Regional traffic flow prediction method and system

Abstract

The invention relates to a regional traffic flow prediction method and system. The method comprises the following steps: acquiring historical traffic associated data of a target region; wherein the historical traffic associated data comprises historical passenger flow data, weather data and holiday and festival data; based on the historical traffic associated data, utilizing an integrated learning method to obtain a passenger flow prediction model; and based on the passenger flow prediction model, predicting passenger flow data of the target area in a period of time in the future. The method can effectively predict and cope with the sudden increase of the passenger flow, avoids traffic congestion, guarantees the service quality, and is of great significance to urban traffic management.

Description

Technical Field

[0001] The present invention relates to the technical field of passenger flow prediction, and in particular to a method and system for regional traffic flow prediction. Background Art

[0002] The core technical problem facing urban traffic management is how to accurately predict and respond to exponentially growing passenger flow. Traditional linear prediction models are difficult to capture sudden surges in passenger flow, resulting in untimely allocation of traffic resources, causing congestion and safety hazards. Exponential passenger flow growth is characterized by rapid and drastic growth, and its triggering factors are complex and changeable, including weather changes, emergencies, holidays, etc. This complexity makes it difficult for a single prediction model to adapt to diverse scenarios. At the same time, the spatial distribution of passenger flow is also highly dynamic, and some areas may become hotspots in a short period of time, posing challenges to accurate early warning. On the other hand, the real-time and adaptability of the prediction model are also key issues. How to quickly identify the characteristic patterns of exponential growth in massive data and adjust the prediction parameters in time to adapt to the changing passenger flow situation. In addition, the generation and transmission of warning information also face the challenge of timeliness. How to make accurate warnings in the early stages of passenger flow surges to gain valuable time for traffic resource allocation, the solution of these problems is of great significance to improving the level of urban traffic management, ensuring public safety and improving service

[0003] Quality is of the essence. Summary of the invention

[0004] The purpose of the present invention is to solve the problems existing in the above-mentioned prior art.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A regional traffic flow prediction method, comprising:

[0007] Acquire historical traffic-related data of the target area; wherein the historical traffic-related data includes: historical passenger flow data, weather data, and holiday data;

[0008] Based on the historical traffic-related data, a passenger flow prediction model is obtained by using an integrated learning method;

[0009] Based on the passenger flow prediction model, passenger flow data of the target area in the future period is predicted.

[0010] Optionally, obtaining historical traffic-related data of the target area includes:

[0011] Obtain historical passenger flow data, weather data, and holiday data for the target area;

[0012] Performing statistical analysis on the historical passenger flow data according to different regions and time periods to obtain the passenger flow mean, variance and peak statistical indicators for each region and time period;

[0013] According to the statistical indicators, determine the passenger flow change trend in each area and each time period;

[0014] If the passenger flow in a certain area during a certain period shows an exponential growth trend, the corresponding area and period will be marked as potential passenger flow peak areas and periods;

[0015] For the potential passenger flow peak areas and time periods, time series features of passenger flow data are extracted; wherein the time series features include the variation patterns of passenger flow mean, variance and peak value on different time scales.

[0016] Optionally, obtaining the passenger flow prediction model includes:

[0017] Classify and quantify weather data according to weather conditions, and convert holiday data into binary features;

[0018] The classified and quantified weather data, the holiday data converted into binary features, and the time series features of the passenger flow data are combined as input to train the preset integrated model. The passenger flow prediction model is obtained.

[0019] Optionally, training the preset integrated model includes:

[0020] Using the merged data, the vector machine, long short-term memory neural network model and convolutional neural network model are trained respectively to obtain three sub-models;

[0021] The prediction performance of each sub-model on the merged data was evaluated through the 5-fold cross-validation method, and the weight coefficient of each sub-model in the integration was determined;

[0022] Based on each sub-model after determining the weight coefficient, the passenger flow prediction model is constructed.

[0023] Optionally, based on the passenger flow prediction model, predicting passenger flow data of a target area within a period of time in the future includes:

[0024] According to the output results of the passenger flow prediction model, determine the high-risk areas and high-risk time periods where the passenger flow rate shows exponential growth;

[0025] For each of the high-risk areas and high-risk time periods, set a corresponding passenger flow warning threshold;

[0026] When the predicted passenger flow in a high-risk area and high-risk period exceeds the corresponding warning threshold, warning information is automatically generated.

[0027] A regional traffic flow prediction system is used to implement a regional traffic flow prediction method, the system comprising: a data acquisition module, a model building module and a passenger flow prediction module;

[0028] The data acquisition module is used to obtain historical traffic-related data of the target area; wherein the historical traffic-related data includes: historical passenger flow data, weather data and holiday data;

[0029] The model building module is used to build a passenger flow prediction model based on the historical traffic related data using an integrated learning method;

[0030] The passenger flow prediction module is used to predict the passenger flow data of the target area within a period of time in the future based on the passenger flow prediction model.

[0031] Optionally, the data acquisition module includes: a data acquisition submodule and a data analysis submodule;

[0032] The data collection submodule is used to obtain historical passenger flow data, weather data and holiday data of the target area;

[0033] The data analysis submodule is used to perform statistical analysis on the historical passenger flow data according to different regions and time periods to obtain the passenger flow mean, variance and peak statistical indicators for each region and time period;

[0034] The data analysis submodule determines the passenger flow change trend of each area and each time period according to the statistical indicators; if the passenger flow of a certain area and a certain time period shows an exponential growth trend, the corresponding area and time period are marked as potential passenger flow peak areas and time periods;

[0035] The data analysis submodule extracts the time series characteristics of the passenger flow data for the potential passenger flow peak areas and time periods; wherein the time series characteristics include the variation patterns of the passenger flow mean, variance and peak value on different time scales.

[0036] Optionally, the model building module includes: a data conversion submodule and a model training submodule;

[0037] The data conversion submodule is used to classify and quantify weather data according to weather conditions and convert holiday data into binary features;

[0038] The model training submodule is used to combine the classified and quantified weather data, the holiday data converted into binary features, and the time series features of the passenger flow data as input to train the preset integrated model. The passenger flow prediction model is obtained.

[0039] Optionally, the model training submodule uses the merged data to train a vector machine, a long short-term memory neural network model and a convolutional neural network model respectively to obtain three sub-models;

[0040] The prediction performance of each sub-model on the merged data was evaluated through the 5-fold cross-validation method, and the weight coefficient of each sub-model in the integration was determined;

[0041] Based on each sub-model after determining the weight coefficient, the passenger flow prediction model is constructed.

[0042] Optionally, the passenger flow prediction module determines the high-risk areas and high-risk time periods where the passenger flow shows exponential growth according to the output results of the passenger flow prediction model;

[0043] For each of the high-risk areas and high-risk time periods, set a corresponding passenger flow warning threshold;

[0044] When the predicted passenger flow in a high-risk area and high-risk period exceeds the corresponding warning threshold, warning information is automatically generated.

[0045] The beneficial effects of the present invention are:

[0046] The present invention discloses a method and system for regional traffic flow prediction. The method obtains historical passenger flow data, adopts time series analysis, identifies characteristic patterns of exponential growth in passenger flow, and combines external factors such as weather and holidays to build a multi-model integrated passenger flow prediction model to improve the accuracy and adaptability of the prediction. According to the prediction results, the present invention can automatically determine high-risk areas and time periods, generate early warning information, and trigger the early scheduling of transportation resources. Through continuous monitoring and feedback mechanisms, the prediction algorithm is dynamically optimized to provide reliable decision-making support for coping with exponential passenger flow. The present invention can effectively predict and respond to passenger flow surges, avoid traffic congestion, and ensure service quality, which is of great significance to urban traffic management. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0048] Figure 1 The present invention is a flow chart of a method for predicting regional traffic flow according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0051] like Figure 1 As shown, this embodiment proposes a regional traffic flow prediction method, which specifically includes:

[0052] Obtain traffic-related data of the target area; wherein the traffic-related data includes: historical passenger flow data, weather data and holiday data;

[0053] Based on traffic-related data, the passenger flow prediction model is obtained using ensemble learning methods;

[0054] Based on the passenger flow prediction model, the passenger flow data of the target area in the future period is predicted.

[0055] Specifically, in this embodiment, a regional traffic flow prediction method first obtains historical passenger flow data, weather data, and holiday data, and uses an integrated learning method to establish an exponential passenger flow prediction model; based on the output results of the exponential passenger flow prediction model, high-risk areas and high-risk time periods where passenger flow increases exponentially are determined; for each high-risk area and high-risk time period, a corresponding passenger flow warning threshold is set; when the predicted passenger flow for a certain high-risk area and high-risk time period exceeds the corresponding warning threshold, a warning information is automatically generated to trigger the early scheduling of transportation resources.

[0056] Furthermore, obtaining traffic-related data of the target area includes:

[0057] Obtain historical passenger flow data, weather data, and holiday data for the target area;

[0058] Conduct statistical analysis on historical passenger flow data by region and time period to obtain the mean, variance and peak statistical indicators of passenger flow in each region and time period;

[0059] Determine the passenger flow trend in each area and time period based on statistical indicators;

[0060] If the passenger flow in a certain area during a certain period shows an exponential growth trend, the corresponding area and period will be marked as potential passenger flow peak areas and periods;

[0061] For potential passenger flow peak areas and time periods, the time series characteristics of passenger flow data are extracted; among them, the time series characteristics include the changing patterns of passenger flow mean, variance and peak value on different time scales.

[0062] Specifically, in this embodiment, firstly, the daily passenger flow data of the past year is obtained from the transportation operation department, and the weather data of the same period, including temperature, humidity, rainfall, etc., is obtained from the meteorological department, and holiday information is extracted from the calendar. When obtaining the historical passenger flow data and real-time passenger flow monitoring data of the target area, the video monitoring system, infrared sensor and other equipment can be used to collect the passenger flow data in real time and transmit the data to the database. For example, by installing an infrared sensor at the entrance of the shopping mall, the sensor will automatically record and upload the data whenever a customer enters or exits. At the same time, the system will also regularly extract the historical passenger flow data from the database and summarize and analyze it with the real-time data. When statistically analyzing the data, the system will divide it according to different areas and time periods, such as dividing the shopping mall into clothing areas, dining areas, entertainment areas, etc., and statistics are performed according to one time period per hour. By calculating the mean, variance and peak values ​​of passenger flow in each area and time period, the system can determine the changing trend of passenger flow. When it is found that the passenger flow in a certain area and time period shows an exponential growth trend, such as the passenger flow in the dining area during lunch time increases at a rate of 20% per hour, the system will automatically mark it as a potential peak area and time period. For these potential peaks, the system will extract the time series features of its passenger flow data, such as calculating the changes in the mean, variance, and peak value of passenger flow every 15 minutes and every 30 minutes.

[0063] Furthermore, obtaining the passenger flow prediction model includes:

[0064] Classify and quantify weather data according to weather conditions, and convert holiday data into binary features;

[0065] Combine the classified and quantified weather data, the holiday data converted into binary features, and the time series features of the passenger flow data as input to train the preset integrated model. Obtain the passenger flow prediction model.

[0066] Furthermore, training the preset integrated model includes:

[0067] Using the merged data, the vector machine, long short-term memory neural network model and convolutional neural network model are trained respectively to obtain three sub-models;

[0068] The prediction performance of each sub-model on the merged data was evaluated through the 5-fold cross-validation method, and the weight coefficient of each sub-model in the integration was determined;

[0069] Based on each sub-model with determined weight coefficients, a passenger flow prediction model is constructed.

[0070] Specifically, in this embodiment, when obtaining the historical data of index passenger flow, in addition to the passenger flow data, it is also necessary to collect external factor data such as weather conditions, holiday information and major event records. For example, the daily weather conditions of the past year can be obtained from the meteorological department and classified and quantified, such as sunny days are recorded as 1, cloudy days are recorded as 2, light rain is recorded as 3, moderate rain is recorded as 4, heavy rain is recorded as 5, etc. Holiday and major event information can be extracted from news reports and converted into binary features, that is, occurrence is recorded as 1, and non-occurrence is recorded as 0. When constructing an index passenger flow prediction model, the passenger flow, weather conditions, holidays and major event features of the past 30 days can be used as input to predict the passenger flow in the next 7 days. Through methods such as grid search, the hyperparameters of the model, such as the number of hidden layers, the number of hidden units, the learning rate, etc., are optimized to improve the prediction performance of the model. In the model evaluation stage, indicators such as MAE and MAPE can be used to measure the prediction error. For example, if the predicted passenger flow in the next 7 days is 1000, 1200, 1500, 1800, 2000, 2200 and 2500 respectively, and the actual passenger flow is 1100, 1300, 1600, 1900, 2100, 2300 and 2600, then the MAE is 100 and the MAPE is 2%. By analyzing the impact of external factors on the prediction results, it can be found that holidays and major events will significantly increase passenger flow, while bad weather will reduce passenger flow. Based on this, the feature engineering method can be further optimized, such as introducing the mean feature of the week before and after the holidays to improve the robustness of the prediction model. After multiple rounds of iterative optimization, an accurate and robust exponential passenger flow prediction model is finally obtained, which can provide important data support for business planning, traffic scheduling, etc.

[0071] More specifically, this embodiment uses an integrated learning method to construct multiple different types of passenger flow prediction sub-models according to the passenger flow prediction business needs. According to the characteristics of passenger flow data, algorithms such as support vector machine (SVM), long short-term memory neural network (LSTM) and convolutional neural network (CNN) are selected, and three sub-models are trained respectively. The prediction performance of each sub-model on the test set is evaluated by the 5-fold cross-validation method, and the mean absolute percentage error (MAPE) of the SVM model is 8%, the MAPE of the LSTM model is 6%, and the MAPE of the CNN model is 7%. According to the prediction accuracy of each sub-model, the weight coefficients of the SVM, LSTM and CNN models in the integration are determined to be 3, 5 and 2 respectively. The prediction results of each sub-model are weighted and fused according to the weight coefficient to obtain the final passenger flow prediction result. A sliding window with a length of 30 days is used to update the training data once a week, retrain each sub-model, and use the passenger flow data of the latest week to update the fusion weight to adapt to the dynamic changes in the passenger flow pattern. By integrating multiple sub-models of different types, and making comprehensive use of the nonlinear fitting ability of the SVM model, the advantage of the LSTM model in capturing time series dependencies, and the ability of the CNN model to extract local features, the robustness and accuracy of the overall prediction are improved. In practical applications, the passenger flow prediction error is controlled within 5%, providing a reliable basis for passenger flow scheduling optimization.

[0072] Furthermore, based on the passenger flow prediction model, the passenger flow data of the target area in the future is predicted to include:

[0073] Based on the output of the passenger flow forecasting model, identify high-risk areas and high-risk periods where passenger flow may increase exponentially;

[0074] Set corresponding passenger flow warning thresholds for each high-risk area and high-risk period;

[0075] When the predicted passenger flow in a high-risk area and high-risk period exceeds the corresponding warning threshold, warning information is automatically generated.

[0076] Specifically, in this embodiment, the model predicts the hourly passenger flow of each area in the next week, and identifies high-risk areas and time periods with exponential growth trends in passenger flow through anomaly detection algorithms such as IsolationForest. For each high-risk area and time period, a passenger flow warning threshold is set according to historical data for the same period. When the predicted passenger flow exceeds the threshold, a warning message is automatically generated and pushed to the capacity dispatching system. The dispatching system combines real-time passenger flow monitoring data and uses reinforcement learning algorithms such as DQN to dynamically adjust the capacity deployment and increase the capacity in high-risk areas and time periods. In key areas with dense passenger flow, video analysis and infrared sensors are used to count the number of people entering and leaving the station in real time. Once the real-time passenger flow data shows that the passenger flow surges beyond the predetermined threshold, such as more than 1,000 passengers per 5 minutes, the emergency plan is immediately activated, and a temporary bus line is opened through the intelligent dispatching system to evacuate passengers and increase security personnel to maintain order. Finally, the cross-validation method is used to evaluate the accuracy of passenger flow warnings and the effectiveness of dispatching measures, and the Bayesian optimization method and other methods are used to automatically adjust the model hyperparameters to continuously optimize the prediction performance and dispatching strategy.

[0077] Furthermore, this embodiment also continuously monitors the deviation between the actual passenger flow and the predicted value, dynamically adjusts the parameters and structure of the prediction model through a feedback mechanism, continuously optimizes and improves the prediction algorithm, improves the real-time and accuracy of the prediction, and provides more reliable decision-making support for responding to exponential passenger flow.

[0078] Real-time passenger flow data is obtained and compared with the predicted value generated by the prediction model to calculate the deviation value; based on the calculated deviation value, it is determined whether the deviation exceeds the preset threshold. If it exceeds the threshold, the dynamic optimization of the prediction model is triggered; the gradient descent algorithm is used to adjust the parameters of the prediction model based on the deviation value, and the deviation between the predicted value and the actual passenger flow is minimized through multiple rounds of iterative optimization; in the model optimization process, a time decay factor is introduced to give a higher weight to recent data to improve the model's sensitivity and real-time performance to passenger flow changes; the optimized prediction model regenerates the passenger flow prediction value for a period of time in the future and continuously compares it with the real-time data to form a dynamic feedback loop; when the deviation between the predicted value and the actual passenger flow continues to decrease and remains within an acceptable range, it is determined that the prediction model has reached a better state and the optimization can be suspended; the optimized prediction results are output to provide an accurate and reliable decision-making basis for coping with possible passenger flow peaks, and to assist relevant departments in formulating emergency plans in advance.

[0079] Specifically, real-time passenger flow data is obtained every 5 minutes, and compared with the predicted value generated by the prediction model to calculate the deviation between the two. If the deviation exceeds the preset 20% threshold, the dynamic optimization of the prediction model is triggered. The gradient descent algorithm is used, and the deviation value is used as the loss function. The loss function is minimized by adjusting the model parameters. In each iteration, the model parameters are updated at a learning rate of 01 to 05 according to the size of the deviation value, and the time decay factor is introduced to give 5 times the weight to the data within the last hour, 2 times the weight to the data between 1 and 2 hours, and 1 weight to the data over 2 hours. After 50 iterations of optimization, the updated prediction model regenerates the passenger flow prediction value within the next 2 hours and continuously compares it with the real-time data. When the deviation between the predicted value and the actual passenger flow is reduced to less than 10% for 3 consecutive cycles, it is determined that the prediction model has reached a better state and the optimization can be suspended. The optimized prediction results are output and combined with the maximum capacity and carrying capacity of the station. If the predicted passenger flow exceeds the threshold, an early warning message will be sent to the subway dispatching center 30 minutes in advance. It is recommended to increase the number of trains in advance and coordinate security personnel to be prepared. If necessary, the emergency plan will be activated to evacuate stranded passengers and ensure safe and orderly operations.

[0080] This embodiment also proposes a regional traffic flow prediction system, including: a data collection module, a model building module and a passenger flow prediction module;

[0081] A data collection module is used to obtain historical traffic-related data of a target area; wherein the historical traffic-related data includes: historical passenger flow data, weather data, and holiday data;

[0082] Model building module, used to build a passenger flow prediction model based on historical traffic correlation data using ensemble learning methods;

[0083] The passenger flow prediction module is used to predict the passenger flow data of the target area in the future based on the passenger flow prediction model.

[0084] Further, the data acquisition module includes: a data acquisition submodule and a data analysis submodule;

[0085] The data collection submodule is used to obtain historical passenger flow data, weather data, and holiday data in the target area;

[0086] The data analysis submodule is used to perform statistical analysis on historical passenger flow data by different regions and time periods, and obtain the mean, variance and peak statistical indicators of passenger flow in each region and time period;

[0087] The data analysis submodule determines the passenger flow change trend of each area and time period based on statistical indicators; if the passenger flow of a certain area and time period shows an exponential growth trend, the corresponding area and time period will be marked as a potential passenger flow peak area and time period;

[0088] The data analysis submodule extracts the time series characteristics of passenger flow data for potential passenger flow peak areas and time periods; among them, the time series characteristics include the changing patterns of passenger flow mean, variance and peak value on different time scales.

[0089] Furthermore, the model building module includes: a data conversion submodule and a model training submodule;

[0090] The data conversion submodule is used to classify and quantify weather data according to weather conditions and convert holiday data into binary features;

[0091] The model training submodule is used to combine the classified and quantified weather data, the holiday data converted into binary features, and the time series features of the passenger flow data as input, train the preset integrated model, and obtain the passenger flow prediction model.

[0092] Furthermore, the model training submodule uses the merged data to train the vector machine, the long short-term memory neural network model and the convolutional neural network model respectively to obtain three sub-models;

[0093] The prediction performance of each sub-model on the merged data was evaluated through the 5-fold cross-validation method, and the weight coefficient of each sub-model in the integration was determined;

[0094] Based on each sub-model after determining the weight coefficient, a passenger flow prediction model is constructed.

[0095] Furthermore, the passenger flow prediction module determines the high-risk areas and high-risk time periods where the passenger flow shows exponential growth based on the output results of the passenger flow prediction model;

[0096] Set corresponding passenger flow warning thresholds for each high-risk area and high-risk period;

[0097] When the predicted passenger flow in a high-risk area and high-risk period exceeds the corresponding warning threshold, warning information is automatically generated.

[0098] This embodiment discloses a method and system for regional traffic flow prediction. This method obtains historical and real-time passenger flow data, uses time series analysis and spatial clustering algorithms to identify characteristic patterns of exponential growth in passenger flow. Combined with external factors such as weather, holidays, etc., a multi-model integrated exponential passenger flow prediction model is constructed to improve the accuracy and adaptability of the prediction. According to the prediction results, this embodiment can automatically determine high-risk areas and time periods, generate early warning information, and trigger the early scheduling of transportation resources. Through continuous monitoring and feedback mechanisms, the prediction algorithm is dynamically optimized to provide reliable decision-making support for coping with exponential passenger flow. The present invention can effectively predict and respond to passenger flow surges, avoid traffic congestion, and ensure service quality, which is of great significance to urban traffic management.

[0099] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A regional traffic flow prediction method, characterized in that: include: Acquire historical traffic-related data of the target area; wherein the historical traffic-related data includes: historical passenger flow data, weather data, and holiday data; Based on the historical traffic-related data, a passenger flow prediction model is obtained by using an integrated learning method; Based on the passenger flow prediction model, passenger flow data of the target area in the future period is predicted.

2. The regional traffic flow prediction method according to claim 1, characterized in that: Obtaining historical traffic-related data for the target area includes: Obtain historical passenger flow data, weather data, and holiday data for the target area; Performing statistical analysis on the historical passenger flow data according to different regions and time periods to obtain the passenger flow mean, variance and peak statistical indicators for each region and time period; According to the statistical indicators, determine the passenger flow change trend in each area and each time period; If the passenger flow in a certain area during a certain period shows an exponential growth trend, the corresponding area and period will be marked as potential passenger flow peak areas and periods; For the potential passenger flow peak areas and time periods, time series features of passenger flow data are extracted; wherein the time series features include the variation patterns of passenger flow mean, variance and peak value on different time scales.

3. The regional traffic flow prediction method according to claim 2, characterized in that: Acquiring the passenger flow prediction model includes: Classify and quantify weather data according to weather conditions, and convert holiday data into binary features; The classified and quantified weather data, the holiday data converted into binary features, and the time series features of the passenger flow data are combined as inputs, and the preset integrated model is trained to obtain the passenger flow prediction model.

4. The regional traffic flow prediction method according to claim 3, characterized in that: Training a preset ensemble model includes: Using the merged data, the vector machine, long short-term memory neural network model and convolutional neural network model are trained respectively to obtain three sub-models; The prediction performance of each sub-model on the merged data was evaluated through the 5-fold cross-validation method, and the weight coefficient of each sub-model in the integration was determined; The passenger flow prediction model is constructed based on each sub-model after the weight coefficient is determined.

5. The regional traffic flow prediction method according to claim 1, characterized in that: Based on the passenger flow prediction model, the passenger flow data of the target area in the future period is predicted to include: According to the output results of the passenger flow prediction model, determine the high-risk areas and high-risk time periods where the passenger flow rate shows exponential growth; For each of the high-risk areas and high-risk time periods, set a corresponding passenger flow warning threshold; When the predicted passenger flow in any high-risk area and high-risk period exceeds the corresponding warning threshold, warning information is automatically generated.

6. A regional traffic flow prediction system, characterized in that: Used to implement the regional traffic flow prediction method as described in any one of claims 1 to 5, the system comprises: a data acquisition module, a model building module and a passenger flow prediction module; The data acquisition module is used to obtain historical traffic-related data of the target area; wherein the historical traffic-related data includes: historical passenger flow data, weather data and holiday data; The model building module is used to build a passenger flow prediction model based on the historical traffic related data using an integrated learning method; The passenger flow prediction module is used to predict the passenger flow data of the target area within a period of time in the future based on the passenger flow prediction model.

7. The regional traffic flow prediction system according to claim 6, characterized in that: The data acquisition module includes: a data acquisition submodule and a data analysis submodule; The data collection submodule is used to obtain historical passenger flow data, weather data and holiday data of the target area; The data analysis submodule is used to perform statistical analysis on the historical passenger flow data according to different regions and time periods to obtain the passenger flow mean, variance and peak statistical indicators for each region and time period; The data analysis submodule determines the passenger flow change trend of each area and each time period according to the statistical indicators; if the passenger flow of a certain area and a certain time period shows an exponential growth trend, the corresponding area and time period are marked as potential passenger flow peak areas and time periods; The data analysis submodule extracts the time series characteristics of the passenger flow data for the potential passenger flow peak areas and time periods; wherein the time series characteristics include the variation patterns of the passenger flow mean, variance and peak value on different time scales.

8. The regional traffic flow prediction system according to claim 7, characterized in that: The model building module includes: a data conversion submodule and a model training submodule; The data conversion submodule is used to classify and quantify weather data according to weather conditions and convert holiday data into binary features; The model training submodule is used to combine the classified and quantified weather data, the holiday data converted into binary features, and the time series features of the passenger flow data as input, train the preset integrated model, and obtain the passenger flow prediction model.

9. The regional traffic flow prediction system according to claim 8, characterized in that: The model training submodule uses the merged data to train the vector machine, the long short-term memory neural network model and the convolutional neural network model respectively to obtain three sub-models; The prediction performance of each sub-model on the merged data was evaluated through the 5-fold cross-validation method, and the weight coefficient of each sub-model in the integration was determined; The passenger flow prediction model is constructed based on each sub-model after the weight coefficient is determined.

10. The regional traffic flow prediction system according to claim 6, characterized in that: The passenger flow prediction module determines the high-risk areas and high-risk time periods where the passenger flow shows exponential growth according to the output results of the passenger flow prediction model; For each of the high-risk areas and high-risk time periods, set a corresponding passenger flow warning threshold; When the predicted passenger flow in a high-risk area and high-risk period exceeds the corresponding warning threshold, warning information is automatically generated.

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