CN110807915A

CN110807915A - Road condition information calculation method and device, storage medium and computer equipment

Info

Publication number: CN110807915A
Application number: CN201910918537.9A
Authority: CN
Inventors: 张勇
Original assignee: Tencent Dadi Tongtu Beijing Technology Co Ltd
Current assignee: Tencent Dadi Tongtu Beijing Technology Co Ltd
Priority date: 2019-09-26
Filing date: 2019-09-26
Publication date: 2020-02-18
Anticipated expiration: 2039-09-26
Also published as: CN110807915B

Abstract

The application relates to a road condition information calculation method, a road condition information calculation device, a storage medium and computer equipment, wherein the method comprises the following steps: acquiring track data of vehicles on a road; selecting a main flow road according to the traffic flow information of the road determined based on the track data; searching a branch road communicated with the main flow direction road; deleting the track data of the vehicle flowing to the branch road from the track data belonging to the main flow road; and determining the road condition information of the main flow road according to the deleted residual track data. The scheme provided by the application can effectively improve the accuracy of the road condition information.

Description

Road condition information calculation method and device, storage medium and computer equipment

Technical Field

The present application relates to the field of computer application technologies, and in particular, to a road condition information calculation method, apparatus, storage medium, and computer device.

Background

Along with the rapid development of science and technology and the continuous improvement of standard of living, the user relies on vehicle more and more when going out, and whether smooth and easy of going out has been decided to the road condition information of road, and when the road condition information of road was seen in the accurate prediction, the user can select suitable road to go, so can save user's travel time, but also can alleviate the traffic jam problem, energy saving etc..

In a traditional road condition information calculation scheme, historical flow data is used for calculating the speed of a vehicle passing through a road, and then road condition information of the road is judged according to a speed threshold value. When vehicles are more on the road, the speeds of different vehicles in the road may have great difference, and the road condition information of the road is judged by calculating the speeds through historical flow data, which may result in inaccurate road information.

Disclosure of Invention

Therefore, it is necessary to provide a traffic information calculation method, a traffic information calculation device, a storage medium, and a computer device, for the technical problem that traffic information is inaccurate due to the fact that traffic information is judged in a mode of calculating a vehicle speed through historical traffic data.

A road condition information calculation method comprises the following steps:

acquiring track data of vehicles on a road;

selecting a main flow road according to the traffic flow information of the road determined based on the track data;

searching a branch road communicated with the main flow direction road;

deleting the track data of the vehicle flowing to the branch road from the track data belonging to the main flow road;

and determining the road condition information of the main flow road according to the deleted residual track data.

A traffic information calculation device, the device comprising:

the data acquisition module is used for acquiring track data of vehicles on the road;

the road selection module is used for selecting a main flow road according to the traffic flow information of the road determined based on the track data;

the road searching module is used for searching a branch road communicated with the main flow direction road;

the data deleting module is used for deleting the track data of the vehicles flowing to the branch road from the track data belonging to the main flow road;

and the road condition information determining module is used for determining the road condition information of the main flow road according to the deleted residual track data.

A computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to execute the steps of the traffic information calculating method.

A computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of the traffic information calculation method.

According to the road condition information calculation method, the road condition information calculation device, the storage medium and the computer equipment, when the track data of the vehicles on the road are obtained, the main flow road is selected, the branch road is searched, and then the track data of the vehicles flowing to the branch road in the main flow road is deleted, so that the influence on the calculation result of the road condition information due to the fact that the speed difference in different flow directions is large is avoided. And determining the road condition information of the main flow road according to the deleted residual track data, wherein the road condition information is not influenced by the change of the vehicle speed when the vehicle flows from the main flow road to the branch road, so that the accuracy of the road condition information is effectively improved.

Drawings

Fig. 1 is an application environment diagram of a traffic information calculation method in an embodiment;

fig. 2 is a schematic flow chart of a traffic information calculation method in one embodiment;

FIG. 3 is a schematic illustration of a roadway in one embodiment;

FIG. 4 is a flowchart illustrating the step of determining track data to delete vehicles flowing to a tributary road based on the number of vehicles in the road segment according to one embodiment;

FIG. 5 is a flowchart illustrating a step of deleting trajectory data for vehicles in lanes merging into a tributary road in one embodiment;

fig. 6 is a schematic flow chart illustrating steps of calculating road condition speed and road condition status, and sending the road condition status and corresponding trajectory data to the client in one embodiment;

fig. 7 is a timing diagram illustrating a traffic information calculation method according to an embodiment;

FIG. 8 is a schematic diagram illustrating trajectory data for a vehicle on a mainstream road in one embodiment;

FIG. 9 is a diagram illustrating trajectory data for vehicles on a lateral roadway in one embodiment;

fig. 10 is a block diagram of a traffic information calculating device according to an embodiment;

fig. 11 is a block diagram of a traffic information calculating device according to another embodiment;

FIG. 12 is a block diagram of a computer device in one embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

Fig. 1 is an application environment diagram of a traffic information calculation method in an embodiment. Referring to fig. 1, the traffic information calculation method is applied to a traffic information calculation system. The traffic information calculation system includes a terminal 110 and a server 120. The server 120 can acquire track points of each vehicle through the terminal 110 in the vehicle to obtain track data of the vehicles on the road; selecting a main flow road according to the traffic flow information of the road determined based on the track data; searching a branch road communicated with the main flow direction road; deleting the track data of the vehicles flowing to the branch roads from the track data belonging to the main flow road; and determining the road condition information of the mainstream road according to the deleted residual track data.

The terminal 110 and the server 120 are connected through a network. The terminal 110 may specifically be a vehicle or a mobile terminal on the vehicle, and the mobile terminal may specifically be at least one of a mobile phone, a tablet computer, a notebook computer, and the like. The vehicle is provided with a positioning device, a display screen and a client. The server 120 may be implemented as a stand-alone server or a server cluster composed of a plurality of servers.

As shown in fig. 2, in an embodiment, a traffic information calculating method is provided. The embodiment is mainly illustrated by applying the method to the server 120 in fig. 1. Referring to fig. 2, the method for calculating road condition information specifically includes the following steps:

s202, track data of vehicles on the road are obtained.

The vehicle can be all motor vehicles driven or pulled by a power device on a road, and specifically can comprise an automobile, a passenger car, a truck, an electric vehicle, a motorcycle and the like. The track data is composed of different track points in the driving process of the vehicle, and the track points are matched with the road marks of the corresponding roads, namely the corresponding roads can be found through the track points. The track data can be historical track data or track data formed by acquiring track points of the vehicle in a driving process in real time. The historical track data refers to track data formed by track points acquired in a time period before the current time and then the track points.

In one embodiment, a server receives a road condition query request sent by a mobile terminal or a positioning device in a target vehicle, wherein the road condition query request at least carries a current track point of the target vehicle, and determines a target area according to the track point, wherein the current track point of the target vehicle is located in the target area. The server acquires track data of vehicles on each road in the target area. The mobile terminal may be a terminal device placed in a target vehicle for navigation. The target vehicle is a vehicle driven by a user needing to inquire road condition information. The trace point may be at the center of the target area, or may be at another position within the target area, which is not specifically limited in the embodiment of the present invention. In the following embodiments, the trajectory data are the trajectory data of the vehicles on each road in the target area, unless otherwise specified.

For example, when a user starts a map application, the default user needs to know road condition information of a road, a positioning device in a mobile terminal or a target vehicle sends a road condition query request to a server, the server determines a target area a, and then obtains trajectory data of vehicles on all roads in the target area a, so as to calculate the road condition information of the road according to the trajectory data. The map application refers to a client application program installed on a mobile terminal or a vehicle for navigation, and may also be referred to as a navigation application.

In one embodiment, S202 may specifically include: the method comprises the steps that a server obtains track points of a vehicle in the driving process; establishing a matching relation between the track point and the road mark of the road corresponding to the track point; sequencing the track points establishing the matching relation according to the track point acquisition time; combining the sequenced track points into track data; the matching relation is used for selecting a main flow road according to the track data.

The matching relationship may be a mapping relationship between the track point and the track point. Due to the existence of the matching relation, the road identification of the corresponding road can be found through the track points. The trace point acquisition time refers to the time when the trace points are acquired. And determining the driving direction of the vehicle through the sequenced track points. If the track point at the time t1 is the track point a, and the track point at the time t2 is the track point b, then the driving direction of the vehicle is a → b.

In one embodiment, when the server acquires the track points of the vehicle in the driving process, the server can obtain the corresponding road identification according to the track points, so that the road where the vehicle is located can be determined. The server establishes a matching relation between the track points and the road identifications so that the server can obtain the corresponding road identifications according to the track points, and then finds out the corresponding roads according to the road identifications.

The track points in the track data may be position information of the vehicle during driving. The position information can be obtained by a positioning system on the vehicle or by a mobile terminal on the vehicle. The mobile terminal may obtain the position information of the vehicle through a built-in positioning system, or may obtain the position information of the vehicle through a mobile Location Service (Location Based Service). The positioning System may be a BDS (BeiDou Navigation Satellite System), a GPS (global positioning System), a Galileo Satellite Navigation System, or other Satellite Navigation systems. The Mobile location service mainly obtains the location information of the vehicle by means of base station positioning, and the base station may be a second-generation to fifth-generation Mobile communication base station, and a subsequent-version Mobile communication base station, such as a GSM (Global System for Mobile Communications ) communication base station, a TD-SCDMA (Time Division-Synchronous Code Division Multiple Access) communication base station, an LTE (Long Term Evolution) base station, and the like.

Therefore, the step of acquiring the position information of the vehicle (i.e. the track point of the vehicle) during the driving process by the server can be divided into the following three ways:

mode 1, position information of a vehicle is obtained by a positioning system on the vehicle.

In one embodiment, the server obtains the position information of the vehicle during driving through a positioning system of the vehicle. Specifically, after the positioning system of the vehicle is started, the positioning system collects the position information of the vehicle at regular intervals, and then the communication module on the vehicle uploads the collected positioning information to the server through the network. In addition, before the communication module on the vehicle uploads the positioning information, the positioning information can be matched with the road identification of the corresponding road, namely, the track point and the road identification of the road corresponding to the track point are matched, and then the matching relation is uploaded to the server together.

The positioning system and the communication module may be two modules independently existing on the vehicle, or two modules integrated on the vehicle for positioning and communicating with the outside.

For example, a user uses a map application to navigate during driving, at which time a GPS installed on a vehicle is automatically turned on, and then location information of the vehicle (e.g., longitude and latitude of the vehicle) may be collected every t seconds, and then the collected location information is uploaded to a server. t may be a number greater than or equal to 5.

Mode 2, the position information of the vehicle is obtained by a positioning system of the mobile terminal.

In one embodiment, when a mobile terminal is placed on a vehicle and a positioning system is started, a server acquires position information of the vehicle in the driving process through the positioning system of the mobile terminal, and the server can acquire the position information of each vehicle driven on a road in this way. Specifically, the above-mentioned mode 1 can be referred to.

For example, during driving, a user uses a map application on a mobile terminal to navigate, at this time, a GPS installed on the mobile terminal is automatically turned on, and then location information of a vehicle (such as longitude and latitude of the vehicle) can be collected once every t seconds, and then the collected location information is uploaded to a server.

Mode 3, the position information of the vehicle is obtained by the mobile position service of the mobile terminal.

In one embodiment, the mobile terminal measures downlink pilot signals of different base stations to obtain TOA (Time of Arrival) or TDOA (Time Difference of Arrival) of downlink pilots of different base stations, and calculates location information of the mobile terminal by combining the measurement result and coordinates of the base stations, so that location information of the vehicle can be obtained, and the mobile terminal transmits the location information to the server. In this way, the server can obtain the position information of each vehicle traveling on the road.

And S204, selecting a main flow road according to the traffic flow information of the road determined based on the track data.

The traffic flow information may be information for measuring density of vehicles on a road, specifically, the traffic flow information refers to the number of vehicles passing through a certain road in a certain time, and the calculation formula is as follows: and y is m/t, wherein y is the traffic flow, m is the number of passing vehicles, and t is time. The main flow road may be a road where the traffic flow reaches a preset condition, for example, among the roads, a road where the traffic flow is greater and greater than or equal to a preset threshold is the main flow road, or a road where the sequence of the traffic flow reaches a preset rank is the main flow road. Alternatively, the main flow road may be a road on which the target vehicle is currently traveling, and a road on which the amount of traffic is largest among roads downstream of the target vehicle. For example, as shown in fig. 3, in all the downstream roads (i.e., the downstream road BC and the downstream road BD) on which the target vehicle travels, when the traffic flow rate of the downstream road BC is greater than that of the downstream road BD, the downstream road BC is the main flow road, and the road AB on which the target vehicle travels is also the main flow road, that is, the road ABC is the main flow road and the BD is the branch road.

For the selection of the mainstream road, as shown in fig. 3, one road has two downstream roads, namely a downstream road BC and a downstream road BD, and when the mainstream road is selected, the following scenes can be divided into a) a scene 1 for dividing the upstream road and the downstream road, judging the mainstream road according to the divided upstream road AB, downstream road BC and downstream road BD, and then selecting the mainstream road, which can be applied to a scene with a long link length of the upstream road AB, and b) without dividing the upstream road and the downstream road, ① can directly judge the mainstream road according to the road ABC and road ABD, which can be applied to a scene with a short link length of the upstream road AB, or ② only acquires the track data of vehicles on the road BC and road BD when the track data is acquired, and then directly judges the mainstream road according to the road BC and road BD.

Therefore, S204 can be divided into the following two scenarios for illustration:

scene 1, an upstream road and a downstream road are divided.

In one embodiment, the server may segment each road into an upstream road and a downstream road according to the direction of traffic.

In one embodiment, the server acquires an upstream road and a downstream road in each road, determines traffic flow information of each downstream road according to the track data, and determines the upstream road and the corresponding target downstream road as a main flow road when the traffic flow information of the target downstream road in each downstream road meets a first preset condition.

For a road, in the track data in a certain period of time, the number of track points can reflect the traffic flow information of the road. The server may determine traffic flow information belonging to each road based on the number of track points in the track data. Wherein, when the number of the track points is larger, the more vehicles flow into the road in the period of time, namely, the larger the traffic flow. When the number of the track points is smaller, it means that there are fewer vehicles flowing into the road during the period of time, that is, the traffic flow is smaller.

In one embodiment, the server acquires an upstream road and a downstream road in each road, and the number of the downstream roads in each road may be plural. Based on the track points in the track data, the server determines traffic flow information belonging to each downstream road, and compares the traffic flow information of each downstream road. And when the traffic flow of the target downstream road in the downstream road is determined to be the maximum traffic flow according to the comparison result, the server determines the upstream road and the corresponding target downstream road as the main flow road.

For example, as shown in fig. 3, if the road AB is a road on which the target vehicle is currently traveling, the road BC and the road BD are two downstream roads of the road AB, and when the traffic flow of the road BC is greater than the traffic flow of the road BD, the road BC is the main flow road. Here, the upstream road AB of the road BC is also the main flow road.

In one embodiment, the server searches for a corresponding road identifier according to track points in the track data, so as to determine the road where the mobile terminal and the positioning device which send the road condition query request are located, and further obtain the road where each vehicle is located.

Scene 2, the upstream road and the downstream road are not divided.

In one embodiment, S204 may specifically include: the server determines traffic flow information belonging to each road based on track points in the track data; comparing traffic flow information between roads; and when the traffic flow of the target road in the roads is determined to be the maximum traffic flow according to the comparison result, determining the target road as the main flow road. It should be noted that the roads participating in the comparison of the traffic flow information should be communicated with each other.

In one embodiment, the server may determine which road has the largest traffic flow by comparing traffic flow information between roads. And when the traffic flow of the target road is greater than the traffic flow of other roads, the server determines the target road with the maximum traffic flow as the main flow road. As shown in fig. 3, for a road AB C and a road ABD, when the traffic flow of the road ABC is greater than the traffic flow of the road ABD, the road ABC is the main flow road.

And S206, searching a branch road communicated with the main flow road.

Where, communicating means that a plurality of roads are connected to each other, that is, a vehicle driven by a user may pass directly from one road to another road. For example, as shown in fig. 3, the road AB, the road BC, and the road BD are communicated with each other.

The tributary road is the non-mainstream direction road, namely: among all the downstream roads of the road on which the vehicle is currently running, the road with relatively small traffic flow is called a branch road; alternatively, the traffic flow is smaller than the main flow road, which is called a branch road.

In one embodiment, the step of searching for a tributary road may further include: and the server determines the traffic flow information of each road according to the track data, and determines the target road as a branch road when the traffic flow information of the target road in each road meets a second preset condition.

In another embodiment, the step of searching for a tributary road may further include: the server determines traffic flow information belonging to each road based on track points in the track data; comparing traffic flow information between roads; and when the traffic flow of the target road in the roads is determined to be the maximum traffic flow according to the comparison result, determining other roads except the target road as branch roads. It should be noted that the roads participating in the comparison of the traffic flow information should be communicated with each other.

In one embodiment, the server may determine which road has the largest traffic flow by comparing traffic flow information between roads. When the traffic flow of the target road is larger than the traffic flow of the other roads, the server determines the other roads as branch roads. As shown in fig. 3, there are only two roads downstream of the road AB, i.e., a road BC and a road BD, and when the traffic flow of the road BC is greater than the traffic flow of the road BD, the road BD is a branch road.

At S208, the trajectory data of the vehicle flowing to the branch road is deleted from the trajectory data belonging to the main flow road.

When the vehicle flow is large and the vehicles flow to different roads from the current road, the vehicle speed difference of the vehicles in different flow directions is large. When all the trajectory data are used to calculate the traffic information, the traffic information is easy to be calculated and deviated. For example, if there is a branch road on the right side of the road, within 100 meters of the intersection of the branch road, the vehicle will usually slow down (the deceleration is large) before merging into the branch road, and then merge into the branch road. When the vehicles going straight pass through the branch road intersection, the vehicles do not decelerate or the deceleration range is small, so the vehicle speed difference of the vehicles in different flow directions is large. The vehicles are guided to the branch road from the main flow to the road and are converged into the branch road.

In one embodiment, when the trajectory data is historical trajectory data, the server may determine vehicles flowing to the branch road from the historical trajectory data and then delete the trajectory data of the vehicles flowing to the branch road. The historical track data is track data of a certain time period, and vehicles may be converged into the branch road from the main flow road or may be driven on the main flow road all the time in the time period, so that the vehicles flowing to the branch road can be determined according to the historical track data.

In one embodiment, when the trajectory data is real-time trajectory data, the server may predict vehicles that may be merged into the tributary road from the real-time trajectory data, and then delete the trajectory data corresponding to the predicted vehicles. Or the server can predict vehicles which are possibly imported into the branch road currently through historical track data, and then delete the track data corresponding to the predicted vehicles.

For example, if the road is composed of 3 lanes, and there is a branch road on the right side of the road, where the 3 lanes are lane a, lane B and lane C, respectively, where lane a is the right side lane, lane B is the middle lane, and lane C is the left side lane. The server can predict whether vehicles are converged into the right branch road in the traffic flow of the lane A and how many vehicles are converged into the right branch road according to the historical track data. In addition, the server can predict whether vehicles are within 100 meters from the junction of the branch road according to the historical track data, and the vehicles are merged into the lane A from the lane B so as to be finally merged into the branch road. It should be noted that the vehicle generally decelerates when merging from lane B to lane a, especially when there are many vehicles in lane a.

In one embodiment, in the trajectory data belonging to the preset distance range of the main flow road, the server may further delete the trajectory data of the vehicle flowing into the main flow road from the branch flow road, so as to avoid affecting the accuracy of the calculation of the main flow road condition information due to a low vehicle speed when the vehicle just flows into the main flow road from the branch flow road.

And S210, determining the road condition information of the mainstream road according to the deleted residual track data.

The traffic information may be used to measure traffic conditions, such as traffic efficiency and traffic status. The traffic information may specifically be the traffic speed of the road or the traffic status of the road. The road condition vehicle speed may be a vehicle speed capable of reflecting road passing efficiency, and specifically may be an average vehicle speed of each vehicle in the road. The road condition state may reflect a traffic state of the road, and may specifically include, but is not limited to, a clear state, a slow-moving state, a congestion state, and the like.

In one embodiment, in the deleted remaining trajectory data, the server calculates the speed of the road condition on the main flow road according to the distance and the time difference between different trajectory points belonging to the same vehicle in the remaining trajectory data. Wherein, different track points can be two adjacent track points, also can be two nonadjacent track points.

In one embodiment, when the mobile terminal or the positioning device uploads the trace points, the corresponding trace point acquisition time is also uploaded to the server. Therefore, the step of calculating the time difference may comprise: the server acquires the time of acquiring different track points of the same vehicle in real time in the positioning process, and calculates the difference between the times of the different track points to obtain the time difference.

In the above embodiment, when the trajectory data of the vehicle on the road is obtained, the main flow road is selected, the branch road is searched, and then the trajectory data of the vehicle flowing to the branch road in the main flow road is deleted, so that the influence on the calculation result of the road condition information due to the large difference of the vehicle speeds in different flow directions is avoided. And determining the road condition information of the main flow road according to the deleted residual track data, wherein the road condition information is not influenced by the change of the vehicle speed when the vehicle flows from the main flow road to the branch road, so that the accuracy of the road condition information is effectively improved.

In an embodiment, as shown in fig. 4, S208 may specifically include:

s402, selecting a road section which is away from a preset range of the branch road from the main flow road.

Wherein the road section is a small section of the main heading road. The preset range may be a default distance and may be set by the map application developer.

In one embodiment, after the main flow road and the branch road are selected, the server takes a certain point on the main flow road as a starting point and a crossing driving to the branch road along the main flow direction as an end point, calculates a distance between the starting point and the end point, and stores the distance as an attribute value. The calculated distance may be n meters, and the value range of n may be 5 to 150, or may be set according to an actual road. Generally, when a user drives a vehicle, the user usually starts to slow down about 100 meters away from an intersection of a branch road to merge into the branch road or merge into a lane for merging into the branch road.

S404, determining the number of vehicles in the road section.

In one embodiment, when the mobile terminal or the positioning device uploads the track point, the corresponding terminal identifier or the corresponding vehicle identifier may also be uploaded to the server. S404 may specifically include: the server determines the number of vehicles according to the number of the terminal identifications and/or the number of the vehicle identifications.

In another embodiment, the server may estimate the number of vehicles based on the number of track points in the remaining track data.

And S406, deleting the track data of the vehicles flowing to the tributary road in the road section when the number of the vehicles is greater than or equal to the preset number.

When the vehicle flow is large and the vehicles flow to different roads from the current road, the vehicle speed difference of the vehicles in different flow directions is large. When all the trajectory data are used to calculate the traffic information, the traffic information is easy to be calculated and deviated.

When the number of vehicles is large and is greater than or equal to the preset data, the vehicle that continues to travel on the main diversion road will usually be on a fast lane (such as a middle lane or a left lane of a bidirectional road), and the vehicle that will merge into the branch road will slowly travel on the lane for merging into the branch road to merge into the branch road. Alternatively, the deceleration is incorporated on a lane for merging into a branch road. For example, if there is a branch road on the right side of the road, within 100 meters of the intersection of the branch road, the vehicle will usually slow down (the deceleration is large) before merging into the branch road, and then merge into the branch road. When the vehicles going straight pass through the branch road intersection, the vehicles do not decelerate or the deceleration range is small, so the vehicle speed difference of the vehicles in different flow directions is large.

In one embodiment, if the track data is historical track data, which vehicles are imported into the branch road from the road section can be determined according to the historical track data, and when the number of the vehicles is large and is larger than or equal to the preset data, the server deletes the track data of the vehicles flowing to the branch road in the road section so as to avoid that the vehicles flowing to the branch road influence the overall average speed of the main flowing road due to slow speed. For example, trajectory data of vehicles flowing to a branch road in a road within 100 meters is deleted.

In one embodiment, when the trajectory data is real-time trajectory data, the server may predict vehicles that may be merged into the tributary road through the real-time trajectory data, and then delete the trajectory data corresponding to the predicted vehicles. For example, it is predicted from the historical trajectory data that the vehicle in that lane is likely to merge into the tributary road.

The method may further comprise: and S408, when the number of the vehicles is less than the preset number, determining the road condition information of the main flow road according to the track data.

In one embodiment, when the number of the vehicles is less than the preset number, the server does not need to delete the trajectory data of the vehicles flowing to the tributary road in the road section, and directly calculates the road condition speed and the road condition state of the main flow road by using the acquired trajectory data.

In one embodiment, in the acquired trajectory data (i.e., the trajectory data acquired in S202), the road condition speed of the main flow of the road is calculated according to the distance and the time difference between different trajectory points belonging to the same vehicle in the trajectory data. Wherein, different track points can be two adjacent track points, also can be two nonadjacent track points.

In the above embodiment, when the number of vehicles is large, the trajectory data of the vehicles flowing to the branch road from the main flow direction can effectively avoid the influence on the calculation result of the road condition information due to the large speed difference in different flow directions, so that the accuracy of the road information can be improved. When the number of the vehicles is small, the difference between the speed of the vehicles flowing to the branch road and the speed of the vehicles flowing to the main flow road is not large, and the road condition information is calculated by using complete track data, so that the condition that the road condition information has contingency due to the fact that the track data are few can be avoided, and the accuracy of the road condition information can be ensured.

In an embodiment, as shown in fig. 5, S208 may specifically include:

and S502, selecting a road section away from a preset range of the branch road on the main flow road.

And S504, determining a lane for merging into the branch road in the road section.

In the case of a main flow road with one-way multi-lane, when a branch road is provided on the left side (or right side) of the main flow road, the left side lane (or right side lane) is a lane for merging into the branch road. When traveling on the lane, the vehicle may decelerate within 100 meters of the branching road intersection to merge into the branching road. For a main flow road with two-way multi-lane (for example, there are 3 lanes toward east, there are 3 lanes toward west, and the roads in both directions are main flow roads, that is, main flow road a and main flow road B), when there is a branch road on the right side of main flow road a, the lane on the right side is the lane for merging into the branch road.

And S506, deleting the track data corresponding to the vehicles in the lane.

In one embodiment, the method further comprises: when a lane for merging into a branch road is determined, calculating a first average vehicle speed of the lane; calculating a second average vehicle speed of the adjacent lanes; the adjacent lane is adjacent to the lane. S506 may specifically include: and when the first average vehicle speed and the second average vehicle speed are both smaller than the preset vehicle speed, deleting the track data corresponding to the vehicles in the lane and the adjacent lane.

When the speed of the vehicles merging into the branch road is slow on the lane for merging into the branch road, other vehicles may slowly run on the adjacent lane to merge into the lane for merging into the branch road, so that the speed of the adjacent lane is affected. Therefore, a first average speed of the lanes used for merging into the branch road is calculated, a second average speed of the adjacent lanes is calculated, when the first average speed and the second average speed are both smaller than a preset speed, vehicles may crawl in the adjacent lanes to merge into the lanes used for merging into the branch road, the server regards the vehicles on the two lanes as the vehicles merging into the branch road, and at the moment, track data corresponding to the vehicles in the lanes and the adjacent lanes are deleted.

In the above embodiment, for the road section away from the preset range of the tributary road, the track data of the vehicle flowing to the tributary road in the road section can effectively avoid the influence on the calculation result of the road condition information due to the large difference of the vehicle speeds in different flow directions, thereby being beneficial to improving the accuracy of the road information.

In one embodiment, the traffic information may include at least one of traffic speed and traffic status; as shown in fig. 6, S210 may specifically include:

and S602, determining time differences among different track points belonging to each vehicle in the deleted residual track data.

In one embodiment, in the deleted remaining trajectory data, the road condition speed on the main flow direction road is calculated according to the distance and the time difference between different trajectory points belonging to the same vehicle in the remaining trajectory data. Wherein, different track points can be two adjacent track points, also can be two nonadjacent track points.

In one embodiment, when the mobile terminal or the positioning device uploads the trace points, the corresponding trace point acquisition time is also uploaded to the server. Therefore, the step of calculating the time difference may comprise: the server acquires two times of acquiring track points of the vehicle in real time in the positioning process, and the two times are differenced to obtain a time difference.

And S604, calculating the road condition speed according to the distance between the different track points and the corresponding time difference.

Specifically, the server divides the distance between different track points belonging to the same vehicle by the corresponding time difference, thereby obtaining the road condition speed of the mainstream road.

After calculating the road condition speed, the method may further include: and S606, determining the road condition state of the mainstream road according to the road condition speed.

The road condition state comprises a smooth state, a slow running state and a congestion state. The different road condition states correspond to speed thresholds, the smooth state corresponds to a first speed threshold, the slow running state corresponds to a second speed threshold, and the congestion state corresponds to a third speed threshold. Wherein the first speed threshold > the second speed threshold > the third speed threshold. The third speed threshold may be a speed value equal to 0 or less than m, m may be 5 kilometers (km)/hour (h), and the first speed threshold, the second speed threshold, and the third speed threshold may be set according to practical situations, and are not particularly limited in the embodiment of the present invention.

In one embodiment, the server determines that the mainstream road is in a clear state when the road condition speed is greater than or equal to a first speed threshold. And when the road condition speed is less than or equal to the second speed threshold value, the server determines that the main flow road is in a slow running state. And when the road condition speed is less than or equal to the third speed threshold value, the server determines that the main flow road is in a congestion state.

And S608, pushing the road condition state and the track data to the client to indicate the client to display the track data in a display mode corresponding to the road condition state.

Among other things, the client may be a mapping application installed on the mobile terminal or the target vehicle, which may be referred to as a navigation application.

In one embodiment, the server obtains a starting point and an end point of the target vehicle, and selects a proper road according to the road condition state to recommend to the client when selecting the road from the starting point to the end point.

In the above embodiment, the road condition speed and the road condition state of the main flow road are determined according to the deleted remaining trajectory data, and the calculation of the road condition speed and the road condition state is not affected by the change of the vehicle speed when the vehicle flows from the main flow road to the branch road, so that the accuracy of the road condition speed and the road condition state is effectively improved, and a user can conveniently select a proper road for traveling according to the accurate road condition speed and the accurate road condition state.

As shown in fig. 7, in an embodiment, a traffic information calculation timing chart is provided, and the traffic information calculation method specifically includes the following steps:

and S702, uploading the track points in the driving process to a server in real time by the vehicle.

The vehicle comprises all motor vehicles in the running process including the target vehicle.

In one embodiment, the vehicle sends track points, which may be position information about the vehicle during driving, to the server in real time through the mobile terminal or a built-in positioning device.

S704, the target vehicle sends a road condition query request to the server.

The target vehicle is a vehicle needing to inquire road condition information. For example, when the user starts a map application of a driven vehicle or uses the map application for navigation, a road condition query request is sent to the server, and then the vehicle driven by the user is a target vehicle.

In one embodiment, the target vehicle may send the road condition query request to the server through a built-in client. In addition, the target vehicle can also send a road condition query request to the server through the client on the mobile terminal. For example, when a user starts a map application installed on a target vehicle or a mobile terminal, or navigates by using the map application installed on the target vehicle or the mobile terminal, the client sends a road condition query request to the server so as to query road condition information of a road where the target vehicle is currently located, and may also query road condition information of all roads in a target area including a location where the target vehicle is located.

S706, the server acquires the track data of the vehicles on the road.

And S708, the server selects a main flow road according to the traffic flow information of the road determined based on the track data.

S710, the server searches a branch road communicated with the main flow road.

S712, when the number of the vehicles is larger than or equal to the preset number, the server deletes the track data of the vehicles flowing to the branch road from the track data belonging to the main flow road; when the number of vehicles is less than the preset number, the trajectory data of the vehicles flowing to the tributary road is not deleted.

The specific implementation process of S706-S712 may refer to S202-S208. In addition, the specific implementation process of S712 may also refer to S402-S406, and S502-S506.

S714, the server determines the time difference between the different trace points belonging to each vehicle.

In one embodiment, when deleting trajectory data of vehicles flowing to a tributary road, the server determines a time difference between different trajectory points belonging to the respective vehicles in the remaining trajectory data after the deletion. When the trajectory data of the vehicles flowing to the branch road is not deleted, the server determines a time difference between different trajectory points belonging to each vehicle in the acquired trajectory data. In addition, the server determines the distance between the different track points of each vehicle.

And S716, the server calculates the road condition speed according to the distance between the different track points and the corresponding time difference.

And S718, the server pushes the road condition state and the track data to the target vehicle.

Specifically, the server may push the road condition status and the trajectory data to a client on the mobile terminal or a client on the target vehicle.

And S720, the target vehicle displays the track data in a display mode corresponding to the road condition state through the client.

Wherein. The specific implementation process of S714-S720 can refer to S602-S608.

As an example, in the conventional scheme, the speed of all vehicles on the road meeting the screening condition is calculated as an average value, and the trajectory data of the vehicles flowing in different directions are not processed differently. When the traffic flow on the road is large, the speed difference of vehicles flowing in different directions is large, and the deviation of road condition calculation is easily caused. Therefore, the embodiment of the invention provides a road condition information calculation scheme, under the condition of abundant samples, track data of vehicles in non-mainstream directions interfering with road condition calculation is removed, and track data of vehicles in the mainstream directions is selected as far as possible to calculate road conditions, and the specific implementation steps are as follows:

(1) excavation main flow direction road

Track data of a vehicle running on a road is acquired, and the track data can be historical track data or real-time track data. The track points in the track data are matched with the roads and are sorted in time order. All the downstream roads of each road, and their traffic flows, may be mined from historical track data, and the traffic flow may be an average traffic flow over a period of time, such as an average daily traffic flow. And excavating the direct downstream road with the maximum traffic flow in each road, taking the excavated downstream road as a main flow road of the road, and corresponding each road with the main flow road to generate a main flow mapping file.

The main flow direction refers to the flow direction of the direct downstream road with the maximum traffic flow after the track of the vehicle passes through the current road. The main flow direction mapping file refers to: a file that relates each road to the immediate downstream road with the largest traffic flow.

(2) Calculating the running distance between the road where the vehicle is currently located and the road entering the non-main flow direction

When a vehicle runs along the current road and enters the main flow road, the track data corresponding to the vehicle is the track data of the main flow of the road, and a main flow sample is generated according to the track data. Similarly, when the vehicle runs along the current road and enters the non-main flow direction road, the track data corresponding to the vehicle is the track data of the non-main flow direction of the road, and a non-main flow direction sample is generated according to the track data.

And according to the mainstream mapping file, finding a first non-mainstream direction road at the downstream of the road where the vehicle is located from the matching road sequence of the mainstream direction sample, and storing the driving distance as an attribute value of the non-mainstream direction sample even if the driving distance from the road where the vehicle is located to the road where the vehicle enters the non-mainstream direction sample.

(3) Identifying non-mainstream direction samples of non-mainstream direction and removing

When the road condition and speed are calculated, the driving distance from the road where the vehicle is located to the intersection of the first non-mainstream direction road is judged, if the driving distance is less than m meters, the fact that the vehicle generating the non-mainstream direction sample is likely to slow down to change the lane for converging into the non-mainstream direction road or slow down to converge into the non-mainstream direction road in the lane for converging into the non-mainstream direction road is indicated, and the speed of the corresponding vehicle cannot reflect the road condition information of the mainstream direction road. Therefore, a non-main flow sample of the vehicle merging into the non-main flow road is removed. Wherein m can be 100, or a positive integer greater than 5 and less than 120,

(4) after the non-main flow direction sample is removed, the main flow direction sample is adopted to calculate the road condition speed

The road condition speed refers to the speed of a plurality of vehicles on a road, and the speed capable of reflecting the road passing efficiency is calculated.

After the non-main flow samples of the vehicles which are converged into the non-main flow road within m meters are removed, when the number of the track points or the number of the vehicles in the main flow samples is larger than n, the road condition speed is calculated by adopting the main flow samples after the non-main flow samples are removed. Wherein n is a positive integer greater than or equal to 6.

After removing the non-main flow samples of the vehicles which are converged into the non-main flow road within m meters, when the number of track points or the number of vehicles in the main flow samples is less than n, in order to ensure that sufficient data are available for calculating the road condition speed, the non-main flow samples are still reserved, and the road condition speed is calculated by combining the non-main flow samples and the main flow samples. In addition, after the road condition speed is calculated, the road condition state of the main flow road can be determined according to the road condition speed. Wherein, this road conditions state includes: a clear state, a slow-moving state and a congested state.

After the road condition speed and the road condition state are calculated, the road condition state and the corresponding track data are sent to a client on the vehicle or a client of a mobile terminal arranged in the vehicle, so that the road condition state of the corresponding road is displayed.

By the embodiment of the invention, the track data interfering the calculation of the road condition speed can be effectively removed, so that the calculated road condition speed is more reasonable, and the accuracy of the calculation of the road condition speed is improved. In addition, the road condition state deduced according to the road condition speed is more accurate.

As shown in fig. 8, trajectory data of vehicles on the main flow road (i.e. trajectory data in the main flow sample) can be used to calculate road speed.

As shown in fig. 9, the trajectory data of the vehicles on the non-primary-flow road (i.e., the trajectory data in the non-primary-flow samples) is removed, and the calculation of the road speed is not involved. Although the speed of the traffic in fig. 9 is low, when the traffic is merged into the non-mainstream road, the speed of the corresponding vehicle is low, but the speed of the straight-going vehicle (i.e. the vehicle continuously runs on the mainstream road) is high, if the non-mainstream sample of the traffic merged into the non-mainstream road is not removed, the speed of the obtained mainstream road is not consistent with the actual speed of the traffic.

While the main flow sample of the straight-ahead vehicle in fig. 8 is more representative of the actual road conditions. The road condition speed and the road condition state calculated by using the main flow direction sample are more accurate, compared with the condition that the non-main flow direction sample is not removed, after the non-main flow direction sample is removed, the road condition state is changed from a slow running state (the slow running state is calculated according to the non-main flow direction sample is not removed) to an unblocked state (the unblocked state is calculated according to the non-main flow direction sample is removed), and the unblocked state is consistent with the real road condition, so that the road condition publishing effect is improved.

Fig. 2 and 4-6 are schematic flow charts of a traffic information calculation method in an embodiment. It should be understood that although the various steps in the flowcharts of fig. 2, 4-6 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2, 4-6 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.

As shown in fig. 10, in an embodiment, a traffic information calculating device is provided, which specifically includes: the system comprises a data acquisition module 1002, a road selection module 1004, a road searching module 1006, a data deleting module 1008 and a road condition information determining module 1010; wherein:

the data acquisition module 1002 is used for acquiring track data of vehicles on a road;

the road selection module 1004 is used for selecting a main flow road according to the traffic flow information of the road determined based on the track data;

a road searching module 1006, configured to search a branch road communicated with the main flow road;

a data deletion module 1008 for deleting the trajectory data of the vehicle flowing to the branch road from the trajectory data belonging to the main flow road;

the road condition information determining module 1010 is configured to determine the road condition information of the mainstream road according to the deleted remaining trajectory data.

In one embodiment, the data acquisition module 1002 is further configured to: acquiring track points of a vehicle in the driving process; establishing a matching relation between the track point and the road mark of the road corresponding to the track point; sequencing the track points establishing the matching relation according to the track point acquisition time; combining the sequenced track points into track data; the matching relation is used for selecting a main flow road according to the track data.

In one embodiment, the track points include position information of the vehicle; the data acquisition module 1002 is further configured to:

acquiring position information of a vehicle in a driving process through a positioning system of the vehicle; or acquiring the position information of the vehicle in the driving process according to a mobile terminal arranged in the vehicle.

In one embodiment, the road selection module 1004 is further configured to: determining traffic flow information belonging to each road based on track points in the track data; comparing traffic flow information between roads; and when the traffic flow of the target road in the roads is determined to be the maximum traffic flow according to the comparison result, determining the target road as the main flow road.

In one embodiment, the road selection module 1004 is further configured to: acquiring an upstream road and a downstream road of a road; each road comprises at least two downstream roads; based on the track points in the track data, the server determines traffic flow information belonging to each downstream road; comparing the traffic flow information between the downstream roads; and when the traffic flow of the target downstream road in the downstream road is determined to be the maximum traffic flow according to the comparison result, determining the upstream road and the corresponding target downstream road as the main flow direction road.

In one embodiment, the data deletion module 1008 is further to: selecting a road section which is away from a preset range of a branch road from a main flow road; determining a number of vehicles in the road segment; when the number of the vehicles is larger than or equal to the preset number, deleting track data of the vehicles flowing to the tributary road in the road section;

the traffic information determining module 1010 is further configured to determine traffic information of the mainstream road according to the trajectory data when the number of vehicles is greater than or equal to a preset number.

In one embodiment, the data deletion module 1008 is further to: selecting a road section away from a preset range of a branch road on a main flow road; determining a lane in the road section for merging into the tributary road; and deleting the track data corresponding to the vehicles in the lane.

In one embodiment, as shown in fig. 11, the apparatus further comprises: an average vehicle speed calculation module 1012; wherein:

an average vehicle speed calculation module 1012 for calculating a first average vehicle speed of the lane when the lane is determined; calculating a second average vehicle speed of the adjacent lanes; the adjacent lanes are adjacent to the lane;

the data deleting module 1008 is further configured to delete the trajectory data corresponding to the vehicles in the lane and the adjacent lane when the first average vehicle speed and the second average vehicle speed are both less than the preset vehicle speed.

In one embodiment, the traffic information determining module 1010 is further configured to: determining time differences among different track points belonging to each vehicle in the deleted residual track data; and calculating the road condition speed according to the distance between different track points and the corresponding time difference.

In one embodiment, as shown in fig. 11, the traffic information further includes a traffic status; the device also includes:

the traffic information determining module 1010 is further configured to determine a traffic state of the mainstream road according to the traffic speed;

the information sending module 1014 is configured to push the road condition status and the trajectory data to the client, so as to instruct the client to display the trajectory data in a display manner corresponding to the road condition status.

FIG. 12 is a diagram illustrating an internal structure of a computer device in one embodiment. The computer device may specifically be the server 120 in fig. 1. As shown in fig. 12, the computer apparatus includes a processor, a memory, a network interface, an input device, and a display screen connected through a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program, and when the computer program is executed by the processor, the processor may implement the road condition information calculating method. The internal memory may also store a computer program, and when the computer program is executed by the processor, the processor may execute the road condition information calculating method.

Those skilled in the art will appreciate that the architecture shown in fig. 12 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, the traffic information calculating apparatus provided in the present application may be implemented in the form of a computer program, and the computer program may be run on a computer device as shown in fig. 12. The memory of the computer device may store various program modules constituting the traffic information calculating apparatus, such as the data obtaining module 1002, the road selecting module 1004, the road searching module 1006, the data deleting module 1008, and the traffic information determining module 1010 shown in fig. 10. The computer program formed by the program modules enables the processor to execute the steps of the traffic information calculating method according to the embodiments of the present application described in the present specification.

For example, the computer device shown in fig. 12 may execute S202 through the data obtaining module 1002 in the traffic information calculating device shown in fig. 10. The computer device may perform S204 through the road selection module 1004. The computer device may perform S206 via the road lookup module 1006. The computer device may perform S208 by the data deletion module 1008. The computer device may perform S210 through the road condition information determining module 1010.

In one embodiment, a computer device is provided, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the traffic information calculating method. Here, the steps of the traffic information calculation method may be steps in the traffic information calculation methods of the above embodiments.

In one embodiment, a computer-readable storage medium is provided, which stores a computer program, and when the computer program is executed by a processor, the processor executes the steps of the traffic information calculating method. Here, the steps of the traffic information calculation method may be steps in the traffic information calculation methods of the above embodiments.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.

The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims

1. A road condition information calculation method comprises the following steps:

acquiring track data of vehicles on a road;

searching a branch road communicated with the main flow direction road;

2. The method of claim 1, wherein the obtaining trajectory data for vehicles on a roadway comprises:

acquiring track points of a vehicle in the driving process;

establishing a matching relation between the track point and the road mark of the road corresponding to the track point;

sequencing the track points establishing the matching relation according to the track point acquisition time;

combining the sequenced track points into track data; and the matching relation is used for selecting a main flow road according to the track data.

3. The method of claim 2, wherein the track points include position information of the vehicle; the method for acquiring the track points of the vehicle in the driving process comprises the following steps:

acquiring position information of the vehicle in the driving process through a positioning system of the vehicle; alternatively, the first and second electrodes may be,

and acquiring the position information of the vehicle in the driving process according to the mobile terminal arranged in the vehicle.

4. The method of claim 1, wherein selecting a mainstream road according to the traffic information of the road determined based on the trajectory data comprises:

determining traffic flow information belonging to each road based on track points in the track data;

comparing the traffic flow information between the roads;

and when the traffic flow of the target road in the roads is determined to be the maximum traffic flow according to the comparison result, determining the target road as the main flow road.

5. The method of claim 1, wherein selecting a mainstream road according to the traffic information of the road determined based on the trajectory data comprises:

acquiring an upstream road and a downstream road of the road; each road comprises at least two downstream roads;

based on the track points in the track data, the server determines traffic flow information belonging to each downstream road;

comparing the traffic flow information between the downstream roads;

and when the traffic flow of the target downstream road in the downstream roads is determined to be the maximum traffic flow according to the comparison result, determining the upstream road and the corresponding target downstream road as the main flow direction road.

6. The method of claim 1, wherein the deleting trajectory data of vehicles flowing to the branch road comprises:

selecting a road section which is away from the branch road within a preset range from the main flow road;

determining a number of vehicles in the road segment;

when the number of the vehicles is larger than or equal to the preset number, deleting track data of the vehicles flowing to the tributary road in the road section;

the method further comprises the following steps: and when the number of the vehicles is less than the preset number, determining the road condition information of the main flow road according to the track data.

7. The method of claim 1, wherein the deleting trajectory data of vehicles flowing to the branch road comprises:

selecting a road section away from the branch road within a preset range on the main flow road;

determining a lane in the road section for merging into the tributary road;

and deleting the track data corresponding to the vehicles in the lane.

8. The method of claim 7, further comprising:

when the lane is determined, calculating a first average vehicle speed of vehicles in the lane;

calculating a second average speed of the vehicles in the adjacent lanes; the adjacent lane is adjacent to the lane;

the deleting the trajectory data corresponding to the vehicle in the lane comprises:

and deleting the track data corresponding to the vehicles in the lane and the adjacent lane when the first average vehicle speed and the second average vehicle speed are both smaller than the preset vehicle speed.

9. The method according to any one of claims 1 to 8, wherein the traffic information comprises traffic speed; the determining the road condition information of the mainstream road according to the deleted residual track data comprises:

determining time differences among different track points belonging to each vehicle in the deleted residual track data;

and calculating the road condition speed according to the distance between the different track points and the corresponding time difference.

10. The method of claim 9, wherein the traffic information further comprises traffic status; the method further comprises the following steps:

determining the road condition state of the main flow road according to the road condition speed;

and pushing the road condition state and the track data to a client to indicate the client to display the track data in a display mode corresponding to the road condition state.

11. A road condition information calculating device, the device comprising:

12. The apparatus of claim 11, wherein the data acquisition module is further configured to:

acquiring track points of a vehicle in the driving process;

13. The apparatus of claim 12, wherein the track points include position information of the vehicle; the data acquisition module is further configured to:

14. A computer-readable storage medium, storing a computer program which, when executed by a processor, causes the processor to carry out the steps of the method according to any one of claims 1 to 10.

15. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method according to any one of claims 1 to 10.