CN112766605A - Multi-source passenger flow prediction system and method based on container cloud platform - Google Patents
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Abstract
The invention discloses a multi-source passenger flow prediction system and method based on a container cloud platform, and particularly relates to the field of rail transit passenger flow prediction. The multi-source passenger flow prediction system based on the container cloud platform comprises the container cloud platform, an artificial intelligence platform, a daily passenger flow prediction module and a theme passenger flow prediction module. The invention also provides a multi-source passenger flow prediction method, S1, collecting basic data needed by the system; s2, carrying out data quality inspection and data preprocessing on the collected data; s3, analyzing and deeply mining the data; s4, extracting the characteristics of the input data; s5, selecting a proper machine learning algorithm according to the analysis result and establishing a passenger flow prediction model; and S6, performing model parameter optimization and model self optimization. According to the method, influence factors and influence weights influencing passenger flow change are fully considered through multi-data fusion input, the self-learning capacity of the model is realized, continuous iterative optimization and updating can be realized, and the prediction accuracy is continuously improved.
Description
Technical Field
The invention belongs to the field of rail transit passenger flow prediction, and particularly relates to a multi-source passenger flow prediction system and method based on a container cloud platform.
Background
In recent years, with the rapid development of urban construction, individual differences become more obvious when passengers select paths, the change period of a path mode becomes shorter and shorter, and the research and development and the improvement of a Dynamic Traffic Management System (DTMS) replace the improvement of a calculation model, so that the problem which is really and urgently needed to be solved is solved. Xiangming Yao of the Beijing university of transportation and transportation, Xinxinati university engineering, Hui Ren of the applied institute and the like propose a dynamic passenger flow distribution model in a transportation network running according to a planning diagram based on simulation, and AFC data of Beijing subway verifies that the simulation model can be used for judging the current network running state and predicting short-term future traffic tendency. Lijun Liu and the like of the editorial university of mansion adopt a prediction model of a deep learning method to carry out prediction modeling and verification on BRT station passenger flow. Florian torque et al propose using a Recurrent Neural Network (RNN) of a time Recurrent Neural network (LSTM) unit to predict a dynamic subway OD matrix, and compare the results with those of a conventional calendar model and a vector autoregressive model, which shows that the results are superior to those of the two methods. Haitao XU of computer academy of Hangzhou electronic science and technology university and the like propose a deep learning method based on a prediction stacked self-encoding (PSAE) model to predict passenger flow of a rapid bus station, a first layer adopts unsupervised learning training, the result is sent to the unsupervised learning of the next layer to train a self-encoding model, finally, a logistic regression prediction layer is constructed and trained, BP and GD are utilized to optimize the model, and the conclusion is that the prediction effect of the PSAE is superior to that of BPNN and SVM.
The research relieves the urgency of broiling to a certain extent in a certain time and region range, but still has the following problems:
(1) time lag of the OD input;
(2) the path model and the parameter adjustment need time-consuming and labor-consuming manual following and are difficult to update in time;
(3) the model is separately established for a normal state and an abnormal state and has no universality;
(4) in recent two years, governments increasingly pay attention to the importance of integrating and planning data of large traffic systems such as buses, subways and urban rails as data input sources of the large systems and utilizing rapidly-developed large data analysis technology and deep learning technology on urban traffic and construction. Each urban rail transit operation unit has a strong desire of mining data value by an artificial intelligence technology. Improving the prediction model by deep learning is a necessary development direction of passenger flow prediction.
Disclosure of Invention
The invention aims to provide a multi-source passenger flow prediction system and method based on a container cloud platform, and solves the problems in the prior art.
The technical scheme of the invention is as follows: a multi-source passenger flow prediction system based on a container cloud platform comprises the container cloud platform, an artificial intelligence platform, a daily passenger flow prediction module and a theme passenger flow prediction module;
the system comprises a container cloud platform, an artificial intelligence platform, a daily passenger flow prediction module and a theme passenger flow prediction module, wherein the container cloud platform is used for storing and processing multi-source data, the artificial intelligence platform is used for providing artificial intelligence algorithms for machine learning and deep learning, the daily passenger flow prediction module is used for establishing a machine learning prediction model aiming at short-term and short-term passenger flows in a daily operation process, and the theme passenger flow prediction module is used for establishing a machine learning prediction model aiming at holidays, large-scale activities, emergencies and special spring transportation scenes.
Further, the multi-source data includes passenger flow data, video data, research data, meteorological data, and external data.
Further, the prediction model is one of an XGBoost prediction model, a markov prediction model, a neural network prediction model, a decision tree prediction model or a time series prediction model.
A multi-source passenger flow prediction method based on a container cloud platform is applied to the multi-source passenger flow prediction system based on the container cloud platform, and comprises the following steps:
s1, collecting basic data needed by the multi-source passenger flow prediction system;
s2, carrying out data quality inspection and data preprocessing on the collected data;
s3, analyzing and deeply mining the data;
s4, extracting the characteristics of the input data;
s5, selecting a proper machine learning algorithm according to the analysis result and establishing a passenger flow prediction model;
and S6, performing model parameter optimization and model self optimization.
Further, the basic data of step S1 includes historical passenger flow data, passenger flow clearing data, weather data, train operation charts, activity and event data, car restriction data, bus and subway data, mobile phone signaling data, and WIFI/video data.
Further, the step S2 is specifically: the data quality inspection comprises the steps of inspecting the integrity and the accuracy of data and inspecting null values, extreme values, discrete values and abnormal values, the data preprocessing mainly comprises the steps of processing the null values, the extreme values, the discrete values and the abnormal values, simultaneously performing data cleaning, data integration, data conversion and data reduction, and finally using a wide table or view form as model input.
Further, the step S3 is specifically: analyzing the passenger flow change rule and the influence degree of influence factors on the passenger flow, and scoring the influence degree of the influence factors on the passenger flow, wherein the influence factors comprise weather, activities, events, historical synchronous passenger flow and previous day passenger flow.
Further, the step S4 is specifically: the feature scored high in the step S3 is selected as an input feature of the model.
Further, the step S5 is specifically: and deeply analyzing and mining the passenger flow data and the influence factor data, selecting an algorithm and establishing a model according to the data magnitude of the passenger flow data and the influence factor data and the requirements of various algorithms on input data, and continuously performing result verification and parameter tuning on the model so as to establish a prediction model.
Further, the step S6 includes the following steps:
s61, visually displaying the prediction result, wherein the prediction result is used for realizing the conversion from data to a graph;
and S62, establishing a model self-optimization system for realizing the self-learning capability of the model so that the model can be continuously optimized and updated in an iterative manner.
After the technical scheme is adopted, the invention has the beneficial effects that: influence factors and influence weights influencing passenger flow change are fully considered through multi-source data fusion input; modeling by adopting an advanced machine learning algorithm, enabling the model to continuously self-learn and self-optimize, carrying out iterative updating at regular intervals, and continuously optimizing a prediction result; resources of the container cloud and the artificial intelligence platform are fully utilized, and self-optimization capacity and prediction efficiency of the model are improved.
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FIG. 1 is a flow chart of the operation of the present invention;
fig. 2 is a system block diagram of the present invention.
Detailed Description
For a better understanding of the present invention by those skilled in the art, the present invention will be described in further detail below with reference to the accompanying drawings and the following examples.
Example 1
Referring to fig. 2, the embodiment provides a multi-source passenger flow prediction system based on a container cloud platform, which includes a container cloud platform, an artificial intelligence platform, a daily passenger flow prediction module and a theme passenger flow prediction module;
specifically, the container cloud platform of the embodiment is used for storing and processing multi-source data, the artificial intelligence platform is used for providing artificial intelligence algorithms for machine learning and deep learning, the daily passenger flow prediction module is used for establishing a machine learning prediction model aiming at short-term and short-term passenger flows in the daily operation process, the theme passenger flow prediction module is used for establishing a machine learning prediction model aiming at holidays, large-scale activities, emergencies and special spring transportation scenes, it should be noted that the multi-source data of the present embodiment includes passenger flow data, video data, research data, meteorological data, external data, and the like, and preferably, the established prediction model may be one of an XGBoost prediction model, a markov prediction model, a neural network prediction model, a decision tree prediction model, or a time series prediction model, and may ensure the accuracy of passenger flow prediction.
According to the embodiment, the multisource data such as passenger flow data, video data, research data, meteorological data and external data are integrated, cleaned, converted and subjected to stipulation through a big data technology to form basic data suitable for model input, then model establishment and model optimization are realized through a machine learning algorithm and a deep learning algorithm, a universal short-term passenger flow prediction model and a short-term passenger flow prediction model are established, and therefore a multisource passenger flow prediction system based on a container cloud platform is established, the accuracy of passenger flow prediction can be guaranteed, and meanwhile the reliability of driving is guaranteed.
Example 2
Based on embodiment 1, referring to fig. 1, this embodiment further provides a multi-source passenger flow prediction method based on a container cloud platform, including the following steps:
s1, collecting basic data needed by the multi-source passenger flow prediction system;
s2, carrying out data quality inspection and data preprocessing on the collected data;
s3, analyzing and deeply mining the data;
s4, extracting the characteristics of the input data;
s5, selecting a proper machine learning algorithm according to the analysis result and establishing a passenger flow prediction model;
and S6, performing model parameter optimization and model self optimization.
Specifically, the basic data collected in step S1 in this embodiment includes historical passenger flow data, passenger flow clearing data, weather data, train operation charts, activity and event data, car restriction data, bus and subway data, mobile phone signaling data, WIFI/video data, and the like;
then the system carries out data quality inspection and data preprocessing on the collected data, wherein the data quality inspection includes but is not limited to the inspection of the integrity, accuracy and the like of the data and the inspection of numerical values such as null values, extreme values, discrete values, abnormal values and the like, the data preprocessing mainly processes the null values, the extreme values, the discrete values, the abnormal values and the like, data cleaning, data integration, data conversion, data reduction and the like are required, and finally the data are used as model input in a form of a wide table or a view;
secondly, after data quality inspection and preprocessing, analyzing and deeply mining the data, mainly analyzing the passenger flow change rule and the influence degree of influence factors on the passenger flow, and grading the influence degree of the influence factors on the passenger flow, wherein the influence factors of the implementation comprise weather, activities, events, historical synchronous passenger flow, previous day passenger flow and the like, and the larger the grading value is, the larger the influence factors are;
moreover, the input feature extraction is performed on the input data, wherein the input feature extraction is to select the feature with higher score in the previous step as the input feature of the model, for example, in the previous step, the influence degree of the meteorological factors on the passenger flow is the largest if the score of the influence degree of the meteorological factors on the passenger flow is the largest, so that the input feature data of the implementation can be determined to be the meteorological factors;
selecting a proper machine learning algorithm according to the analysis result, establishing a passenger flow prediction model, wherein the selection of the prediction model is to clearly know the composition and the rule of data by performing deep analysis and mining on passenger flow data and influence factor data, and then selecting the algorithm to establish the model by combining the requirements of various algorithms on input data according to the data magnitude of the passenger flow data and the influence factor data;
finally, parameter tuning and model self-optimization are carried out on the passenger flow prediction model, specifically, the prediction result is displayed in a visual mode, data are converted into graphs, the display of the prediction result is more visual, a model self-optimization system is established, the self-learning capacity of the model is achieved, the passenger flow prediction model can be continuously optimized and updated in an iterative mode, and the prediction accuracy is continuously improved.
According to the method, through multi-source data fusion input, influence factors and influence weights influencing passenger flow changes are fully considered, advanced machine learning algorithm modeling is achieved, the model can continuously learn by itself and optimize by itself, iteration updating is carried out periodically, prediction results are continuously optimized, resources of a container cloud and an artificial intelligence platform are fully utilized, and self-optimization capability and prediction efficiency of the model are improved.
Claims (10)
1. The utility model provides a multisource passenger flow prediction system based on container cloud platform which characterized in that: the system comprises a container cloud platform, an artificial intelligence platform, a daily passenger flow prediction module and a theme passenger flow prediction module;
the system comprises a container cloud platform, an artificial intelligence platform, a daily passenger flow prediction module and a theme passenger flow prediction module, wherein the container cloud platform is used for storing and processing multi-source data, the artificial intelligence platform is used for providing artificial intelligence algorithms for machine learning and deep learning, the daily passenger flow prediction module is used for establishing a machine learning prediction model aiming at short-term and short-term passenger flows in a daily operation process, and the theme passenger flow prediction module is used for establishing a machine learning prediction model aiming at holidays, large-scale activities, emergencies and special spring transportation scenes.
2. The multi-source passenger flow prediction system based on the container cloud platform according to claim 1, characterized in that: the multi-source data comprises passenger flow data, video data, research data, meteorological data and external data.
3. The multi-source passenger flow prediction system based on the container cloud platform according to claim 1, characterized in that: the prediction model is one of an XGboost prediction model, a Markov prediction model, a neural network prediction model, a decision tree prediction model or a time sequence prediction model.
4. A multi-source passenger flow prediction method based on a container cloud platform is applied to the multi-source passenger flow prediction system based on the container cloud platform in claims 1 to 3, and is characterized in that: the method comprises the following steps:
s1, collecting basic data needed by the multi-source passenger flow prediction system;
s2, carrying out data quality inspection and data preprocessing on the collected data;
s3, analyzing and deeply mining the data;
s4, extracting the characteristics of the input data;
s5, selecting a proper machine learning algorithm according to the analysis result and establishing a passenger flow prediction model;
and S6, performing model parameter optimization and model self optimization.
5. The multi-source passenger flow prediction method based on the container cloud platform according to claim 4, characterized in that: the basic data of the step S1 includes historical passenger flow data, passenger flow clearing data, meteorological data, train operation charts, activity and event data, car restriction data, bus and subway data, mobile phone signaling data, and WIFI/video data.
6. The multi-source passenger flow prediction method based on the container cloud platform according to claim 4, characterized in that: the step S2 specifically includes: the data quality inspection comprises the inspection of the integrity and the accuracy of the data and the inspection of null values, extreme values, discrete values and abnormal values;
the data preprocessing comprises the processing of null values, extreme values, discrete values and abnormal values, and simultaneously carries out data cleaning, data integration, data conversion and data reduction, and the data is used as model input in a form of a wide table or a view.
7. The multi-source passenger flow prediction method based on the container cloud platform according to claim 4, characterized in that: the step S3 specifically includes: analyzing the passenger flow change rule and the influence degree of influence factors on the passenger flow, and scoring the influence degree of the influence factors on the passenger flow, wherein the influence factors comprise weather, activities, events, historical synchronous passenger flow and previous day passenger flow.
8. The multi-source passenger flow prediction method based on the container cloud platform according to claim 4, characterized in that: the step S4 specifically includes: the feature scored high in the step S3 is selected as an input feature of the model.
9. The multi-source passenger flow prediction method based on the container cloud platform according to claim 4, characterized in that: the step S5 specifically includes: and deeply analyzing and mining the passenger flow data and the influence factor data, selecting an algorithm and establishing a model according to the data magnitude of the passenger flow data and the influence factor data and the requirements of various algorithms on input data, and continuously performing result verification and parameter tuning on the model so as to establish a prediction model.
10. The multi-source passenger flow prediction method based on the container cloud platform according to claim 4, characterized in that: the step S6 includes the following steps:
s61, visually displaying the prediction result, wherein the prediction result is used for realizing the conversion from data to a graph;
and S62, establishing a model self-optimization system for realizing the self-learning capability of the model so that the model can be continuously optimized and updated in an iterative manner.
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