CN111986490A - Road condition prediction method and device, electronic equipment and storage medium - Google Patents
Road condition prediction method and device, electronic equipment and storage medium Download PDFInfo
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- CN111986490A CN111986490A CN202010986785.XA CN202010986785A CN111986490A CN 111986490 A CN111986490 A CN 111986490A CN 202010986785 A CN202010986785 A CN 202010986785A CN 111986490 A CN111986490 A CN 111986490A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/049—Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
Abstract
The application discloses a road condition prediction method, a road condition prediction device, electronic equipment and a storage medium, and relates to the technical field of intelligent traffic. The specific implementation scheme is as follows: determining historical road traffic information of the current time period in a historical period; determining real-time road traffic information of the current time period in the current period; determining historical road traffic information of a time period to be predicted in the historical period; wherein the time period to be predicted is after the current time period; and predicting real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period according to the historical road traffic information and the real-time road traffic information of the current time period and the historical road traffic information of the time period to be predicted. By adopting the technical means, the accuracy of road condition prediction can be improved, and the requirements of users on road condition prediction are met.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a road condition prediction method, apparatus, electronic device, and storage medium.
Background
With the continuous and rapid increase of the automobile holding quantity in China, the urban road traffic jam problem is increasingly serious, and the importance and the demand of people on road condition prediction are higher and higher.
Traffic managers pay more and more attention to monitoring and predicting traffic road conditions so as to better manage traffic; common users pay more and more attention to future road condition prediction so as to more reasonably arrange travel planning.
Disclosure of Invention
The application provides a road condition prediction method, a road condition prediction device, electronic equipment and a storage medium.
According to a first aspect of the present application, a road condition prediction method is provided, including:
determining historical road traffic information of the current time period in a historical period;
determining real-time road traffic information of the current time period in the current period;
determining historical road traffic information of a time period to be predicted in the historical period; wherein the time period to be predicted is after the current time period;
and predicting real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period according to the historical road traffic information and the real-time road traffic information of the current time period and the historical road traffic information of the time period to be predicted.
According to a second aspect of the present application, there is provided a traffic prediction apparatus, comprising:
the historical road traffic information determining module of the current time period is used for determining the historical road traffic information of the current time period in a historical period;
the real-time road traffic information determining module of the current time period is used for determining the real-time road traffic information of the current time period in the current period;
the historical road traffic information determining module of the time period to be predicted is used for determining the historical road traffic information of the time period to be predicted in the historical period; wherein the time period to be predicted is after the current time period;
and the real-time road condition information prediction module is used for predicting the real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period according to the historical road traffic information of the current time period, the real-time road traffic information and the historical road traffic information of the time period to be predicted.
According to a third aspect of the present application, there is provided an electronic apparatus, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method of any one of the present applications.
According to a fourth aspect of the present application, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any of the present applications.
According to the technology of the application, the accuracy of road condition prediction can be improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present application, nor do they limit the scope of the present application. Other features of the present application will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
fig. 1 is a schematic flow chart illustrating a road condition prediction method according to an embodiment of the present application;
fig. 2a is a schematic flow chart illustrating another road condition prediction method according to an embodiment of the present disclosure;
FIG. 2b is a schematic diagram of a model framework of an LSTM provided in accordance with an embodiment of the present application;
fig. 3 is a schematic flow chart illustrating another road condition prediction method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a road condition prediction device according to an embodiment of the present application;
fig. 5 is a block diagram of an electronic device according to a road condition prediction method provided in an embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic flow chart of a road condition prediction method according to an embodiment of the present application. The embodiment can be applied to the situation of predicting the road condition of the road in the future time period. The road condition prediction method disclosed in this embodiment may be executed by an electronic device, and specifically may be executed by a road condition prediction apparatus, where the apparatus may be implemented by software and/or hardware and configured in the electronic device. Referring to fig. 1, the road condition prediction method provided in this embodiment includes:
and S110, determining historical road traffic information of the current time period in a historical period.
In this embodiment, a single time period may include at least two time segments, and a single time segment may include at least two road conditions. The time period may be divided by day, or may be divided by week or other manners, where, under the condition of dividing the time period by day, the current day is the current period, each day before the current day may be a history period, and under the condition of dividing the time period by week, the current week is the current period, and each week before the current week may be a history period. A single time segment may be divided into at least two road condition prediction times every fixed time period, for example, a 1-hour time segment may be divided into 12 road condition prediction times every 5 minutes.
The current time period refers to a time period to which the current time belongs, and the duration of the current time period may be a fixed value. Specifically, the time interval between the starting time and the current time of the current time period may be a fixed value, and the ending time of the current time period may be the current time. Taking the current time as 10 o ' clock and 15 min, dividing the time period by days, and the duration of the time period as 1 hour as an example, the current time period can be 9 o ' clock and 15 min to 10 o ' clock and 15 min.
The road traffic information in the historical road traffic information and the subsequent real-time road traffic information refers to road condition information of roads, and the road traffic information can include speeds and tracks of the roads to be predicted and adjacent roads. Specifically, the adjacent road is an upstream road of the road to be predicted or a downstream road of the road to be predicted, wherein the number of the upstream roads may be M, and the number of the downstream roads may be N, and M and N have different values. Illustratively, both M and N have a value of greater than or equal to 3. The historical road traffic information of the current time period in the historical period refers to the historical road traffic information of the road in the current time period of the historical period, for example, the historical road traffic information of the road in the time period from 9 o 'clock 15 to 10 o' clock 15 before today. In this embodiment, the road traffic information includes not only the traffic information of the road to be predicted, but also the traffic information of the adjacent roads. Therefore, the influence of the spatial topological relation on the road condition of the road to be predicted can be better described by utilizing the relation between the road to be predicted and the adjacent road.
Specifically, historical road traffic information of at least two road conditions in a historical period in the current time period is obtained. For example, the historical road traffic information of at least two road conditions from 9: 15 to 10: 15 in the historical period can be obtained.
And S120, determining the real-time road traffic information of the current time period in the current period.
In this embodiment, the real-time road traffic information of the current time period in the current cycle refers to the real-time road traffic information of the road in the current time period of the current cycle, for example, the real-time road traffic information of the road in the today's 9 o ' clock 15 to 10 o ' clock 15 time period.
S130, determining historical road traffic information of the time period to be predicted in the historical period; wherein the time period to be predicted is after the current time period.
In this embodiment, the time period to be predicted refers to a time period after the current time period, and the length of the time period to be predicted is the same as that of the current time period, and may be a fixed value. The starting time of the time period to be predicted may be the current time, and the time interval between the ending time of the time period to be predicted and the current time may be a fixed value. For example, the current time period is 9: 15 to 10: 15, and the time period to be predicted may be 10: 15 to 11: 15. The historical road traffic information of the time period to be predicted in the historical period refers to the historical road traffic information of the road in the time period to be predicted in the historical period.
S140, predicting real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period according to the historical road traffic information and the real-time road traffic information of the current time period and the historical road traffic information of the time period to be predicted.
In this embodiment, the time period to be predicted may include at least two road condition prediction times, that is, times to be predicted, and for example, one time to be predicted may be taken every 5 minutes. Still taking the time period to be predicted as 10: 15 to 11: 15 as an example, the time to be predicted may be 12 times, including 10: 20, 10: 25, … 11, 11: 10 and 11: 15.
In this embodiment, the current time period has both historical road traffic information in the historical period and real-time road traffic information in the current period, and the traffic relationship between the current period and the historical period can be determined by processing the historical road traffic information and the real-time road traffic information, for example, the traffic conditions between the current period and the historical period are similar or different. And determining real-time road condition information of the time to be predicted in the current period in the time period to be predicted according to the road condition relation between the current period and the historical road traffic information of the time period to be predicted in the historical period. The real-time road condition information of the time to be predicted is determined based on the road condition relation between the current period and the historical period, and the road condition conditions of the current period and the historical period are fully considered, so that the accuracy of the road condition of the time to be predicted in the current period in the time period to be predicted can be improved by combining the historical laws and the real-time characteristics of the road, and the road condition information of the road to be predicted is more precise due to the fact that different multiple times to be predicted in the time period to be predicted can be predicted.
According to the technical scheme of the embodiment of the application, historical road traffic information of the current time period in a historical period is determined; determining real-time road traffic information of the current time period in the current period; determining historical road traffic information of a time period to be predicted in the historical period; wherein the time period to be predicted is after the current time period; and predicting real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period according to the historical road traffic information and the real-time road traffic information of the current time period and the historical road traffic information of the time period to be predicted. By adopting the technical means, the accuracy of road condition prediction can be improved, and the requirements of users on road condition prediction are met.
Fig. 2a is a schematic flow chart of a road condition prediction method according to an embodiment of the present application. The present embodiment is an alternative proposed on the basis of the above-described embodiments. Referring to fig. 2a, the road condition prediction method provided in this embodiment includes:
and S210, determining historical road traffic information of the current time period in a historical period.
And S220, determining the real-time road traffic information of the current time period in the current period.
S230, determining historical road traffic information of the time period to be predicted in the historical period; wherein the time period to be predicted is after the current time period.
S240, determining the historical weight of the time period to be predicted according to the historical road traffic information of the current time period and the real-time road traffic information.
In this embodiment, the historical weight of the time period to be predicted is used to measure the similarity between the time period to be predicted and the road condition of the current time period. The higher the historical weight is, the higher the similarity degree between the road condition of the time period to be predicted and the road condition of the current time period is. Specifically, if the similarity between the historical road traffic information of the current time period and the real-time road traffic information is higher, the possibility that the similarity between the historical road traffic information of the time period to be predicted and the real-time road traffic information of the time period to be predicted is higher. Accordingly, the historical weight of the time period to be predicted is relatively high.
In this embodiment, the historical weight can be dynamically calculated to measure the similarity between the current time period and the time period to be predicted.
Optionally, the determining the historical weight of the time period to be predicted according to the historical road traffic information of the current time period and the real-time road traffic information includes:
determining a historical time sequence hidden feature vector of the current time period according to the historical road traffic information of the current time period;
determining a real-time sequence hidden feature vector of the current time period according to the real-time road traffic information of the current time period;
and taking the product of the historical time sequence hidden feature vector of the current time period and the real-time sequence hidden feature vector as the historical weight of the time period to be predicted.
In this embodiment, the historical time sequence hidden feature vector of the current time period is obtained by inputting the historical road traffic information of the current time period into an LSTM (Long-Short Term Memory). Wherein, the LSTM is a special RNN (Recurrent Neural Network) model, which is proposed to solve the problem of RNN model gradient diffusion; in the conventional RNN, a BPTT (Back Propagation Time, Time-based Back Propagation) is used in the training algorithm, and when the Time is longer, the residual exponent that needs to be returned decreases, which results in slow updating of the network weight and failure to exhibit the long-term memory effect of the RNN. The real-time sequence hidden feature vector of the current time period is obtained by inputting the real-time road traffic information of the current time period into the LSTM. The historical weight of the time period to be predicted can be obtained by multiplying the historical time sequence hidden feature vector of the current time period by the real-time sequence hidden feature vector.
The real-time traffic characteristics of the current time period are compared with the historical traffic characteristics to obtain a similarity, and the reference value of the historical characteristics can be introduced, so that the road condition prediction of the time period to be predicted is more accurate.
And S250, predicting real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period according to the real-time road traffic information of the current time period, the historical weight of the time period to be predicted and the historical road traffic information of the time period to be predicted.
In this embodiment, according to the historical weight of the time period to be predicted and the historical road traffic information of the time period to be predicted, whether the real-time traffic information of the time period to be predicted in the current cycle can be determined according to the historical road traffic information of the time period to be predicted can be determined. If the historical weight of the time period to be predicted is lower, the weight of the real-time road traffic information of the current time period is higher, namely the similarity between the time period to be predicted and the real-time road traffic information of the current time period is higher.
In particular, see FIG. 2b for a schematic model framework of an LSTM. The specific numerical value can be determined by the historical time sequence hidden feature vector and the real-time sequence hidden feature vector of the current time period and can be used as the historical weight of the time period to be predicted. And then performing dimensionality combination on the historical weight of the time period to be predicted and the historical road traffic information of the time period to be predicted through aggregation in the LSTM model, and outputting.
Optionally, the predicting, according to the real-time road traffic information of the current time period, the historical weight of the time period to be predicted, and the historical road traffic information of the time period to be predicted, real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current cycle includes:
determining historical correction characteristics of the time period to be predicted according to the historical weight of the time period to be predicted and the historical road traffic information of the time period to be predicted;
aggregating the real-time road traffic information of the current time period and the historical correction characteristics of the time period to be predicted to obtain aggregated road characteristics;
and performing semantic coding on the aggregated road characteristics to obtain real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period.
In this embodiment, the historical correction feature of the time period to be predicted is used as an index of the historical road traffic condition of the time period to be predicted, and the historical correction feature of the time period to be predicted can more accurately reflect the historical rule of the time period to be predicted. The aggregate road characteristic refers to a comprehensive characteristic combining the characteristic of the real-time road traffic information of the current time period and the historical correction characteristic of the time period to be predicted. And determining real-time road condition information of the time period to be predicted by using the aggregated road characteristics.
In the embodiment, the change of the traffic law can be well and dynamically depicted by using the real-time characteristic and the historical characteristic.
According to the technical scheme of the embodiment of the application, the historical weight of the time period to be predicted is determined according to the historical road traffic information and the real-time road traffic information of the current time period, and then the real-time road condition information of at least two moments to be predicted in the current period in the time period to be predicted is predicted according to the real-time road traffic information of the current time period, the historical weight of the time period to be predicted and the historical road traffic information of the time period to be predicted. By adopting the technical means, the real-time characteristics and the historical rules can be used at the same time, the change of the traffic rules can be well dynamically depicted, the road condition change caused by the real-time traffic environment change can be reflected in time, and the periodic rules of the historical road conditions can also be reflected.
Fig. 3 is a schematic flow chart of a road condition prediction method according to an embodiment of the present application. The present embodiment is an alternative proposed on the basis of the above-described embodiments. Referring to fig. 3, the road condition prediction method provided in this embodiment includes:
and S310, determining historical road traffic information of the current time period in a historical period.
And S320, determining the real-time road traffic information of the current time period in the current period.
S330, determining historical road traffic information of the time period to be predicted in the historical period; wherein the time period to be predicted is after the current time period.
And S340, determining static attribute information of the road to be predicted.
In this embodiment, the static attribute information of the road to be predicted includes a road grade and/or a lane number of the road to be predicted. The road grade is that the road to be predicted is segmented, and each segment is different grade. Illustratively, the road grade may be grade 1, grade 2 up to grade N. After the static attribute information of the road to be predicted is taken as the reference value, the static attribute of the road to be predicted can be synthesized, so that the analyzed data are more comprehensive, and the accuracy of road condition prediction is further improved.
And S350, predicting real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period according to the historical road traffic information and the real-time road traffic information of the current time period, the historical road traffic information of the time period to be predicted and the static attribute information of the road to be predicted.
In the embodiment, the static attribute information of the road to be predicted is introduced, so that the influence of the spatial topological relation on the road condition of the road to be predicted can be better described.
Optionally, the predicting, according to the historical road traffic information and the real-time road traffic information of the current time period and the historical road traffic information of the time period to be predicted, real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current cycle includes:
predicting the traffic flow of the road to be predicted at least two moments to be predicted in the time period to be predicted according to the navigation request data;
and predicting the real-time road condition information of the at least two moments to be predicted in the current period in the time period to be predicted according to the historical road traffic information and the real-time road traffic information of the current time period, the historical road traffic information of the time period to be predicted and the traffic flow of the road to be predicted at the at least two moments to be predicted in the time period to be predicted.
In this embodiment, the navigation request data refers to a navigation request message sent by a navigation search engine. The traffic flow of the road to be predicted at least two moments to be predicted in the time period to be predicted can be obtained. The data of the traffic flow is accurate, but relatively sparse. In this embodiment, the accuracy of road condition prediction can be further improved by introducing data information of traffic flow.
Optionally, the predicting, according to the historical road traffic information and the real-time road traffic information of the current time period and the historical road traffic information of the time period to be predicted, real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current cycle includes:
predicting the vehicle density of the road to be predicted at least two moments to be predicted in the time period to be predicted according to the positioning data of the user equipment;
and predicting the real-time road condition information of the at least two moments to be predicted in the current period in the time period to be predicted according to the historical road traffic information and the real-time road traffic information of the current time period, the historical road traffic information of the time period to be predicted and the vehicle density of the roads to be predicted at the at least two moments to be predicted in the time period to be predicted.
In this embodiment, the user equipment may be a smart phone, a tablet, or a notebook computer, etc. that the user carries with him. After the user equipment is connected with the network, the vehicle is positioned according to the built-in positioning of the user equipment so as to obtain the vehicle density of at least two roads to be predicted in the time period to be predicted. In this embodiment, by introducing the data information of the vehicle density, the accuracy of road condition prediction can be further improved by analyzing the vehicle condition of the road to be predicted.
Fig. 4 is a schematic structural diagram of a traffic prediction apparatus according to an embodiment of the present disclosure, which may be configured in an electronic device. Referring to fig. 4, the traffic prediction apparatus 400 according to the embodiment of the present disclosure may include:
a historical road traffic information determination module 410 for determining the historical road traffic information of the current time period in the historical period;
a real-time road traffic information determining module 420 for determining real-time road traffic information of the current time period in the current cycle;
the historical road traffic information determining module 430 of the time period to be predicted is used for determining the historical road traffic information of the time period to be predicted in the historical period; wherein the time period to be predicted is after the current time period;
and the real-time traffic information prediction module 440 is configured to predict real-time traffic information of at least two moments to be predicted in the time period to be predicted in the current cycle according to the historical road traffic information of the current time period, the real-time road traffic information, and the historical road traffic information of the time period to be predicted.
Optionally, the real-time traffic information prediction module 440 includes:
the historical weight determining submodule is used for determining the historical weight of the time period to be predicted according to the historical road traffic information of the current time period and the real-time road traffic information;
and the real-time road condition information prediction sub-module is used for predicting the real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period according to the real-time road traffic information of the current time period, the historical weight of the time period to be predicted and the historical road traffic information of the time period to be predicted.
Optionally, the historical weight determining submodule is configured to determine a historical time sequence hidden feature vector of the current time period according to the historical road traffic information of the current time period;
determining a real-time sequence hidden feature vector of the current time period according to the real-time road traffic information of the current time period;
and taking the product of the historical time sequence hidden feature vector of the current time period and the real-time sequence hidden feature vector as the historical weight of the time period to be predicted.
Optionally, the real-time traffic information prediction sub-module is configured to determine a historical correction feature of the time period to be predicted according to the historical weight of the time period to be predicted and the historical road traffic information of the time period to be predicted;
aggregating the real-time road traffic information of the current time period and the historical correction characteristics of the time period to be predicted to obtain aggregated road characteristics;
and performing semantic coding on the aggregated road characteristics to obtain real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period.
Optionally, the historical road traffic information includes historical speeds and historical tracks of the road to be predicted and the adjacent road; the real-time road traffic information comprises real-time speed and real-time track of the road to be predicted and the adjacent road.
Optionally, the real-time traffic information prediction module 440 is configured to determine static attribute information of a road to be predicted;
and predicting real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period according to the historical road traffic information and the real-time road traffic information of the current time period, the historical road traffic information of the time period to be predicted and the static attribute information of the road to be predicted.
Optionally, the static attribute information of the road to be predicted includes a road grade and/or a number of lanes of the road to be predicted.
Optionally, the real-time traffic information prediction module 440 is configured to predict traffic flows of the road to be predicted at least two moments to be predicted in the time period to be predicted according to the navigation request data;
and predicting the real-time road condition information of the at least two moments to be predicted in the current period in the time period to be predicted according to the historical road traffic information and the real-time road traffic information of the current time period, the historical road traffic information of the time period to be predicted and the traffic flow of the road to be predicted at the at least two moments to be predicted in the time period to be predicted.
Optionally, the real-time traffic information predicting module 440 is configured to predict, according to the positioning data of the user equipment, vehicle densities of roads to be predicted at least two moments to be predicted in the time period to be predicted;
and predicting the real-time road condition information of the at least two moments to be predicted in the current period in the time period to be predicted according to the historical road traffic information and the real-time road traffic information of the current time period, the historical road traffic information of the time period to be predicted and the vehicle density of the roads to be predicted at the at least two moments to be predicted in the time period to be predicted.
According to the technical scheme of the embodiment of the application, historical road traffic information of the current time period in a historical period is determined; determining real-time road traffic information of the current time period in the current period; determining historical road traffic information of a time period to be predicted in the historical period; wherein the time period to be predicted is after the current time period; and predicting real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period according to the historical road traffic information and the real-time road traffic information of the current time period and the historical road traffic information of the time period to be predicted. By adopting the technical means, the accuracy of road condition prediction can be improved, and the requirements of users on road condition prediction are met.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
Fig. 5 is a block diagram of an electronic device according to the road condition prediction method of the embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 5, the electronic apparatus includes: one or more processors 501, memory 502, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 5, one processor 501 is taken as an example.
The memory 502, which is a non-transitory computer-readable storage medium, may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the traffic condition prediction method in the embodiment of the present application (for example, the historical road traffic information determination module 410 for the current time period, the real-time road traffic information determination module 420 for the current time period, the historical road traffic information determination module 430 for the time period to be predicted, and the real-time traffic condition information prediction module 440 shown in fig. 4). The processor 501 executes the non-transitory software programs, instructions and modules stored in the memory 502 to execute various functional applications of the server and the road condition prediction, so as to implement the road condition prediction method in the above method embodiments.
The memory 502 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the electronic device displayed by the point of interest, and the like. Further, the memory 502 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 502 may optionally include memory located remotely from processor 501, which may be connected to point of interest display electronics over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the road condition prediction method may further include: an input device 503 and an output device 504. The processor 501, the memory 502, the input device 503 and the output device 504 may be connected by a bus or other means, and fig. 5 illustrates the connection by a bus as an example.
The input device 503 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the electronic equipment for which the point of interest is displayed, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointer, one or more mouse buttons, a track ball, a joystick, or other input device. The output devices 504 may include a display device, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
According to the technical scheme of the embodiment of the application, historical road traffic information of the current time period in a historical period is determined; determining real-time road traffic information of the current time period in the current period; determining historical road traffic information of a time period to be predicted in the historical period; wherein the time period to be predicted is after the current time period; and predicting real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period according to the historical road traffic information and the real-time road traffic information of the current time period and the historical road traffic information of the time period to be predicted. By adopting the technical means, the accuracy of road condition prediction can be improved, and the requirements of users on road condition prediction are met.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (20)
1. A road condition prediction method comprises the following steps:
determining historical road traffic information of the current time period in a historical period;
determining real-time road traffic information of the current time period in the current period;
determining historical road traffic information of a time period to be predicted in the historical period; wherein the time period to be predicted is after the current time period;
and predicting real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period according to the historical road traffic information and the real-time road traffic information of the current time period and the historical road traffic information of the time period to be predicted.
2. The method of claim 1, wherein the predicting the real-time traffic information of the current period of time at least two moments to be predicted in the current period of time according to the historical road traffic information and the real-time road traffic information of the current period of time and the historical road traffic information of the period of time to be predicted comprises:
determining the historical weight of the time period to be predicted according to the historical road traffic information of the current time period and the real-time road traffic information;
and predicting real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period according to the real-time road traffic information of the current time period, the historical weight of the time period to be predicted and the historical road traffic information of the time period to be predicted.
3. The method of claim 2, wherein the determining the historical weight of the time period to be predicted according to the historical road traffic information of the current time period and the real-time road traffic information comprises:
determining a historical time sequence hidden feature vector of the current time period according to the historical road traffic information of the current time period;
determining a real-time sequence hidden feature vector of the current time period according to the real-time road traffic information of the current time period;
and taking the product of the historical time sequence hidden feature vector of the current time period and the real-time sequence hidden feature vector as the historical weight of the time period to be predicted.
4. The method according to claim 2, wherein the predicting real-time traffic information of at least two to-be-predicted moments in the to-be-predicted time period in the current cycle according to the real-time traffic information of the current time period, the historical weight of the to-be-predicted time period and the historical traffic information of the to-be-predicted time period comprises:
determining historical correction characteristics of the time period to be predicted according to the historical weight of the time period to be predicted and the historical road traffic information of the time period to be predicted;
aggregating the real-time road traffic information of the current time period and the historical correction characteristics of the time period to be predicted to obtain aggregated road characteristics;
and performing semantic coding on the aggregated road characteristics to obtain real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period.
5. The method according to any one of claims 1-4, wherein the historical road traffic information comprises historical speeds and historical trajectories of the road to be predicted and the adjacent road; the real-time road traffic information comprises real-time speed and real-time track of the road to be predicted and the adjacent road.
6. The method according to any one of claims 1 to 4, wherein the predicting real-time road condition information in the current cycle of at least two to-be-predicted moments in the to-be-predicted time period according to the historical road traffic information and the real-time road traffic information of the current time period and the historical road traffic information of the to-be-predicted time period comprises:
determining static attribute information of a road to be predicted;
and predicting real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period according to the historical road traffic information and the real-time road traffic information of the current time period, the historical road traffic information of the time period to be predicted and the static attribute information of the road to be predicted.
7. The method according to claim 6, wherein the static attribute information of the road to be predicted comprises a road grade and/or a number of lanes of the road to be predicted.
8. The method according to any one of claims 1 to 4, wherein the predicting real-time road condition information in the current cycle of at least two to-be-predicted moments in the to-be-predicted time period according to the historical road traffic information and the real-time road traffic information of the current time period and the historical road traffic information of the to-be-predicted time period comprises:
predicting the traffic flow of the road to be predicted at least two moments to be predicted in the time period to be predicted according to the navigation request data;
and predicting the real-time road condition information of the at least two moments to be predicted in the current period in the time period to be predicted according to the historical road traffic information and the real-time road traffic information of the current time period, the historical road traffic information of the time period to be predicted and the traffic flow of the road to be predicted at the at least two moments to be predicted in the time period to be predicted.
9. The method according to any one of claims 1 to 4, wherein the predicting real-time road condition information in the current cycle of at least two to-be-predicted moments in the to-be-predicted time period according to the historical road traffic information and the real-time road traffic information of the current time period and the historical road traffic information of the to-be-predicted time period comprises:
predicting the vehicle density of the road to be predicted at least two moments to be predicted in the time period to be predicted according to the positioning data of the user equipment;
and predicting the real-time road condition information of the at least two moments to be predicted in the current period in the time period to be predicted according to the historical road traffic information and the real-time road traffic information of the current time period, the historical road traffic information of the time period to be predicted and the vehicle density of the roads to be predicted at the at least two moments to be predicted in the time period to be predicted.
10. A road condition prediction device includes:
the historical road traffic information determining module of the current time period is used for determining the historical road traffic information of the current time period in a historical period;
the real-time road traffic information determining module of the current time period is used for determining the real-time road traffic information of the current time period in the current period;
the historical road traffic information determining module of the time period to be predicted is used for determining the historical road traffic information of the time period to be predicted in the historical period; wherein the time period to be predicted is after the current time period;
and the real-time road condition information prediction module is used for predicting the real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period according to the historical road traffic information of the current time period, the real-time road traffic information and the historical road traffic information of the time period to be predicted.
11. The apparatus of claim 10, wherein the real-time traffic information prediction module comprises:
the historical weight determining submodule is used for determining the historical weight of the time period to be predicted according to the historical road traffic information of the current time period and the real-time road traffic information;
and the real-time road condition information prediction sub-module is used for predicting the real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period according to the real-time road traffic information of the current time period, the historical weight of the time period to be predicted and the historical road traffic information of the time period to be predicted.
12. The device according to claim 11, wherein the historical weight determination sub-module is configured to determine a historical timing hidden feature vector of the current time period according to historical road traffic information of the current time period;
determining a real-time sequence hidden feature vector of the current time period according to the real-time road traffic information of the current time period;
and taking the product of the historical time sequence hidden feature vector of the current time period and the real-time sequence hidden feature vector as the historical weight of the time period to be predicted.
13. The device according to claim 11, wherein the real-time traffic information prediction sub-module is configured to determine a historical correction feature of the time period to be predicted according to the historical weight of the time period to be predicted and the historical road traffic information of the time period to be predicted;
aggregating the real-time road traffic information of the current time period and the historical correction characteristics of the time period to be predicted to obtain aggregated road characteristics;
and performing semantic coding on the aggregated road characteristics to obtain real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period.
14. The apparatus according to any one of claims 10-13, wherein the historical road traffic information includes historical speeds and historical trajectories of the road to be predicted and the adjacent road; the real-time road traffic information comprises real-time speed and real-time track of the road to be predicted and the adjacent road.
15. The device according to any one of claims 10 to 13, wherein the real-time traffic information prediction module is configured to determine static attribute information of a road to be predicted;
and predicting real-time road condition information of at least two moments to be predicted in the time period to be predicted in the current period according to the historical road traffic information and the real-time road traffic information of the current time period, the historical road traffic information of the time period to be predicted and the static attribute information of the road to be predicted.
16. The apparatus of claim 15, wherein the static attribute information of the road to be predicted comprises a road grade and/or a number of lanes of the road to be predicted.
17. The device according to any one of claims 10 to 13, wherein the real-time traffic information prediction module is configured to predict traffic flow of the road to be predicted at least two moments to be predicted in the time period to be predicted according to the navigation request data;
and predicting the real-time road condition information of the at least two moments to be predicted in the current period in the time period to be predicted according to the historical road traffic information and the real-time road traffic information of the current time period, the historical road traffic information of the time period to be predicted and the traffic flow of the road to be predicted at the at least two moments to be predicted in the time period to be predicted.
18. The device according to any one of claims 10 to 13, wherein the real-time traffic information prediction module is configured to predict vehicle densities of roads to be predicted at least two times to be predicted in the time period to be predicted according to positioning data of user equipment;
and predicting the real-time road condition information of the at least two moments to be predicted in the current period in the time period to be predicted according to the historical road traffic information and the real-time road traffic information of the current time period, the historical road traffic information of the time period to be predicted and the vehicle density of the roads to be predicted at the at least two moments to be predicted in the time period to be predicted.
19. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.
20. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-9.
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