WO2020103064A1

WO2020103064A1 - Method, apparatus and terminal device for predicting transportation event

Info

Publication number: WO2020103064A1
Application number: PCT/CN2018/116851
Authority: WO
Inventors: 蒋新春; 刘文涛
Original assignee: 深圳市锐明技术股份有限公司
Priority date: 2018-11-22
Filing date: 2018-11-22
Publication date: 2020-05-28
Also published as: CN109661692A; CN109661692B

Abstract

A method, an apparatus and a terminal device for predicting transportation event are disclosed, comprising: acquiring transportation data, wherein the transportation data include basic road network data and floating car data; fusing the basic road network data and the floating car data and obtaining a corresponding event feature; comparing the event feature with a predefined event feature of the transportation event; determining the transportation event corresponding to the event feature to be a predicted transportation event if the event feature is the same as the predefined event feature of the transportation event. By using the above described method, the accuracy of the prediction of the transportation event can be enhanced.

Description

Traffic incident prediction method, device and terminal equipment

Technical field

The present application belongs to the technical field of traffic data processing, and particularly relates to a traffic event prediction method, device, terminal device, and computer-readable storage medium.

Background technique

With the development of the Internet and the Internet of Things, the interaction between data is faster, and intelligent transportation is also possible.

Intelligent Transportation System (Intelligent Transportation System, ITS) is a kind of large-scale establishment established by effectively integrating advanced information technology, data communication transmission technology, electronic sensing technology, control technology and computer technology into the entire ground transportation management system. A real-time, accurate and efficient comprehensive transportation management system that functions within the scope and in all directions.

The existing intelligent transportation system mainly obtains the vehicle operation information of the road section through sensors installed in the road section, processes the collected vehicle operation information of the road section, and then predicts the traffic event. Because traffic incidents are only predicted based on data obtained by sensors and other devices installed on road sections, and the data obtained by sensors is limited, the accuracy of traffic incident predictions is low.

Therefore, a new technology needs to be proposed to solve the above technical problems.

technical problem

In view of this, the embodiments of the present application provide a traffic event prediction method to solve the existing traffic event prediction method only based on data obtained by sensors and other devices installed on the road section, and the data obtained by the sensors is limited, which results in The problem of low accuracy of traffic event prediction.

Technical solution

The first aspect of the embodiments of the present application provides a traffic event prediction method, including:

Collect traffic data, which includes basic road network data and floating car data;

Fuse the basic road network data and floating vehicle data to obtain corresponding event characteristics;

Compare the event characteristics with the event characteristics of a predefined traffic event;

If the event feature is the same as the event feature of the predefined traffic event, the traffic event corresponding to the event feature is used as the predicted traffic event.

A second aspect of an embodiment of the present application provides a traffic incident prediction device, including:

The traffic data collection unit is used to collect traffic data, and the traffic data includes basic road network data and floating car data;

The event feature determination unit is used to fuse the basic road network data and floating car data to obtain corresponding event features;

An event feature comparison unit, used to compare the event feature with a predefined traffic event event feature;

The traffic event prediction unit is configured to use the traffic event corresponding to the event feature as the predicted traffic event if the event feature is the same as the event feature of the predefined traffic event.

A third aspect of the embodiments of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, when the processor executes the computer program Implement the steps of the traffic incident prediction method as described.

A fourth aspect of the embodiments of the present application provides a computer-readable storage medium that stores a computer program, which when executed by a processor implements the steps of the traffic event prediction method.

Beneficial effect

Because traffic events are combined with basic road network data and floating car data for prediction, the traffic data used for prediction is guaranteed to be more comprehensive, thereby improving the accuracy of traffic event prediction.

BRIEF DESCRIPTION

In order to more clearly explain the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only for the application For some embodiments, those of ordinary skill in the art can obtain other drawings based on these drawings without paying any creative labor.

1 is a flowchart of a traffic event prediction method provided in Embodiment 1 of the present application;

2 is a schematic structural diagram of a traffic event prediction device according to Embodiment 2 of the present application;

FIG. 3 is a schematic diagram of a terminal device provided in Embodiment 3 of the present application.

Embodiments of the invention

In the following description, for the purpose of illustration rather than limitation, specific details such as specific system structure and technology are proposed to thoroughly understand the embodiments of the present application. However, those skilled in the art should understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details hindering the description of the present application.

In order to explain the technical solutions described in the present application, the following will be described with specific embodiments.

It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements, and / or components, but does not exclude one or more other features , Wholes, steps, operations, elements, components and / or their existence or addition.

It should also be understood that the terminology used in the description of this application is for the purpose of describing particular embodiments only and is not intended to limit this application. As used in the specification of the present application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

It should also be further understood that the term "and / or" used in the specification of the present application and the appended claims refers to any and all possible combinations of one or more of the associated listed items and includes these combinations .

As used in this specification and the appended claims, the term "if" may be interpreted as "when" or "once" or "in response to determination" or "in response to detection" depending on the context . Similarly, the phrase "if determined" or "if [described condition or event] is detected" can be interpreted in the context to mean "once determined" or "in response to a determination" or "once detected [described condition or event ] "Or" In response to detection of [the described condition or event] ".

Example one :

FIG. 1 shows a flowchart of a traffic event prediction method provided in Embodiment 1 of the present application. Details are as follows:

Step S11: Collect traffic data, which includes basic road network data and floating car data;

Specifically, collecting traffic data in the road network equipment system, the transportation vehicle system, and the traveling public system, for example, collecting basic road network data in the road network equipment system, and collecting floating vehicle data in the traveling vehicle system and the traveling public system.

Optionally, the format of the collected traffic data is unified.

Specifically, since the format of the traffic data of each system is different, it is helpful to improve the efficiency of subsequent data processing after unifying the format of the traffic data collected from different systems.

Step S12, fuse the basic road network data and floating vehicle data to obtain corresponding event characteristics;

In this step, only basic road network data and floating vehicle data in the same area are fused. Specifically, the basic road network data and floating car data in the same area are determined from the collected traffic data, and then the basic road network data and floating car data in the same area are fused. For example, assuming that the area A includes the basic road network data A and the floating car data A, only the basic road network data A and the floating car data A are fused.

Step S13, comparing the event characteristics with the event characteristics of a predefined traffic event;

Among them, the pre-defined traffic events are related to regions. Different regions may have different traffic events under the same event characteristics.

Specifically, this step compares the event feature with the event feature of a predefined traffic event corresponding to the area where the event feature is located.

Among them, the event characteristics of the pre-defined traffic events are determined by analyzing historical data in different regions, and the historical data includes historical basic road network data and historical floating car data.

In step S14, if the event feature is the same as the event feature of the predefined traffic event, the traffic event corresponding to the event feature is used as the predicted traffic event.

It should be pointed out that the number of predefined traffic events is greater than or equal to 1, and the event characteristics of different traffic events are different. For example, suppose that the event characteristics corresponding to traffic event X are X1 and X2, and the event characteristics corresponding to traffic event Y It is Y1 and Y2. If the event feature obtained in step S12 is the same as the event feature X1 and the event feature X2, the traffic event X corresponding to the event feature X1 and the event feature X2 is determined as the predicted traffic event.

Optionally, the basic road network data in step S11 includes data reported by various traffic lights, data reported by speed monitoring, and data reported by flow monitoring equipment; the floating vehicle data includes: deployed in various types of GPS and / or Or the location information reported by the equipment on the video surveillance vehicle; in this case, the step S12 includes:

A1, according to the data reported by the various types of traffic lights and the data reported by the flow monitoring device and the position information included in the floating car data, determine the area of the asymmetrical access to the vehicle; Lights), compare the data reported by the flow monitoring device with the location information included in the floating car data, specifically compare the vehicles entering and exiting the same traffic light, and then determine whether the area where the traffic light is located is an asymmetric area entering and exiting the vehicle.

A2. According to the data reported to the vehicles in the asymmetrical area of the vehicle by the speed monitoring, determine the type of congestion in the asymmetrical area of the vehicle, the type of congestion includes long-term and periodical;

Optionally, before the step A2, the area where the difference between the in and out of the vehicle is greater than the specified difference threshold is filtered from the determined area of the in and out of the vehicle asymmetry. At this time, the step A2 is specifically: judging from the data reported by the speed monitoring to the vehicle in the area where the vehicle enters and exits a serious asymmetry, determine the type of congestion in the area where the vehicle enters or exits a serious asymmetry And time period; the step A3 is specifically: combining the data reported by the speed monitoring on the vehicles in the area of serious asymmetry in and out of the vehicle with at least one of the following data to obtain the event characteristics corresponding to the combined data: basis Facility data, time period data, weather data, season data, key festival data.

A3. Combine the data reported by the speed monitoring on the vehicle in and out of the asymmetric area of the vehicle with at least one of the following data to obtain the event characteristics corresponding to the combined data: infrastructure data, time period data, weather data, and season data , Key holiday data.

Optionally, if the vehicle is a bus, the A3 is specifically: combining the data reported by the speed monitoring on the vehicles in and out of the asymmetric area of the vehicle with the number of passengers and at least one of the following data, The event characteristics corresponding to the combined data are obtained: infrastructure data, time period data, weather data, season data, and key holiday data.

Taking the bus as an example, the historical bus data is used to analyze the speed and vehicle distribution of the bus on the road under different weathers (such as rainy days, foggy days, etc.) and under different seasons (such as spring, summer, autumn, and winter seasons) , The number of passengers and other information, and then facilitate the subsequent planning of bus travel, for example, control the bus departure interval, bus line density, etc.

Optionally, after the step S14, it includes:

The scheduling strategy corresponding to the predicted traffic event is started through the scheduling strategy configuration, and the scheduling strategy configuration includes at least one of the following: period configuration, area range configuration, traffic vehicle configuration, traffic direction, and long-term configuration.

Among them, the time period configuration is mainly used to identify the time period when the scheduling strategy takes effect, and different scheduling strategies are obtained in different time periods. Time slot configuration refers to the time slot generated by the scheduling strategy, which can be the morning peak, the evening peak, or a special time period in different festivals. The area-wide configuration is mainly used to identify the area where the scheduling strategy is in effect. For long-term congested areas, highways, and residential areas, different scheduling strategies should be used; it should be noted that the scheduling strategy configuration includes the area-wide configuration, so the application of the scheduling strategy needs to be defined in advance range. The configuration of passing vehicles mainly refers to adopting different passing strategies for different types of vehicles; for example, buses, taxis and online cars, line buses, long-distance buses, dangerous vehicles, and trucks should be treated differently. The direction and duration of traffic are mainly used to define the direction and duration of vehicles allowed to pass in different scenarios; for example, the morning peak for a long-term congested area such as an office area, allowing vehicles to drive out, restricting large numbers of vehicles to enter, and restricting non-transit Traffic vehicles drive in; according to the duration of the morning rush hour, define the duration of restricted traffic, such as 8:00 is not allowed to pass, 9:00 is allowed to partially pass, etc.

Specifically, a configuration page is provided on which the user starts the corresponding scheduling strategy by scheduling different strategy configurations. Because users can quickly configure a scheduling strategy through scheduling strategy configuration, they can quickly respond to predicted traffic events.

Optionally, since there are many types of traffic events, when the predicted traffic event is an emergency traffic event, in order to notify the designated personnel as soon as possible, when the scheduling strategy corresponding to the predicted traffic event is activated through the scheduling strategy configuration, Include at least one of the following:

Notify designated management departments, coordinate notification to nearby hospitals, and send predicted traffic events to the public.

Among them, the designated management department here includes the department where the traffic police are located.

Optionally, the predicted traffic event includes: traffic event type, traffic feature value, traffic event level; the traffic feature value includes at least one of the following: regional traffic index, average vehicle speed, vehicle density per road segment, current vehicle Speed and historical speed fluctuate.

Among them, the types of traffic incidents are divided into: sudden and regular. The suddenness is that the area has smoother traffic and fewer accidents. According to historical traffic data analysis, it is determined that the area is a safe area, but sudden traffic congestion and vehicle running speed are too slow. Such traffic incidents are generally temporary, but Need to designate the management department, such as the traffic management department to pay attention to the solution immediately; the routine is that the traffic conditions are not optimistic during certain periods (such as morning and evening peaks), and under certain weather conditions (such as rain), congestion, slow driving, or frequent incidents. The problem of regional traffic incidents is a long-term improvement action, which has a wide impact and requires long-term attention.

Traffic incident levels are defined as mild, normal, and severe dimensions according to the type of traffic incident, changes in traffic characteristic values, duration, impact surface, and linkage, and are used to describe the degree of attention that the current traffic incident should receive. For example, at a relatively smooth intersection, there are data such as congestion, slow vehicles, and high vehicle density, which last for 15 minutes, 30 minutes, 1 hour, etc. The level of the traffic event definition is different.

In the embodiment of the present application, traffic data is collected, and the traffic data includes basic road network data and floating car data, and the basic road network data and floating car data are fused to obtain corresponding event characteristics, and the event characteristics are defined in advance Comparison of the event characteristics of the traffic event of, if the event characteristics are the same as the event characteristics of the predefined traffic event, the traffic event corresponding to the event feature is used as the predicted traffic event. Because traffic events are combined with basic road network data and floating car data for prediction, the traffic data used for prediction is guaranteed to be more comprehensive, thereby improving the accuracy of traffic event prediction.

Example 2:

Corresponding to the traffic event prediction method of Embodiment 1, Embodiment 2 of the present application provides a structural diagram of a traffic event prediction apparatus. For ease of description, only parts related to the embodiment of the present application are shown.

The traffic event prediction device includes: a traffic data collection unit 21, an event feature determination unit 22, an event feature comparison unit 23, and a traffic event prediction unit 24, wherein:

The traffic data collection unit 21 is used to collect traffic data, and the traffic data includes basic road network data and floating car data;

Optionally, the traffic event prediction device includes:

Unified format unit, used to unify the format of the collected traffic data.

The event feature determination unit 22 is used to fuse the basic road network data and floating vehicle data to obtain corresponding event features;

Specifically, the basic road network data and floating car data in the same area are determined from the collected traffic data, and then the basic road network data and floating car data in the same area are fused.

The event feature comparison unit 23 is used to compare the event feature with a predefined traffic event event feature;

The traffic event prediction unit 24 is configured to use the traffic event corresponding to the event feature as the predicted traffic event if the event feature is the same as the event feature of the predefined traffic event.

It should be pointed out that the number of predefined traffic events is greater than or equal to 1, and the characteristics of different traffic events are different.

Optionally, the basic road network data includes data reported by various types of traffic lights, data reported by speed monitoring and data reported by flow monitoring equipment; the floating vehicle data includes: deployed in various vehicles with GPS and / or video surveillance The location information reported by the device on the device; at this time, the event feature determination unit 22 includes:

An asymmetric area determination module, configured to determine the asymmetric area of the vehicle in and out based on the comparison between the data reported by the various traffic lights and the data reported by the flow monitoring device and the location information included in the floating vehicle data;

The congestion type determination module is used to determine the congestion type of the asymmetrical area of the in and out vehicles according to the data reported by the speed monitoring on the vehicles in the asymmetrical area of the in and out vehicles, the congestion type includes long-term and period ;

The data fusion module is used to combine the data reported by the speed monitoring on the vehicles in and out of the asymmetric area with at least one of the following data to obtain the event characteristics corresponding to the combined data: infrastructure data, time period data, weather Data, season data, key holiday data.

Optionally, the event feature determination unit 22 includes:

The module for determining the area of serious asymmetry in and out of the vehicle is used to filter the area where the difference between the in and out of the determined vehicle is greater than the specified difference threshold, and the filtered area is used as the area for the in and out of vehicle.

The congestion type determination module is specifically configured to determine the congestion type of the area where the vehicle enters and exits a severe asymmetry based on the data reported by the speed monitoring on the vehicle in the area where the vehicle enters and exits a severe asymmetry. Sex and time.

The data fusion module is specifically used to combine the data reported by the speed monitoring on the vehicles in the area where the vehicle is in and out of the asymmetric area with at least one of the following data to obtain the event characteristics corresponding to the combined data: infrastructure data, time period Data, weather data, season data, key festival data.

Optionally, if the vehicle is a bus, the data fusion module is specifically used to report the speed monitoring data to the vehicles in and out of the asymmetric area of the vehicle and the number of passengers and at least one of the following data Combining, the event characteristics corresponding to the combined data are obtained: infrastructure data, period data, weather data, season data, and key holiday data.

Optionally, the traffic event prediction device further includes:

The scheduling strategy starting unit is configured to start a scheduling strategy corresponding to the predicted traffic event through the scheduling strategy configuration, the scheduling strategy configuration including at least one of the following: period configuration, area range configuration, traffic vehicle configuration, traffic direction and long-term configuration.

Optionally, when the scheduling strategy corresponding to the predicted traffic event is activated through the scheduling strategy configuration, it further includes at least one of the following:

In the embodiment of the present application, since traffic events are combined with basic road network data and floating car data for prediction, the traffic data used for prediction is guaranteed to be more comprehensive, thereby improving the accuracy of traffic event prediction.

It should be understood that the size of the sequence numbers of the steps in the above embodiments does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

Example three:

FIG. 3 is a schematic diagram of a terminal device provided in Embodiment 3 of the present application. As shown in FIG. 3, the terminal device 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30. When the processor 30 executes the computer program 32, the steps in the above embodiments of each traffic event prediction method are implemented, for example, steps S11 to S14 shown in FIG. 1. Alternatively, when the processor 30 executes the computer program 32, the functions of the modules / units in the foregoing device embodiments are realized, for example, the functions of the modules 21 to 24 shown in FIG. 2.

Exemplarily, the computer program 32 may be divided into one or more modules / units, and the one or more modules / units are stored in the memory 31 and executed by the processor 30 to complete This application. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 32 in the terminal device 3. For example, the computer program 32 may be divided into a traffic data collection unit, an event feature determination unit, an event feature comparison unit, and a traffic event prediction unit. The specific functions of each unit are as follows:

The terminal device 3 may be a computing device such as a desktop computer, a notebook, a palmtop computer and a cloud server. The terminal device may include, but is not limited to, the processor 30 and the memory 31. Those skilled in the art may understand that FIG. 3 is only an example of the terminal device 3, and does not constitute a limitation on the terminal device 3, and may include more or fewer components than those illustrated, or a combination of certain components, or different components. For example, the terminal device may further include an input and output device, a network access device, a bus, and the like.

The so-called processor 30 may be a central processing unit (Central Processing Unit (CPU), can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (Application Specific Integrated Circuit (ASIC), ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 31 may be an internal storage unit of the terminal device 3, such as a hard disk or a memory of the terminal device 3. The memory 31 may also be an external storage device of the terminal device 3, such as a plug-in hard disk equipped on the terminal device 3, a smart memory card (Smart Media Card, SMC), and a secure digital (SD) Flash card Card) etc. Further, the memory 31 may also include both an internal storage unit of the terminal device 3 and an external storage device. The memory 31 is used to store the computer program and other programs and data required by the terminal device. The memory 31 may also be used to temporarily store data that has been or will be output.

Those skilled in the art can clearly understand that, for convenience and conciseness of description, only the above-mentioned division of each functional unit and module is used as an example for illustration. In practical applications, the above-mentioned functions may be allocated by different functional units, Module completion means that the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The above integrated unit may use hardware It can also be implemented in the form of software functional units. In addition, the specific names of each functional unit and module are only for the purpose of distinguishing each other, and are not used to limit the protection scope of the present application. For the specific working processes of the units and modules in the above system, reference may be made to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

In the above embodiments, the description of each embodiment has its own emphasis. For a part that is not detailed or recorded in an embodiment, you can refer to the related descriptions of other embodiments.

Those of ordinary skill in the art may realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application of the technical solution and design constraints. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

In the embodiments provided in this application, it should be understood that the disclosed device / terminal device and method may be implemented in other ways. For example, the device / terminal device embodiments described above are only schematic. For example, the division of the module or unit is only a logical function division, and in actual implementation, there may be another division manner, such as multiple units Or components can be combined or integrated into another system, or some features can be ignored or not implemented. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or software functional unit.

If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the processes in the methods of the above embodiments, and can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the program is executed by the processor, the steps of the foregoing method embodiments may be implemented. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file, or some intermediate form. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signals, telecommunication signals, and software distribution media. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in jurisdictions. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media Does not include electrical carrier signals and telecommunications signals.

The above-mentioned embodiments are only used to illustrate the technical solutions of the present application, not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still implement The technical solutions described in the examples are modified, or some of the technical features are equivalently replaced; and these modifications or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Within the scope of protection of this application.

Claims

A traffic incident prediction method, which is characterized by including:

Collect traffic data, which includes basic road network data and floating car data;

Fuse the basic road network data and floating vehicle data to obtain corresponding event characteristics;

Compare the event characteristics with the event characteristics of a predefined traffic event;

If the event feature is the same as the event feature of the predefined traffic event, the traffic event corresponding to the event feature is used as the predicted traffic event.
The traffic event prediction method according to claim 1, wherein the basic road network data includes data reported by various types of traffic lights, data reported by speed monitoring and data reported by flow monitoring equipment; and the floating vehicle data includes: Location information reported by devices deployed on various vehicles with GPS and / or video surveillance; at this time, the basic road network data and floating vehicle data are merged to obtain corresponding event characteristics, including:

According to the data reported by the various types of traffic lights and the data reported by the flow monitoring device and the location information included in the floating car data, determine the area where the vehicle enters and exits the asymmetry;

Determine the type of congestion in the area where the vehicle is asymmetric according to the speed monitoring data reported to the vehicle in the area where the vehicle is asymmetric, and the type of congestion includes long-term and periodical;

Combining the data reported by the speed monitoring on the vehicles in and out of the asymmetrical area of the vehicle with at least one of the following data to obtain the event characteristics corresponding to the combined data: infrastructure data, time period data, weather data, season data, key Holiday data.
The traffic event prediction method according to claim 2, characterized in that, if the vehicle is a bus, the data and the number of passengers on and off the vehicle that report the speed monitoring to the area where the vehicle enters and exits the asymmetry And at least one of the following data combinations to obtain event characteristics corresponding to the combined data: infrastructure data, time period data, weather data, season data, and key holiday data.
The traffic event prediction method according to claim 1, wherein after the traffic event corresponding to the event feature is used as the predicted traffic event, the method includes:

The scheduling strategy corresponding to the predicted traffic event is started through the scheduling strategy configuration, and the scheduling strategy configuration includes at least one of the following: period configuration, area range configuration, traffic vehicle configuration, traffic direction, and long-term configuration.
The traffic event prediction method according to claim 4, wherein when the scheduling strategy corresponding to the predicted traffic event is activated by the scheduling strategy configuration, the method further includes at least one of the following:

Notify designated management departments, coordinate notification to nearby hospitals, and send predicted traffic events to the public.
The traffic event prediction method according to any one of claims 1 to 5, wherein the predicted traffic event includes: a traffic event type, a traffic feature value, and a traffic event level; the traffic feature value includes at least one of the following Species: regional traffic index, average vehicle speed, vehicle density per unit section, current vehicle speed and historical speed fluctuations.
A traffic incident prediction device, characterized in that it includes:

The traffic data collection unit is used to collect traffic data, and the traffic data includes basic road network data and floating car data;

The event feature determination unit is used to fuse the basic road network data and floating car data to obtain corresponding event features;

An event feature comparison unit, used to compare the event feature with a predefined traffic event event feature;

The traffic event prediction unit is configured to use the traffic event corresponding to the event feature as the predicted traffic event if the event feature is the same as the event feature of the predefined traffic event.
The traffic event prediction device according to claim 7, wherein the basic road network data includes data reported by various traffic lights, data reported by speed monitoring and data reported by flow monitoring equipment; and the floating vehicle data includes: Location information reported by devices deployed on various vehicles with GPS and / or video surveillance; at this time, the event feature determination unit includes:

An asymmetric area determination module, configured to determine the asymmetric area of the vehicle in and out based on the comparison between the data reported by the various traffic lights and the data reported by the flow monitoring device and the location information included in the floating vehicle data;

The congestion type determination module is used to determine the congestion type of the asymmetrical area of the in and out vehicles according to the data reported by the speed monitoring on the vehicles in the asymmetrical area of the in and out vehicles, the congestion type includes long-term and period ;

The data fusion module is used to combine the data reported by the speed monitoring on the vehicles in and out of the asymmetric area with at least one of the following data to obtain the event characteristics corresponding to the combined data: infrastructure data, time period data, weather Data, season data, key holiday data.
A terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, it is implemented as claimed in claims 1 to 6. The steps of any one of the methods.
A computer-readable storage medium storing a computer program, characterized in that when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.