CN110751828A

CN110751828A - Road congestion measuring method and device, computer equipment and storage medium

Info

Publication number: CN110751828A
Application number: CN201910850202.8A
Authority: CN
Inventors: 林岳鹏
Original assignee: Ping An International Smart City Technology Co Ltd
Current assignee: Ping An International Smart City Technology Co Ltd
Priority date: 2019-09-10
Filing date: 2019-09-10
Publication date: 2020-02-04
Anticipated expiration: 2039-09-10
Also published as: CN110751828B

Abstract

The embodiment of the application belongs to the technical field of artificial intelligence and relates to a road congestion measuring method. According to the method, the vehicle passing record data of the road section to be predicted and the relevant road section of the road section to be predicted are collected through the road gate, the collected vehicle passing record data are processed, the vehicle flow in each relevant road section and the road section to be predicted are counted, the congestion index of each relevant road section and the congestion weight caused by the road section to be predicted are calculated, and therefore the future congestion condition of the road section to be predicted is predicted. The application also provides a road congestion measuring device, computer equipment and a storage medium. According to the embodiment of the application, the future congestion condition of the road is predicted based on the actual vehicle condition of the road to be predicted, the data accuracy is high, and the accuracy rate of the prediction result is high.

Description

Road congestion measuring method and device, computer equipment and storage medium

Technical Field

The application relates to the technical field of artificial intelligence, in particular to the technical field of traffic road congestion prediction, and particularly relates to a road congestion measuring method, a road congestion measuring device, computer equipment and a storage medium.

Background

The road gate refers to an electronic device distributed at a road gate for collecting vehicle information data, an electronic eye at the road gate is one of the road gates, and the road gate collects information data such as an image of each passing vehicle by adopting a photoelectric technology, an image processing technology, a pattern recognition technology and the like.

The information data of the vehicles collected by the road gate is stored in a traffic flow database, the vehicles can be accurately identified through the traffic flow database, and the number of the vehicles passing through a road section in unit time can be acquired.

At present, mainstream map software in the market comprises Baidu and Gao De, and the modes of judging whether a road is congested by the mainstream map software are that real-time road conditions are judged by information transmitted by taxis, buses, user apps and the like almost through arrangement of a plurality of information nodes, and a solution for judging the road conditions through vehicle passing record data is not provided.

Disclosure of Invention

The embodiment of the application aims to provide a road section congestion measuring method, a road section congestion measuring device, computer equipment and a storage medium, wherein vehicle passing record data of a road section to be predicted and a road section related to the road section to be predicted are collected through a road gate, and the future congestion condition of the road section to be predicted is determined according to the vehicle passing record data.

In order to solve the above technical problem, an embodiment of the present application provides a road congestion measuring method, which adopts the following technical scheme:

a road section congestion measuring method comprises the following steps:

extracting vehicle passing record data reported by road bayonets arranged at an entrance and an exit of a road section to be predicted and an associated road section of the road section to be predicted from a vehicle flow database, wherein the associated road section is all communication road sections of the road section to be predicted, and the traveling path direction of the associated road section is the same as that of the road section to be predicted;

acquiring the number of vehicles and the traffic flow on the associated road section and the road section to be predicted within a preset time period according to the vehicle passing record data;

calculating congestion weight of each associated road section to the road section to be predicted according to the associated road section and the traffic flow on the road section to be predicted;

respectively calculating the congestion index of each associated road section according to the number of vehicles of each associated road section;

calculating a congestion prediction value of the road section to be predicted according to the congestion weight of each associated road section on the road section to be predicted and the congestion index of each associated road section;

and determining the congestion condition of the road section to be predicted according to the size relation between the congestion predicted value and a preset congestion threshold value.

Further, the vehicle passing record data comprises checkpoint information, license plate information and time information; the method for extracting the vehicle passing record data reported by the road bayonets arranged at the entrance and the exit of the road section to be predicted and the associated road section from the traffic flow database specifically comprises the following steps:

acquiring all checkpoint information of the road section to be predicted and all relevant road sections thereof;

and acquiring license plate information of the vehicles passing through the corresponding road gate and time information of the passing of the vehicles based on the gate information.

Further, the acquiring the number of vehicles and the traffic flow on the associated road section and the road section to be predicted within the predetermined time period specifically includes:

respectively determining the number of vehicles of the associated road section and the road section to be predicted according to the number of license plate information included in the vehicle passing record data reported by the entrance road gate in the predetermined time period of the associated road section and the road section to be predicted minus the number of license plate information included in the vehicle passing record data reported by the exit road gate;

and respectively determining the traffic flow of the associated road section and the road section to be predicted in the preset time period according to the ratio of the number of vehicles in the preset time period to the preset time on the associated road section and the road section to be predicted.

and respectively determining the traffic flow of the associated road section and the road section to be predicted in the preset time period according to the ratio of the number of license plate information included in the vehicle passing record data reported by the road gates at the entrance and the exit of the associated road section and the road gate at the exit of the road section to be predicted to the preset time.

Further, the calculating the congestion index of each associated road section according to the number of vehicles in each associated road section specifically includes:

and respectively determining the congestion index of each associated road section from a preset congestion index mapping table according to the number of vehicles of each associated road section, wherein the congestion index mapping table comprises different numbers of vehicles and congestion indexes corresponding to the numbers of the vehicles.

Further, the calculating congestion weight of each associated road section on the road section to be predicted according to the traffic flow of the associated road section and the road section to be predicted specifically includes:

calculating the congestion weight of each associated road section to the road section to be predicted according to the following formula:

wherein Q is_iRepresenting the congestion weight of the ith associated road section to the road section to be predicted, D representing the traffic flow of the road section to be predicted, C_iAnd the traffic flow of the ith associated road section is represented, n is the total number of the associated road sections, and i and n are integers which are more than or equal to 1.

Further, the calculating the congestion prediction value of the road section to be predicted according to the congestion weight of each associated road section on the road section to be predicted and the congestion index of each associated road section comprises:

calculating the congestion prediction value of the road section to be predicted according to the following formula:

wherein Y is the congestion prediction value, Q_iRepresenting the congestion weight of the ith associated road section to the road section to be predicted, G_iAnd the congestion index of the ith associated road section is represented, n is the total number of the associated road sections, and i and n are integers which are more than or equal to 1.

In order to solve the above technical problem, an embodiment of the present application provides a road congestion measuring device, which adopts the following technical solutions:

a road segment congestion measuring device comprising:

the data extraction module is used for extracting vehicle passing record data reported by road bayonets arranged at an inlet and an outlet of a road section to be predicted and an associated road section of the road section to be predicted from a traffic flow database, wherein the associated road section is all communication road sections of the road section to be predicted, and the traveling path direction of the associated road section is the same as that of the road section to be predicted;

the acquisition module is used for acquiring the number of vehicles and the traffic flow on the associated road section and the road section to be predicted within a preset time period according to the vehicle passing record data;

the congestion weight calculation module is used for calculating congestion weight of each associated road section on the road section to be predicted according to the traffic flow of the associated road section and the road section to be predicted;

the congestion index calculation module is used for calculating the congestion index of each associated road section according to the number of vehicles of each associated road section;

the congestion prediction value calculation module is used for calculating the congestion prediction value of the road section to be predicted according to the congestion weight of each associated road section on the road section to be predicted and the congestion index of each associated road section;

and the congestion judgment module is used for determining the congestion condition of the road section to be predicted according to the size relation between the congestion predicted value and a preset congestion threshold value.

In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:

a computer device comprising a memory in which a computer program is stored and a processor that implements the steps of the road segment congestion measuring method described above when executing the computer program.

In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:

a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the road segment congestion measuring method described above.

Compared with the prior art, the embodiment of the application mainly has the following beneficial effects:

the embodiment of the application provides a road section congestion measuring method, a road section congestion measuring device, computer equipment and a storage medium, vehicle passing record data of a road section to be predicted and an associated road section of the road section to be predicted are collected through a road card port, the vehicle passing record data are processed, congestion indexes of the associated road sections and congestion weights caused by the road section to be predicted are calculated, a congestion predicted value of the road section to be predicted is further calculated, and the future congestion condition of the road section to be predicted is determined according to the size relation between the congestion predicted value and a preset congestion threshold value. The method and the device for predicting the future congestion condition of the road based on the actual vehicle condition of the road to be predicted have the advantages of high data accuracy and high accuracy rate of the prediction result.

Drawings

In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow diagram of one embodiment of a road segment congestion measurement method according to the application;

FIG. 3 is a flow diagram for one embodiment of step 202 of FIG. 2;

FIG. 4 is a schematic illustration of a road segment of the present application;

FIG. 5 is a flow diagram for one embodiment of step 202 of FIG. 2;

FIG. 6 is a schematic block diagram of an embodiment of a road congestion measuring device according to the present application;

FIG. 7 is a schematic block diagram of one embodiment of a computer device according to the present application.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, the system architecture 100 may include

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.

The

terminal devices

101, 102, 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to user devices, network devices, or devices formed by integrating user devices and network devices through a network. The user equipment includes, but is not limited to, any mobile electronic product, such as a smart phone, a tablet computer, and the like, which can perform human-computer interaction with a user through a touch panel, and the mobile electronic product may employ any operating system, such as an android operating system, an IOS operating system, and the like. The network device includes an electronic device capable of automatically performing numerical calculation and information processing according to preset or stored instructions, and the hardware includes but is not limited to a microprocessor, an Application Specific Integrated Circuit (ASIC), a programmable gate array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like. The network device comprises but is not limited to a computer, a network host, a single network server, a plurality of network server sets or a cloud formed by a plurality of servers; here, the Cloud is composed of a large number of computers or web servers based on Cloud Computing (Cloud Computing), which is a kind of distributed Computing, one virtual supercomputer consisting of a collection of loosely coupled computers. Including, but not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a VPN network, a wireless Ad Hoc network (Ad Hoc network), etc. Of course, those skilled in the art should understand that the above terminal device is only an example, and other existing or future terminal devices may be applicable to the present application, and are included in the scope of the present application and are incorporated herein by reference.

The server 105 may be a server, a server cluster composed of several servers, or a cloud computing service center. It may also be a server providing various services, such as a background server providing support for pages displayed on the

terminal devices

101, 102, 103.

It should be noted that the road congestion measuring method provided by the embodiment of the present application is generally executed by a server, and accordingly, the road congestion measuring device is generally disposed in the server.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to fig. 2, a flow diagram of one embodiment of a road segment congestion measurement method according to the present application is shown. The road congestion measuring method comprises the following steps.

Step 201, vehicle passing record data reported by road gates arranged at the entrance and the exit of the road section to be predicted and the related road section are extracted from the traffic flow database.

In this embodiment, the electronic device (for example, the terminal device shown in fig. 1) on which the road congestion measuring method operates may extract the traffic record data reported by the road gate at the entrance and the exit of the road segment to be predicted and the associated road segment thereof in a wired connection manner or a wireless connection manner. It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a uwb (ultra wideband) connection, and other wireless connection means now known or developed in the future.

Specifically, the associated road segment is a communication path segment of the road segment to be predicted, which is directly communicated with the road segment to be predicted, and has a traffic flow added to the road segment to be predicted, or can shunt the traffic flow of the road segment to be predicted. In the present application, a road segment to be predicted and an associated road segment should be understood as having directionality, and at least two or more, one is used for vehicles to enter the road segment to be predicted, and the other is used for vehicles to exit the road segment to be predicted.

Further, the road card port is disposed at an entrance and an exit of the road segment to be predicted and the associated road segment, the road card port includes, but is not limited to, an electronic eye, other monitoring devices such as a Video Recorder (DVR), a Digital Video Recorder (DVR), and the like, the road card port collects information data such as images of each passing vehicle by using a photoelectric technology, an image processing technology, a pattern recognition technology, and the like, and the collected information data of the vehicles is stored in a traffic database. Vehicles can be identified through the traffic flow database, and the number of vehicles passing through a road section in unit time can be acquired.

Referring to fig. 3, a flowchart of an embodiment of step 201 is shown, where step 201 is to extract traffic log data reported by road gates arranged at an entrance and an exit of a road segment to be predicted and an associated road segment thereof, and includes the following steps.

Step 2011: and acquiring all checkpoint information of the road section to be predicted and all relevant road sections thereof.

Step 2012: and acquiring license plate information of the vehicles passing through the corresponding road gate and time information of the passing of the vehicles based on the gate information.

In this embodiment, the vehicle passing record data includes bayonet information, license plate information and time information, wherein the bayonet information includes the bayonet number and the position information of road bayonet, the bayonet number is used for only marking road bayonet, the position information is used for recording the position of road bayonet. The license plate information includes a license plate number as a vehicle uniqueness characteristic in this embodiment, and is used for identifying the vehicle. The time information refers to the specific time when the vehicle passes through the road gate.

Further, the acquired vehicle passing record data is stored in a database, and a mapping relation is established among the checkpoint information, the license plate information and the time information. Specifically, in this embodiment, the gate information is obtained according to the position information, all gate information of the road segment to be predicted and all associated road segments thereof are obtained, the gate information is further used as a filtering condition, vehicle passing record data of all associated road gates are obtained, and the obtained information is specifically shown in table 1. The vehicle passing record data can be further extracted according to preset conditions based on the acquired information.

TABLE 1

Bayonet numbering	License plate number	Time of occurrence
			025	X.A55689	1 month 1-7: 12am in 2018
025	X.A56214	1 month 1-7: 15am in 2018
			026	X.A55689	1 month 1 day-7: 30am in 2018
026	X.P21463	1 month 1-7: 21am in 2018
			031	X.A55689	1 month 1 day-7: 45am in 2018
031	X.P21463	1 month 1, 2018-7: 33am
			014	X.A55689	1 month 1 st-7: 59am in 2018
014	X.P21463	1 month 1 st-7: 43am in 2018

The embodiment performs predictive analysis on the intersection to be predicted according to the driving amounts in different time periods, and specifically, extracts data of the road gates of the road sections to be predicted and the associated road sections, which are acquired in the corresponding time periods. When the vehicle passing record data is extracted, the starting time and the ending time of the vehicle passing record data are preset and obtained, and then the vehicle passing record data of each road gate of the road section to be predicted and the vehicle passing record data of each road gate of all the related road sections are obtained according to the starting time and the ending time. For example, if the preset starting time for acquiring the vehicle record data is 7:00 am, the ending time is 10 am: 00, then get 7:00 morning to 10 morning: and 00, vehicle passing record data recorded by a road gate.

Taking the road shown in fig. 4 as an example, if the set time is 7: 00-10: 00, acquiring road blocks of a road section to be predicted, an associated road section I, an associated road section II, an associated road section III and an associated road section IV in the graph, wherein the road blocks are from 7:00 in the morning to 10 in the morning: 00 recorded vehicle passing record data.

It should be noted that a plurality of the start times and a plurality of the end times may be preset to form a plurality of time periods, and the vehicle passing record data of each road gate of the road section to be predicted and the vehicle passing record data of each road gate of the associated road section are acquired in each time period.

Step 202: and acquiring the number of vehicles and the traffic flow on the associated road section and the road section to be predicted within a preset time period according to the vehicle passing record data.

In this embodiment, the vehicles are identified by the vehicle number plate information of the vehicles according to the vehicle passing record data of each road gate, and the time information describes the appearance time of the vehicles appearing at each road gate, that is, in this embodiment, when the vehicle information passes through one of the road gates, the appearance time of the vehicles appearing at the road gate is recorded and identified. Further, the position information associates the positions of the road checkpoints in the road section to be predicted or the associated road section, so as to determine the positions of the identified vehicles in the road section to be predicted or the associated road section. Specifically, in this embodiment, a gate number is associated with the road segment to be predicted or the associated road segment.

Referring to fig. 5, which shows a flowchart of an embodiment of step 202, obtaining the number of vehicles and the traffic flow on the associated road section and the road section to be predicted in a predetermined time period according to the vehicle passing record data may be as follows.

And when any vehicle is identified, acquiring the time information of the vehicle passing through the road gate and the gate number of the road gate, and sequencing all the acquired appearance times of the same vehicle according to the sequence of time. The passing record data of the same vehicle is arranged in chronological order, and the sequence of the gate numbers corresponding to all the appearance times corresponds to the sequence of all the appearance times, for example, as shown in table 2 below.

TABLE 2

Taking the vehicle x.a55689 shown in table 2 as an example, the sequence of the gate numbers corresponding to all the appearance times is 025. Positions of road checkpoints corresponding to checkpoint numbers 025, 026, 031 and 014 in the road segment to be predicted, the associated road segment I and the associated road segment IV are shown in FIG. 4. .

Step 2021: and respectively determining the number of vehicles of the associated road section and the road section to be predicted according to the number of license plate information included in the vehicle passing record data reported by the entrance road gate in the predetermined time period of the associated road section and the road section to be predicted minus the number of license plate information included in the vehicle passing record data reported by the exit road gate.

Since only the entrance and exit of each associated road segment and the road segment to be predicted are provided with road bayonets, that is, the intersection of the two road segments is provided with the road bayonets, for the same-direction path of each road segment, if the license plate information of a vehicle is only recorded at the entrance road bayonets and not recorded at the exit road bayonets within the predetermined time period, the vehicle is explained to travel on the road segment, and therefore, the vehicle number of the associated road segment and the road segment to be predicted can be determined according to the license plate information number included in the vehicle passing record data reported by the entrance road bayonets within the predetermined time period of the associated road segment and the road segment to be predicted minus the license plate information number included in the vehicle passing record data reported by the exit road bayonets. To avoid missing a calculation of a vehicle that enters a road segment before the predetermined time starts and has not yet exited the road segment at the expiration of the predetermined time, the predetermined time is optionally set according to the distance of the road segment, for example, the predetermined time may be set to 1 hour for a road segment of 1000 meters.

Step 2022: and respectively determining the traffic flow of the associated road section and the road section to be predicted in the preset time period according to the ratio of the number of vehicles in the preset time period to the preset time on the associated road section and the road section to be predicted.

The determination of the traffic flow on the associated road section and the road section to be predicted in the preset time period according to the ratio of the number of vehicles in the associated road section and the road section to be predicted in the preset time period to the preset time can be realized by the following formula: :

traffic flow-number of vehicles/predetermined time

In another embodiment of the present application, for any one of the associated road segment and the road segment to be predicted, traffic flows at an entrance and an exit of the road segment may also be respectively calculated, that is, the number of vehicles passing through in unit time is calculated according to the number of license plate information included in the vehicle passing record data reported by road gates at the entrance and the exit of the road segment, that is, calculated according to the following formula:

vehicle flow rate (number of vehicles passing through bayonet)/predetermined time

When the traffic flow of the associated road segment and the road segment to be predicted is calculated through the number of vehicles at the gate, the traffic flow at the inlet or the outlet of each of the associated road segment and the road segment to be predicted may be respectively used as the traffic flow of the associated road segment and the road segment to be predicted, or the average value of the traffic flow at the inlet and the outlet of each of the associated road segment and the road segment to be predicted may be respectively used as the traffic flow of the associated road segment and the road segment to be predicted, which is not limited in this embodiment.

In another embodiment of the application, the vehicle traveling speed of each associated road section in the predetermined time period may also be obtained, for example, vehicles of each associated road section are identified based on license plate information, the vehicle speed of each vehicle is calculated according to time information that each vehicle of each associated road section passes through two road gates at the entrance and the exit of the same associated road section and the distance between the two road gates, and the vehicle traveling speed of each associated road section is calculated according to the vehicle speeds of all vehicles on each associated road section.

For example, the average vehicle travel speed of all the vehicles of each associated section is calculated, that is, the vehicle travel speed of each associated section according to the following formula:

wherein V is the average vehicle of an associated road segmentSpeed of travel, v_iThe vehicle travel speed of the i-th vehicle of the associated section, and n is the number of all vehicles of the associated section.

For example, for the vehicle x.a55689 in table 2, distances of road gates corresponding to gate numbers 025, 026, 031, and 014 may be called in preset map data, for example, the distance of the road gate corresponding to the gate number 025 and the gate number 026 is l1, the distance of the road gate corresponding to the gate number 026 is l2, and the distance of the road gate corresponding to the gate number 031 and the gate number 014 is l3, and then the distance of the vehicle in the road segment to be predicted, the associated road segment i, and the associated road segment iv is l1+ l2+ l 3. The travel speed of the same vehicle in the road segment to be predicted and at least one of the associated road segments can then be calculated in a plurality of calculation manners.

In an alternative form of this embodiment, the speed of the same vehicle between two adjacent road gates is obtained in segments. For example, the average speed value is the speed of the vehicle on the road section when l1 is divided by the speed v1 obtained by the time difference between the first appearance time (1/2018-7: 12AM) and the second appearance time (1/2018-1/7: 30AM), l2 is divided by the speed v2 obtained by the time difference between the second appearance time (1/2018-1-7: 30AM) and the third appearance time (1/2018-1-7: 45AM), and l3 is divided by the speed v3 obtained by the time difference between the third appearance time (1/2018-7: 45AM) and the fourth appearance time (1/7/59 AM 2018), and then the average speed values of v1, v2 and v3 are calculated.

Step 203: and calculating congestion weight of each associated road section to the road section to be predicted.

For example, the congestion weight of each associated road section to the road section to be predicted is calculated according to the associated road section and the traffic flow on the road section to be predicted.

In some embodiments of the present application, the obtained vehicle data may be analyzed using a neural network model in the time dimension. The neural network model in the time dimension is capable of processing time series data, which refers to data collected in order of time. In the present application, the vehicle-passing record data acquired by the road gate is substantially represented in time sequence. For example, the vehicle passing record data is expressed as [ appearance time ] + [ bayonet number ] according to the sequence of time: [ 1/2018-7: 12AM ] + [ 025 ], [ 1/2018-7: 30AM ] + [ 026 ], [ 1/2018-7: 45AM ] + [ 031 ], [ 1/2018-7: 59AM ] + [ 014 ].

As shown in fig. 4, the dotted line portions with arrows indicate the flow trajectory entering the section to be predicted and the flow trajectory exiting from the section to be predicted. The solid line part with the arrow indicates a traffic flow track which does not enter the road section to be predicted but can affect the congestion condition of the road section to be predicted, namely the congestion conditions of the associated road section I and the associated road section II can affect the traffic flow entering the road section to be predicted. When the traffic flow of the associated road section I and the associated road section II entering the road section to be predicted is increased, the congestion condition of the road section to be predicted depends on the traffic flow flowing out of the road section to be predicted to the associated road section III and the associated road section IV. The congestion conditions of the associated road section III and the associated road section IV influence the traffic flow flowing out of the road section to be predicted.

The weight of each associated road section to the congestion of the road section to be predicted depends on: (1) for the associated road sections (for example, the associated road section I and the associated road section II) which input the traffic flow to the road section to be predicted, the weight of the congestion of the road section to be predicted depends on the input traffic flow to the road section to be predicted, and when the traffic flow input to the road section to be predicted by the associated road sections is larger, the weight of the congestion of the road section to be predicted is larger. (2) For the associated road sections (for example, the associated road section III and the associated road section IV) which are used for shunting the traffic flow from the road section to be predicted, the weight of congestion of the road section to be predicted depends on the traffic flow output from the road section to be predicted to the associated road sections, and the weight of congestion of the road section to be predicted is larger when the traffic flow output from the road section to be predicted to the associated road sections is larger.

For example, the congestion weight of each associated road segment to the road segment to be predicted is calculated according to the following formula:

It should be noted that the congestion weight of each associated link may change in different time periods, and the main reason for the change is related to the traffic volume. Since the traffic flow rate is different in different time periods, the congestion weight of each associated link is also changed in different time periods, and therefore the congestion weight of each associated link is used for calculating the congestion weight of the predetermined time period.

Step 204: and respectively calculating the congestion index of each associated road section according to the number of vehicles of each associated road section.

In this embodiment, in a predetermined time period, the congestion index of each associated link is determined according to the number of vehicles in each associated link and from a preset congestion index mapping table, where the congestion index mapping table includes congestion indexes corresponding to different numbers of vehicles and numbers of vehicles, so that the congestion index of each associated link can be quickly found according to the number of vehicles, for example, the number of vehicles and congestion index mapping table may be as follows in table 3:

TABLE 3

Number of vehicles	Congestion index
		10	0.1
20	0.2
		30	0.3
……
		100	1

The congestion index in the congestion index mapping table may be set according to a distance of a road segment, for example, 1000 meters, and the congestion index of 30 vehicles may be set to 0.1, and so on, which is not described herein again.

Step 205: and calculating the congestion prediction value of the road section to be predicted according to the congestion weight of each associated road section to the road section to be predicted and the congestion index of each associated road section.

In this embodiment, congestion weights of the associated links to the links to be predicted at different time periods are different, and congestion indexes of the associated links at different time periods are different. Therefore, when the congestion condition of the road section to be predicted is predicted, the congestion weight of each associated road section to the congestion of the road section to be predicted and the congestion index of each associated road section are taken according to different time periods. Specifically, during prediction, the current time period of the road section to be predicted is obtained, the weight of each associated road section matched with the current time information of the road section to be predicted for congestion of the road section to be predicted is selected according to the current time information of the road section to be predicted, the future congestion situation of the road is predicted based on the actual vehicle situation of the road to be predicted, the data accuracy is high, the prediction result accuracy rate is high, the congestion weight and the congestion index of each associated road section need to be updated, and the existing congestion weight and congestion index are replaced.

For example, based on the congestion weight and congestion index, according to a formula

Obtaining a congestion predicted value of the road section to be predicted, wherein Y is the congestion predicted value, and Q is_iRepresenting the congestion weight of the ith associated road section to the road section to be predicted, G_iAnd the congestion index of the ith associated road section is represented, n is the total number of the associated road sections, and i and n are integers which are more than or equal to 1.

Step 206: and determining the congestion condition of the road section to be predicted according to the size relation between the congestion predicted value and a preset congestion threshold value.

For example, when the congestion predicted value is greater than or equal to a preset congestion index threshold value of the road section to be predicted, it is determined that the road section to be predicted will be congested, and when the congestion predicted value is smaller than the congestion index threshold value of the road section to be predicted, it is determined that the road section to be predicted will not be congested but will be unblocked.

In another embodiment of the present application, a plurality of congestion index threshold value ranges and a congestion level mapping relation table may be further provided, and a congestion level of the road segment to be predicted is determined according to which congestion index threshold value range the congestion prediction value is in, for example, as shown in table 4 below.

TABLE 4

Congestion index threshold	Congestion level
		1-20	First stage
21-50	Second stage
		50-100	Third stage
>100	Fourth stage

According to the method and the device, the vehicle-passing record data of the road section to be predicted and the associated road sections of the road section to be predicted are collected through the road gate, the vehicle-passing record data are processed, the congestion index of each associated road section and the congestion weight caused by the road section to be predicted are calculated, and then the future congestion condition of the road section to be predicted is predicted.

To solve the above technical problem, an embodiment of a device corresponding to the method for measuring road segment congestion in fig. 2 is further provided in the present application, specifically referring to fig. 6, and fig. 6 is a schematic structural diagram of the road segment congestion measuring device in this embodiment.

The road section congestion prediction 600 comprises a data extraction module 601, an acquisition module 602, a congestion weight calculation module 603, a congestion index calculation module 604, a congestion prediction value calculation module 605 and a congestion judgment module 606, wherein the data extraction module 601, the acquisition module 602, the congestion weight calculation module 603, the congestion index calculation module 604, the congestion prediction value calculation module 605 and the congestion judgment module 606 are connected with each other through buses, and each module is realized through a circuit, a chip or a processor.

The data extraction module 601 is configured to extract vehicle passing record data reported by road gates arranged at an entrance and an exit of a road segment to be predicted and an associated road segment from a traffic flow database, where the associated road segment is all communication road segments of the road segment to be predicted and the direction of the travel path of the associated road segment is the same as that of the road segment to be predicted.

For example, the data extraction module 601 is configured to obtain all gate information of the road segment to be predicted and all associated road segments thereof, and obtain license plate information of a vehicle passing through a corresponding road gate and time information of the vehicle passing through the corresponding road gate based on the gate information, which may specifically refer to the content described in step 201 of the foregoing method embodiment and is not described herein again.

The obtaining module 602 is configured to obtain, according to the vehicle passing record data, the number of vehicles and the traffic flow on the associated road segment and the road segment to be predicted within a predetermined time period.

For example, the obtaining module 602 is configured to determine the number of vehicles in the associated road segment and the road segment to be predicted according to the number of license plate information included in the vehicle passing record data reported by the entrance road gate in the predetermined time period of the associated road segment and the road segment to be predicted minus the number of license plate information included in the vehicle passing record data reported by the exit road gate.

The obtaining module 602 is further configured to determine traffic flows on the associated road segment and the road segment to be predicted in the predetermined time period according to a ratio of the number of vehicles in the associated road segment and the road segment to be predicted in the predetermined time period to the predetermined time.

The obtaining module 602 obtains the number of vehicles and the traffic flow on the associated road segment and the road segment to be predicted in the predetermined time period may specifically refer to the content described in step 202 of the foregoing method embodiment, and details are not repeated here.

The congestion weight calculation module 603 is configured to calculate congestion weights of the associated road segments for the road segments to be predicted according to the associated road segments and the traffic flow on the road segments to be predicted.

For example, when the congestion weight calculation module 603 is configured to calculate the congestion weight of each associated road segment for the road segment to be predicted according to the traffic flow on the associated road segment and the road segment to be predicted, the congestion weight of each associated road segment for the road segment to be predicted may be calculated according to the following formula:

The congestion weight calculation module 603 may refer to the content described in step 203 of the foregoing method embodiment for calculating the congestion weight of each associated road segment for the road segment to be predicted, which is not described herein again.

The congestion index calculation module 604 is configured to calculate congestion indexes of the associated road sections according to the number of vehicles in the associated road sections.

For example, the congestion index calculation module 604 is configured to determine the congestion index of each associated link according to the number of vehicles in each associated link and from a preset congestion index mapping table, where the congestion index mapping table includes congestion indexes corresponding to different numbers of vehicles and the number of vehicles, in a predetermined time period, so that the congestion index of each associated link can be quickly found according to the number of vehicles.

The definition of the congestion index mapping table may refer to what is described in step 204 of the foregoing method embodiment, and is not described herein again.

The congestion prediction value calculation module 605 is configured to calculate a congestion prediction value of the road segment to be predicted according to the congestion weight of each associated road segment on the road segment to be predicted and the congestion index of each associated road segment.

For example, the congestion prediction value calculation module 605 is configured to calculate the congestion prediction value of the road segment to be predicted according to the following formula:

The specific process of the congestion prediction value calculation module 605 calculating the congestion prediction value of the road segment to be predicted may refer to the content described in step 205 of the foregoing method embodiment, and is not described herein again.

The congestion judging module 606 is configured to determine a congestion condition of the road segment to be predicted according to a size relationship between the congestion predicted value and a preset congestion threshold.

For example, when the congestion judgment module 606 judges that the congestion predicted value is greater than or equal to the congestion index threshold of the road segment to be predicted, it is determined that the road segment to be predicted will be congested, and when the congestion predicted value is smaller than the congestion index threshold of the road segment to be predicted, it is determined that the road segment to be predicted will not be congested but will be smooth.

In another embodiment of the present application, a plurality of congestion index threshold value ranges and a congestion level mapping relationship table may be further provided, and the congestion judging module 606 is configured to determine a congestion level of the road segment to be predicted according to which congestion index threshold value range the congestion predicted value is in.

The process of determining the congestion condition by the congestion determining module 606 may be as described in step 206 of the above method embodiment, and is not described herein again.

In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 7, fig. 7 is a block diagram of a basic structure of a computer device according to the present embodiment.

The computer device 7 comprises a memory 71, a processor 72, a network interface 73, which are communicatively connected to each other via a system bus. It is noted that only a computer device 7 having components 71-73 is shown, but it is to be understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable gate array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.

The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.

The memory 71 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the storage 71 may be an internal storage unit of the computer device 7, such as a hard disk or a memory of the computer device 7. In other embodiments, the memory 71 may also be an external storage device of the computer device 7, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a flash Card (FlashCard), and the like, which are provided on the computer device 7. Of course, the memory 71 may also comprise both an internal storage unit of the computer device 7 and an external storage device thereof. In this embodiment, the memory 71 is generally used for storing an operating system installed in the computer device 7 and various types of application software, such as program codes of a road congestion measuring method. Further, the memory 71 may also be used to temporarily store various types of data that have been output or are to be output.

The processor 72 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 72 is typically used to control the overall operation of the computer device 7. In this embodiment, the processor 72 is configured to run a program code stored in the memory 71 or process data, for example, a program code for running the road congestion measuring method.

The network interface 73 may comprise a wireless network interface or a wired network interface, and the network interface 73 is generally used for establishing a communication connection between the computer device 7 and other electronic devices.

The present application further provides another embodiment, which is to provide a computer-readable storage medium storing a logistics insurance program, where the logistics insurance program is executable by at least one processor to cause the at least one processor to execute the steps of the road section congestion measurement method as described above.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.

It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.

Claims

1. A road congestion measuring method is characterized by comprising the following steps:

2. The road section congestion measuring method according to claim 1, wherein the passing record data includes gate information, license plate information and time information; the method for extracting the vehicle passing record data reported by the road bayonets arranged at the entrance and the exit of the road section to be predicted and the associated road section from the traffic flow database specifically comprises the following steps:

3. The method for measuring road segment congestion according to claim 2, wherein the obtaining the number of vehicles and the traffic flow on the associated road segment and the road segment to be predicted in the predetermined time period specifically comprises:

4. The method for measuring road segment congestion according to claim 2, wherein the obtaining the number of vehicles and the traffic flow on the associated road segment and the road segment to be predicted in the predetermined time period specifically comprises:

5. The link congestion measurement method according to any one of claims 1 to 4, wherein the calculating the congestion index of each associated link according to the number of vehicles in each associated link comprises:

6. The method for measuring the congestion of the road segment according to any one of claims 1 to 4, wherein the calculating the congestion weight of each associated road segment on the congestion of the road segment to be predicted according to the traffic flow of the associated road segment and the traffic flow of the road segment to be predicted specifically comprises:

wherein Q is_iRepresenting the congestion weight of the ith associated road section to the road section to be predicted, and D representing the traffic flow of the road section to be predicted，C_iAnd the traffic flow of the ith associated road section is represented, n is the total number of the associated road sections, and i and n are integers which are more than or equal to 1.

7. The method for measuring congestion of a road segment as claimed in any one of claims 1 to 4, wherein the calculating of the congestion prediction value of the road segment to be predicted according to the congestion weight of each associated road segment to the road segment to be predicted and the congestion index of each associated road segment comprises:

8. A road congestion measuring device, comprising:

9. A computer device, characterized by comprising a memory in which a computer program is stored and a processor which, when executing the computer program, implements the steps of the road segment congestion measurement method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the steps of the road segment congestion measuring method according to any one of claims 1 to 7.