CN116596113A - Intelligent prediction-based traffic junction comprehensive radiation model construction method - Google Patents

Intelligent prediction-based traffic junction comprehensive radiation model construction method Download PDF

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CN116596113A
CN116596113A CN202310376453.3A CN202310376453A CN116596113A CN 116596113 A CN116596113 A CN 116596113A CN 202310376453 A CN202310376453 A CN 202310376453A CN 116596113 A CN116596113 A CN 116596113A
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任家博
陈大山
邹劲柏
谢鲲
陈文�
庞茂盛
黄宇轩
汪静
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Shanghai Institute of Technology
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Abstract

The application discloses a traffic junction comprehensive radiation model construction method based on intelligent prediction, which comprises the steps of selecting a traffic junction and a surrounding city range, and acquiring traffic flow data in a designated time range of a research area according to big data; according to traffic flow data, an ARIMA model is used for realizing traffic flow prediction between a traffic hub and surrounding cities; according to traffic flow prediction, constructing a comprehensive radiation model of the traffic junction by adopting a linear regression model; and according to the prediction data of the comprehensive radiation model of the transportation junction, the comprehensive radiation capacity of the transportation junction is predicted. Based on the existing problems, the application creatively provides a traffic junction comprehensive radiation model construction method based on intelligent prediction, which can effectively solve the traffic junction comprehensive radiation capability calculation problem. The comprehensive radiation capacity of the transportation hub to surrounding cities can be calculated scientifically, and a new idea is provided for defining the comprehensive radiation capacity of the transportation hub.

Description

Intelligent prediction-based traffic junction comprehensive radiation model construction method
Technical Field
The application belongs to the technical field of traffic model research, and particularly relates to a traffic hub comprehensive radiation model construction method based on intelligent prediction.
Background
The modern comprehensive transportation hub is connected with various passenger and freight modes such as aviation, high-speed rail, interurban trains, subways and the like, so that multifunctional integration is realized, interconnection and intercommunication among urban clusters are promoted, and co-development of co-city is further promoted. Under the background of prosperous development of urban transportation hubs, urban groups taking transportation hubs as passenger flow centers are connected with the urban groups with larger-scale multi-factor comprehensive radiation. The comprehensive radiation capability of the transportation hub to surrounding cities is explored, which is favorable for supporting urban road planning decisions and provides important basis for expanding the space planning of new economy.
In the traffic junction comprehensive radiation model based on intelligent prediction, an obvious linear relation exists between the traffic junction comprehensive radiation capacity and traffic flow, and the future radiation capacity can be predicted by using a linear regression model. In addition, traffic flow prediction is also particularly important. At present, traffic flow prediction can be classified into three types according to different methods. The first is a statistical-based method, wherein traffic flow prediction is regarded as a time sequence problem, such as ARIMA model, kalman filtering model and the like, and the model is simple and quick to calculate; the second type is a machine learning-based method, such as a support vector machine regression algorithm, a K-nearest neighbor algorithm, and the like, wherein the selection of algorithm parameters has a great influence on model accuracy. The third category is related to deep learning methods, including two traffic flow prediction methods based on spatio-temporal sequences and graph structures, which are advantageous in terms of handling relatively complex problems. The current research on the comprehensive radiation capability of the urban transportation junction is deficient, the scientific basis for defining the comprehensive radiation capability of the transportation junction is lacking, and the configuration unbalance of the transportation junction capability is easy to cause. Therefore, based on the existing problems, the application creatively provides a traffic junction comprehensive radiation model construction method based on intelligent prediction, which can effectively solve the traffic junction comprehensive radiation capability calculation problem. The comprehensive radiation capacity of the transportation hub to surrounding cities can be calculated scientifically, and a new idea is provided for defining the comprehensive radiation capacity of the transportation hub.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the application and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application.
The present application has been made in view of the above-mentioned and/or existing problems associated with intelligent prediction-based comprehensive radiation model construction methods for transportation hubs.
Therefore, the application aims to provide a traffic junction comprehensive radiation model construction method based on intelligent prediction. In order to solve the technical problems, the application provides the following technical scheme: a traffic junction comprehensive radiation model construction method based on intelligent prediction comprises the steps of selecting a traffic junction and a surrounding city range, and acquiring traffic flow data in a specified time range of a research area according to big data; according to traffic flow data, an ARIMA model is used for realizing traffic flow prediction between a traffic hub and surrounding cities; according to traffic flow prediction, constructing a comprehensive radiation model of the traffic junction by adopting a linear regression model; and according to the prediction data of the comprehensive radiation model of the transportation junction, the comprehensive radiation capacity of the transportation junction is predicted.
As a preferable scheme of the intelligent prediction-based traffic junction comprehensive radiation model construction method, the application comprises the following steps: the method for acquiring traffic flow data in the appointed time range of the research area according to the big data comprises the following steps: an API for accessing the big data of the vacation position; analyzing JSON data returned by the API; and acquiring traffic flow data of various traffic travel modes.
As a preferable scheme of the intelligent prediction-based traffic junction comprehensive radiation model construction method, the application comprises the following steps: the method for predicting the traffic flow between the traffic junction and the surrounding cities by using the ARIMA model according to the traffic flow data comprises the following steps: preprocessing traffic flow data, including processing missing values and abnormal values, and sorting the missing values and the abnormal values into time sequence data; checking the stability of the time sequence data, and if a non-stable sequence exists, further performing differential processing on the data; dividing the stable time series data into a training set and a testing set, taking the data before (m-7) days as the training set, and taking the data of the last 7 days as the testing set; fitting an ARIMA model, and further determining parameters and checking the model; and predicting the traffic flow by using the ARIMA model after training, and recovering the differential data to finally obtain traffic flow prediction data.
As a preferable scheme of the intelligent prediction-based traffic junction comprehensive radiation model construction method, the application comprises the following steps: the stability of the time sequence data is checked by adopting a unit root checking method to judge the sequence data; if the unit root does not exist, the sequence is stable; if a unit root exists, differential processing is required for the non-stationary sequence.
As a preferable scheme of the intelligent prediction-based traffic junction comprehensive radiation model construction method, the application comprises the following steps: the ARIMA model is fitted, parameters are further determined and model verification is carried out, and the method comprises the following steps: cross-validation is adopted to determine the optimal parameters of the ARIMA model, wherein the optimal parameters comprise an autoregressive term number p, a difference frequency d and a moving average term number q; and further carrying out validity checking and adjustment on the model until an ARIMA model meeting the checking requirement is obtained.
As a preferable scheme of the intelligent prediction-based traffic junction comprehensive radiation model construction method, the application comprises the following steps: the formula of the comprehensive radiation model of the transportation junction is as follows:
wherein F is the comprehensive radiation capacity of the transportation hub to one city around; x represents an x-th travel mode, and only comprises an automobile, a train, an airplane, a high-speed rail, a subway and an inter-city train; e is the total number of the transportation modes between the city and the transportation junction; pjx is the sharing ratio of the traffic flow of the x-th travel mode in the city j; mx is the cost average value of the x-th travel mode between two places; lx is the actual mileage of the x-th travel mode between two places; tx is the average value of time cost required by the x-th travel mode between two places; i represents an i-th traffic infrastructure, which only includes main trunk roads, light rails, railways, and highways in the urban central area and the peripheral areas; ki is the number of i-th infrastructure; h is the total number of the traffic infrastructure categories in the urban central area and the peripheral areas; dj is the number of large or famous tourist attraction resources in city j; fj is the radiation potential of city j; beta is a random error term; gx, px, ux, bi, cj are the coefficients of their corresponding influencing factors, respectively.
As a preferable scheme of the intelligent prediction-based traffic junction comprehensive radiation model construction method, the application comprises the following steps: the expression of the sharing ratio Pjx of the x-th travel mode traffic flow is as follows:
in which Q jx The traffic flow of the x-th travel mode between two places in the unit time in the city j.
As a preferable scheme of the intelligent prediction-based traffic junction comprehensive radiation model construction method, the application comprises the following steps: the expression of the radiation potential fj of city j is as follows:
wherein n is the number of cities around the transportation junction; e (E) j The annual total economic amount of the city j around the transportation junction; r is R j Is the total amount of resident population of the city j around the transportation junction.
As a preferable scheme of the intelligent prediction-based traffic junction comprehensive radiation model construction method, the application comprises the following steps: the gx, px, ux, bi, cj coefficients corresponding to the influence factors comprise a cost influence coefficient gx, a distance influence coefficient px, a time influence coefficient ux, an infrastructure level influence coefficient bi and a travel resource coefficient cj, which are all obtained by adopting an expert scoring method.
As a preferable scheme of the intelligent prediction-based traffic junction comprehensive radiation model construction method, the application comprises the following steps: the method for predicting the comprehensive radiation capacity of the transportation junction according to the predicted data of the comprehensive radiation model of the transportation junction comprises the following steps: taking the unit time traffic flow in the transportation hub comprehensive radiation model as a variable, and taking the cost of charge, the distance, the time cost, the convenience of transportation infrastructure, travel resources, the number of cities around the transportation hub, the total annual urban amount and the resident population as constants; determining a characteristic variable as traffic flow, a target variable as comprehensive radiation capacity of a traffic junction, preprocessing a related data set, and converting the related data set into a two-dimensional array; dividing the historical data set into a training set and a testing set, using the data before (m-7) days for training, and using the data of the last 7 days for testing the performance of the linear rR check model in the scikit-learn library; constructing a linear regression model and training, testing and optimizing; and predicting the comprehensive radiation capacity of the target variable traffic hub based on the characteristic variable traffic flow prediction data.
The application has the beneficial effects that: the intelligent prediction-based comprehensive radiation model for the traffic junction is built by creatively introducing a traffic flow prediction model, and the influence of factors such as cost, distance, time cost, convenience of traffic infrastructure, traffic flow, travel resources and the like on the comprehensive radiation capacity of the traffic junction can be comprehensively considered. The traffic junction comprehensive radiation model construction method based on intelligent prediction can effectively solve the problem of calculating the comprehensive radiation capacity of the urban traffic junction, and can provide a new idea for defining the comprehensive radiation capacity of the traffic junction.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a flowchart of a method for constructing a comprehensive radiation model of a transportation junction based on intelligent prediction in embodiment 1.
FIG. 2 is a flow chart of traffic flow prediction in ARIMA model of the intelligent prediction-based traffic junction comprehensive radiation model construction method in embodiment 1.
FIG. 3 is a flow chart of the comprehensive radiation capacity prediction of the traffic junction based on the linear regression model of the traffic junction comprehensive radiation model construction method based on intelligent prediction in the embodiment 1.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of the application will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present application is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the application. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Example 1
Referring to fig. 1 to 3, a first embodiment of the present application provides a method for constructing a comprehensive radiation model of a traffic junction based on intelligent prediction, which comprises the following steps:
s1, selecting a traffic hub and a surrounding city range, and acquiring traffic flow data in a designated time range of a research area according to big data.
Specifically, the method for acquiring traffic flow data within a specified time range of a research area according to big data comprises the following steps: and accessing an API of the flight position big data, analyzing JSON data returned by the API, and obtaining traffic flow data of various traffic traveling modes.
S2, according to traffic flow data, the ARIMA model is used for realizing traffic flow prediction between the traffic hub and surrounding cities.
Specifically, the prediction of traffic flow between a traffic hub and surrounding cities using the ARIMA model comprises the steps of: preprocessing traffic flow data includes: the missing values and the outliers are processed and sorted into time-series data.
Further, the stability of the time series data is checked, and if a non-stationary sequence exists, the data is further subjected to differential processing.
Further, the stationary time series data is divided into a training set and a test set, the data before (m-7) days is used as the training set, and the data of the last 7 days is used as the test set; fitting an ARIMA model, and further determining parameters and checking the model; and predicting the traffic flow by using the ARIMA model after training, and recovering the differential data to finally obtain traffic flow prediction data.
Furthermore, the stability of the time sequence data is checked, and the sequence data can be judged by adopting a unit root check method; if the unit root does not exist, the sequence is stable; if a unit root exists, differential processing is required for the non-stationary sequence.
Further, cross-validation is adopted to determine the optimal parameters of the ARIMA model, including an autoregressive term p, a difference number d and a moving average term q; and further carrying out validity checking and adjustment on the model until an ARIMA model meeting the checking requirement is obtained.
And S3, constructing a comprehensive radiation model of the traffic junction by adopting a linear regression model according to traffic flow prediction.
The formula of the constructed comprehensive radiation model of the transportation junction is as follows:
wherein F is the comprehensive radiation capacity of the transportation hub to one city around; x represents an x-th travel mode, and only comprises an automobile, a train, an airplane, a high-speed rail, a subway and an inter-city train; e is the total number of the traffic travel modes between the city and the traffic junction; pjx is the sharing ratio of the traffic flow of the x-th travel mode in the city j; mx is the cost average value of the x-th travel mode between two places; lx is the actual mileage of the x-th travel mode between two places; tx is the average value of time cost required by the x-th travel mode between two places; i represents an i-th traffic infrastructure, which only includes main trunk roads, light rails, railways, and highways in the urban central area and the peripheral areas; ki is the number of i-th infrastructure; h is the total number of traffic infrastructure types in the urban central area and the peripheral areas; dj is the number of large or famous tourist attraction resources in city j; fj is the radiation potential of city j; beta is a random error term; gx, px, ux, bi, cj are the coefficients of their corresponding influencing factors, respectively.
Furthermore, the construction of the comprehensive radiation model of the urban transportation junction merges a plurality of influence factors, so that a decision maker can be helped to understand the comprehensive influence capacity of the transportation junction more intuitively, and the decision is more scientific and objective; the model can help evaluate the radiation capability of the urban transportation junction, provide scientific basis for urban planning and transportation facility construction, be beneficial to optimizing the configuration of traffic resources and promote the development of urban transportation and economy.
S3.1, the sharing ratio Pjx of the x-th travel mode traffic flow is expressed as follows:
in which Q jx The traffic flow of the x-th travel mode between two places in the unit time in the city j.
Furthermore, the sharing ratio Pjx of the traffic flows of the various traffic travel modes in the city has an important influence on the urban traffic, and the situation of the urban traffic travel can be better known by quantifying and analyzing the traffic flows of the various traffic travel modes, so that a scientific basis is provided for making traffic policies and planning.
S3.2, the expression of the radiation potential fj of the city j is as follows:
wherein n is the number of cities around the transportation junction; e (E) j The annual total economic amount of the city j around the transportation junction; r is R j Is the total amount of resident population of the city j around the transportation junction.
Furthermore, the urban radiation potential fj integrates two important indexes of measuring the economic total quantity of the urban development level and the total quantity of the resident population, and can intuitively reflect the attractive force and influence of the urban on the economic, cultural and social development of the surrounding areas.
S3.3, the cost influence coefficient gx, the distance influence coefficient px, the time influence coefficient ux, the infrastructure level influence coefficient bi and the travel resource coefficient cj are all obtained by adopting an expert scoring method.
S3.4, predicting future comprehensive radiation capacity of the transportation junction specifically comprises the following steps:
taking the traffic flow per unit time in the comprehensive radiation model of the transportation hub as a variable, and taking the cost of fees, distance, time cost, convenience of transportation infrastructure, travel resources, the number of cities around the transportation hub, the total annual economic amount of cities and the resident population as constants; determining a characteristic variable as traffic flow, a target variable as comprehensive radiation capacity of a traffic junction, preprocessing a related data set, and converting the related data set into a two-dimensional array; dividing the historical data set into a training set and a testing set, using the data before (m-7) days for training, and using the data of the last 7 days for testing the performance of the linear rR check model in the scikit-learn library; constructing a linear regression model and training, testing and optimizing; and predicting the comprehensive radiation capacity of the target variable traffic hub based on the characteristic variable traffic flow prediction data.
Furthermore, the test set is used for evaluating the performance of the trained linear regression model, and if the performance of the model is not good enough, the performance of the model can be improved by optimizing model parameters, optimizing algorithms and the like.
Further, inputting the test set into an optimized linear regression model to obtain prediction data of comprehensive radiation capacity of the transportation junction, performing error analysis on a prediction result, if the prediction result is not accurate enough, re-performing data preprocessing, and attempting to improve model prediction accuracy by using different models and parameter combinations.
The intelligent prediction-based comprehensive radiation model for the traffic junction is built by creatively introducing a traffic flow prediction model, and the influence of factors such as cost, distance, time cost, convenience of traffic infrastructure, traffic flow, travel resources and the like on the comprehensive radiation capacity of the traffic junction can be comprehensively considered. The traffic junction comprehensive radiation model construction method based on intelligent prediction can effectively solve the problem of calculating the comprehensive radiation capacity of the urban traffic junction, and can provide a new idea for defining the comprehensive radiation capacity of the traffic junction.
Example 2
Referring to table 1, for one embodiment of the present application, based on the above method, a comparative description with the conventional scheme is provided in order to verify the advantageous effects thereof.
Table 1 comparison table
Through the above description, the advantages of the scheme on the aspects of accuracy, calculation capability and scientificity compared with the traditional transportation junction model can be clearly seen, the prediction of the comprehensive radiation capability of the urban transportation junction is effectively realized, and the credibility of the scheme on the my side is enhanced.
Through the description of the embodiments, it can be clearly understood by those skilled in the art that the application can effectively solve the problem of calculating the comprehensive radiation capacity of the transportation junction according to the comprehensive radiation model of the transportation junction based on intelligent prediction, scientifically calculate the comprehensive radiation capacity of the transportation junction to surrounding cities, and provide a new idea for defining the comprehensive radiation capacity of the transportation junction.
It should be noted that the above embodiments are only for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the technical solution of the present application, which is intended to be covered by the scope of the claims of the present application.

Claims (10)

1. A traffic hub comprehensive radiation model construction method based on intelligent prediction is characterized in that: comprising the following steps: selecting a traffic hub and a surrounding city range, and acquiring traffic flow data in a designated time range of a research area according to big data;
according to traffic flow data, an ARIMA model is used for realizing traffic flow prediction between a traffic hub and surrounding cities;
according to traffic flow prediction, constructing a comprehensive radiation model of the traffic junction by adopting a linear regression model;
and according to the prediction data of the comprehensive radiation model of the transportation junction, the comprehensive radiation capacity of the transportation junction is predicted.
2. The method for constructing the comprehensive radiation model of the transportation junction based on intelligent prediction according to claim 1, wherein the method comprises the following steps: the method for acquiring traffic flow data in the appointed time range of the research area according to the big data comprises the following steps:
an API for accessing the big data of the vacation position;
analyzing JSON data returned by the API;
and acquiring traffic flow data of various traffic travel modes.
3. The method for constructing the comprehensive radiation model of the transportation junction based on intelligent prediction according to claim 1 or 2, wherein the method comprises the following steps of: the method for predicting the traffic flow between the traffic junction and the surrounding cities by using the ARIMA model according to the traffic flow data comprises the following steps:
preprocessing traffic flow data, including processing missing values and abnormal values, and sorting the missing values and the abnormal values into time sequence data;
checking the stability of the time sequence data, and if a non-stable sequence exists, further performing differential processing on the data;
dividing the stable time series data into a training set and a testing set, taking the data before (m-7) days as the training set, and taking the data of the last 7 days as the testing set;
fitting an ARIMA model, and further determining parameters and checking the model;
and predicting the traffic flow by using the ARIMA model after training, and recovering the differential data to finally obtain traffic flow prediction data.
4. The method for constructing the comprehensive radiation model of the transportation junction based on intelligent prediction according to claim 3, wherein the method comprises the following steps: the stability of the time sequence data is tested by adopting a unit root test method to judge the sequence data;
if the unit root does not exist, the sequence is stable;
if a unit root exists, differential processing is required for the non-stationary sequence.
5. The method for constructing the comprehensive radiation model of the transportation junction based on intelligent prediction according to claim 3, wherein the method comprises the following steps: the ARIMA model is fitted, parameters are further determined and model verification is carried out, and the method comprises the following steps:
cross-validation is adopted to determine the optimal parameters of the ARIMA model, wherein the optimal parameters comprise an autoregressive term number p, a difference frequency d and a moving average term number q;
and further carrying out validity checking and adjustment on the model until an ARIMA model meeting the checking requirement is obtained.
6. The method for constructing the comprehensive radiation model of the transportation junction based on intelligent prediction according to claim 1, wherein the method comprises the following steps: the formula of the comprehensive radiation model of the transportation junction is as follows:
wherein F is the comprehensive radiation capacity of the transportation hub to one city around; x represents an x-th travel mode, and only comprises an automobile, a train, an airplane, a high-speed rail, a subway and an inter-city train; e is the total number of the traffic travel modes between the city and the traffic junction; pjx is the sharing ratio of the traffic flow of the x-th travel mode in the city j; mx is the cost average value of the x-th travel mode between two places; lx is the actual mileage of the x-th travel mode between two places; tx is the average value of time cost required by the x-th travel mode between two places; i represents an i-th traffic infrastructure, which only includes main trunk roads, light rails, railways, and highways in the urban central area and the peripheral areas; ki is the number of i-th infrastructure; h is the total number of traffic infrastructure types in the urban central area and the peripheral areas; dj is the number of large or famous tourist attraction resources in city j; fj is the radiation potential of city j; beta is a random error term; gx, px, ux, bi, cj are the coefficients of their corresponding influencing factors, respectively.
7. The intelligent prediction-based traffic hub comprehensive radiation model construction method according to claim 6, wherein: the expression of the sharing ratio Pjx of the x-th travel mode traffic flow is as follows:
in which Q jx The traffic flow of the x-th travel mode between two places in the unit time in the city j.
8. The intelligent prediction-based traffic hub comprehensive radiation model construction method according to claim 6, wherein: the expression of the radiation potential fj of the city j is as follows:
wherein n is the number of cities around the transportation junction; e (E) j The annual total economic amount of the city j around the transportation junction; r is R j Is the total amount of resident population of the city j around the transportation junction.
9. The intelligent prediction-based traffic hub comprehensive radiation model construction method according to claim 6, wherein: the gx, px, ux, bi, cj coefficients corresponding to the influence factors comprise a cost influence coefficient gx, a distance influence coefficient px, a time influence coefficient ux, an infrastructure level influence coefficient bi and a travel resource coefficient cj, which are all obtained by adopting an expert scoring method.
10. The method for constructing the comprehensive radiation model of the traffic hub based on intelligent prediction according to any one of claims 1, 2, 4, 5, 6, 7, 8 or 9, wherein the method comprises the following steps: the method for predicting the comprehensive radiation capacity of the transportation junction according to the predicted data of the comprehensive radiation model of the transportation junction comprises the following steps:
considering the traffic flow per unit time in the traffic hub integrated radiation model as a variable, and considering the cost of charge, distance, time cost, traffic infrastructure convenience, travel resources, the number of cities around the traffic hub, the total annual urban amount and the resident population as constants;
determining a characteristic variable as traffic flow, a target variable as comprehensive radiation capacity of a traffic junction, preprocessing a related data set, and converting the related data set into a two-dimensional array;
dividing the data set into a training set and a testing set, using the data before (m-7) days for training, and using the data of the last 7 days for testing the performance of the linear rR check model in the scikit-learn library;
constructing a linear regression model and training, testing and optimizing;
and predicting the comprehensive radiation capacity of the target variable traffic hub based on the characteristic variable traffic flow prediction data.
CN202310376453.3A 2023-04-10 2023-04-10 Intelligent prediction-based traffic junction comprehensive radiation model construction method Pending CN116596113A (en)

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