CN117612386A - Highway traffic flow prediction method, device, computer equipment and storage medium - Google Patents
Highway traffic flow prediction method, device, computer equipment and storage medium Download PDFInfo
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Abstract
The disclosure relates to the technical field of computers, and provides a method, a device, computer equipment and a storage medium for predicting traffic flow of an expressway. The highway traffic flow prediction method comprises the following steps: collecting traffic data and weather data of a corresponding driving interval by using data collecting equipment on a highway; the expressway is divided into a plurality of driving intervals, and each driving interval is provided with a data processing device and at least one data acquisition device; carrying out data fusion on traffic data and weather data of each driving interval; and inputting the data fusion result into a traffic flow prediction model, and outputting a traffic flow prediction result of each driving interval. The invention can improve the accuracy of traffic flow prediction, realize the real-time performance of traffic flow prediction and improve traffic management and travel efficiency.
Description
Technical Field
The disclosure relates to the field of computer technology, and in particular, to a method, a device, computer equipment and a storage medium for predicting traffic flow of an expressway.
Background
The traffic flow prediction is based on traffic information of past time periods to predict traffic conditions of a certain time period in the future, plays an important role in the intelligent expressway field, and the accurate prediction result can help expressway operation management departments to better arrange road resources and provide more accurate road information for travelers, so that the traffic flow prediction method is an effective means for improving traffic safety and efficiency.
Along with the intelligent development of highways, traffic flow prediction technology based on cloud computing and big data is widely applied in the traffic field. Traffic flow prediction technology based on cloud computing and big data has great limitation, and mainly shows the following aspects:
1) Delay and response time: in the prior art, data is transmitted from road side acquisition equipment to a cloud for processing and analysis, and the data can be summarized to a large data platform for analysis and prediction after being processed by a special data acquisition system, and the transmission and processing delay can lead to longer response time and is not suitable for quick response and decision-making of real-time traffic.
2) The prediction accuracy is not enough: the prior art cannot timely fuse various original information, only pays attention to traffic flow data, lacks sufficient fusion of other data sources (such as weather, road conditions and the like), influences the accuracy of traffic flow analysis and prediction, and cannot realize real-time and accurate traffic flow prediction.
3) Network bandwidth requirements are high: in the prior art, all data generated by the road side are required to be uploaded to a center for processing, so that huge pressure is inevitably brought to network bandwidth, and particularly in a large-scale traffic system, the data volume required to be transmitted and processed can be very large, and high requirements are placed on the use of network resources and the cost.
4) Network disconnection and reliability: the prior art relies on stable network connections, but in certain traffic environments the network connection may be unstable or risk disconnection, leading to failure of data transmission or an affected prediction result.
Therefore, there is a need for a method for predicting traffic flow on a highway, so as to solve the above-mentioned shortcomings in the prior art and achieve the purpose of improving the accuracy, real-time performance, safety and reliability of traffic flow prediction on a highway.
Disclosure of Invention
In view of the above, the embodiments of the present disclosure provide a method, an apparatus, a computer device, and a storage medium for predicting traffic flow on an expressway, so as to solve the problems of delayed response, low prediction accuracy, high network requirements, and poor reliability of the existing traffic flow prediction technology.
In a first aspect of embodiments of the present disclosure, there is provided a method for predicting traffic flow on an expressway, the method being applied to a data processing apparatus, the method including: collecting traffic data and weather data of a corresponding driving interval by using data collecting equipment on a highway; the expressway is divided into a plurality of driving intervals, and each driving interval is provided with a data processing device and at least one data acquisition device; carrying out data fusion on traffic data and weather data of each driving interval; and inputting the data fusion result into a traffic flow prediction model, and outputting a traffic flow prediction result of each driving interval.
A second aspect of the embodiments of the present disclosure provides an expressway traffic control apparatus for implementing the expressway traffic flow prediction method of the first aspect, where the expressway traffic control apparatus includes a plurality of data acquisition units, a plurality of data processing units, and a control unit: the data acquisition unit is arranged in a corresponding driving interval of the expressway, and is used for acquiring traffic data and environment data in the corresponding driving interval and sending the traffic data and the environment data to the control unit; the expressway is divided into a plurality of running intervals by intervals; each data processing unit is arranged in a corresponding driving interval of the expressway, and is used for receiving the traffic data and the environment data sent by at least one data acquisition unit in the corresponding driving interval, carrying out data processing on the traffic data and the environment data, generating a traffic flow prediction result and sending the traffic flow prediction result to the control unit; the control unit is used for receiving traffic flow prediction results sent by the data processing unit corresponding to each driving interval and generating traffic control strategies based on the traffic flow prediction results.
In a third aspect of the embodiments of the present disclosure, there is provided an expressway traffic flow prediction apparatus, including: the data acquisition module is used for acquiring traffic data and weather data of a corresponding driving interval by using data acquisition equipment on the expressway; the expressway is divided into a plurality of driving intervals, and each driving interval is provided with a data processing device and at least one data acquisition device; the data fusion module is used for carrying out data fusion on traffic data and weather data of each driving interval; and the traffic flow prediction result output module is used for inputting the data fusion result into the traffic flow prediction model and outputting the traffic flow prediction result of each driving interval.
In a fourth aspect of the disclosed embodiments, there is provided an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the above method when the computer program is executed.
In a fifth aspect of the disclosed embodiments, a computer readable storage medium is provided, which stores a computer program which, when executed by a processor, implements the steps of the above method.
Compared with the prior art, the embodiment of the disclosure has the beneficial effects that:
1) Reducing delay and response time: according to the invention, calculation and analysis can be moved to a place closer to a data source, and delay of data transmission and processing can be reduced based on the calculation capability and the prediction model deployed at the edge of the road side, so that traffic flow is predicted and analyzed in real time, and faster response and decision are realized.
2) The prediction accuracy is improved: according to the invention, the optimal expressway space segments can be divided according to specific scene characteristics, road side information such as traffic running states, meteorological environment states, road structure states, vehicle microscopic states and the like is collected for different road segments, and more accurate prediction results are obtained by fusing and analyzing original data of different sources.
3) Bandwidth requirements are reduced: according to the method, the collected information is calculated and processed at the edge, and the key information and the prediction result are transmitted to the cloud end, so that the transmitted data volume can be greatly reduced, dependence on network bandwidth is reduced, and bandwidth requirements are reduced.
4) Enhancing reliability and disconnect tolerance: the invention supports real-time closed-loop control of the edge, and even if the network is disconnected or unstable, the prediction and decision can be continued through the stored data and the model, so that the reliability of the prediction is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are required for the embodiments or the description of the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings may be obtained according to these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a flow diagram of a highway traffic flow prediction method according to the present disclosure;
FIG. 2 is a flow diagram of one particular embodiment of a highway traffic flow prediction method according to the present disclosure;
FIG. 3 is a flow diagram of one particular embodiment of a highway traffic flow prediction method according to the present disclosure;
FIG. 4 is a flow diagram of one particular embodiment of a highway traffic flow prediction method according to the present disclosure;
FIG. 5 is a schematic structural view of a highway traffic control device according to the present disclosure;
FIG. 6 is a schematic structural diagram of an edge cloud computing platform according to the present disclosure;
FIG. 7 is a schematic structural view of a highway traffic flow prediction apparatus according to the present disclosure;
fig. 8 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings. Embodiments of the present disclosure and features of embodiments may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
In this embodiment, a method for predicting traffic flow of an expressway is provided, which may be used in a data processing device, and fig. 1 is a flowchart of a method for predicting traffic flow of an expressway according to an embodiment of the present invention, as shown in fig. 1, where the flowchart includes the following steps:
step S101, collecting traffic data and weather data of a corresponding driving interval by using data collecting equipment on the expressway.
Specifically, the expressway is divided into a plurality of driving intervals in advance, wherein each driving interval is provided with a data processing device and at least one data acquisition device; the data acquisition equipment is equipment which is deployed along the expressway and used for carrying out data acquisition, comprises a vehicle detector, a camera, a radar, a weather detector and the like, and also comprises a lane control lamp, a variable information board and the like, and an OBU, an auxiliary driving terminal, an automatic driving terminal, a navigation terminal and other vehicle-mounted terminals, and is used for acquiring traffic data and weather data in a corresponding driving interval in real time. The data processing equipment is used for receiving traffic data and weather data which are acquired by each data acquisition equipment in real time in a corresponding driving interval, wherein the traffic data comprise the driving speed of the vehicle, the lane occupation condition and the number of the vehicles, and the weather data comprise temperature, rainfall, wind speed, visibility and the like.
In some preferred embodiments, after traffic data and weather data corresponding to a driving interval are collected, the data thereof needs to be preprocessed to remove invalid data therein, so as to ensure the accuracy of traffic flow prediction.
Step S102, data fusion is carried out on traffic data and weather data of each driving interval.
Specifically, the time characteristics of traffic flow data can be extracted in a periodic, trending and seasonal mode, and the space characteristics can be extracted through the division of the driving interval of the expressway; and considering the influence of weather changes on traffic flow, taking weather data as external characteristics, and fusing the traffic data based on different characteristics.
More specifically, in the process of extracting temporal features of traffic flow data by means of periodicity, trending, seasonal, and the like, a temporal sequence analysis method such as a moving average method, an exponential smoothing method, and the like is used to perform periodicity and trending analysis on the traffic data, thereby extracting temporal features; seasonal features in the traffic flow data are identified using a seasonal decomposition method, such as the seasonal decomposition method or the X-12-ARIMA method.
More specifically, in extracting the spatial feature by the driving section division of the expressway, the road section may be divided into different spatial regions, such as a city region, a suburban region, a mountain region, and the like, according to the specific situation of the expressway, and the spatial feature may be extracted. Or a Geographic Information System (GIS) technology can be used for carrying out spatial analysis on the road segments by combining traffic flow and road network data so as to extract spatial characteristics.
More specifically, in the process of fusing traffic data based on different characteristics with weather data as external characteristics in consideration of the influence of weather changes on traffic flow:
firstly, considering the influence of weather changes on traffic flow, a statistical analysis method such as correlation analysis or regression analysis can be used to explore the relationship between the weather changes and traffic flow and extract weather features;
secondly, associating the extracted weather features with traffic flow data to construct an external feature data set; the external characteristic data set is fused with the original traffic data, and the comprehensive data set containing time, space and weather characteristics can be obtained by adopting methods of data merging, connection or splicing;
finally, performing feature engineering and model training on the comprehensive data set by using a machine learning or deep learning algorithm to mine rules and modes in the data; and combining different characteristics to fuse the data, and adopting a weighted average method or a characteristic combination method to improve the prediction and analysis capacity of the model.
Step S103, inputting the data fusion result into a traffic flow prediction model, and outputting a traffic flow prediction result of each driving interval.
Specifically, the data fusion result can be understood as a comprehensive data set including time features, space features and weather features, and the comprehensive data set is input into a trained traffic flow prediction model to output a traffic flow prediction result corresponding to a driving interval. The traffic flow prediction result may include a traffic state prediction result and a traffic flow prediction result; the traffic state prediction result is used for representing the real-time traffic state in the corresponding driving interval; the real-time traffic state comprises road congestion conditions and is used for providing functions of real-time traffic condition monitoring, congestion early warning, road condition assessment and the like, so that the purpose of helping traffic management departments and drivers to make reasonable traffic decisions is achieved. The traffic flow prediction result is used for representing the traffic flow of the corresponding driving interval in a preset time period in the future so as to help traffic planning, traffic management, route selection and other decisions.
In some preferred embodiments, as shown in fig. 2, the highway traffic flow prediction method further comprises the steps of:
and step S104, judging whether the traffic flow prediction result meets a preset precision threshold.
Specifically, the above-mentioned preset accuracy threshold may be set according to actual situations, and is not specifically limited herein. Judging whether the traffic flow prediction result meets a preset precision threshold or not, wherein the purpose is to ensure the prediction accuracy of a traffic flow prediction model, and if the prediction accuracy is low, model training and model optimization are needed to be carried out again.
Step S105, if the traffic flow prediction result meets a preset precision threshold, the traffic flow prediction result is sent to a highway cloud control platform; the expressway cloud control platform is used for receiving the traffic flow prediction result and determining a traffic control strategy of a corresponding driving interval according to the traffic flow prediction result.
Specifically, when the traffic flow prediction result meets the preset precision threshold, the prediction accuracy of the traffic flow prediction model is high, the user requirements can be met, and the traffic flow prediction result can be sent to the cloud control platform. The cloud control platform is used for taking the traffic flow prediction result of each driving interval as the basis for executing the overall traffic decision, so as to obtain the global optimal traffic strategy.
And S106, if the traffic flow prediction result does not meet a preset precision threshold, performing precision optimization on the traffic flow prediction model until the traffic flow prediction result output by the optimized traffic flow prediction model meets the preset precision threshold.
Specifically, as shown in fig. 3: when the prediction result meets the precision requirement, the prediction result can be directly output to the cloud control platform; when the prediction result does not meet the precision requirement, model training and model optimization are needed to be carried out on the traffic flow prediction model again, the accuracy and reliability of prediction are further improved, the prediction model and parameters are updated, and a new round of prediction and optimization are carried out until an optimal flow prediction model is generated; when the model training reaches the optimal, the trained model can be utilized to predict and analyze the traffic flow in real time. And predicting the traffic flow condition of the specific road section in a future time period according to the current traffic flow data and weather data, and providing an accurate traffic flow prediction result.
In some preferred embodiments, as shown in fig. 4, determining a traffic control policy for a corresponding driving interval according to the traffic flow prediction result includes:
step S401, determining a traffic flow control strategy of a corresponding driving interval based on the traffic state prediction result and the traffic flow prediction result; the traffic flow control strategy comprises a signal lamp control strategy, a lane management strategy and a dynamic traffic restriction strategy.
Specifically, by monitoring the real-time traffic condition, congestion pre-warning and road condition of the corresponding driving interval, the traffic flow of a local road section of a certain period of time in the future is predicted according to the real-time traffic condition, so that a signal lamp control strategy, a lane management strategy and a dynamic traffic limiting strategy are adopted adaptively, for example, the green light time is prolonged when the traffic is crowded, and the problem of relieving the traffic jam is solved; or the traffic lane is crowded on one side and the traffic lane is few, the occupied road running instruction can be issued temporarily, so that the vehicles can run on the road with few vehicles, and the problem of traffic jam is solved. Thereby achieving the purpose of helping traffic planning, traffic management and route selection.
Step S402, determining a driving path guiding and controlling strategy of a corresponding driving interval based on the traffic flow prediction result; the driving path guiding and controlling strategy comprises a driving path reminding strategy and a dynamic path optimizing strategy.
Specifically, by monitoring the abnormal events of each lane on the expressway and the traffic flow of each lane, route optimization information is reasonably sent to a driver end, for example, when the A road section is crowded, prompt information is sent to the driver end for prompting the A road section crowding; and re-planning the driving path to ensure that the vehicle is on the optimal driving path in real time.
In some preferred embodiments, the method further comprises: initializing network weights of the traffic flow prediction model, and determining input dimensions, output dimensions, hidden layer numbers, convolution kernel dimensions, step sizes, training batches and residual error unit numbers; training the traffic flow prediction model by using a training set; inputting the data fusion result into a trained traffic flow prediction model, and outputting a predicted value; according to the training batch, training a traffic flow prediction model by using a back propagation algorithm, and updating the weight value of the traffic flow prediction model; and after training, testing the prediction precision of the traffic flow prediction model by using a test set.
In particular, the above steps may be understood as specific embodiments of model training or model re-optimization due to prediction accuracy failure. The training set and the testing set are comprehensive data sets containing time, space and weather characteristics in a historical time period, and the proportion of the comprehensive data sets is 80% of the training set and 20% of the testing set. The traffic flow prediction model may be a convolutional neural network, and the specific network structure is not particularly limited herein.
In some preferred embodiments, the traffic data includes traffic running state data, road structure state data, vehicle microscopic data; the weather data includes weather environmental state data; the data fusion of the traffic data and the weather data of each driving interval comprises the following steps: and carrying out data fusion on the traffic running state data, the road structure state data, the vehicle microscopic data and the meteorological environment state data of each running interval.
The invention has the following technical effects:
1. the control model of the edge cloud computing platform is a technical key for constructing the edge intelligence, the model can fuse traffic data of different sources in real time, supports local real-time decision and control, and optimizes a flow prediction model and an algorithm through cloud-edge cooperation and cloud feedback, and the feedback loop can continuously improve the performance and accuracy of the edge intelligence, so that the effect of traffic flow prediction is improved.
2. According to the invention, a plurality of data sources, such as expressway monitoring equipment, weather monitoring equipment, vehicle-mounted terminals and the like, are utilized to acquire traffic flow data and weather data, and the multi-source data are fused from a characteristic layer, so that a more comprehensive and more accurate traffic flow prediction result is obtained.
3. Aiming at the characteristics of the expressway, the invention introduces a method of space segmentation and scene perception. By dividing the expressway into different space sections and combining the space-time distribution characteristics of traffic flow, the traffic flow change rule and congestion condition can be captured better, and the prediction accuracy is improved.
4. The invention predicts and analyzes the traffic flow in real time on the edge node based on the edge intelligence, and can utilize the dynamic real-time traffic flow data to predict the future traffic flow in combination with the weather data, thereby providing the traffic manager and the driver with the timely traffic state and congestion early warning information.
There is also provided in this embodiment a highway traffic control apparatus including: a plurality of data acquisition units, a plurality of data processing units, and a control unit:
the data acquisition unit is arranged in a corresponding driving interval of the expressway, and is used for acquiring traffic data and environment data in the corresponding driving interval and sending the traffic data and the environment data to the control unit; the expressway is divided into a plurality of running intervals by intervals;
Each data processing unit is arranged in a corresponding driving interval of the expressway, and is used for receiving the traffic data and the environment data sent by at least one data acquisition unit in the corresponding driving interval, carrying out data processing on the traffic data and the environment data, generating a traffic flow prediction result and sending the traffic flow prediction result to the control unit;
the control unit is used for receiving traffic flow prediction results sent by the data processing unit corresponding to each driving interval and generating traffic control strategies based on the traffic flow prediction results.
Specifically, the data acquisition unit (for example, front end equipment in fig. 5) may be a vehicle detector, a camera, a weather detector, a lane control lamp, a variable information board, and the like, and an on-board terminal such as an OBU, an assisted driving terminal, an automatic driving terminal, a navigation terminal, and the like; the data processing units (for example, the intelligent edge all-in-one machine in fig. 5 is a device which is deployed in the range of a highway along a line domain and realizes a road side edge computing function, a plurality of intelligent edge all-in-one machines deployed at different positions form an edge cloud computing platform through an optical fiber private network and realize edge intelligence), and each data processing unit comprises a road side edge server, and a front-end equipment interface and a user interface which are connected with the road side edge server; the control unit (for example, the highway cloud control platform in fig. 5) is deployed in a highway monitoring center, adopts a 'Yun Bianduan' collaborative architecture, performs hierarchical processing and decision analysis on mass information uploaded by roadside edge computing equipment and other roadside equipment by using key technologies such as artificial intelligence, big data and cloud computing, and has a basic support platform for accurately controlling and efficiently operating and managing the highway.
More specifically, as can be seen from fig. 5: the edge cloud computing platform can be connected into front-end equipment of different types and quantity downwards through rich front-end equipment interfaces and user interfaces, performs data acquisition, processing and control on the connected front-end equipment, reduces the loss of network transmission on calculation power instantaneity and bandwidth requirements through autonomous decision control on the edge side, and accordingly achieves the functions of accurately sensing traffic states, accurately predicting and controlling traffic flows and the like in a local area. The edge cloud computing platform is upwards connected with the highway cloud control platform in a butt joint mode, processed data such as flow prediction results, event analysis results and the like are converged to the cloud control platform to carry out overall analysis, and global traffic decision is completed through cloud edge cooperation.
More specifically, the control model of the edge cloud computing platform is shown in fig. 6. Based on the model, the edge safety control service and cloud edge cooperative control can be implemented. The edge cloud computing platform starts a road segment segmentation algorithm by applying scene analysis, and divides the optimal expressway space segmentation according to specific scene characteristics. On the basis, traffic data such as traffic running states, meteorological environment states, road structure states and vehicle microscopic states are collected for local road sections, and sensing fusion analysis is carried out by combining feedback information of a central cloud control platform, wherein the analysis modules comprise traffic state analysis, traffic flow prediction and the like, and analysis results are output to the central cloud control platform; meanwhile, according to the result of the perception fusion analysis, a local road section strategy set such as local optimal traffic flow control and driving path guiding can be generated locally and provided for a central cloud control platform to serve as a basis for making overall decisions, so that a global optimal traffic strategy is obtained. Based on cloud edge task division, under the authority of a central cloud, edge cloud can also provide high-efficiency edge safety control service with driving control and decision suggestion for automatic driving/assisted driving vehicles, so that the timeliness of the service is improved, and the user experience is improved.
The invention also provides a specific implementation mode, which is as follows:
and arranging a plurality of intelligent edge integrated machines along the expressway at intervals of 3-5 km, and connecting the intelligent edge integrated machines together through a special optical fiber ring network. The intelligent edge all-in-one machine has basic resources such as calculation, storage and network, can provide edge calculation functions such as front-end equipment access, edge management, edge service and cloud edge cooperation, and is basic equipment for constructing an edge cloud calculation platform and realizing edge intelligence. A variety of front-end devices are deployed along the highway, including vehicle detectors, cameras, radars, weather detectors, etc., that collect traffic and weather data in real-time. The intelligent edge all-in-one machine is connected with front-end equipment in a responsible road section area through a front-end equipment interface, and traffic data and weather data acquired by all the equipment in real time are collected. The cloud control platform is deployed in the expressway monitoring center, adopts a Yun Bianduan collaborative architecture, performs hierarchical processing and decision analysis on data uploaded by the edge cloud computing platform, and can also send feedback information to the edge cloud computing platform.
An edge cloud computing platform is built on the intelligent edge all-in-one machine, edge intelligence is achieved based on the edge cloud computing platform, and the intelligent edge cloud computing platform is achieved through Go language design and comprises functional modules of road section segmentation, data acquisition, data fusion, intelligent analysis, local decision making, edge safety control and the like.
Road segment segmentation module: according to the application scene analysis, road section segmentation is carried out on the expressway space, the optimal local road sections are divided, and the traffic flow prediction scene can be divided based on geographic positions, road network topology structures, clustering analysis and the like. The road section dividing method is determined according to the characteristics of the expressway, such as a traffic flow prediction task aiming at a specific geographic area, a dividing method based on geographic positions is adopted, and a dividing method based on cluster analysis is adopted for road sections with similar traffic characteristics. After road section division is completed, the intelligent edge all-in-one machine is responsible for data acquisition, processing and analysis of the divided local road sections.
And a data acquisition module: and the edge computing middleware edgeX Foundry is used as a front-end equipment access tool, various front-end equipment is managed and connected through an open standard and a framework, and the front-end equipment with different types and brands and the edge cloud computing platform are integrated, so that unified acquisition, processing and transmission of front-end equipment data are realized.
The data fusion module utilizes the KubeEdge as a cloud edge cooperative tool to realize cooperative work of the cloud end and the edge nodes, tasks, configurations and instructions can be sent to the edge nodes by the cloud end through the tool, and the edge nodes can be processed and executed according to scheduling and control of the cloud end. And carrying out perception fusion analysis on the feedback information of the cloud and the traffic running state, the meteorological environment state, the road structure state and the vehicle microscopic state acquired in the data acquisition stage to acquire more comprehensive, accurate and comprehensive information, and providing more dimensions and visual angles for the subsequent stage, so that the analysis and application capacity of the data are enhanced.
And an intelligent analysis module: the integrated data is intelligently analyzed by adopting methods such as data mining, machine learning, deep learning and the like, and functions such as traffic state analysis, traffic flow prediction and the like are included according to different scenes. The traffic state analysis is to evaluate and analyze the real-time traffic state of each road section or area in the traffic network, and can provide the functions of real-time traffic state monitoring, congestion early warning, road condition evaluation and the like, so as to help traffic management departments and drivers to make reasonable traffic decisions; traffic flow prediction predicts traffic flow of local road segments at a certain time period in the future according to real-time traffic states to help traffic planning, traffic management, route selection and other decisions. The execution result of the intelligent analysis module can be output to the central cloud control platform through the cloud edge cooperative function provided by the KubeEdge, and can also be used as input of the local decision module and the edge safety control module.
And a local decision module: and formulating a traffic control strategy according to the intelligent analysis result, wherein the traffic control strategy comprises functions of traffic flow control, driving path guidance and the like. Traffic flow control means that according to the real-time traffic state and the traffic prediction result, measures such as dynamic signal control, lane management, dynamic restriction and the like are adopted to control traffic flow so as to relieve congestion and optimize the running efficiency of a traffic system; the driving path guiding refers to providing driving path suggestion, dynamic path optimization and the like according to traffic flow prediction results and road conditions, and guiding a driver to select an optimal route so as to avoid a congestion road section.
Edge safety control module: the results of the data fusion module, the intelligent analysis module and the local decision module can be output to the edge safety control module, an edge safety control strategy is generated according to the results, and high-efficiency edge safety control service is provided for driving control and decision suggestion for automatic driving/assisted driving vehicles, so that the timeliness of the service is improved, and the user experience is improved.
The expressway traffic flow prediction method based on the edge cloud computing platform comprises the following steps of:
step 1: according to highway section division, traffic flow data and weather data of local sections are collected, wherein the traffic flow data comprise traffic flow data such as running speed, lane occupation condition and number of vehicles, and the weather data such as temperature, rainfall, wind speed and visibility, and the like, and the original data are preprocessed, so that invalid data in the data are removed, and the accuracy of traffic flow prediction is ensured.
Step 2: the method comprises the steps of extracting time features of traffic flow data in a periodic, trending and seasonal mode and the like, extracting space features through highway section division, taking the influence of weather changes on traffic flow into consideration, taking the weather data as external features, and fusing the data based on different features.
Step 3: and calculating and analyzing the fused data by using a pre-deployed traffic flow prediction algorithm model, and predicting the traffic flow of the road section to obtain a preliminary prediction result.
Step 4: judging a prediction result according to a set precision threshold, and if the precision requirement is met, outputting the prediction result to application modules such as traffic flow control and driving path guidance, or uploading the prediction result to a central cloud control platform; and if the accuracy requirement is not met, outputting a feedback result to the model training and optimizing module.
Step 5: the model training and optimizing module retrains and optimizes the traffic flow prediction model according to the feedback result, further improves the accuracy and reliability of prediction, updates the prediction model and parameters, and performs a new round of prediction and optimization until an optimal flow prediction model is generated.
Step 6: when the model training reaches the optimal, the trained model can be utilized to predict and analyze the traffic flow in real time. And predicting the traffic flow condition of the specific road section in a future time period according to the current traffic flow data and weather data, and providing an accurate traffic flow prediction result.
Compared with the prior art, the embodiment of the disclosure has the beneficial effects that:
1. the accuracy of traffic flow prediction is improved: the invention utilizes the edge intelligent algorithm and the multi-source data fusion to more comprehensively and accurately predict the traffic flow of the expressway. Compared with the traditional method, the method considers the spatial characteristics of traffic flow and various influencing factors, utilizes the fusion analysis of the highway spatial segmentation and the original data of different sources, better captures the change rule of the traffic flow, and improves the prediction accuracy.
2. Real-time performance of traffic flow prediction is realized: the traffic flow prediction method based on the intelligent edge technical scheme can predict and analyze traffic flow on the road side edge in real time. Compared with the traditional method which needs to rely on a cloud control platform for data processing, the method and the device fully utilize the edge computing capability, reduce the data transmission delay and realize faster real-time prediction.
3. Traffic management and travel efficiency are improved: the technical scheme of the invention can accurately obtain the traffic flow prediction result in real time and apply the prediction result to traffic management decisions. By timely knowing road congestion, traffic condition prediction and flow distribution information, traffic managers can take corresponding measures according to prediction results so as to improve traffic management efficiency and reduce congestion conditions. Meanwhile, for travelers, more reasonable journey planning can be performed according to traffic flow prediction results, traffic jam areas are avoided, and traveling efficiency is improved.
The following are device embodiments of the present disclosure that may be used to perform method embodiments of the present disclosure. For details not disclosed in the embodiments of the apparatus of the present disclosure, please refer to the embodiments of the method of the present disclosure.
Fig. 7 is a schematic structural diagram of some embodiments of a highway traffic flow prediction apparatus according to the present disclosure. As shown in fig. 7, the expressway traffic flow prediction apparatus includes:
the data acquisition module is used for acquiring traffic data and weather data of a corresponding driving interval by using data acquisition equipment on the expressway; the expressway is divided into a plurality of driving intervals, and each driving interval is provided with a data processing device and at least one data acquisition device;
the data fusion module is used for carrying out data fusion on traffic data and weather data of each driving interval;
and the traffic flow prediction result output module is used for inputting the data fusion result into the traffic flow prediction model and outputting the traffic flow prediction result of each driving interval.
In some preferred embodiments, the apparatus further comprises:
the precision judging module is used for judging whether the traffic flow prediction result meets a preset precision threshold value or not;
the prediction result sending module is used for sending the traffic flow prediction result to a highway cloud control platform if the traffic flow prediction result meets a preset precision threshold; the expressway cloud control platform is used for receiving the traffic flow prediction result and determining a traffic control strategy of a corresponding driving interval according to the traffic flow prediction result;
And the model optimization module is used for carrying out precision optimization on the traffic flow prediction model if the traffic flow prediction result does not meet a preset precision threshold value, until the traffic flow prediction result output by the optimized traffic flow prediction model meets the preset precision threshold value.
In some preferred embodiments, the traffic flow predictions include traffic state predictions and traffic flow predictions; the traffic state prediction result is used for representing the real-time traffic state in the corresponding driving interval; the real-time traffic state comprises road congestion; and the traffic flow prediction result is used for representing the traffic flow of the corresponding driving interval in a preset time period in the future.
In some preferred embodiments, the prediction result transmitting module includes:
the traffic flow control strategy determining unit is used for determining a traffic flow control strategy of a corresponding driving interval based on the traffic state prediction result and the traffic flow prediction result; the traffic flow control strategy comprises a signal lamp control strategy, a lane management strategy and a dynamic restriction strategy;
the driving path guiding and controlling strategy determining unit is used for determining driving path guiding and controlling strategies of the corresponding driving interval based on the traffic flow prediction result; the driving path guiding and controlling strategy comprises a driving path reminding strategy and a dynamic path optimizing strategy.
In some preferred embodiments, the traffic data includes traffic running state data, road structure state data, vehicle microscopic data; a data fusion module comprising: and the data fusion unit is used for carrying out data fusion on the traffic running state data, the road structure state data, the vehicle microscopic data and the meteorological environment state data of each running interval.
In some preferred embodiments, the apparatus further comprises:
the weight initialization module is used for initializing the network weight of the traffic flow prediction model and determining the input dimension, the output dimension, the number of hidden layer layers, the convolution kernel dimension, the step length, the training batch and the residual error unit number;
the model training module is used for training the traffic flow prediction model by utilizing a training set;
the predicted value output module is used for inputting the data fusion result into the trained traffic flow predicted model and outputting a predicted value;
the updating weight value module is used for training the traffic flow prediction model by using a back propagation algorithm according to the training batch and updating the weight value of the traffic flow prediction model;
and the prediction precision testing module is used for testing the prediction precision of the traffic flow prediction model by using a testing set after training is completed.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not constitute any limitation on the implementation process of the embodiments of the disclosure.
Referring now to fig. 8, a schematic diagram of an electronic device 800 suitable for use in implementing some embodiments of the present disclosure is shown. The server illustrated in fig. 8 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present disclosure in any way.
As shown in fig. 8, the electronic device 800 may include a processing means (e.g., a central processor, a graphics processor, etc.) 801, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 802 or a program loaded from a storage means 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic device 800 are also stored. The processing device 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.
In general, the following devices may be connected to the I/O interface 805: input devices 806 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 807 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, etc.; storage 808 including, for example, magnetic tape, hard disk, etc.; communication means 809. The communication means 809 may allow the electronic device 800 to communicate wirelessly or by wire with other devices to exchange data. While fig. 8 shows an electronic device 800 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead. Each block shown in fig. 8 may represent one device or a plurality of devices as needed.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such embodiments, the computer program may be downloaded and installed from a network via communication device 809, or from storage device 808, or from ROM 802. The above-described functions defined in the methods of some embodiments of the present disclosure are performed when the computer program is executed by the processing device 801.
It should be noted that, in some embodiments of the present disclosure, the computer readable medium may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, the computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.
The computer readable medium may be embodied in the apparatus; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: collecting traffic data and weather data of a corresponding driving interval by using data collecting equipment on a highway; the expressway is divided into a plurality of driving intervals, and each driving interval is provided with a data processing device and at least one data acquisition device; carrying out data fusion on traffic data and weather data of each driving interval; and inputting the data fusion result into a traffic flow prediction model, and outputting a traffic flow prediction result of each driving interval.
Computer program code for carrying out operations for some embodiments of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts 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 flowchart or block diagrams may represent a module, segment, or 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 functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above technical features, but encompasses other technical features formed by any combination of the above technical features or their equivalents without departing from the spirit of the invention. Such as the above-described features, are mutually substituted with (but not limited to) the features having similar functions disclosed in the embodiments of the present disclosure.
Claims (10)
1. A method of highway traffic flow prediction, the method being applied to a data processing apparatus, the method comprising:
collecting traffic data and weather data of a corresponding driving interval by using data collecting equipment on a highway; the expressway is divided into a plurality of driving intervals, and each driving interval is provided with a data processing device and at least one data acquisition device;
Carrying out data fusion on traffic data and weather data of each driving interval;
and inputting the data fusion result into a traffic flow prediction model, and outputting a traffic flow prediction result of each driving interval.
2. The method of highway traffic flow prediction according to claim 1, further comprising:
judging whether the traffic flow prediction result meets a preset precision threshold value or not;
if the traffic flow prediction result meets a preset precision threshold, the traffic flow prediction result is sent to a highway cloud control platform; the expressway cloud control platform is used for receiving the traffic flow prediction result and determining a traffic control strategy of a corresponding driving interval according to the traffic flow prediction result;
and if the traffic flow prediction result does not meet the preset precision threshold, performing precision optimization on the traffic flow prediction model until the traffic flow prediction result output by the optimized traffic flow prediction model meets the preset precision threshold.
3. The expressway traffic flow prediction method according to claim 1 or 2, wherein the traffic flow prediction result includes a traffic state prediction result and a traffic flow prediction result;
The traffic state prediction result is used for representing the real-time traffic state in the corresponding driving interval; the real-time traffic state comprises road congestion;
and the traffic flow prediction result is used for representing the traffic flow of the corresponding driving interval in a preset time period in the future.
4. The method for predicting traffic flow on highway according to claim 3, wherein determining the traffic control strategy for the corresponding driving interval according to the traffic flow prediction result comprises:
determining a traffic flow control strategy of a corresponding driving interval based on the traffic state prediction result and the traffic flow prediction result; the traffic flow control strategy comprises a signal lamp control strategy, a lane management strategy and a dynamic restriction strategy;
determining a driving path guiding and controlling strategy of a corresponding driving interval based on the traffic flow prediction result; the driving path guiding and controlling strategy comprises a driving path reminding strategy and a dynamic path optimizing strategy.
5. The expressway traffic flow prediction method according to claim 1 or 2, wherein the traffic data includes traffic running state data, road structure state data, vehicle microscopic data;
The weather data includes weather environmental state data; the data fusion of the traffic data and the weather data of each driving interval comprises the following steps:
and carrying out data fusion on the traffic running state data, the road structure state data, the vehicle microscopic data and the meteorological environment state data of each running interval.
6. The method of predicting highway traffic flow according to claim 1 or 2, wherein the method further comprises:
initializing network weights of the traffic flow prediction model, and determining input dimensions, output dimensions, hidden layer numbers, convolution kernel dimensions, step sizes, training batches and residual error unit numbers;
training the traffic flow prediction model by using a training set;
inputting the data fusion result into a trained traffic flow prediction model, and outputting a predicted value;
according to the training batch, training a traffic flow prediction model by using a back propagation algorithm, and updating the weight value of the traffic flow prediction model;
and after training, testing the prediction precision of the traffic flow prediction model by using a test set.
7. A highway traffic control apparatus for implementing the highway traffic flow prediction method according to any one of claims 1 to 6, comprising a plurality of data acquisition units, a plurality of data processing units, and a control unit:
The data acquisition unit is arranged in a corresponding driving interval of the expressway, and is used for acquiring traffic data and environment data in the corresponding driving interval and sending the traffic data and the environment data to the control unit; the expressway is divided into a plurality of running intervals by intervals;
each data processing unit is arranged in a corresponding driving interval of the expressway, and is used for receiving the traffic data and the environment data sent by at least one data acquisition unit in the corresponding driving interval, carrying out data processing on the traffic data and the environment data, generating a traffic flow prediction result and sending the traffic flow prediction result to the control unit;
the control unit is used for receiving traffic flow prediction results sent by the data processing unit corresponding to each driving interval and generating traffic control strategies based on the traffic flow prediction results.
8. A highway traffic flow prediction apparatus, comprising:
the data acquisition module is used for acquiring traffic data and weather data of a corresponding driving interval by using data acquisition equipment on the expressway; the expressway is divided into a plurality of driving intervals, and each driving interval is provided with a data processing device and at least one data acquisition device;
The data fusion module is used for carrying out data fusion on traffic data and weather data of each driving interval;
and the traffic flow prediction result output module is used for inputting the data fusion result into the traffic flow prediction model and outputting the traffic flow prediction result of each driving interval.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method for predicting highway traffic flow according to any one of claims 1-6 when executing the computer program.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the highway traffic flow prediction method according to any one of claims 1-6.
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