CN113723191A

CN113723191A - Road congestion prediction method, location-based service providing method, and program product

Info

Publication number: CN113723191A
Application number: CN202110871171.1A
Authority: CN
Inventors: 刘羽飞
Original assignee: Autonavi Software Co Ltd
Current assignee: Autonavi Software Co Ltd
Priority date: 2021-07-30
Filing date: 2021-07-30
Publication date: 2021-11-30
Anticipated expiration: 2041-07-30
Also published as: CN113723191B

Abstract

The embodiment of the disclosure discloses a road congestion prediction method, a location-based service providing method and a program product, wherein the method comprises the following steps: acquiring current road traffic characteristics of a target road in a current time period and road congestion image characteristics of the target road; predicting congestion occurrence time and congestion tendency of the target road based on the current road traffic characteristics and the road congestion image characteristics; optimizing the congestion occurrence time and/or congestion tendency based on one or more combinations of the attributes of the target road, the current road conditions of the associated roads of the target road and the predicted historical traffic characteristics of the congestion occurrence time. According to the technical scheme, the traffic pressure of the congested road can be relieved, the travel time cost can be saved for the user, and the travel experience of the user is improved.

Description

Road congestion prediction method, location-based service providing method, and program product

Technical Field

The present disclosure relates to the field of dynamic traffic technologies, and in particular, to a road congestion prediction method, a location-based service providing method, and a program product.

Background

With the continuous expansion of the urban scale and the continuous increase of the private car reserves, traffic jam becomes a problem of the urban management.

The existing dynamic traffic (real-time traffic) system can predict road conditions (congestion, slow running or smooth running) and provide the predicted road conditions for navigation calculation or traffic management and other related service systems. Taking the provision of the navigation route calculation service as an example, the navigation route calculation service can avoid a road in a congestion state (congested road) based on predicted road conditions when planning a navigation route, so that vehicles driving into the congested road section can be reduced, and the traffic pressure of the congested road can be relieved.

Therefore, how to accurately predict the road condition is one of the technical problems that the skilled person needs to continuously solve and optimize.

Disclosure of Invention

The disclosed embodiments provide a road congestion prediction method, a location-based service providing method, and a program product.

In a first aspect, an embodiment of the present disclosure provides a method for predicting road congestion, including:

acquiring current road traffic characteristics of a target road in a current time period and road congestion image characteristics of the target road;

predicting congestion occurrence time and congestion tendency of the target road based on the current road traffic characteristics and the road congestion image characteristics;

optimizing the congestion occurrence time and/or congestion tendency based on one or more combinations of the attributes of the target road, the current road conditions of the associated roads of the target road and the predicted historical traffic characteristics of the congestion occurrence time.

Further, predicting congestion occurrence time and congestion tendency of the target road based on the current road traffic characteristics and the road congestion image characteristics comprises the following steps:

and performing similarity comparison on the current road traffic characteristics and road congestion image characteristics within a set time length from the current time, if similar road congestion image characteristics are compared, predicting the congestion occurrence time of the target road based on the road congestion image characteristics, and taking the congestion tendency corresponding to the road congestion image characteristics as the predicted congestion tendency of the target road.

Further, optimizing the congestion occurrence time and/or congestion tendency based on one or more of the combination of the attributes of the target road, the current road conditions of the associated roads of the target road, and the predicted historical traffic characteristics of the congestion occurrence time, comprises:

obtaining attributes of the target road, wherein the attributes at least comprise: a road grade;

acquiring the current road condition of an upstream road and/or a downstream road of the target road;

and correcting the congestion tendency of the target road according to the attribute of the target road, the current road condition of the upstream road and/or the downstream road of the target road and the predicted historical traffic characteristics of the congestion occurrence time, and the influence weight of the attribute, the current road condition and the historical traffic characteristics on the congestion tendency.

Further, the road congestion image characteristics comprise road congestion image characteristics of the target road before congestion, in congestion and/or after congestion dissipation; predicting the congestion occurrence time and congestion tendency of the target road based on the current road traffic characteristics and the road congestion image characteristics, wherein the predicting comprises the following steps:

respectively comparing the similarity between the current road traffic characteristics and the road congestion image characteristics before congestion, in congestion and/or after congestion dissipation;

and taking the congestion tendency corresponding to the most similar road congestion image characteristics in the road congestion image characteristics before congestion, during congestion and/or after congestion dissipation as the congestion tendency of the target road, and predicting the congestion occurrence time of the target road based on the most similar road congestion image characteristics.

determining a characteristic day corresponding to the current time period;

carrying out similarity comparison on the current road traffic characteristics and the road congestion image characteristics corresponding to the characteristic days;

and determining the congestion tendency corresponding to the road congestion image feature corresponding to the characteristic day with the maximum similarity value as the congestion tendency of the target road, and predicting the congestion occurrence time of the target road based on the road congestion image feature corresponding to the characteristic day with the maximum similarity value.

determining one or more comparison periods of a current period; the comparison period comprises one or more congestion periods within a preset time range with the current period; the congestion time interval is a time interval corresponding to the road congestion image characteristics;

carrying out similarity comparison on the current road traffic characteristic and the road congestion image characteristic corresponding to the comparison time period;

and determining the congestion tendency corresponding to the road congestion image feature corresponding to the comparison time period with the maximum similarity value as the congestion tendency of the target road, and predicting the congestion occurrence time of the target road based on the road congestion image feature corresponding to the comparison time period with the maximum similarity value.

screening out the road jam image characteristics with the similarity higher than a preset threshold value to the current road traffic characteristics based on the similarity between the current road traffic characteristics and the road jam image characteristics;

and predicting the congestion tendency and the congestion occurrence time of the target road based on the screened road congestion image characteristics and the closeness degree between the corresponding congestion time interval and the current time interval.

Further, the congestion tendency comprises a combination of one or more of:

congestion stage, congestion degree, congestion estimated duration, congestion estimated formation time and confidence degree of congestion tendency; wherein the congestion stage comprises one or more of congestion non-forming, congestion exacerbating, congestion severity, daily congestion, and congestion resolving.

In a second aspect, an embodiment of the present invention provides a method for providing a location-based service, where the method provides a location-based service for a served object by using a congestion tendency predicted by the method in the first aspect, and the location-based service includes: one or more of navigation, map rendering, route planning.

In a third aspect, an embodiment of the present invention provides a road congestion prediction apparatus, including:

the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is configured to acquire current road traffic characteristics of a current time period of a target road and road congestion image characteristics of the target road;

the prediction module is configured to predict the congestion occurrence time and the congestion tendency of the target road based on the current road traffic characteristics and the road congestion image characteristics;

an optimization module configured to optimize the congestion occurrence time and/or the congestion tendency based on one or more combinations of attributes of the target road, current road conditions of a road associated with the target road, and predicted historical traffic characteristics of the congestion occurrence time.

In a fourth aspect, an embodiment of the present invention provides a location-based service providing device, for providing a location-based service for a served object by using a congestion tendency predicted by the road congestion prediction device, where the location-based service includes: one or more of navigation, map rendering, route planning.

The functions can be realized by hardware, and the functions can also be realized by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the above-described functions.

In one possible design, the apparatus includes a memory configured to store one or more computer instructions that enable the apparatus to perform the corresponding method, and a processor configured to execute the computer instructions stored in the memory. The apparatus may also include a communication interface for the apparatus to communicate with other devices or a communication network.

In a fifth aspect, the disclosed embodiments provide an electronic device comprising a memory, a processor, and a computer program stored on the memory, wherein the processor executes the computer program to implement the method of any of the above aspects.

In a sixth aspect, the disclosed embodiments provide a computer-readable storage medium for storing computer instructions for use by any of the above apparatuses, the computer instructions, when executed by a processor, being configured to implement the method of any of the above aspects.

In a seventh aspect, the disclosed embodiments provide a computer program product comprising computer instructions, which when executed by a processor, are configured to implement the method of any one of the above aspects.

The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:

in the congestion tendency prediction process of the target road, the road congestion image characteristics of the target road are determined offline, then the current road traffic characteristics of the target road are collected during real-time prediction, the similarity between the current road traffic characteristics and the road congestion image characteristics is compared, and the congestion tendency of the target road is predicted based on the comparison result of the similarity; and in order to further improve the accuracy of the congestion tendency, the congestion tendency is optimized by one or more combinations of the attributes of the target road, the image characteristics of the current time period, the current road conditions of the associated roads and the like. According to the method, a complex real environment is shielded by using historical congestion aggregation logic in an offline process in a mode of combining offline prediction with online prediction, the result that the road is prone to periodic congestion is directly drawn based on historical data, and finally historical experience which is difficult to learn by a machine model, namely road congestion drawing characteristics, is formed; in the real-time prediction process, the road congestion occurrence time and the congestion tendency of the road are predicted by combining the road congestion image characteristics with the real-time road traffic characteristics, the predicted congestion tendency is optimized by utilizing the attributes of the road, the current road conditions of the associated road, the historical traffic characteristics of the predicted congestion occurrence time and the like, and finally the congestion condition of the road in a period of time in the future can be accurately fed back in real time, so that the traffic pressure of the congested road can be relieved, the travel time cost can be saved for a user, and the travel experience of the user can be improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

Other features, objects, and advantages of the present disclosure will become more apparent from the following detailed description of non-limiting embodiments when taken in conjunction with the accompanying drawings. In the drawings:

fig. 1 illustrates a flowchart of a road congestion prediction method according to an embodiment of the present disclosure;

fig. 2 is a schematic diagram illustrating an application of a road congestion prediction method in a navigation scenario according to an embodiment of the disclosure;

fig. 3 is a block diagram illustrating a configuration of a road congestion prediction apparatus according to an embodiment of the present disclosure;

fig. 4 is a schematic structural diagram of an electronic device suitable for implementing a road congestion prediction method and/or a location-based service providing method according to an embodiment of the present disclosure.

Detailed Description

Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. Also, for the sake of clarity, parts not relevant to the description of the exemplary embodiments are omitted in the drawings.

In the present disclosure, it is to be understood that terms such as "including" or "having," etc., are intended to indicate the presence of the disclosed features, numbers, steps, actions, components, parts, or combinations thereof, and do not preclude the possibility that one or more other features, numbers, steps, actions, components, parts, or combinations thereof are present or added.

It should be further noted that the embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

The details of the embodiments of the present disclosure are described in detail below with reference to specific embodiments.

Fig. 1 illustrates a flowchart of a road congestion prediction method according to an embodiment of the present disclosure. As shown in fig. 1, the road congestion prediction method includes the steps of:

in step S101, obtaining a current road traffic characteristic of a current time period of a target road and a road congestion image characteristic of the target road;

in step S102, a congestion occurrence time and a congestion tendency of the target road are predicted based on the current road traffic characteristics and the road congestion image characteristics;

in step S103, the congestion occurrence time and/or congestion tendency is optimized based on one or more combinations of the attributes of the target road, the current road conditions of the associated roads of the target road, and the predicted historical traffic characteristics of the congestion occurrence time.

In this embodiment, the road congestion prediction method may be executed on a server. The link may be one of directional logical link units divided according to actual links, each link may have an independent link identification, and each link may include an entrance and an exit. The target road may be any one of the roads.

The current road traffic characteristic may be a road traffic characteristic on the target road acquired in real time. In some embodiments, the current road traffic characteristics may be obtained based on real-time traffic information, navigation data returned by the navigation system, communication traffic of the vehicle, and other data. Road traffic characteristics may include, but are not limited to: the average traffic volume of the vehicles, the average traffic speed of the vehicles passing through the target road, the average transit time of the vehicles passing through the target road, the distribution of the vehicle volume at different transit speeds, the distribution of the vehicle volume at different transit times, and the average vehicle volume expected to reach the target road within a preset time period. The distribution of the vehicle quantity of different transit times can be, for example, the distribution of the transit speeds of the vehicles passing through the target road in a plurality of transit speed ranges, and the transit speed ranges can include but are not limited to less than 10 km/h, 10-20 km/h, 20-40 km/h, 40-60 km/h, more than 60km/h and the like. The distribution of the transit time of the vehicles passing through the target road may be, for example, the distribution of the transit time of the vehicles passing through the target road in one or more transit time ranges, and the transit time ranges may include, but are not limited to, less than 30 seconds, 30-60 seconds, 60-120 seconds, 120-180 seconds, 180-240 seconds, more than 240 seconds, and the like.

The road congestion profile characteristics may be traffic characteristics corresponding to a period of time when the target road is congested, and may include, but are not limited to, an average traffic volume of the vehicle, an average traffic speed of the vehicle passing through the target road, an average traffic time of the vehicle passing through the target road, a distribution of vehicle volumes at different traffic speeds, a distribution of vehicle volumes at different traffic times, and an average vehicle volume expected to reach the target road within a preset time period.

In some embodiments, the road congestion imaging characteristics may also include the distribution of congestion batches and the delivery relationships associated with roads. The distribution of the user batches may include, but is not limited to, the number of times congestion occurs within a preset period of time. The delivery of the associated link may include, but is not limited to, an identification of the associated link, a distance between the associated link and the target link, a delivery weight for vehicle traffic, a congestion formation offset (i.e., a difference between a time at which the associated link is congested and a time at which the target link is congested), and a congestion dissipation offset (i.e., a difference between a time at which the associated link is dissipating congestion and a time at which the target link is dissipating congestion).

The congestion period may be one or more time periods in which congestion periodically occurs on the target road, which are determined by performing statistical analysis on the historical road condition data of the target road. The congestion period may be one or more time periods of the day.

In some embodiments, the road congestion profile features are obtained as follows:

acquiring congested roads and corresponding congested time periods thereof based on historical road condition information of the roads in the historical time periods;

according to a set congestion image time range, aggregating congestion time periods corresponding to congested roads to generate congestion time characteristics of the congested roads in the congestion image time range, wherein the congestion time characteristics comprise: a pre-congestion formation time characteristic, a congestion duration characteristic, and a congestion dissipation time characteristic;

and processing the road traffic characteristics of the congested road according to the time in the congestion time characteristics to obtain the road congestion image characteristics at the corresponding time.

The historical time period may be a time period in days, which includes one day or a plurality of consecutive days or a plurality of discrete days.

The historical traffic information may refer to various information describing actual traffic conditions of each road in the historical time period, for example, the historical traffic information may include traffic conditions of each road in the historical time period or some information capable of reflecting the traffic conditions, the traffic conditions may include congestion states and non-congestion states, and the non-congestion states may include a plurality of states such as a smooth state and a slow state. The historical traffic information reflecting the traffic status may include traffic speed or traffic time, and generally, the traffic status of each road is related to the traffic speed or traffic time of the vehicle on the road. Taking the traffic information as the traffic speed as an example, for the suburban road link1, the traffic state corresponding to the traffic speed [ 70-80 km/h ] is an unblocked state, the traffic state corresponding to the traffic speed [ 50-70 km/h ] is a slow traffic state, and the traffic state corresponding to the traffic speed [ 30-50 km/h ] is a congested state. For the urban center road link2, the road condition state corresponding to the traffic speed [ 40-60 km/h ] is an unblocked state, the road condition state corresponding to the traffic speed [ 30-40 km/h ] is a slow traffic state, and the road condition state corresponding to the traffic speed [ 20-30 km/h ] is a congestion state.

The historical traffic information can be obtained based on traffic information published in real time by the location based service system.

In the process of representing the road, the congestion time periods corresponding to the congested road can be aggregated in the congestion image time range, so that the congested road is determined to have several times of congestion in the congestion image time range, congestion time characteristics corresponding to the several times of congestion are generated, and each congestion corresponds to one congestion time characteristic. Each congestion time feature correspondingly comprises three features, namely a time feature before congestion formation, a congestion duration feature and a congestion dissipation time feature, wherein the time corresponding to the time feature before congestion formation, the congestion duration feature and the congestion dissipation time feature is a period of continuous time, and the period of time before congestion formation, the period of congestion duration and the period of time after congestion dissipation of the current congestion are respectively indicated. The congestion duration characteristic is a characteristic representing a congestion duration of the current congestion, and may be, for example, a time between a congestion start time and a congestion end time, the pre-congestion-formation-time characteristic is a characteristic representing a time before the current congestion starts, and may be, for example, a time between the congestion start time and a first preset time period before the current congestion start time, and the congestion-dissipation-time characteristic is a characteristic representing a time after the current congestion ends, and may be, for example, a time between the congestion end time and a second preset time period after the current congestion end time.

Assuming that the congestion image time range is one day, the congestion duration characteristics corresponding to the congested roads on that day are aggregated, and for congested roads with the same road ID, the congestion time periods corresponding to the congested roads may be combined into one or more congestion duration characteristics, as corresponding to link1, for example, there is 7: 30-8: 30 and 17: 30-18: and 30 congestion duration characteristics, each of which can be extended forwards and backwards for a period of time, wherein the first preset time period for forward extension, such as 30 minutes, obtains the time characteristic before congestion formation, and the second preset time period for backward extension, such as 30 minutes, obtains the time characteristic before congestion dissipation. This results in a first set of congestion time characteristics for the link1 during the day: pre-congestion formation time feature 7: 00-7: 30, congestion duration feature 7: 30-8: 30, congestion dissipation time characteristic 8: 30-9: 30, of a nitrogen-containing gas; and a second set of congestion time characteristics: pre-congestion formation time characteristic 17: 00-17: 30, congestion duration feature 17: 30-18: 30, congestion dissipation time characteristic 18: 30-19: 30.

in some embodiments, the road congestion profile features refer to information that can describe attributes of congestion conditions of the road, such as congestion occurrence probability, congestion time, congestion duration or traffic capacity at the time of congestion. The road congestion features may include road congestion profile features before congestion is formed, while congestion continues, and after congestion is resolved.

In the embodiment of the disclosure, in an offline situation, the road congestion profile characteristics can be determined in advance based on the historical data of the target road. In the real-time prediction online process, the current road traffic characteristics of the target road can be collected in real time, and the current road traffic characteristics correspond to the road congestion image characteristics obtained in advance as can be known from the above description. Therefore, after the current road traffic characteristics on the target road are collected in real time, the current road traffic characteristics can be compared with the road congestion image characteristics. For example, the similarity between the average traffic speeds of the vehicles passing through the target road, the similarity between the average transit times of the vehicles passing through the target road, the similarity between the vehicle quantity distribution situations of different transit speeds, the similarity of the vehicle quantity distribution situations of different transit times, and/or the similarity of the average vehicle quantity expected to reach the target road within a preset time period in the current road traffic characteristic and the road congestion profile characteristic may be compared respectively. After the comparison, whether the current road condition of the target road is similar to the road congestion image characteristic or not can be determined according to the comparison result, if so, whether the target road is congested in a future period of time or not can be predicted based on the road congestion image characteristic, and if so, congestion trends such as congestion duration and the like can be predicted. For example, if the road congestion map features being compared describe the features of the target road before congestion, then the current situation of the target road before congestion can be determined based on the features, and the congestion tendency of the target road in a future period is that congestion is forming.

In some embodiments, the congestion tendency may include, but is not limited to, a combination of one or more of a congestion stage, a congestion level, a predicted duration of congestion, a confidence in the congestion tendency; wherein the congestion phase includes a combination that may include, but is not limited to, one or more of congestion formation, congestion acceleration, congestion severity, daily congestion, and congestion resolution. The congestion occurrence time may be a time when the target road is predicted to be congested, for example, when a current situation of the target road before congestion is predicted according to the road congestion image feature, the road congestion time when the target road is to be congested may be further predicted based on the road congestion image feature.

Consider the situation where predicting congestion trends solely from the comparison between the current road traffic characteristics of the target road and the road congestion map characteristics may result in inaccurate prediction results. Accordingly, the disclosed embodiments also provide a way to optimize the predicted congestion tendency.

In some embodiments, the congestion tendency of the target road may be optimized based on one or more of the attributes of the target road, the current road conditions of the associated roads of the target road, and the predicted historical traffic characteristics of the congestion occurrence time.

The attributes of the target road may include, but are not limited to, road width, whether there are traffic lights, whether there are expressway expressways, daily traffic, free flow speed, and the like. The portrait features of the current time period may include, but are not limited to, weekdays, holidays, morning and evening peak periods, flat peak periods, nights, and the like. The associated roads of the target road may include, but are not limited to, upstream roads, downstream roads, etc. of the target road vehicle traffic.

The associated roads of the target road may refer to respective roads through which vehicles entering and exiting from the target road pass and respective roads through which vehicles pass before entering the target road, in the road network within a preset geographical range. In some embodiments, the current traffic information of the associated road may be obtained, and the current traffic information may include, but is not limited to, an average traffic speed of the vehicle passing through the associated road, an average transit time of the vehicle passing through the associated road, a distribution of vehicle amounts at different transit speeds, a distribution of vehicle amounts at different transit times, an average vehicle amount expected to reach the associated road within a preset time period, and the like. In some embodiments, the current traffic information may be determined according to the traffic information published in real time by the location-based service system.

Further, an influence factor of the associated road and the target road may be determined in advance. The impact factor may be used to represent the degree of impact of congestion formation or dissipation of the associated link on congestion formation or dissipation of the target link. The congestion formation and dissipation of each target road can be influenced by the associated road, when the congestion formation and the congestion dissipation are carried out, the transfer relationship of the flow of the carrier is very important, the larger the flow of the carrier entering and exiting between the associated road and the target road is, the larger the influence factor is, and the smaller the distance between the associated road and the target road is, the larger the influence factor is, so that the influence factor can be predetermined based on the flow of the carrier entering and exiting between the associated road and the target road, the distance between the associated road and the target road and the like. It should be noted that, the associated road and the target road may be separated by one or more other roads or may be directly adjacent to each other, when the associated road is adjacent to the target road, the distance from the exit of the associated road to the entrance of the target road is 0, and when the associated road is possibly separated by one or more other roads, the distance between the associated road and the target road is the sum of the lengths of the one or more other roads separated from each other.

In some embodiments, the historical traffic characteristics of the predicted congestion occurrence time may include, but are not limited to, characteristics such as the amount of probability that the target link will be congested at the congestion occurrence time. In some embodiments, the historical traffic characteristics of the congestion occurrence time may be represented as traffic characteristics that are likely to have congestion during the day, traffic characteristics that are not likely to have congestion, and the like. If the congestion tendency of the target road is predicted to be formed due to congestion, and the occurrence time of the congestion is night, the target road has historical traffic characteristics that the congestion is not easy to occur, and the attribute of the target road is a fast road, the current road condition of the associated road is good, and the congestion does not exist or the phenomena of large traffic of a carrying tool, low traffic speed, long traffic time and the like do not exist, the actual road condition of the target road has a large probability that the congestion does not exist, and has a certain difference with the predicted congestion tendency, so that corresponding optimization can be performed, for example, the adjustment can be performed by adjusting the weight of a factor used for predicting the congestion tendency and the like. For example, the following steps are carried out: if the factors influencing the congestion tendency prediction include the similarity between the current road traffic characteristics and the road congestion image characteristics, the proximity between the current time period and the time period corresponding to the road congestion image characteristics, and the like, the congestion tendency can be obtained by recalculating after adjusting the weights of the factors.

In an optional implementation manner of this embodiment, in step S102, the step of predicting the congestion occurrence time and the congestion tendency of the target road based on the current road traffic characteristic and the road congestion image characteristic further includes the following steps:

In this alternative implementation, the road congestion image feature may include, but is not limited to, image features corresponding to one or more time periods in a day where congestion is likely to occur. In order to improve the prediction efficiency and accuracy, the current profile feature at the current time may be compared with one or more road congestion profile features within a set time length range from the current time in the day, and the set time length may be set in advance based on experience or predicted actual requirements, which is not specifically limited herein. And if the current time is compared to be similar to the road congestion image characteristics in a certain time period, predicting the congestion tendency and the congestion occurrence time of the target road based on the similar road congestion image characteristics. In some embodiments, the congestion tendency corresponding to the relatively similar road congestion image feature may be used as the congestion tendency of the target road, and the congestion occurrence time of the target road at which congestion is about to occur may be predicted based on the congestion time period corresponding to the road congestion image feature in the day. For example, if a relatively similar image feature of road congestion corresponds to an image feature before the target road congestion and the image feature before the congestion is N minutes away from the image feature in the target congestion, the target road congestion can be predicted to occur N minutes later, and therefore the predicted congestion occurrence time of the target road can be the current time plus the time obtained after N minutes.

In an optional implementation manner of this embodiment, in step S103, that is, the step of optimizing the congestion occurrence time and/or the congestion tendency based on one or more combinations of the attribute of the target road, the current road condition of the road associated with the target road, and the predicted historical traffic characteristics of the congestion occurrence time further includes the following steps:

In this alternative implementation, the attributes of the target road may include, but are not limited to, road grades, which may be divided based on, for example, road width, whether there are traffic lights, whether there are expressways or expressways, daily traffic, free flow speed, and the like.

The associated road of the target road may include an upstream road and/or a downstream road. Generally, the congestion status of the upstream road and the downstream road is transmitted to the target road, so that the congestion tendency of the target road can be corrected based on the current traffic information of the upstream road and/or the downstream road. The current traffic information of the upstream road and the downstream road may be the current actual traffic, and may be determined by the traffic information issued by the location-based service system in real time and/or the navigation data in the location-based service system. For example, it is predicted in real time that the congestion tendency of the target road in a future period is not formed, and the current traffic information of the downstream road is currently congested, so that it can be determined based on this that the congestion condition of the downstream road may be transmitted to the target road in the future period, and therefore the congestion tendency can be corrected based on the current traffic information of the upstream and downstream roads, the current traffic information of the target road and the predicted historical traffic history of the congestion occurrence time, for example, the congestion tendency can be corrected to be the congestion formation in the congestion stage where the target road is currently located. It is understood that the above examples are only for illustration, and the influence of other factors such as the attribute of the target road, the historical traffic characteristics, etc. on the congestion tendency may also be considered in the actual correction process.

In some embodiments, in the process of optimizing the predicted congestion tendency of the target road, attributes of the target road, current road conditions of an upstream road and/or a downstream road of the target road, and historical traffic characteristics of the predicted congestion occurrence time may be used as influence factors influencing the congestion tendency, different influence weights may be given to the influence factors in advance, and after the congestion tendency of the target road is predicted, the congestion tendency of the target road is corrected by obtaining the influence factors in the current time period and based on the influence factors and the influence weights thereof.

In an optional implementation manner of this embodiment, the road congestion image feature includes a road congestion image feature of the target road before congestion, in congestion and/or after congestion dissipation; step S102, which is a step of predicting congestion occurrence time and congestion tendency of the target road based on the current road traffic characteristics and the road congestion image characteristics, and further includes the following steps:

In this optional implementation, the predetermined road congestion image features may include road congestion image features of three congestion stages, namely, before congestion, during congestion and after congestion dissipation, which correspond to congestion time periods. When the similarity is compared, the current road traffic characteristics can be respectively compared with the road congestion image characteristics before congestion, in congestion and/or after congestion dissipation in each congestion time period, and the congestion tendency of the target road can be determined according to the comparison result. If the current road traffic characteristic of the target road is most similar to the road congestion image characteristic of one of the stages before congestion, during congestion and/or after congestion dissipation in a certain congestion period, the congestion tendency of the target road can be preliminarily determined to be similar to the one of the stages, for example, the congestion tendency is similar to the road congestion image characteristic before congestion, the congestion tendency of the target road can be preliminarily determined to be a congestion formation stage, and congestion may be formed on the target road in a future period of time, so that the congestion tendency corresponding to the road congestion image characteristic before congestion can be determined to be the congestion tendency of the target road, and the congestion occurrence time of the target road can be predicted based on the road congestion image characteristic.

determining a characteristic day corresponding to the current time period;

In this alternative implementation, the feature day may be description information that expresses the characteristics of the sky road traffic in units of days. In some embodiments, the characteristic day may include, but is not limited to, a working day, a non-working day, a long holiday, a summer holiday, a cold holiday, and the like, and may be specifically set according to an actual condition of the road, which is not limited herein.

In the embodiment of the disclosure, in order to accurately represent roads, different road congestion representation features may be obtained in advance for different feature days.

In some embodiments, in order to portray a road in units of characteristic days, a strongly connected subgraph can be respectively established based on one or more congestion periods of each day, wherein one vertex of the strongly connected subgraph represents a day corresponding to one congestion period, an edge of the strongly connected subgraph represents an associated time period existing between two congestion periods in two days corresponding to two vertices, and the associated time period existing between the two congestion periods can refer to a coincidence time period between the two congestion periods which exceeds a preset threshold value; the preset threshold may be 1/2 for the shorter of the two congestion periods; after the strongly connected subgraph is established, the road congestion image characteristics corresponding to the congestion time periods in one or more days with the associated time periods can be aggregated by traversing the strongly connected subgraph to obtain the road congestion image characteristics corresponding to the characteristic days, and the road congestion image characteristics corresponding to the characteristic days can also comprise average congestion starting time, average congestion duration, congestion occurrence days, congestion occurrence confidence, congestion formation time deviation time, congestion dissipation time deviation time and the like.

In some embodiments, the road congestion imaging features acquired for the feature day may also include, but are not limited to, a starting batch (the number of congestion in the work day), congestion duration, congestion occurrence days, congestion frequency, congestion formation deviation, congestion ending deviation, and the like.

When the congestion tendency of the target road is predicted in real time, the similarity comparison can be carried out on the road congestion image characteristics in the characteristic day corresponding to the current time period and the current road traffic characteristics. For example, if the current time interval is a time interval in a working day, similarity comparison is carried out on the current road traffic characteristic and the road congestion image characteristic in the working day, if the current time interval is a time interval in a non-working day, similarity comparison is carried out on the current road traffic characteristic and the road congestion image characteristic in the non-working day, then the congestion tendency corresponding to the road congestion image characteristic corresponding to the characteristic day with the maximum similarity value is determined as the congestion tendency of the target road, and the congestion occurrence time of the target road is predicted based on the road congestion image characteristic corresponding to the characteristic day with the maximum similarity value. By the method, different days with similar congestion conditions on the road can be aggregated in advance to form the characteristic day, the corresponding road portrait characteristics are obtained according to the characteristic day, the road portrait characteristics corresponding to the characteristic day of the current time period are further utilized to predict the congestion tendency of the target road, and the prediction accuracy of the congestion tendency can be further improved.

In the optional implementation manner, in the process of forming the road congestion image feature, the actual congestion start time and end time of each road each day can be obtained by analyzing and screening the road condition information issued by the location-based service system in real time, and the congestion time period of each road each day, each week or each month can be obtained by performing statistical analysis on the start time and the end time. The congestion periods within a time range may include one or more, for example, the congestion periods on a weekday day may include early peak periods and late peak periods, and the like.

After the congestion time period is determined, the road congestion image feature corresponding to the congestion time period can be obtained based on the historical road condition information collected in the congestion time period. In some embodiments, road congestion image features corresponding to different congestion time periods can be obtained for each day, and then the road congestion image features corresponding to the congestion time periods on different days can be obtained in a manner of aggregating the road congestion image features corresponding to the congestion time periods on different days.

Congestion conditions may be more similar between the same or similar time periods each day, while congestion conditions may be less similar between time periods that are further away. Therefore, in the embodiment of the disclosure, in the real-time prediction process, the similarity comparison can be performed on the current road traffic characteristic and the road congestion image characteristic in the same or similar time period. The current road traffic characteristic is a characteristic of a current time period, a time range can be preset, a congestion time period in the same time range as the current time period is determined as a comparison time period which is the same as or similar to the current time period, similarity comparison is further performed on the current road traffic characteristic and a road congestion image characteristic corresponding to the comparison time period, a congestion tendency corresponding to the road congestion image characteristic corresponding to the comparison time period with the maximum similarity value is determined as a congestion tendency of a target road, and congestion occurrence time of the target road is predicted based on the road congestion image characteristic corresponding to the comparison time period with the maximum similarity value.

In some embodiments, the comparison period may be one or more congestion periods that are of the same characteristic day as the current period.

In this alternative implementation, the current road traffic characteristics may be compared with the similarity of the road congestion profile characteristics for a plurality of congestion periods. In the process of predicting the congestion tendency in real time, the congestion image features of the roads with low similarity can be ignored, and the congestion tendency of the target road can be predicted based on the congestion image features of the roads with high similarity.

In some embodiments, a similarity threshold may be preset, a road congestion image feature with a similarity greater than the similarity threshold is screened, and based on the principle that the road conditions between two time periods with close time are similar, the degree of closeness between the congestion time period of the screened road congestion image feature and the current time period may be used as a factor of congestion tendency prediction, so as to finally obtain the congestion tendency of the target road. For example, although the current road traffic characteristic of the current time interval is similar to the road congestion image characteristic of a certain congestion time interval, if the distance between the current time interval and the congestion time interval is long, for example, the current time interval is night, and the congestion time interval is day, the road congestion image characteristic of the congestion time interval does not have a great reference to the current time interval, so the weight of the road congestion image characteristic corresponding to the congestion time interval is set to be small, or may be set to be 0.

In some embodiments, a calculation weight may be determined based on a closeness degree between a current time interval and a congestion time interval corresponding to the screened road congestion image feature, the calculation weight of the congestion time interval with a higher closeness degree (that is, two time intervals overlap or a time distance is shorter) is set to be larger, the calculation weight of the congestion time interval with a lower closeness degree (that is, two time intervals do not overlap and a time distance is longer) is set to be larger, and a congestion tendency of the target road is predicted by considering congestion characteristics of the screened road congestion image feature. In some embodiments, the congestion characteristics of the road congestion profile feature may include, but are not limited to, congestion stages (e.g., before congestion, during congestion, and after congestion has dissipated), congestion duration, average transit speed during congestion, average transit time, etc., as characterized by the road congestion profile feature.

In an optional implementation manner of the embodiment, the congestion tendency includes one or more of the following combinations:

In the optional implementation mode, the congestion tendency of the target road is calculated by comparing the similarity between the current road traffic characteristic and the road congestion image characteristic. The current congestion stage can be determined based on the congestion stages corresponding to the one or more groups of road congestion image features which are similar to the current road traffic features, for example, if the one or more groups of road congestion image features which are most similar to the current road traffic features are all features before congestion, it can be determined that the target road is currently in the congestion formation stage. The congestion stages at which the current or future time is located may also include congestion acceleration, congestion severity, daily congestion, congestion dispersion, etc.

In the embodiment of the disclosure, the congestion tendency may further include a congestion degree, and the congestion degree may determine the current congestion degree based on an average transit time, an average transit speed, an average transit amount, a vehicle amount expected to reach the target road in a period of time in the current road transit characteristics, and an average transit time, an average transit speed, an average transit amount, a vehicle amount expected to reach the target road in a period of time before congestion, and the like.

The congestion tendency also comprises the predicted congestion duration and the predicted congestion formation time, and the predicted congestion duration can be calculated based on the predicted duration corresponding to the road congestion image feature of the target road (namely, the road congestion image feature similar to the current road traffic feature) and the predicted duration corresponding to the road congestion image feature of the associated road of the target road.

The congestion tendency further comprises a confidence level of the congestion tendency, and the confidence level of the congestion tendency (which can also be understood as the probability of congestion of the target road) can be calculated based on the calculated congestion degree of the target road, the current road condition state of the associated road, and the congestion probability corresponding to the road congestion image feature of the target road. The current road condition state of the associated road can be obtained by calculation based on the congestion degree and the congestion occurrence probability of the associated road.

One way of calculating congestion tendency is illustrated below:

the relative congestion degree of the target road is calculated as follows:

v1＝speed/G1.speed，t1＝travel_time/G1.ttime，u1＝tp_users/G1.users，e1＝c10/G1.c10

level＝v1＊w1+t1＊w2+u1＊w3+e1＊w4，

wherein v1 represents a speed factor, speed represents the average traffic speed of vehicles in the current road traffic characteristic, g1.speed represents the average traffic speed of vehicles in the target road before congestion, t1 represents a time factor, travel _ time represents the average traffic time of vehicles in the current road traffic characteristic, g1. time represents the average traffic time of vehicles in the target road before congestion, u1 represents a vehicle traffic volume factor, tp _ users represents the average traffic volume of vehicles in the current road traffic characteristic, g1.users represents the average traffic volume of vehicles in the target road before congestion, e1 represents a vehicle estimated arrival volume factor, c10 represents the average vehicle volume estimated to arrive at the target road within 10 minutes of the current road traffic characteristic (which data can be determined based on navigation information in a navigation system), and g1.c10 represents the average vehicle volume to arrive at the target road within 10 minutes before congestion; level represents the relative congestion degree of the target road, and w1, w2, w3 and w4 are preset weights.

Confidence in congestion tendency is calculated as follows:

relib＝level＊0.4+related_state＊0.3+hist_relib＊0.3

related_state＝∑w_i*relib_i*level_i

the method comprises the steps of obtaining a road congestion model, obtaining a road congestion confidence coefficient, obtaining a road congestion model, and obtaining a road congestion model_iWeight, relib, representing the ith associated road_iIndicating the congestion probability, level, of the ith associated road_iIndicating the congestion degree of the ith associated road. In some embodiments, the current traffic status of the associated road may be calculated according to the actual traffic information, and the calculation method may be similar to the above prediction method of the target road.

The predicted duration of congestion is calculated as follows:

offset＝(1-w6)＊hist_offset+w6＊related_offset

w6＝related_reib＊w5

where offset represents the predicted expected duration of the road trend of the predicted target road, hist _ offset represents the trend duration of the target road before congestion, related _ offset represents the congestion formation offset between the related road and the target road, w6 represents the influence factor of the related road, related _ relib represents the congestion probability of the related road, and w5 represents the weight of the congestion probability of the related road.

In some embodiments, the congestion phase may be refined into an uncommitted congestion phase, a phase 1/2 before the congestion (if the period before the congestion is regarded as a period of time, the 1/2 phase before the congestion may be regarded as half of the period before the congestion), a period during the congestion, and a phase 1/4 before the congestion (if the period before the congestion is regarded as a period of time, the 1/4 phase before the congestion may be regarded as 3/4 that is the period before the congestion is over, that is, the congestion is about to occur).

In an optional implementation manner of this embodiment, the method further includes:

road congestion profile features are predetermined.

The aggregating of the congestion time periods corresponding to the congested roads to generate the congestion time characteristics of the congested roads in the congestion image time range includes:

sequencing congestion time periods corresponding to the congested roads in sequence according to each congested road, and merging continuous congestion time periods in the congested roads to obtain alternative congestion duration time characteristics corresponding to the congested roads;

when only one alternative congestion duration characteristic corresponding to the congested road exists, taking the alternative congestion duration characteristic as a congestion duration characteristic corresponding to the congested road;

when at least two alternative congestion duration characteristics corresponding to the congested road exist, merging alternative congestion duration characteristics meeting merging conditions in the at least two alternative congestion duration characteristics for a continuous time period to serve as congestion duration characteristics corresponding to the congested road, wherein each alternative congestion duration characteristic which is not merged in the at least two alternative congestion duration characteristics serves as other congestion duration characteristics corresponding to the congested road, and the merging conditions include that the time interval between one alternative congestion duration characteristic and the adjacent alternative congestion duration characteristic in the merged alternative congestion duration characteristics is smaller than a preset time length or the traffic condition in the time interval meets the congestion condition;

and acquiring a time feature before congestion formation and a time feature before congestion dissipation corresponding to the congestion duration feature based on the congestion duration feature for each congestion duration feature.

The method comprises the following steps of processing road traffic characteristics of the congested road according to the time in the congestion time characteristics to obtain road congestion image characteristics at corresponding time, and further comprises the following steps:

acquiring flow distribution of vehicles entering and exiting between a congested road and a related road within time corresponding to congestion time characteristics according to a running track of the vehicles on the road;

acquiring an influence factor between the associated road and a congested road based on the flow distribution and the distance between the associated road and the congested road;

and acquiring the congestion occurrence advance time and the congestion dissipation advance time of the associated road compared with the congested road based on the congestion duration characteristics of the associated road and the congested road within the congestion imaging time range.

establishing a strongly connected subgraph based on congestion duration characteristics corresponding to the congested roads every day, wherein one vertex of the strongly connected subgraph represents a day corresponding to one congestion duration characteristic, an edge of the strongly connected subgraph represents a time period for which two congestion duration characteristics in two days corresponding to two vertices exist, and the time period for which the two congestion duration characteristics exist is that a coincidence time period between the two congestion duration characteristics exceeds a preset threshold value; the preset threshold is 1/2 of the shorter time period of the two congestion duration time periods;

traversing the strongly connected subgraph, and aggregating road congestion image characteristics corresponding to time in the same type of congestion duration characteristics with associated time periods to obtain road congestion image characteristics corresponding to characteristic days, wherein the road congestion image characteristics corresponding to the characteristic days comprise date characteristics of the days where the same type of congestion duration characteristics are located, and the average congestion starting time, the average congestion duration time, the congestion occurrence days, the confidence coefficient of congestion occurrence, the deviation time of the congestion formation time and the deviation time of the congestion dissipation time corresponding to the same type of congestion duration characteristics.

A location-based service providing method according to an embodiment of the present disclosure includes: the congestion tendency predicted by the road congestion prediction method is used for providing a location-based service for a served object, and the location-based service comprises the following steps: one or more of navigation, map rendering, route planning.

In this embodiment, the location-based service providing method may be executed on a terminal, where the terminal is a mobile phone, an ipad, a computer, a smart watch, a vehicle, or the like. According to the embodiment of the disclosure, the congestion tendency in a future period of time can be predicted by using the above-mentioned road congestion prediction method for any road or a pre-selected road such as a hot road, and then in a location-based service process, the congestion tendency can be used for providing more accurate location services for a served object, such as a navigation service, a route planning service and/or a map rendering service.

The served object can be a mobile phone, ipad, computer, smart watch, vehicle, robot, etc. When a route is navigated and planned for a served object or a road on a map is rendered, a road congestion tendency of the road can be obtained based on a road congestion prediction method, then a road section which is about to form congestion is avoided based on the congestion tendency during navigation or planning of the route, the road section which is about to dissipate congestion or does not have congestion is selected, and the road congestion tendency of the road is rendered on an electronic map to play a warning role during map rendering, wherein specific details can be referred to the description of the road congestion prediction method, and are not described herein again.

Fig. 2 is a schematic diagram illustrating an application of a road congestion prediction method in a navigation scenario according to an embodiment of the disclosure. As shown in fig. 2, in the off-line phase, the prediction server uses off-line information to represent roads that are likely to be congested, and obtains road congestion representation features of the congested roads. The off-line information can be obtained from the navigation server, and can include, but is not limited to, real-time traffic information, navigation data, vehicle traffic information and the like for congested road sections in a congested time period, the road congestion image features are off-line features, and the off-line features of different congested roads are all stored in a feature library. The prediction server also obtains perception characteristics by utilizing offline characteristic aggregation, wherein the perception characteristics can be road congestion image characteristics formed aiming at characteristic days, unexpected situations and the like, and the perception characteristics are also stored in a characteristic library.

In the real-time phase, the prediction server collects real-time information, which may also be obtained from the navigation server. The real-time information may include, but is not limited to, real-time traffic information, navigation data, vehicle traffic information, etc. received in real time. And the prediction server extracts the current road traffic characteristics based on the real-time information, compares the similarity of the current road traffic characteristics on the same road with the road congestion image characteristics, and obtains the congestion tendency of the road based on the comparison result. In addition, the prediction server optimizes the congestion tendency based on the perception characteristics and the like to obtain the congestion tendency predicted in real time.

The prediction server sends the congestion tendency to the navigation server, and the navigation server adjusts the navigation path in real time based on the congestion tendency, so that the vehicle is guided to avoid the road to be congested, and the road without congestion or with congestion to be dissipated is selected for running.

The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods.

Fig. 3 is a block diagram illustrating a configuration of a road congestion prediction apparatus according to an embodiment of the present disclosure. As shown in fig. 3, the apparatus may be implemented as part or all of an electronic device by software, hardware, or a combination of both. The road congestion prediction device includes:

an obtaining module 301, configured to obtain a current road traffic characteristic of a current time period of a target road and a road congestion profile characteristic of the target road;

a prediction module 302 configured to predict congestion occurrence time and congestion tendency of the target road based on the current road traffic characteristics and the road congestion image characteristics;

an optimizing module 303 configured to optimize the congestion occurrence time and/or the congestion tendency based on one or more of the combination of the attribute of the target road, the current road condition of the road associated with the target road, and the predicted historical traffic characteristics of the congestion occurrence time.

In an optional implementation manner of this embodiment, the prediction module includes:

and the first comparison sub-module is configured to perform similarity comparison on the current road traffic characteristic and a road congestion image characteristic within a set time length from the current time, predict the congestion occurrence time of the target road based on the road congestion image characteristic if the similar road congestion image characteristic is compared, and take the congestion tendency corresponding to the road congestion image characteristic as the predicted congestion tendency of the target road.

In an optional implementation manner of this embodiment, the optimization module includes:

a first obtaining sub-module configured to obtain attributes of the target road, the attributes including at least: a road grade;

the second obtaining submodule is configured to obtain the current road condition of an upstream road and/or a downstream road of the target road;

and the correction submodule is configured to correct the congestion tendency of the target road according to the attribute of the target road, the current road condition of the upstream road and/or the downstream road of the target road and the predicted historical traffic characteristics of the congestion occurrence time, and the influence weight of the attribute, the current road condition and the historical traffic characteristics on the congestion tendency.

In an optional implementation manner of this embodiment, the road congestion image feature includes a road congestion image feature of the target road before congestion, in congestion and/or after congestion dissipation; the prediction module comprises:

a second comparison submodule configured to compare similarities between the current road traffic characteristic and the road congestion representation characteristics before congestion, during congestion and/or after congestion dissipation, respectively;

the first prediction submodule is configured to take a congestion tendency corresponding to the most similar road congestion image feature in the road congestion image features before congestion, during congestion and/or after congestion dissipation as the congestion tendency of the target road, and predict the congestion occurrence time of the target road based on the most similar road congestion image feature.

the first determining submodule is configured to determine a characteristic day corresponding to the current time period;

the third comparison submodule is configured to perform similarity comparison on the current road passing characteristic and the road congestion image characteristic corresponding to the characteristic day;

the second prediction submodule is configured to determine a congestion tendency corresponding to the road congestion image feature corresponding to the characteristic day with the largest similarity value as the congestion tendency of the target road, and predict the congestion occurrence time of the target road based on the road congestion image feature corresponding to the characteristic day with the largest similarity value.

a second determination submodule configured to determine one or more comparison periods of the current period; the comparison period comprises one or more congestion periods within a preset time range with the current period; the congestion time interval is a time interval corresponding to the road congestion image characteristics;

the fourth comparison submodule is configured to perform similarity comparison on the current road passing characteristic and the road congestion image characteristic corresponding to the comparison time period;

the third prediction sub-module is configured to determine a congestion tendency corresponding to the road congestion image feature corresponding to the comparison time period with the largest similarity value as the congestion tendency of the target road, and predict the congestion occurrence time of the target road based on the road congestion image feature corresponding to the comparison time period with the largest similarity value.

the screening submodule is configured to screen out the road congestion image characteristics with the similarity higher than a preset threshold value based on the similarity between the current road passing characteristics and the road congestion image characteristics;

and the second prediction submodule is configured to predict the congestion tendency and the congestion occurrence time of the target road based on the screened road congestion image characteristics and the closeness degree between the corresponding congestion time interval and the current time interval.

The road congestion prediction apparatus in this embodiment corresponds to the above road congestion prediction method, and specific details may refer to the description of the road congestion prediction method in the foregoing, which are not described herein again.

According to the location-based service providing apparatus of an embodiment of the present disclosure, the apparatus may be implemented as part or all of an electronic device by software, hardware, or a combination of both. The location-based service providing device provides a location-based service for a served object by using the congestion tendency predicted by the road congestion prediction device, wherein the location-based service comprises: one or more of navigation, map rendering, route planning.

The location-based service providing device in this embodiment corresponds to the location-based service providing method described above, and specific details may refer to the description of the location-based service providing method described above, which are not described herein again.

As shown in fig. 4, electronic device 400 includes a processing unit 401, which may be implemented as a CPU, GPU, FPGA, NPU, or other processing unit. The processing unit 401 may execute various processes in the embodiment of any one of the above-described methods of the present disclosure according to a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. In the RAM403, various programs and data necessary for the operation of the electronic apparatus 400 are also stored. The processing unit 401, the ROM402, and the RAM403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.

The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. A driver 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary.

In particular, according to embodiments of the present disclosure, any of the methods described above with reference to embodiments of the present disclosure may be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a machine-readable medium, the computer program comprising program code for performing any of the methods of the embodiments of the present disclosure. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 409, and/or installed from the removable medium 411.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, a program segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units or modules described in the embodiments of the present disclosure may be implemented by software or hardware. The units or modules described may also be provided in a processor, and the names of the units or modules do not in some cases constitute a limitation of the units or modules themselves.

As another aspect, the present disclosure also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the apparatus in the above-described embodiment; or it may be a separate computer readable storage medium not incorporated into the device. The computer readable storage medium stores one or more programs for use by one or more processors in performing the methods described in the present disclosure.

The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is possible without departing from the inventive concept. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.

Claims

1. A road congestion prediction method includes:

2. The method of claim 1, wherein predicting congestion occurrence time and congestion tendency of the target road based on the current road traffic characteristics and the road congestion profile characteristics comprises:

3. The method of claim 1, wherein optimizing the congestion occurrence time and/or congestion tendency based on a combination of one or more of attributes of the target road, current road conditions of the target road associated with the target road, and predicted historical traffic characteristics of the congestion occurrence time comprises:

and correcting the congestion tendency of the target road according to the attribute of the target road, the current road condition of the upstream road and/or the downstream road of the target road and the predicted historical traffic characteristics of the congestion occurrence time, and the influence weight on the congestion tendency of the attribute, the current road condition and the historical traffic characteristics.

4. The method of claim 1 or 2, wherein the road congestion imaging characteristic comprises a road congestion imaging characteristic of the target road before congestion, in congestion and/or after congestion dissipation; predicting the congestion occurrence time and congestion tendency of the target road based on the current road traffic characteristics and the road congestion image characteristics, wherein the predicting comprises the following steps:

5. The method as claimed in claim 1 or 2, wherein predicting congestion occurrence time and congestion tendency of the target road based on the current road traffic characteristics and the road congestion profile characteristics comprises:

determining a characteristic day corresponding to the current time period;

6. The method as claimed in claim 1 or 2, wherein predicting congestion occurrence time and congestion tendency of the target road based on the current road traffic characteristics and the road congestion profile characteristics comprises:

7. The method as claimed in claim 1 or 2, wherein predicting congestion occurrence time and congestion tendency of the target road based on the current road traffic characteristics and the road congestion profile characteristics comprises:

8. The method of claim 1 or 2, wherein the congestion tendency comprises a combination of one or more of:

9. A location-based service providing method, wherein the method provides a location-based service for a served object using the congestion tendency predicted by the method of any one of claims 1 to 8, the location-based service comprising: one or more of navigation, map rendering, route planning.

10. A computer program product comprising computer instructions, wherein the computer instructions, when executed by a processor, implement the method of any one of claims 1-9.