passengers of vehicles moving in traffic, or on-board systems (if equipped with such systems) that provide such information. Although this method carries the threat of "violation of privacy", it offers an effective solution as the problems of maintaining secrecy are resolved. European Patent No. EP 1938296 B1, titled “Assessing Road Traffic Conditions using Data from Mobile Data Source”, describes such a system. Similarly, the article "SmartRoad: Smartphone-Based Crowd Sensing for Traffic Regulator Detection and Identification", published in ACM Transactions on Sensor Networks, Volume 11, No. 4, Article 55, July 2015, provides information on this technology. "Tracking Traffic with Cell Phones," published November 11, 2008 in the MIT Technology Review, describes a research project carried out by the University of California at Berkeley that has proven the effectiveness of this technology.

The above crowdsourced traffic monitoring technologies can be implemented using methods for collecting data from devices owned by passengers in vehicles (e.g. mobile phones), or through built-in vehicle functions that do not depend on data received from passengers' devices. Information obtained through the use of any of these methods can also be transmitted either directly from the vehicle to external infrastructure elements (vehicle-to-infrastructure technology - "V2I"), or in the form of data transmitted between vehicles or a group vehicles to an external receiver (Vehicle to Vehicle - “V2V” system). The general category of systems covering both types is called "V2x".

Although these emerging approaches are capable of solving the problems of monitoring the entire transport network, they nevertheless have certain limitations that reduce their effectiveness. These are, in particular:

1) a very limited ability to recognize the “composition of the traffic flow” (i.e. a heterogeneous composition of vehicle types) - to distinguish between two-wheeled, three-wheeled, cars and trucks, public transport (especially with regard to its low occupancy), tractors, horse-drawn vehicles, etc. This limitation is exacerbated during actual traffic congestion when all types of vehicles are moving at the same slow speed. Since the composition of the traffic flow is an important factor influencing congestion, this is a significant limitation that has also been present in previous "adaptive" and "phasing/automatic" systems.

2) the inability to recognize unexpected events and conditions that pose a potential threat of traffic congestion but occur when there is either no or very low traffic on the roads (for example, at night or in the early morning, when traffic density is usually little or no). Such events include, for example, vehicle breakdowns, accidents or natural obstacles (falling trees, etc.). Such cases are not detected until a traffic jam occurs, and therefore it is not possible to prevent the occurrence of traffic jams.

These evolving systems are designed as stand-alone systems, operating independently of and replacing existing systems. In this regard, the implementation of such solutions, as a rule, is an expensive undertaking.

The proposed solution enhances the effectiveness of existing solutions and, for the most part, helps to prevent traffic jams and more effectively manage the inevitable occurrence of traffic jams. This improvement achieves economic benefits by eliminating the need to replace existing solutions.

SUMMARY OF THE INVENTION

The proposed solution makes it possible to inexpensively improve existing traffic control systems in a certain area by using an interface to an additional control layer that synergistically combines the work of these resources with additional resources that enhance the system's capabilities by overcoming the limitations in existing systems described above. This solution provides monitoring of traffic characteristics throughout the area under consideration. An additional control layer analyzes monitoring data in real time using artificial intelligence technologies, including Reinforcement learning and heuristics to predict the location and causes of situations that can lead to traffic jams, as well as potential traffic delay situations that can be avoided. Additional artificial intelligence technologies are used to determine the actions needed to prevent further development of such situations, taking into account known locations and the capabilities of available traffic control mechanisms. As a result, relevant existing traffic control mechanisms will be forced to perform certain actions using improved interfaces.

This option also allows the proposed system to increase its efficiency by receiving data on travel requirements and destinations from users who choose to subscribe to a trip planning and destination navigation program based on actual traffic control data.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects and advantages of the present invention will be better understood upon reading the following detailed description with reference to the accompanying drawings, in which the same reference numbers represent the same elements throughout the drawings and in which the following is shown:

Fig. 1 illustrates a limitation of the prior art with examples of traffic congestion causes that cannot be detected until a traffic congestion occurs.

Fig. 2 shows a "bird's eye view" feature of the present invention, showing details of the traffic flow and its composition not described in the prior art.

Fig. 3 illustrates some preferred embodiments of an unmanned aerial platform used to implement the proposed solution.

Fig. 4 illustrates the steps of the traffic control method described in the present invention.

Fig. 5 is a block diagram of a traffic control system in accordance with the present invention.

Fig. 6 is a block diagram of a user program provided by the present invention.

DESCRIPTION

The following description is presented solely for the purpose of explaining certain aspects, advantages and novel features of the invention. It is to be understood that not all of the advantages of the invention may be achieved by any one embodiment of the invention. Thus, the present invention can be implemented in such a way as to realize or optimize one advantage or group of advantages without the need to achieve other advantages.

Those skilled in the art will also appreciate that the invention is not limited to the specific embodiments disclosed herein and may be embodied in many different forms. The embodiments and description used in the disclosure of the invention are not intended to limit the scope of the invention.

In the present description, references to "an embodiment", "one embodiment" or "some embodiments" may refer to several places. This does not necessarily mean that each such reference refers to the same embodiment(s), or that the feature applies to only one embodiment. Separate features of different embodiments may also be combined to provide other embodiments.

The singular as used in this document includes plural forms unless the context clearly dictates otherwise. It is further to be understood that the terms "comprises", "comprising" and/or "comprises", "comprising" as used herein indicate the presence of certain features, integers, steps, operations, elements and/or or components, but do not exclude the presence or addition of one or more features, integers, steps, operations, elements, components and/or groups thereof. It will be understood that when one element is referred to as "connected" or "associated" with another element, it may be connected or associated with another element directly, or intermediate elements may be present. In addition, the terms "connected" or "connected" as used herein may include the concept of operably connected or connected. As used herein, the term "and/or" includes any and all combinations and configurations of one or more of the listed matched elements.

One skilled in the art will appreciate that the exemplary design may also contain other functions and designs. It should be understood that the functions, structures, elements and protocols used for data transmission are not relevant to the present invention. Therefore, there is no need to discuss them in detail in the present description.

All described devices include software and/or hardware components necessary for their operation. In addition, each device may contain within itself one or more components that are understandable in meaning. These components may be operatively linked to each other and may be configured to communicate data to perform the function of said device.

Specialists in the art should be obvious that in the embodiments disclosed in the present description, can be made various modifications and changes in accordance with the present invention, without departing from its essence and scope.

Unless otherwise indicated, all terms (including technical and scientific terms) used herein have the same meanings as are commonly understood by those skilled in the art to which this invention pertains. It is also to be understood that terms normally defined in dictionaries should be construed in the sense that corresponds to their meaning in the context of the relevant state of the art, and should not be construed in an idealized or overly formal sense, unless expressly defined herein. .

The following definitions apply in the context of this disclosure:

1. Mechanism - any device or set of devices or any functional behavior that provides a desired result. The mechanism may be implemented in hardware, software, or any combination of hardware and software.

2. Traffic monitoring is any set of activities for obtaining real-time data on certain characteristics and parameters of traffic at a certain one or more points or in a certain area. Any sensing element or combination of sensing elements can be used to perform these actions. Image-based video surveillance and monitoring systems are preferred, as well as monitoring based on combined data obtained from GPS sensors and non-GPS mobile phone sensors located on separate vehicles and capable of reporting the location, speed and direction of the vehicle. .

3. Traffic control mechanism - any device or set of devices operating centrally or in a distributed environment to regulate the flow of vehicles at one or more interchanges, regardless of whether it functions as an "Intelligent Agent" or as part of a hierarchical or centrally controlled system , such as SCOOT, SCATA, UTOPIA, RHODES or any other.

4. A traffic monitoring engine is any engine that monitors traffic.

5. A traffic information navigation system is any system that transmits information to vehicles or drivers of vehicles to facilitate their travel conditions and (or) improve traffic conditions - such mechanisms are not limited to functioning only at intersections and include messages, as well as Voice / text notification via mobile communication channels and (or) through car navigators. Preferred embodiments of such traffic information navigation systems include smartphone "apps" (and similar applications installed in on-board systems) that provide interfaces for obtaining detailed trip data from a registered user and provide audio/video/text based navigation instructions.

6. A computing engine is any computing device, software, or any combination thereof.

7. An Analytical Engine is any computational engine that analyzes input data to determine characteristics of interest for a particular application. Such data may include images, streaming video, text, audio or voice messages, or any other type of data.

8. An evaluation engine is any computational mechanism that determines the actions required to achieve a desired outcome.

9. A control system is any computational mechanism that monitors other mechanisms that are part of the system and influences their behavior in accordance with certain rules and requirements.

In FIG. 1 shows a block diagram of the transport network according to the existing technology with a series of interchanges at road intersections. The boundaries drawn around each intersection define the area that is controlled by the traffic control mechanisms installed there. Incidents that occur outside these boundaries are not detected by the system's sensors. In particular, if incidents occur when there is little or no traffic on the roads, then FCD-based systems and crowdsourced data communication systems, such as mobile phone data programs, are also unable to perceive such cases until traffic jams will not actually occur. Thus, none of the existing solutions is capable of preventing traffic jams in such situations. A traffic accident or breakdown (105) of a vehicle in the early morning when there was no traffic, a break (106) of a water main, an abandoned construction site (107) are examples of such situations. These examples clearly demonstrate the limitations of existing systems.

In FIG. 2 shows an implementation in accordance with one embodiment of the proposed solution in the form of an aerial photograph (200) taken at night. This “Eyes in the Sky” aerial survey technique provides a complete overview of the area and uses both data from existing sensor systems, including crowdsourced data, and aerial imagery to provide continuous and complete information on traffic conditions at all points throughout the area. The system is able to simultaneously view congested sections (202) and (204) of movement, as well as the causes of congestion (203) and (201) in movement, respectively. At the same time, it can also predict the occurrence of traffic congestion by identifying potential or "emerging" areas that have not yet become congested, as shown in example (205). Prediction is performed using an appropriate combination of known methods, including artificial intelligence technologies such as reinforcement learning, heuristic methods, and formal procedures for analyzing monitoring data. Further processing is carried out on the results of the analysis by applying a suitable combination of known methods, including artificial intelligence technologies, for example, reinforcement learning, heuristic methods and formal procedures to determine the actions to be performed by one or more available existing and / or additional control mechanisms. . These actions are then performed by passing input/instructions to the selected control mechanisms/navigation systems using the provided interfaces.

In FIG. 3 shows some preferred air platform embodiments. (3A) shows a tethered unmanned balloon, and (3B) shows a tethered unmanned balloon. These are examples of fixed platforms that can be used as low cost options for providing aerial surveillance. Since the payload is extremely small, air platforms consisting only of video equipment with remote control can be very small and inexpensive. The power consumption of the video equipment is also low, and power is easily supplied through the rope cable. (3C) depicts a untethered drone that can be positioned over certain points with the proposed solution to provide more “accurate” input data with greater detail.

In FIG. 4 is a block diagram explaining the functioning of the proposed solution. The operation is initialized in step (401) by collecting information that identifies the location, performance, and control interface of each available traffic control engine, traffic information navigation system, and traffic monitoring engine. This action is carried out initially during system commissioning and then as programs are updated. Existing mechanisms are equipped with appropriate interfaces that are compatible with the upper management level introduced by the new system. New monitoring mechanisms are being introduced to improve and overcome the limitations of existing mechanisms. These new mechanisms also interact with the upper level of management. Next, the system continuously monitors the characteristics of the traffic flow throughout the defined area in real time (402) and analyzes the characteristics of the tracked traffic flow to predict and/or detect traffic jams (403). Monitoring is carried out using all available traffic monitoring mechanisms that were connected in step (401). Analysis is performed based on monitored data to determine the location, nature and causes of potential and existing traffic jams. This analysis also identifies critical areas that require increased attention to optimize the deployment and operation of monitoring mechanisms. The results obtained are used to determine the functions required to be performed by the selected traffic control mechanisms and traffic information navigation systems to avoid or reduce traffic congestion and/or minimize travel delays (404). These actions are then implemented using the interfaces of available traffic control mechanisms, traffic information navigation systems and traffic monitoring mechanisms, so that each of them performs certain functions (405).

In FIG. 5 is a top-level block diagram of the structure of a new control element that interacts with available resources for traffic monitoring, traffic control, and traffic information navigation systems to implement the proposed solution. The complex of one or more Analytical Engines (501) processes the data received from the available traffic monitoring mechanisms and identifies potential traffic jams, as well as existing traffic jams, as well as the causes of the traffic jams themselves. The data provided by monitoring mechanisms is analyzed to determine the nature, intensity, location and causes of any traffic jam that can be identified as existing or emerging or likely. The analysis uses both formal and heuristic methods, and self-learning AI methodologies such as Reinforcement Learning can be applied. The main approach is to detect and predict global and local traffic patterns in a given context, and the consequences associated with the identified vehicle composition and distribution. In particular, information about the trajectory of an individual vehicle or selected groups of vehicles (lots of vehicles) is evaluated for the potential or existing impact of traffic congestion. External factors, including weather conditions, planned and unexpected events such as vehicle breakdowns, road repairs, landslides, which are recognized as significant factors influencing traffic conditions, are also included in the analysis.

To perform its functions, the Analytical Engine (501) is capable of dynamically configuring or moving those monitoring mechanisms that can be configured or moved. One or more Evaluation Engines (502) analyze the identified congestion characteristics as well as the causes of the congestion to determine the actions needed to eliminate them as efficiently as possible. The actions defined in this way are then passed to the Control Engine (503) which converts each of them into a sequence of input signals for each target traffic control engine in accordance with the specified interface requirements.

Each of the Analytical Engines (501), the Evaluation Engine (502), and the Control Engine (503) may use known artificial intelligence technologies, including, but not limited to, reinforced learning technologies, genetic programming, and various heuristic methods. Analytical engines (501) receive real-time traffic monitoring data (A) from monitoring mechanisms deployed in a particular area. Controllable monitoring mechanisms receive control inputs (B) to optimize their performance. Analytical engines (501) analyze the received data (A) and predict possible locations and causes of traffic jams, and detect existing traffic jam phenomena and related causes. This data is fed to the Evaluation Engines (502) which determine the corrective actions to be taken by the most appropriate means selected from the available set of control mechanisms/navigation systems, depending on their capabilities and location. The results obtained are passed to the Control Engine (503) for transmission to the selected engines (C) using separate interfaces available for this purpose.

In FIG. 6 shows a basic diagram of an optimized trip planning and route management system provided by the system to subscribers to such a service. The smartphone application (601) provided to the user is used to download the set of requirements to the system (602). These requirements include details of the planned trip and guidance on how to navigate to the destination of the trip. Such parameters may include details of the start and end destination, preferred travel dates/days and times, arrival time limits at the destination, optimization requirements - shortest trip duration, minimum fuel consumption, etc. The system (602) is aware of the available traffic routes, traffic patterns and related external factors, and obtains the current status of all traffic/navigation systems (603) along each possible route. It then dynamically analyzes all available options in terms of desired optimization criteria and provides guidance to the user via the application and/or via the on-board systems (604).

The solution described in this disclosure is designed with sufficient flexibility to work in conjunction with any traffic control systems and controls that are currently operating in a particular area. There is no need to replace existing equipment to take advantage of the new system. All that needs to be done is to precisely define the capabilities, limitations, and behavior of the existing system, and to make available/update an interface that, in interaction with the control(s), allows you to influence the behavior of the existing system in a given order. Therefore, the advantages and benefits of large installed base systems SCOOT, SCATS, MOVA, UTOPIA and other solutions can be used cost-effectively.

At the same time, the proposed solution also uses additional monitoring devices/mechanisms that expand the capabilities of the system and overcome existing limitations. The proposed solution implements a top-level structure that ensures the synergistic functioning of existing and new resources and devices, which contributes to obtaining the optimal technical result.

The locations, capabilities, and limitations of the installed base of traffic control systems operating within a particular area are identified in the same way as the capabilities of other available devices that may be used. The proposed solution will be functionally interfaced with each of the identified mechanisms/devices through suitable interfaces that will be available or created. These interfaces may be implemented in hardware or software, or any combination thereof, and allow the exchange of information and instructions.

Claims (37)

1. A method for optimizing the movement of vehicles in a certain area, which includes the following steps: interfacing with every available traffic monitoring and traffic management/traffic traffic information navigation system; real-time monitoring, through one or more monitoring mechanisms, of traffic flow characteristics over a defined area in real time, whereby traffic flow monitoring includes monitoring based on collective data obtained from GPS-based sensors and non-GPS mobile phone sensors located in separate vehicles; analyzing, by means of one or more analytical engines, the characteristics of the tracked traffic flow to predict and/or detect traffic jams and identify avoidable time losses; determining, by means of one or more evaluation machines, the functions to be performed by the selected one or more of said traffic control/traffic information navigation systems in order to prevent or reduce traffic congestion and/or minimize travel delays based on capabilities and locations of said traffic control/guidance mechanisms; as well as issuing, by means of a control mechanism, a command to the selected traffic control mechanisms/traffic information navigation systems to perform the specified functions, wherein one or more analytical engine and/or evaluation engine and/or control engine are implemented as distributed systems. 2. The method according to claim 1, characterized in that interfacing with available traffic monitoring and traffic control/traffic navigation system mechanisms includes an operational connection with each available existing mechanism, as well as with each new mechanism provided for use, based on its location and performance data. 3. The method of claim 2, wherein the interface with the traffic monitoring engine comprises a device for receiving real-time traffic data from it and the ability to determine its operating parameters, where possible, and in relation to the traffic control / navigation system of the road information contains a circuit that allows you to perform any function within its capabilities. 4. The method of claim 1, wherein the monitoring of traffic stream characteristics comprises dynamically selecting, using, and retrieving real-time data from available monitoring mechanisms. 5. The method according to claim. 1, in which the analysis of the characteristics of the tracked traffic stream includes processing the characteristics of the tracked stream using a combination of one or more known and / or heuristic methods and / or artificial intelligence tools, including reinforced learning, to detect and predict traffic jams and avoidable travel delays. 6. The method according to claim 1, in which the determining functions to be performed are achieved by applying a combination of one or more known and / or heuristic methods and / or artificial intelligence tools, including reinforced learning, to identify actions necessary to prevent the occurrence predicted traffic jam, as well as corrective actions necessary to minimize the impact of the detected traffic jam and prevent loss of time. 7. The method of claim 1, wherein the selected traffic control/traffic information navigation system mechanisms are initiated by determining and issuing control signals to each such mechanism/device in accordance with the identified individual performance characteristics and capabilities in a manner that ensures the performance of the established function. 8. The method of claim 1, wherein the traffic performance monitoring (402) comprises real-time video footage analysis from one or more platforms positioned and equipped to provide continuous and unhindered surveillance of all vehicles within an entire defined area. 9. The method of claim 1, wherein said traffic monitoring characteristics (402) comprise the physical positioning of one or more dynamically relocatable traffic detection mechanisms. 10. The method according to claim 8, characterized in that the location of the platform is at a height that prevents the effect of shading by large vehicles of smaller vehicles, and the adjustable zoom capabilities in the video capture devices make it possible to clearly identify the general and selected trajectories of the vehicle. 11. The method of claim 1, wherein said monitoring includes receiving destination parameters and travel requirements from one or more vehicles that are subscribed to routing services, including destination parameters and travel requirements in the analysis process, tracking the location and trajectory of each subscribed vehicle participating in controlled traffic, and navigation of each mentioned vehicle while scheduling its journey and reaching its destination in accordance with the requirements. 12. The method of claim. 1, in which the analysis of the characteristics of the traffic flow includes additional requirements in the event of foreseeable/unforeseen weather conditions, repair/construction works, natural emergencies, social events, as well as other circumstances requiring the provision of priority in traffic. 13. The method of claim 12, wherein the additional requirements include trip planning and routing guidance for vehicles that are subscribed to receive such a service and whose trip parameters and requirements are precisely determined or derived from data from previous schemes. trips. 14. A traffic management system for a certain area, containing one or more monitoring mechanisms that monitor real-time data on the characteristics of traffic flow throughout a certain area, and the monitoring is carried out by one or more monitoring mechanisms based on collective data received from GPS-based sensors and sensors based on non-GPS mobile phones located in separate vehicles, one or more traffic control mechanisms/traffic information navigation systems having a specific interface for signaling any device with a set or several specific sets of functionality located in a known location in a particular area, a set of one or more analytical engines configured to predict and/or detect traffic congestion or traffic delays based on real-time data received from each of the monitoring mechanisms, set of one or more evaluation machines capable of determining the functions to be performed by a selected one or more available traffic control mechanisms/traffic information navigation systems in a certain area in order to prevent or minimize travel delays, based on capabilities and the locations of said traffic control/guidance mechanisms, and a control mechanism that controls a certain interface of each of the selected traffic control mechanisms / navigation systems and causes them to perform certain functions, wherein one or more analytical engine and/or evaluation engine and/or control engine are implemented as distributed systems. 15. The system of claim 14, wherein the monitoring mechanisms include vehicle location and movement data crowdsourced from a plurality of vehicles in a defined area. 16. The system of claim. 14, in which the monitoring mechanisms include one or more tethered or mobile air platforms capable of providing real-time images and/or video footage of one or more areas in a particular area. 17. The system of claim. 16, in which one or more tethered or mobile air platforms are controllable in relation to the functions of capturing images and/or video communication. 18. The system of claim. 16, in which one or more mobile air platforms are controllable in terms of their position, as well as image capture and / or video streaming functions. 19. The system of claim 14, wherein the traffic control mechanisms include one or more vehicle-controlled individual interchanges. 20. The system of claim 14, wherein the traffic control mechanisms include one or more vehicle-controlled intersection coordination systems. 21. The system of claim. 14, in which one or more analytical engines use heuristic methods and/or one or more known artificial intelligence methods, including reinforcement learning. 22. The system of claim 14, wherein the one or more scoring machines use one or more known artificial intelligence techniques, including reinforcement learning.

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