CN115691094A - Road condition information processing method, device, equipment and storage medium - Google Patents
Road condition information processing method, device, equipment and storage medium Download PDFInfo
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Abstract
The present disclosure relates to a road condition information processing method, apparatus, device and storage medium, the method comprising: acquiring original road condition information of a target road section and portrait characteristic information of an equipment user; and processing the original road condition information of the target road section according to the portrait characteristic information of the equipment user to obtain target road condition information for displaying on the equipment, wherein the target road condition information accords with the cognition of the equipment user. According to the road condition information processing method, the purpose that the target road condition information displayed on the equipment is matched with the cognition of the equipment user is achieved, different target road condition information can be displayed for equipment users with different cognition, and the credibility of the equipment users on the target road condition information is favorably improved.
Description
Technical Field
The present disclosure relates to the field of dynamic traffic technologies, and in particular, to a method, an apparatus, a device, and a storage medium for processing road condition information.
Background
With the continuous development of science and technology, intelligent terminals have become indispensable devices in people's daily life. The trip object can install various different types of Application (APP) programs on the intelligent terminal to meet different requirements of the trip object.
Travel-class applications, such as map navigation applications, are widely used. Generally, a map navigation application can not only plan a navigation route from a starting point to a destination, but also identify road condition information of the route on the navigation route.
At present, on the same road, the road conditions seen by different travel objects are the same, but the inventor finds that the experience of different travel objects on the road conditions is different, for example, the road conditions display is slow traveling, but some travel objects are considered to be congested, the road conditions display is congested, but some travel objects are considered to be slow traveling, which causes the travel objects to consider the road conditions provided by the application program to be inaccurate, so that the trust level of the travel objects on the accuracy of the road conditions information is reduced.
Disclosure of Invention
In order to solve the technical problems or at least partially solve the technical problems, the present disclosure provides a road condition information processing method, apparatus, device and storage medium, which achieves the purpose of matching target road condition information displayed on a device with the cognition of a device user, thereby achieving the purpose of displaying different target road condition information for different device users with different cognitions, and facilitating to improve the trust level of the device user on the target road condition information.
In a first aspect, an embodiment of the present disclosure provides a traffic information processing method, including:
acquiring original road condition information of a target road section and portrait characteristic information of an equipment user;
and processing the original road condition information of the target road section according to the portrait feature information of the equipment user to obtain target road condition information for displaying on the equipment, wherein the target road condition information accords with the cognition of the equipment user.
In a second aspect, an embodiment of the present disclosure provides a traffic information processing apparatus, including:
the acquisition module is used for acquiring original road condition information of a target road section and portrait characteristic information of an equipment user;
and the processing module is used for processing the original road condition information of the target road section according to the portrait feature information of the equipment user to obtain target road condition information for displaying on the equipment, wherein the target road condition information accords with the cognition of the equipment user.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of the first aspect.
In a fourth aspect, the present disclosure provides a computer-readable storage medium, on which a computer program is stored, the computer program being executed by a processor to implement the method of the first aspect.
According to the traffic information processing method provided by the embodiment of the disclosure, the original traffic information of the target road section is processed by combining the image characteristic information of the equipment user, so that the target traffic information matched with the image characteristic information used by the equipment can be obtained, that is, the target traffic information conforming to the cognition of the equipment user can be obtained, and the trust level of the equipment user on the target traffic information can be improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present disclosure, the drawings used in the embodiments or technical solutions in the prior art description will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
Fig. 1 is a flow chart of a traffic information processing method according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a traffic information processing method according to an embodiment of the present disclosure;
fig. 3 is a flowchart of a traffic information processing method according to an embodiment of the disclosure;
fig. 4 is a schematic structural diagram of a traffic information processing apparatus according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an embodiment of an electronic device according to an embodiment of the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, aspects of the present disclosure will be further described below. It should be noted that the embodiments and features of the embodiments of the present disclosure may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced in other ways than those described herein; it is to be understood that the embodiments disclosed in the specification are only a few embodiments of the present disclosure, and not all embodiments.
Generally, a travel application, a car booking application or a living service application with a map navigation function, for example, the map navigation application may not only plan a navigation route from a starting point to a destination, but also display road condition information of the route on the navigation route, where the road condition information is, for example, congestion, slow traveling or clear traveling. In the related existing scheme, the road condition information seen by different travel objects is the same for the same road and at the same time. However, different trip objects have different subjective feelings about the road condition, or different traveling objects have different cognition about the road condition, or different trip objects have different tolerance degrees about the road condition, for example, the road condition information displayed by the application program is slow traveling, but the personal feelings of some trip objects are congested; the road condition information displayed by the application program is congested, but the in-person experience of some travel objects is slow, so that the travel objects consider that the road condition information provided by the application program is inaccurate, and the trust of the travel objects on the road condition information is reduced.
In view of the above problems, embodiments of the present disclosure provide a method for processing road condition information, which is described below with reference to specific embodiments by taking a map navigation application as an example.
Fig. 1 is a flowchart of a traffic information processing method according to an embodiment of the present disclosure. The embodiment is applicable to the condition of processing the road condition information in the client, the method can be executed by a road condition information processing device, the device can be implemented in a software and/or hardware manner, and the device can be configured in an electronic device, such as a terminal, specifically including a mobile phone, a computer or a tablet computer. Alternatively, the embodiment may be applicable to a case where the traffic information is processed in the server, and the method may be executed by the traffic information processing apparatus, which may be implemented in a software and/or hardware manner, and may be configured in an electronic device, such as a server. The following describes the traffic information processing method by taking a terminal as an example.
As shown in fig. 1, the method comprises the following specific steps:
s101, acquiring original road condition information of a target road section and portrait feature information of an equipment user.
The original road condition information can also be understood as real-time road condition information, which indicates the current objective traffic state of the target road section, and can be expressed by a preset algorithm according to the average speed and/or the average traffic duration of the target road section, which are determined by the driving speed of each vehicle driving on the target road section at the current time or within a period of time before and after the current time, or can be calculated or predicted by a more complex real-time road condition system.
Specifically, the original traffic information includes: the road section traffic information and the road condition expression (such as clear, slow running or congestion) are obtained, wherein the road section traffic information comprises traffic speed and/or traffic duration. The passing speed specifically refers to the running speed of a vehicle running on the road section; the transit time refers to the time taken for the vehicle to pass through the road segment. It can be understood that if the vehicle traveling on the road section has a higher traveling speed, the road section is more clear, and the time taken for the vehicle to pass through the road section (i.e. the passing time duration) is shorter, the passing capacity of the road section is stronger; if the speed of the vehicle running on the road section is slower, the road section is more congested, and the time taken by the vehicle to pass through the road section (namely the passing time length) is longer, so that the passing capacity of the road section is weaker.
The road condition expression comprises a first road condition or a second road condition, and the road section traffic capacity represented by the first road condition is stronger than the road section traffic capacity represented by the second road condition. Specifically, for example, the first road condition is smooth, and the second road condition is slow; or the first road condition is slow running, and the second road condition is congestion; or the first road condition is smooth at the highest speed, and the second road condition is smooth. The embodiment of the present application also does not limit the definition of the road condition expression.
The device may specifically refer to a terminal running a map navigation application, or a terminal displaying target road condition information, such as a smart phone or a vehicle-mounted display device.
The portrait feature information refers to information representing awareness of the equipment user about the road condition, or information representing tolerance of the equipment user about the road condition, for example, when the vehicle speed is large, the equipment user thinks that the road condition information is smooth, slow to move or jammed. The portrait characteristic information may be obtained by analyzing historical driving data of the device user, which is authorized by the device user, or by analyzing information actively input by the device user, for example, when the device user uses a map navigation application having a traffic information processing function for the first time, a questionnaire is displayed, and portrait characteristic information of the device user is determined based on information input by the device user with respect to the questionnaire.
And S102, processing the original road condition information of the target road section according to the portrait feature information of the equipment user to obtain target road condition information for displaying on the equipment, wherein the target road condition information accords with the cognition of the equipment user.
Specifically, according to the portrait characteristic information of the equipment user, the original road condition information of the target road section is corrected to obtain the target road condition information which accords with the cognition of the equipment user, and the target road condition information is displayed on the equipment, so that the equipment user can see the road condition information which accords with the cognition of the equipment user, the trust degree of the equipment user on the road condition information is improved, and the use experience of the equipment user on application software with a road condition information processing function is improved.
The road condition information processing method provided by the embodiment of the disclosure corrects the objective traffic state of the target road section detected in real time by combining the portrait characteristic information of the equipment user, so that the target road condition information presented to the equipment user is matched with the portrait characteristic information used by the equipment, the presenting effect of thousands of people is realized, the target road condition information presented by the terminal equipment of different travel objects is different for the same target road section at the same time, the trust of the equipment user on the target road condition information is favorably improved,
fig. 2 is a flowchart of a traffic information processing method according to another embodiment of the disclosure. In this embodiment, two specific embodiments are provided for the step S102 "of processing the original traffic information of the target road segment according to the image feature information of the device user to obtain the target traffic information for displaying on the device, where the target traffic information conforms to the cognition of the device user". As shown in fig. 2, the method comprises the following specific steps:
s201, acquiring original road condition information of a target road section and portrait feature information of an equipment user.
S202, under the condition that the road section passing information of the target road section belongs to the preset passing range of the first road condition and the proximity of the road section passing information of the target road section to the preset passing range of the second road condition is smaller than a preset threshold value, if the image characteristic information of the equipment user is aggressive, the road condition expression of the target road section is corrected from the first road condition to the second road condition, and the target road condition information used for being displayed on the equipment is obtained.
S203, under the condition that the road section passing information of the target road section belongs to the preset passing range of the second road condition and the proximity of the road section passing information of the target road section to the preset passing range of the first road condition is smaller than a preset threshold value, if the portrait feature information of the equipment user is conservative, the road condition expression of the target road section is modified from the second road condition to the first road condition, and the target road condition information used for being displayed on the equipment is obtained.
For example, the first road condition is smooth, the preset passing speed range corresponding to the smooth condition is greater than or equal to 60km/h, the second road condition is slow running, the preset passing speed range corresponding to the slow running is 30km/h-60km/h, the preset threshold value is 5, and the preset threshold value may be calculated by taking a difference between the passing speed included in the road section passing information and an upper limit value of the preset passing speed range, and determining the obtained difference value as the proximity of the road section passing information and the preset passing range. Based on the above assumptions, if the objective traffic state of the target road section detected in real time, i.e. the original traffic information is the traffic speed 63km/h and the traffic expression is the first road condition (i.e. smooth), since the traffic speed 63km/h belongs to the preset traffic range corresponding to "smooth" and the proximity to the preset traffic range (30 km/h-60 km/h) of the second road condition (slow traffic) (the difference between the traffic speed 63km/h and the upper limit 60km/h of the preset traffic range (30 km/h-60 km/h) is 3) is smaller than the preset threshold (5), at this time, if the image characteristic information of the device user is aggressive, the traffic expression of the target road section is corrected from the first road condition (smooth) to the second road condition (slow traffic) so that the expression of the target road section conforms to the cognition (aggressive) of the device user, and the target road condition information (i.e. slow traffic) for displaying on the device is obtained.
Aiming at the travel object with the image characteristic information as a sharp object, the subjective feeling of the object in the cognition of the travel object usually means that the vehicle speed is too slow, but the actual vehicle speed is fast, so that the road condition expression is corrected from smooth to slow in the cognition of the travel object, and the purpose of conforming to the cognition of the travel object is achieved. And aiming at the travel object with the conservative portrait characteristic information, the subjective feeling of the travel object is that the vehicle speed is usually perceived to be too fast, but the actual vehicle speed is not fast in the cognition of the travel object, so that the road condition expression is corrected from slow running to smooth running when the objective traffic state of the target road section is slow running and the proximity of the traffic information to the preset traffic range corresponding to the smooth running is smaller than the preset threshold value, and the purpose of conforming to the cognition of the travel object is achieved.
It should be noted that, if the image feature information of the device user is robust, the original road condition information of the target road segment is not processed, and the original road condition information is directly fed back to the travel object.
Specifically, the implementation manner and specific principle of S201 and S101 are consistent, and are not described herein again.
The road condition information processing method provided by the embodiment of the disclosure aims at a travel object with image characteristic information as an aggressive travel object, and corrects the road condition expression from unblocked to slow to fulfill the aim of conforming to the cognition of the aggressive travel object when the objective traffic state of a target road section is unblocked and the proximity of the traffic information to the preset traffic range corresponding to slow is smaller than a preset threshold value, namely, the traffic information is close to the preset traffic range corresponding to slow; aiming at a travel object with the portrait characteristic information being conservative, when the objective traffic state of the target road section is slow traffic and the proximity of the traffic information to a preset traffic range corresponding to the smooth traffic is smaller than a preset threshold value, the road condition expression is corrected from slow traffic to smooth traffic so as to achieve the purpose of conforming to the conservative travel object cognition. By combining the portrait characteristic information of the equipment user, the objective traffic state of the target road section is corrected, and the target road condition information matched with the portrait characteristic information of the equipment user can be obtained, namely the target road condition information conforming to the cognition of the equipment user can be obtained, so that the personalized target road condition information is presented for different travel objects, and the credibility of the travel objects to the target road condition information is improved.
Fig. 3 is a flowchart of a traffic information processing method according to another embodiment of the disclosure. An alternative embodiment of determining portrait characteristics of a user of the device is further presented in this embodiment. As shown in fig. 3, the method comprises the following specific steps:
s301, determining historical traffic information of the equipment user on the sample road section according to historical positioning information of the equipment user.
The historical positioning information of the equipment user refers to the position of the equipment user at a certain time in the past. The sample road section refers to a directed road which is divided according to a certain rule in a road network. The historical traffic information may specifically include a historical traffic speed, that is, an average traffic speed (which may be a driving speed during driving or a riding speed during riding) when the device user passes through the sample road section in the past. The historical transit information may also include historical transit time, i.e., the time required to traverse the sample road segment. It can be understood that the higher the historical passage speed, the shorter the historical passage time, and if the historical passage speed is lower, the longer the historical passage time.
S302, determining historical passing information of the sample road section according to the positioning information of one or more sample traveling objects passing through the sample road section in the historical time.
The sample travel object passing through the sample section in the historical time refers to the sample travel object appearing on the sample section in the historical time, and includes the sample travel object passing through the sample section completely in the historical time and also includes the sample travel object not passing through the sample section completely in the historical time. For example, history time is yesterday 9; also included is a sample travel object that does not completely walk linkA within 9.
Illustratively, the determining the historical traffic information of the sample road segment according to the positioning information of one or more sample travel objects passing through the sample road segment in the historical time includes: determining historical passing information of each sample travel object in the one or more sample travel objects on the sample road section according to positioning information of one or more sample travel objects passing through the sample road section within historical time; and determining the historical traffic information of the sample road section according to the historical traffic information of each sample travel object on the sample road section. For example, the sample travel object passing through the sample section in the historical time includes a sample travel object B, a sample travel object C, and a sample travel object D, and the historical passage information (for example, the average passage speed 1) of the sample travel object B on the sample section, the historical passage information (for example, the average passage speed 2) of the sample travel object C on the sample section, and the historical passage information (for example, the average passage speed 3) of the sample travel object D on the sample section are respectively determined. And then, the historical traffic information (for example, the average traffic speed 1) of the sample travel object B on the sample road section, the historical traffic information (for example, the average traffic speed 2) of the sample travel object C on the sample road section and the historical traffic information (for example, the average traffic speed 3) of the sample travel object D on the sample road section are fused to obtain the historical traffic information of the sample road section. The fusion method may be, for example, determining an average value of the average traffic speed 1, the average traffic speed 2, and the average traffic speed 3 as the historical traffic information of the sample road segment. The fusion method may also be a weighted summation, for example, 100 sample travel objects that have passed through the sample road segment in the historical time exist, and if the average passing speeds corresponding to 99 sample travel objects in the 100 sample travel objects are relatively close, and the average passing speed of 1 sample travel object deviates from the group, the weight occupied by the average passing speed of the 1 sample travel object in the weighted summation is reduced.
S303, determining portrait feature information of the equipment user based on the historical traffic information of the equipment user on the sample road section and the historical traffic information of the sample road section.
Specifically, if the historical passing speed of the equipment user on the sample road section is greater than the historical passing speed of the sample road section, determining that the image characteristic information of the equipment user is aggressive; or
If the historical passing speed of the equipment user on the sample road section is equal to the historical passing speed of the sample road section, determining that the image characteristic information of the equipment user is stable; or alternatively
And if the historical passing speed of the equipment user on the sample road section is smaller than that of the sample road section, determining that the portrait feature information of the equipment user is conservative.
Furthermore, according to the above steps S301-S303, the image feature information of the equipment user passing through other sample road sections can be counted, in order to further improve the determination accuracy of the image feature information of the equipment user, the corresponding image feature information of the equipment user passing through each sample road section in the past month or even two months can be counted, and then the image feature information with higher ratio can be determined as the final image feature information of the equipment user. For example, the user of the device passes through 600 sample road sections in the past month, the user of the device corresponds to the portrait feature information labels on each of the 600 sample road sections, and the portrait feature information labels corresponding to the user of the device on each of the 600 sample road sections are fused to obtain the final portrait feature information label of the user of the device. For example, if the "aggressive" tag accounts for 70% of 600 portrait feature information tags, the final portrait feature information of the user of the apparatus is determined to be "aggressive".
S304, acquiring original road condition information of the target road section and portrait characteristic information of the equipment user.
And S305, processing the original road condition information of the target road section according to the portrait feature information of the equipment user to obtain target road condition information for displaying on the equipment, wherein the target road condition information accords with the cognition of the equipment user.
Specifically, the implementation manners and specific principles of S304 and S101 are the same, and the implementation manners and specific principles of S305 and S102 are the same and will not be described herein again.
The embodiment further provides an optional implementation mode for determining portrait characteristic information of the equipment user, and specifically, historical traffic information of the equipment user on a sample road section is determined according to historical positioning information of the equipment user; determining historical passing information of the sample road section according to positioning information of one or more sample traveling objects passing through the sample road section within historical time; determining portrait feature information of the device user based on historical traffic information of the device user on the sample road segment and historical traffic information of the sample road segment. If the historical passing speed of the equipment user on the sample road section is greater than that of the sample road section, determining the image characteristic information of the equipment user as an acceleration; or if the historical passing speed of the equipment user on the sample road section is equal to the historical passing speed of the sample road section, determining that the image characteristic information of the equipment user is stable; or if the historical passing speed of the equipment user on the sample road section is smaller than that of the sample road section, determining that the portrait feature information of the equipment user is conservative. The technical scheme provided by the embodiment realizes accurate determination of the portrait characteristic information of the equipment user, and provides a reference basis for determining the target road condition information matched with the cognition of the equipment user.
In other words, the process of the traffic information processing method can be described as follows:
the method comprises the following steps: and classifying the driving sample track recycled historically by taking a travel object label (aggressive, robust or conservative) as a key value. The driving behavior of each sample travel object in a plurality of sample travel objects in historical time is subjected to statistical analysis, the driving behavior of each sample travel object is marked according to the contract time and the fusion speed of the same road section, and the label comprises: aggressive, robust and conservative. Yesterday is taken as an example, a target travel object is included in the multiple sample travel objects, and the target travel object starts from home at 9 am and arrives at a company at 10 am. The route from home to the company includes a plurality of road segments, and for each road segment, an average speed at which the target travel object passes is calculated. For example, the target travel object passes through the link a, linkA, during the time period from 9 to 10 to 9. As other travel objects may not enter segment a at the same time as the target travel object while leaving segment a. Therefore, it is further determined that during the time period from a time instant before 9. Among them, the "travel object through the section a" may be understood as 9: within 20, the travel object of the section a is completely taken, for example, the travel object B enters the section a at 9; travel object C enters road segment a,9:20 leave the section a. The "travel object that has moved on the route section a" can be understood as 9: within 20, the travel object that has not completely traveled the segment a, for example, travel object D is located right in the middle of segment a at time 9. Further calculate yesterday 9:20, an average speed of each of all travel objects through section a (e.g., an average speed of travel object B through section a, an average speed of travel object C through section a), and/or an average speed of each of all travel objects that have traveled through section a while traveling on section a (e.g., an average speed of travel object D while traveling on section a). Further, for yesterday 9: and 20, merging the average speed of each travel object in all travel objects passing through the road section A in the period of time and/or the average speed of each travel object in all travel objects moving on the road section A in the period of time to obtain the merged passing speed of the road section A in the period of time. Among these, the fusion method may be weighted summation, for example, 9: in 20, there are 100 travel objects passing through the road segment a and/or 100 travel objects moving on the road segment a, and if the average speeds corresponding to 99 travel objects in the 100 travel objects are relatively close and the average speed of 1 travel object is outlier, the weight occupied by the average speed of 1 travel object in the weighted sum is reduced.
Marking: and if the average speed of the target travel object passing through the road section A is greater than the fusion speed of the road section A, determining that the corresponding label of the target travel object on the road section A is radical. And if the average speed of the target travel object passing through the road section A is equal to the fusion speed of the road section A, determining that the corresponding label of the target travel object on the road section A is stable. And if the average speed of the target travel object passing through the road section A is less than the fusion speed of the road section A, determining that the corresponding label of the target travel object on the road section A is conservative.
The marking process of the target trip object on one road section in one day of yesterday is described above. And by analogy, the labels of the target travel object on other road sections in one day of yesterday can be determined. For example, the driving distance of the target trip object within the previous day of today, i.e. yesterday, comprises 20 road segments. In the same way, the driving distance of the target trip object in the first two days, the driving distance of the target trip object in the first three days and the like can be counted, so that the total driving distance of the target trip object in the past month is obtained. For example, the total driving distance of the target travel object in the past month includes 600 road segments, and the tags are respectively corresponding to the 600 road segments of the target travel object. And fusing the labels respectively corresponding to the target trip object on the 600 road sections to obtain the final label of the target trip object. For example, if the "aggressive" of 600 tags accounts for more than 70%, the final tag of the target trip object is determined to be "aggressive", so that the determination accuracy of the tag of the target trip object, that is, the determination accuracy of the portrait feature information corresponding to the target trip object, can be improved.
Step two, counting the current (for example, today) road conditions
1. Matching the sample running track recovered today to each road section on the road network, and calculating the speed passing through each road section through a two-point model aiming at each road section. For example, the average speed of each of all travel objects passing through the road segment a and/or all travel objects moving on the road segment a in a period of time before the current time (11 o 'clock) (e.g. in 5 minutes before 11 o' clock) is counted. In addition, the time length used by each travel object in all travel objects passing through the road section A and/or all travel objects moving on the road section A in a period of time before the current time can be counted. Namely, the step is to calculate the average speed and the time length of the current single travel object passing through the road section A.
2. A regular fusion of the samples passed on the route section a results in a fused transit speed and/or a fused transit time for the route section a during this time (e.g. within 5 minutes before 11 o' clock). The method comprises the following steps: and fusing the average speed of each travel object in all travel objects passing through the road section A or all travel objects moving on the road section A in a period of time before the current moment to obtain the fused passing speed of the road section A. And fusing the time length of each passing road section A in all the travel objects passing through the road section A or all the travel objects moving on the road section A in a period of time before the current moment to obtain the fused passing time length of the road section A. In general, if a certain road segment is long, the road segment can be divided into a plurality of small segments, and each small segment can correspond to the fused traffic speed of the small segment or the average traffic speed of a single sample travel object. And fusing the fused passing speeds respectively corresponding to the plurality of small sections or fusing the average passing speeds of the single sample trip objects respectively corresponding to the plurality of small sections to obtain the fused passing speed of the road section in a certain period of time. For example, a link is long, within 10-56-11, there are 3 travel objects on the link, travel object 1 in the first third of the link, travel object 2 in the middle third of the link, and travel object 3 in the last third of the link. Calculating the average speed of the traveling object 1 in the first third section, the average speed of the traveling object 2 in the middle third section, and the average speed of the traveling object 3 in the last third section, and further, fusing the average speeds of the 3 traveling objects in the respective third sections to obtain a fused passing speed of the section within 10-11. Furthermore, the passing time lengths of the 3 trip objects in respective one-third sections are fused and then multiplied by 3, so that the fused passing time length of the road section can be obtained. Further, according to the fusion passing speed of the road section in a certain period of time or the fusion passing time of the road section, the original road condition information of the road section in a certain period of time is determined. The original road condition information can be divided into 3 grades, smooth, slow and congested. For example, if the merged traffic speed of the road segment in a certain time is within 0-30km/h, the original traffic information of the road segment in the certain time may be congestion. If the fusion passing speed of the road section in a certain period of time is between 30km/h and 60km/h, the original road condition information of the road section in the certain period of time can be slow-moving. If the fusion passing speed of the road section in a certain period of time is more than 60km/h, the original road condition information of the road section in the certain period of time can be smooth. The preset threshold value of the fusion passing speed corresponding to each grade is preset. Specifically, the preset threshold may be determined according to the traffic capacity of the road or the road section. For example, the preset threshold corresponding to each of 3 levels of an expressway is high, the preset threshold corresponding to each of 3 levels of an ordinary road is medium, and the preset threshold corresponding to each of 3 levels of a similar expressway is low.
Step three, for example, at the current time 11 of today, after the target trip object (i.e. the device user) inputs the starting point and the ending point on the map navigation APP, the map navigation APP plans a path from the starting point to the ending point, where the path includes a plurality of links. The navigation APP is installed on the terminal or the vehicle-mounted equipment. Further, the navigation APP sends the tag of the target trip object to a server through a terminal or a vehicle-mounted device. The server determines a final label of the target travel object according to the label of the target travel object, and determines target traffic information of each Link according to the final label of the target travel object and the original traffic information of each Link in the plurality of links at the current moment, wherein the target traffic information indicates the state of each Link presented to the target travel object. Further, the server sends the final tag of the target trip object and the target road condition information of each Link to the navigation APP. Or planning a path from a starting point to a terminal point by the navigation APP, and sending the label of the target travel object to the server by the navigation APP through the terminal or the vehicle-mounted equipment. And the server sends the final label of the target travel object and the original road condition information of each Link to the navigation APP, and the navigation APP determines the target road condition information of each Link according to the final label of the target travel object and the original road condition information of each Link. Or at the current time 11 today, after the target travel object inputs the starting point and the end point on the navigation APP, the navigation APP sends the starting point and the end point to the server, and the server plans a path from the starting point to the end point, where the path includes multiple links. Further, the server determines the target road condition information of each Link according to the final tag of the target travel object and the original road condition information of each Link, and sends the path, the target road condition information of each Link in the links and the final tag of the target travel object to the navigation APP. Or the server plans a path from the starting point to the end point, sends the final label of the target travel object and the original road condition information of each Link to the navigation APP, and the navigation APP determines the target road condition information of each Link according to the final label of the target travel object and the original road condition information of each Link. Specifically, the process of determining the target traffic information of each Link according to the final tag of the target travel object and the original traffic information of each Link is as follows: if the final label is aggressive, the server sends the Link with the fused traffic speed in the smooth section but close to the slow section to the line object in the slow state, and sends the Link with the fused traffic speed in the slow section but close to the congestion section to the line object in the congestion state; that is, the original traffic information and the target traffic information of a link are different. Taking a common road as an example, the fusion traffic speed is more than 60km/h, and the traffic is smooth. The fusion traffic speed is between 30km/h and 60km/h and belongs to slow traffic. The fusion traffic speed is between 0 and 30km/h, and belongs to congestion. When the merged traffic speed of the link falls within the threshold range of smooth traffic, further, it is determined whether the merged traffic speed of the link is close to slow traffic, if so (for example, the merged traffic speed of the link is between 60 and 70), it is further seen what the line object tag is, and if the line object tag is aggressive, the road condition information of the link is sent to the line object in a slow traffic state. Further, the navigation APP presents the link in a lazy state. And if the travel object label is robust, the original road condition information of Link is sent to the travel object. If the travel object tag is conservative, a link with the fused traffic speed within the threshold range of slow travel but close to the smooth interval is sent to the line object in a smooth state (for example, the link with the fused traffic speed between 50-60km/h is sent to the line object in a smooth state), and a link with the fused traffic speed within the threshold range of congestion but close to the slow travel interval is sent to the line object in a slow travel state (for example, the link with the fused traffic speed between 20-30km/h is sent to the line object in a slow travel state). It should be noted that links affected by traffic lights, toll stations or inspection stations and other control facilities are subjected to state division in combination with transit time and are sent to travel objects. Namely, the fusion passing speed of the process is replaced by the fusion passing time. Namely, the traffic light, the toll station or the inspection station respectively correspond to a preset passing time threshold range. Taking a traffic light as an example, the traffic light corresponds to a smooth traffic time threshold range, a slow traffic time threshold range and a congested traffic time threshold range. And calculating the current fusion passing time of the traffic light. And comparing the fusion passing time with the passing time threshold ranges of 3 grades (clear, slow running or congestion) of the traffic light to see which grade of the passing time threshold range the traffic light falls in. If the traffic light is in the grade of smooth traffic, whether the current fusion traffic time of the traffic light is close to the threshold range of the traffic time corresponding to slow running is further seen, if so, the traffic object label is further seen, and if the traffic object label is aggressive, the traffic information of the traffic light is sent to the traffic object in a 'slow running' state.
Fig. 4 is a schematic structural diagram of a traffic information processing device according to an embodiment of the present disclosure. The traffic information processing apparatus provided in the embodiment of the present disclosure may execute the processing procedure provided in the embodiment of the traffic information processing method, as shown in fig. 4, the apparatus includes: an acquisition module 410 and a processing module 420.
The acquiring module 410 is configured to acquire original road condition information of a target road segment and portrait feature information of an equipment user; and the processing module 420 is configured to process the original road condition information of the target road segment according to the portrait feature information of the device user to obtain target road condition information for displaying on the device, where the target road condition information conforms to the cognition of the device user.
Optionally, the original traffic information includes: the road section traffic information comprises traffic speed and/or traffic duration, the road condition expression comprises a first road condition or a second road condition, and the traffic capacity of the road section represented by the first road condition is higher than that of the road section represented by the second road condition.
Optionally, the processing module 420 includes: and the first processing unit is used for modifying the road condition expression of the target road section from the first road condition to the second road condition to obtain target road condition information for displaying on the equipment if the image characteristic information of the equipment user is aggressive under the condition that the road section passing information of the target road section belongs to a preset passing range of the first road condition and the proximity of the road section passing information of the target road section to the preset passing range of the second road condition is smaller than a preset threshold value.
Optionally, the processing module 420 includes: and the second processing unit is used for modifying the road condition expression of the target road section from the second road condition to the first road condition to obtain target road condition information for displaying on the equipment if the portrait feature information of the equipment user is conservative under the condition that the road section passing information of the target road section belongs to a preset passing range of the second road condition and the proximity of the road section passing information of the target road section to the preset passing range of the first road condition is smaller than a preset threshold value.
Optionally, the apparatus further comprises: the first determination module is used for determining historical traffic information of the equipment user on a sample road section according to historical positioning information of the equipment user; the second determination module is used for determining historical traffic information of the sample road section according to the positioning information of one or more sample travel objects which pass through the sample road section within the historical time; a third determining module, configured to determine portrait feature information of the device user based on historical traffic information of the device user on the sample road segment and historical traffic information of the sample road segment.
Optionally, the second determining module includes: a first determining unit, configured to determine, according to location information of one or more sample travel objects that have passed through the sample road segment within a historical time, historical passage information of each sample travel object of the one or more sample travel objects on the sample road segment; the second determining unit is used for determining the historical traffic information of the sample road section according to the historical traffic information of each sample travel object on the sample road section.
Optionally, the third determining module is specifically configured to: if the historical passing speed of the equipment user on the sample road section is greater than that of the sample road section, determining that the image characteristic information of the equipment user is aggressive; or alternatively
If the historical passing speed of the equipment user on the sample road section is equal to the historical passing speed of the sample road section, determining that the image characteristic information of the equipment user is stable; or alternatively
And if the historical passing speed of the equipment user on the sample road section is smaller than that of the sample road section, determining that the portrait feature information of the equipment user is conservative.
The traffic information processing apparatus in the embodiment shown in fig. 4 can be used to implement the technical solutions of the above method embodiments, and the implementation principle and technical effects are similar, which are not described herein again.
The internal functions and structure of the traffic information processing apparatus, which can be implemented as an electronic device, are described above. Fig. 5 is a schematic structural diagram of an embodiment of an electronic device according to an embodiment of the present disclosure. As shown in fig. 5, the electronic device includes a memory 151 and a processor 152.
And a memory 151 for storing a program. In addition to the above-described programs, the memory 151 may also be configured to store other various data to support operations on the electronic device. Examples of such data include instructions for any application or method operating on the electronic device, contact data, phonebook data, messages, pictures, videos, and so forth.
The memory 151 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
A processor 152, coupled to the memory 151, that executes programs stored by the memory 151 to:
acquiring original road condition information of a target road section and portrait characteristic information of an equipment user;
and processing the original road condition information of the target road section according to the portrait feature information of the equipment user to obtain target road condition information for displaying on the equipment, wherein the target road condition information accords with the cognition of the equipment user.
Further, as shown in fig. 5, the electronic device may further include: communication components 153, power components 154, audio components 155, a display 156, and other components. Only some of the components are schematically shown in fig. 5, and it is not meant that the electronic device comprises only the components shown in fig. 5.
The communication component 153 is configured to facilitate wired or wireless communication between the electronic device and other devices. The electronic device may access a wireless network based on a communication standard, such as WiFi,2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 153 receives a broadcast signal or broadcast associated information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 153 further includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.
A power supply component 154 provides power to the various components of the electronic device. The power components 154 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for electronic devices.
The display 156 includes a screen, which may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a traveling object. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.
In addition, the embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the traffic information processing method described in the above embodiment.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present disclosure, which enable those skilled in the art to understand or practice the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A road condition information processing method comprises the following steps:
acquiring original road condition information of a target road section and portrait characteristic information of an equipment user;
and processing the original road condition information of the target road section according to the portrait characteristic information of the equipment user to obtain the target road condition information which accords with the cognition of the equipment user.
2. The method as claimed in claim 1, wherein the original traffic information comprises: the road section traffic information comprises traffic speed and/or traffic duration, the road condition expression comprises a first road condition or a second road condition, and the traffic capacity of the road section represented by the first road condition is higher than that of the road section represented by the second road condition.
3. The method as claimed in claim 2, wherein processing the original traffic information of the target road segment according to the portrait characteristic information of the user of the device to obtain the target traffic information for displaying on the device comprises:
and under the condition that the road section passing information of the target road section belongs to a preset passing range of a first road condition and the proximity of the road section passing information of the target road section to a preset passing range of a second road condition is smaller than a preset threshold value, if the image characteristic information of the equipment user is aggressive, correcting the road condition expression of the target road section from the first road condition to the second road condition to obtain target road condition information for displaying on the equipment.
4. The method as claimed in claim 2, wherein processing the original traffic information of the target road segment according to the portrait characteristic information of the user of the device to obtain the target traffic information for displaying on the device comprises:
and under the condition that the road section passing information of the target road section belongs to a preset passing range of a second road condition and the proximity of the road section passing information of the target road section to the preset passing range of the first road condition is smaller than a preset threshold value, if the portrait feature information of the equipment user is conservative, correcting the road condition expression of the target road section from the second road condition to the first road condition to obtain target road condition information for displaying on the equipment.
5. The method of claim 1, wherein the method further comprises:
determining historical traffic information of the equipment user on a sample road section according to historical positioning information of the equipment user;
determining historical traffic information of the sample road section according to positioning information of one or more sample travel objects passing through the sample road section within historical time;
determining portrait feature information of the device user based on historical traffic information of the device user on the sample road segment and historical traffic information of the sample road segment.
6. The method of claim 5, wherein determining historical transit information for the sample segment from location information for one or more sample travel objects that passed over the sample segment over a historical time comprises:
determining historical passing information of each sample travel object in the one or more sample travel objects on the sample road section according to positioning information of one or more sample travel objects passing through the sample road section within historical time;
and determining historical traffic information of the sample road section according to the historical traffic information of each sample travel object on the sample road section.
7. The method of claim 5, wherein determining portrait feature information of the device user based on historical traffic information of the device user on the sample road segment and historical traffic information of the sample road segment comprises:
if the historical passing speed of the equipment user on the sample road section is greater than that of the sample road section, determining that the image characteristic information of the equipment user is aggressive; or
If the historical passing speed of the equipment user on the sample road section is equal to the historical passing speed of the sample road section, determining that the image characteristic information of the equipment user is stable; or
And if the historical passing speed of the equipment user on the sample road section is smaller than that of the sample road section, determining that the portrait feature information of the equipment user is conservative.
8. A traffic information processing apparatus, comprising:
the acquisition module is used for acquiring original road condition information of a target road section and portrait characteristic information of an equipment user;
and the processing module is used for processing the original road condition information of the target road section according to the portrait feature information of the equipment user to obtain target road condition information for displaying on the equipment, wherein the target road condition information accords with the cognition of the equipment user.
9. An electronic device, comprising:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of any one of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method of any one of claims 1-7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116153082A (en) * | 2023-04-18 | 2023-05-23 | 安徽省中兴工程监理有限公司 | Expressway road condition acquisition, analysis and processing system based on machine vision |
| CN116448138A (en) * | 2023-06-19 | 2023-07-18 | 北京云行在线软件开发有限责任公司 | Running coordinate prediction method, server side and running coordinate prediction system |
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2021
- 2021-07-27 CN CN202110853030.7A patent/CN115691094A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116153082A (en) * | 2023-04-18 | 2023-05-23 | 安徽省中兴工程监理有限公司 | Expressway road condition acquisition, analysis and processing system based on machine vision |
| CN116153082B (en) * | 2023-04-18 | 2023-06-30 | 安徽省中兴工程监理有限公司 | Expressway road condition acquisition, analysis and processing system based on machine vision |
| CN116448138A (en) * | 2023-06-19 | 2023-07-18 | 北京云行在线软件开发有限责任公司 | Running coordinate prediction method, server side and running coordinate prediction system |
| CN116448138B (en) * | 2023-06-19 | 2023-09-01 | 北京云行在线软件开发有限责任公司 | Running coordinate prediction method, server side and running coordinate prediction system |
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