Intelligent Transportation Management System and Method for Travel Based on Big Data

Abstract

The present invention discloses an intelligent transportation management system and method for travel based on big data, belonging to the technical field of traffic travel management. First, the present invention identifies the vehicle import relationship between road segments, constructs traffic flow data sets in three dimensions of year, month, and day, calculates the maximum value, average value, and minimum value in each dimension, introduces trend change rate analysis, and combines with a road segment traffic flow prediction neural network for traffic flow prediction to achieve more accurate traffic flow prediction. By analyzing the flow ratio between connected road segments and dynamically correcting the prediction results, the traffic flow prediction of the monitored road segments is finally obtained more accurately. The present invention solves the problem of low accuracy of traffic flow prediction in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic management, and particularly relates to an intelligent traffic management system and method for travel based on big data. Background Art

[0002] In modern urban traffic management, accurately predicting traffic flow has always been a complex and challenging problem. Traditional traffic flow prediction methods mainly rely on static statistical models and simple time series analysis, and these methods often fail to effectively capture the dynamics and complexity of the traffic system. Existing technologies usually face multiple limitations: one is the lack of in-depth consideration of the road network, making it difficult to accurately reflect the mutual influence between different road sections; the other is that the prediction model's processing of the time dimension is too single, and it fails to fully consider the multi-scale time characteristics of year, month, and day. Therefore, existing technologies have the problem of low accuracy in predicting traffic flow. Summary of the Invention

[0003] Aiming at the above deficiencies in the prior art, an intelligent traffic management system and method for travel based on big data provided by the present invention solves the problem of low accuracy in predicting traffic flow existing in the prior art.

[0004] To achieve the above object of the invention, the technical solution adopted by the present invention is: an intelligent traffic management system for travel based on big data, including: a road section acquisition subsystem, a set construction subsystem, a change rate acquisition subsystem, a prediction subsystem, and a correction subsystem;

[0005] The road section acquisition subsystem is used to obtain the connecting road sections having a vehicle import relationship with the monitored road section according to the position of the monitored road section in the traffic route;

[0006] The set construction subsystem is used to extract the historical traffic flow of each road section according to the travel time, and construct sets of the maximum value, average value, and minimum value of the traffic flow in three dimensions of year, month, and day;

[0007] The change rate acquisition subsystem is used to obtain the trend change rate for each set, and obtain the maximum value, average value, and minimum value trend change rates in three dimensions of year, month, and day;

[0008] The prediction subsystem is used to process the sets of the maximum value, average value, and minimum value of the traffic flow in three dimensions by using a neural network for predicting road section flow, perform feature enhancement based on the maximum value, average value, and minimum value trend change rates in three dimensions, and obtain the predicted traffic flow of each road section;

[0009] The correction subsystem is used to obtain the flow ratio of the monitored road section and the connecting road section according to the set of the average value of the traffic flow in the day dimension, and correct the predicted traffic flow of the monitored road section based on the predicted traffic flow of the connecting road section to obtain the corrected predicted traffic flow of the monitored road section.

[0010] Further, the specific process of the set construction subsystem includes:

[0011] Obtain the travel time period according to the travel time;

[0012] Extract the maximum traffic flow, average traffic flow, and minimum traffic flow in the same travel time period for each adjacent historical year of each road segment, and arrange them in chronological order to obtain the annual maximum traffic flow set, annual average traffic flow set, and annual minimum traffic flow set;

[0013] Extract the maximum traffic flow, average traffic flow, and minimum traffic flow in the same travel time period for each adjacent historical month of each road segment, and arrange them in chronological order to obtain the monthly maximum traffic flow set, monthly average traffic flow set, and monthly minimum traffic flow set;

[0014] Extract the maximum traffic flow, average traffic flow, and minimum traffic flow at the same travel time for each adjacent historical day of each road segment, and arrange them in chronological order to obtain the daily maximum traffic flow set, daily average traffic flow set, and daily minimum traffic flow set.

[0015] Further, the specific process of the change rate acquisition subsystem includes:

[0016] Obtain the trend change rates for the annual maximum traffic flow set, annual average traffic flow set, and annual minimum traffic flow set respectively to obtain the annual maximum value trend change rate, annual average value trend change rate, and annual minimum value trend change rate;

[0017] Obtain the trend change rates for the monthly maximum traffic flow set, monthly average traffic flow set, and monthly minimum traffic flow set respectively to obtain the monthly maximum value trend change rate, monthly average value trend change rate, and monthly minimum value trend change rate;

[0018] Obtain the trend change rates for the daily maximum traffic flow set, daily average traffic flow set, and daily minimum traffic flow set respectively to obtain the daily maximum value trend change rate, daily average value trend change rate, and daily minimum value trend change rate.

[0019] Further, the formula for obtaining the trend change rate is: , where θ is the trend change rate, v i is the i-th traffic flow in the set, v i+1 is the (i + 1)-th traffic flow in the set, N is the number of traffic flows in the set, and i is a positive integer.

[0020] Further, the road segment traffic flow prediction neural network includes: a first fully connected unit, a second fully connected unit, a third fully connected unit, a first splicing layer, a second splicing layer, a fusion layer E1, a first convolutional layer, a second convolutional layer, and an output layer;

[0021] The first fully-connected unit is used to process the sets of the maximum, average, and minimum values of vehicle flow in the annual dimension to obtain the annual maximum feature, the annual average feature, and the annual minimum feature; the second fully-connected unit is used to process the sets of the maximum, average, and minimum values of vehicle flow in the monthly dimension to obtain the monthly maximum feature, the monthly average feature, and the monthly minimum feature; the third fully-connected unit is used to process the sets of the maximum, average, and minimum values of vehicle flow in the daily dimension to obtain the daily maximum feature, the daily average feature, and the daily minimum feature;

[0022] The first splicing layer is used to splice the output features of the three fully-connected units into the feature matrix; the second splicing layer is used to splice the trend change rates of the maximum, average, and minimum values in the three dimensions into the change rate matrix;

[0023] The input end of the fusion layer E1 is respectively connected to the output end of the first splicing layer and the output end of the second splicing layer, and its output end is connected to the input end of the first convolutional layer;

[0024] The output end of the first convolutional layer is connected to the input end of the second convolutional layer;

[0025] The input end of the output layer is connected to the output end of the second convolutional layer, and its output end serves as the output end of the road section traffic flow prediction neural network.

[0026] Furthermore, the first fully-connected unit, the second fully-connected unit, and the third fully-connected unit all contain: the first fully-connected layer, the second fully-connected layer, and the third fully-connected layer; in the first fully-connected unit, the first fully-connected layer processes the set of the annual maximum vehicle flow values to obtain the annual maximum feature, the second fully-connected layer processes the set of the annual average vehicle flow values to obtain the annual average feature, and the third fully-connected layer processes the set of the annual minimum vehicle flow values to obtain the annual minimum feature;

[0027] In the second fully-connected unit, the first fully-connected layer processes the set of the monthly maximum vehicle flow values to obtain the monthly maximum feature, the second fully-connected layer processes the set of the monthly average vehicle flow values to obtain the monthly average feature, and the third fully-connected layer processes the set of the monthly minimum vehicle flow values to obtain the monthly minimum feature;

[0028] In the third fully-connected unit, the first fully-connected layer processes the set of the daily maximum vehicle flow values to obtain the daily maximum feature, the second fully-connected layer processes the set of the daily average vehicle flow values to obtain the daily average feature, and the third fully-connected layer processes the set of the daily minimum vehicle flow values to obtain the daily minimum feature.

[0029] Furthermore, the fusion layer E1 is used to multiply the feature matrix and the change rate matrix element by element;

[0030] The convolution kernel size of the first convolutional layer is ;

[0031] The convolution kernel size of the second convolutional layer is .

[0032] Furthermore, the specific process of the correction subsystem includes:

[0033] Obtain the flow ratio of the monitoring section and the connecting section according to the set of traffic flow means in the daily dimension;

[0034] Calculate the mean of the predicted traffic flows of each connecting section to obtain the mean predicted traffic flow of the connecting section;

[0035] Multiply the mean predicted traffic flow of the connecting section by the flow ratio to obtain the comparison flow;

[0036] Take the mean of the comparison flow and the predicted traffic flow of the monitoring section to obtain the corrected predicted traffic flow of the monitoring section.

[0037] Furthermore, the specific process of obtaining the flow ratio of the monitoring section and the connecting section includes: calculating the mean of the traffic flows with the same number in the set of traffic flow means in the daily dimension of each connecting section to obtain the average traffic flow, taking the ratio of the traffic flow with the same number in the set of traffic flow means in the daily dimension of the monitoring section to the average traffic flow with the same number as the flow ratio of this number, and taking the mean of the flow ratios of each number as the flow ratio.

[0038] An intelligent traffic management method for travel based on big data includes the following steps:

[0039] S1. According to the position of the monitoring section in the traffic route, obtain the connecting sections that have a vehicle import relationship with the monitoring section;

[0040] S2. According to the travel time, extract the historical traffic flow of each section and construct sets of the maximum, mean, and minimum values of the traffic flow in the three dimensions of year, month, and day;

[0041] S3. Obtain the trend change rate for each set to obtain the maximum, mean, and minimum trend change rates in the three dimensions of year, month, and day;

[0042] S4. Use the section traffic flow prediction neural network to process the sets of the maximum, mean, and minimum values of the traffic flow in the three dimensions, and perform feature enhancement based on the maximum, mean, and minimum trend change rates in the three dimensions to obtain the predicted traffic flow of each section;

[0043] S5. According to the set of traffic flow means in the daily dimension, obtain the flow ratio of the monitoring section and the connecting section, and correct the predicted traffic flow of the monitoring section based on the predicted traffic flow of the connecting section to obtain the corrected predicted traffic flow of the monitoring section.

[0044] The beneficial effects of the present invention are as follows:

[0045] 1. Starting from the three dimensions of year, month, and day, the present invention obtains the maximum, average, and minimum value sets of traffic flow in the three dimensions, can comprehensively capture the periodicity and trend of traffic flow on different time scales, and then extracts the trend change rates in the three dimensions to deeply explore the dynamic evolution characteristics of traffic flow. The present invention then uses a section traffic flow prediction neural network to process the maximum, average, and minimum value sets of traffic flow in the three dimensions, and improves the accuracy of predicting the traffic flow of each section based on the feature enhancement of the trend change rates in the three dimensions.

[0046] 2. Vehicles will flow between different sections, and the change in traffic flow of the connecting section will directly or indirectly affect the monitored section. By calculating the flow ratio through the average value set of traffic flow in the daily dimension, this correlation can be quantified, and the proportional law of traffic flow between the monitored section and the connecting section under normal conditions can be reflected. Thus, the predicted traffic flow of the monitored section can be corrected through the predicted traffic flow of the connecting section, further improving the accuracy of traffic flow prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a system block diagram of an intelligent traffic management system for travel based on big data;

[0048] Figure 2 is a relationship diagram between the monitored section and the connecting section;

[0049] Figure 3 is a schematic structural diagram of a section traffic flow prediction neural network;

[0050] Figure 4 is a schematic structural diagram of the first fully connected unit;

[0051] Figure 5 is a schematic structural diagram of the second fully connected unit;

[0052] Figure 6 is a schematic structural diagram of the third fully connected unit. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] The following describes the specific embodiments of the present invention to facilitate those skilled in the art of the present technology to understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art of the present technology, as long as various changes are within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are within the scope of protection.

[0054] Example 1, as Figure 1As shown, an intelligent transportation management system for travel based on big data includes: a road section acquisition subsystem, a set construction subsystem, a change rate acquisition subsystem, a prediction subsystem, and a correction subsystem;

[0055] The road section acquisition subsystem is used to obtain the connecting road sections that have a vehicle import relationship with the monitored road section according to the position of the monitored road section in the traffic route;

[0056] The set construction subsystem is used to extract the historical traffic flow of each road section according to the travel time, and construct sets of the maximum, average, and minimum traffic flows in three dimensions of year, month, and day;

[0057] The change rate acquisition subsystem is used to obtain the trend change rate for each set, and obtain the maximum, average, and minimum trend change rates in three dimensions of year, month, and day;

[0058] The prediction subsystem is used to process the sets of the maximum, average, and minimum traffic flows in three dimensions by using a road section traffic flow prediction neural network, and perform feature enhancement based on the maximum, average, and minimum trend change rates in three dimensions to obtain the predicted traffic flow of each road section;

[0059] The correction subsystem is used to obtain the traffic flow ratio of the monitored road section and the connecting road section according to the set of the average traffic flow in the day dimension, and correct the predicted traffic flow of the monitored road section based on the predicted traffic flow of the connecting road section to obtain the corrected predicted traffic flow of the monitored road section.

[0060] As Figure 2 shown, the vehicles on the connecting road section can drive into the monitored road section.

[0061] In this embodiment, the specific process of the set construction subsystem includes:

[0062] Obtain the travel time period according to the travel time;

[0063] Extract the maximum traffic flow, average traffic flow, and minimum traffic flow of each road section in the same travel time period in each adjacent historical year, and arrange them in chronological order to obtain the annual maximum traffic flow set, annual average traffic flow set, and annual minimum traffic flow set;

[0064] Extract the maximum traffic flow, average traffic flow, and minimum traffic flow of each road section in the same travel time period in each adjacent historical month, and arrange them in chronological order to obtain the monthly maximum traffic flow set, monthly average traffic flow set, and monthly minimum traffic flow set;

[0065] Extract the maximum traffic flow, average traffic flow, and minimum traffic flow of each road section at the same travel time in each adjacent historical day, and arrange them in chronological order to obtain the daily maximum traffic flow set, daily average traffic flow set, and daily minimum traffic flow set.

[0066] The travel time period can be defined as a continuous time interval intercepted before and after a given travel time, centered around the given travel time.

[0067] For example, the travel time is 11:00 on May 9, 2025. In this embodiment, the travel time period is set as 10:00 - 12:00 on May 9, 2025. The adjacent historical years extracted are 2024, 2023, 2022, 2021, and 2020. The maximum traffic flow, average traffic flow, and minimum traffic flow are extracted respectively from 10:00 - 12:00 on May 9 of 2024, 2023, 2022, 2021, and 2020. The maximum traffic flows in each year dimension are arranged in chronological order to construct a set of annual maximum traffic flows; the average traffic flows in each year dimension are arranged in chronological order to construct a set of annual average traffic flows; the minimum traffic flows in each year dimension are arranged in chronological order to construct a set of annual minimum traffic flows.

[0068] The adjacent historical months extracted are April 2025, March 2025, February 2025, January 2025, and December 2024. The maximum traffic flow, average traffic flow, and minimum traffic flow of each road section are extracted respectively from the travel time period of these five adjacent historical months: 10:00 - 12:00 on April 9, 2025; 10:00 - 12:00 on March 9, 2025; 10:00 - 12:00 on February 9, 2025; 10:00 - 12:00 on January 9, 2025; and 10:00 - 12:00 on December 9, 2024.

[0069] The adjacent historical days refer to multiple consecutive dates selected forward based on the currently occurred date.

[0070] If the current date is June 5, 2025 (a date that has occurred, and June 7, 2025 and June 8, 2025 have not occurred and are not considered), then the adjacent historical days are: June 6, 2025; June 4, 2025; June 3, 2025; June 2, 2025; and June 1, 2025. The maximum traffic flow, average traffic flow, and minimum traffic flow are extracted respectively from 10:00 - 12:00 of these five dates: June 6, 2025; June 4, 2025; June 3, 2025; June 2, 2025; and June 1, 2025.

[0071] In this embodiment, the specific process of the change rate acquisition subsystem includes:

[0072] Obtain the trend change rates for the annual maximum traffic flow set, the annual average traffic flow set, and the annual minimum traffic flow set respectively, to obtain the annual maximum trend change rate, the annual average trend change rate, and the annual minimum trend change rate;

[0073] Obtain the trend change rates for the monthly maximum traffic flow set, the monthly average traffic flow set, and the monthly minimum traffic flow set respectively, to obtain the monthly maximum trend change rate, the monthly average trend change rate, and the monthly minimum trend change rate;

[0074] Obtain the trend change rates for the daily maximum traffic flow set, the daily average traffic flow set, and the daily minimum traffic flow set respectively, to obtain the daily maximum trend change rate, the daily average trend change rate, and the daily minimum trend change rate.

[0075] In this embodiment, the formula for obtaining the trend change rate is: , where θ is the trend change rate, v i is the i-th traffic flow in the set, v i+1 is the (i + 1)-th traffic flow in the set, N is the number of traffic flows in the set, and i is a positive integer.

[0076] The present invention obtains the trend change rates for the maximum, average, and minimum traffic flow sets at different time scales of year, month, and day respectively. The annual dimension grasps the long-term change law, the monthly dimension reflects the monthly periodicity, and the daily dimension captures the short-term fluctuations, comprehensively covering the analysis requirements of traffic flow change characteristics.

[0077] As Figure 3 shown, the road section traffic flow prediction neural network includes: a first fully connected unit, a second fully connected unit, a third fully connected unit, a first splicing layer, a second splicing layer, a fusion layer E1, a first convolutional layer, a second convolutional layer, and an output layer;

[0078] The first fully connected unit is used to process the annual maximum, average, and minimum traffic flow sets to obtain the annual maximum feature, the annual average feature, and the annual minimum feature; the second fully connected unit is used to process the monthly maximum, average, and minimum traffic flow sets to obtain the monthly maximum feature, the monthly average feature, and the monthly minimum feature; the third fully connected unit is used to process the daily maximum, average, and minimum traffic flow sets to obtain the daily maximum feature, the daily average feature, and the daily minimum feature;

[0079] The first splicing layer is used to splice the output features of the three fully connected units into feature matrix; the second splicing layer is used to splice the maximum, average, and minimum trend change rates in the three dimensions into change rate matrix;

[0080] The input end of the fusion layer E1 is respectively connected to the output ends of the first splicing layer and the second splicing layer, and its output end is connected to the input end of the first convolutional layer;

[0081] The output end of the first convolutional layer is connected to the input end of the second convolutional layer;

[0082] The input end of the output layer is connected to the output end of the second convolutional layer, and its output end serves as the output end of the road section traffic flow prediction neural network.

[0083] In the present invention, the first, second, and third fully connected units respectively process the maximum value, average value, and minimum value sets of the traffic flow in the annual, monthly, and daily dimensions, comprehensively capturing the traffic flow characteristics at different time scales. Then, through the first splicing layer, the features of each dimension are integrated into a feature matrix, and the second splicing layer integrates the trend change rate into a change rate matrix, realizing the systematic fusion of multi-dimensional data, enabling the model to comprehensively consider various aspects of traffic flow information and improving the prediction accuracy.

[0084] Each of the first fully connected unit, the second fully connected unit, and the third fully connected unit includes: a first fully connected layer, a second fully connected layer, and a third fully connected layer; as Figure 4 shown, in the first fully connected unit, the first fully connected layer processes the annual traffic flow maximum value set to obtain the annual maximum value feature, the second fully connected layer processes the annual traffic flow average value set to obtain the annual average value feature, and the third fully connected layer processes the annual traffic flow minimum value set to obtain the annual minimum value feature.

[0085] As Figure 5 shown, in the second fully connected unit, the first fully connected layer processes the monthly traffic flow maximum value set to obtain the monthly maximum value feature, the second fully connected layer processes the monthly traffic flow average value set to obtain the monthly average value feature, and the third fully connected layer processes the monthly traffic flow minimum value set to obtain the monthly minimum value feature.

[0086] As Figure 6 shown, in the third fully connected unit, the first fully connected layer processes the daily traffic flow maximum value set to obtain the daily maximum value feature, the second fully connected layer processes the daily traffic flow average value set to obtain the daily average value feature, and the third fully connected layer processes the daily traffic flow minimum value set to obtain the daily minimum value feature.

[0087] In this embodiment, the fusion layer E1 is used to multiply the feature matrix and the change rate matrix element by element;

[0088] The convolution kernel size of the first convolutional layer is ;

[0089] The convolution kernel size of the second convolutional layer is .

[0090] The fusion layer E1 outputs The fused feature matrix, and the convolution kernel size of the first convolutional layer is , so the first convolutional layer performs a convolution operation on each row of the fused feature matrix.

[0091] In this embodiment, the output layer is implemented using a fully connected layer.

[0092] The feature matrix is , where C is the feature matrix, c y,max is the annual maximum value feature, c y,avg is the annual average value feature, c y,min is the annual minimum value feature, c m,max is the monthly maximum value feature, c m,avg is the monthly average value feature, c m,min is the monthly minimum value feature, c s,max is the daily maximum value feature, c s,avg is the daily average value feature, c s,min is the daily minimum value feature.

[0093] The rate of change matrix is , where R is the rate of change matrix, r y,max is the annual maximum value trend rate of change, r y,avg is the annual average value trend rate of change, r y,min is the annual minimum value trend rate of change, r m,max is the monthly maximum value trend rate of change, r m,avg is the monthly average value trend rate of change, r m,min is the monthly minimum value trend rate of change, r s,max is the daily maximum value trend rate of change, r s,avg is the daily average value trend rate of change, r s,min is the daily minimum value trend rate of change.

[0094] The present invention multiplies the feature matrix and the rate of change matrix element by element to multiply the annual maximum value feature by the annual maximum value trend rate of change, multiply the annual average value feature by the annual average value trend rate of change, multiply the annual minimum value feature by the annual minimum value trend rate of change, and so on, to enhance the corresponding features, enhance the feature expression ability, and improve the prediction accuracy.

[0095] In this embodiment, the specific process of the correction subsystem includes:

[0096] Obtain the flow ratio of the monitoring section and the connecting section according to the set of traffic flow average values in the daily dimension;

[0097] Take the average of the predicted traffic flows of each connecting section to obtain the average predicted traffic flow of the connecting section;

[0098] Multiply the predicted traffic flow mean value of the connection section by the flow ratio to obtain the comparison flow.

[0099] Take the mean value of the comparison flow and the predicted traffic flow of the monitoring section to obtain the corrected predicted traffic flow of the monitoring section.

[0100] In this embodiment, the specific process of obtaining the flow ratio of the monitoring section and the connection section includes: taking the mean value of the traffic flow with the same number in the set of daily traffic flow mean values of each connection section to obtain the average traffic flow, and taking the ratio of the traffic flow with the same number in the set of daily traffic flow mean values of the monitoring section to the average traffic flow with the same number as the flow ratio of this number, and taking the mean value of the flow ratios of each number as the flow ratio.

[0101] The calculation formula of the flow ratio is: , where θ is the flow ratio, I mo,avg,k,n is the traffic flow with number n in the set of daily traffic flow mean values of the kth connection section (i.e., the nth traffic flow), I co,avg,n is the traffic flow with number n in the set of daily traffic flow mean values of the monitoring section, M is the number of traffic flows in the set of daily traffic flow mean values, n and k are positive integers, and K mo is the number of connection sections.

[0102] By obtaining the flow ratio of the monitoring section and the connection section, the present invention considers the correlation between the traffic flows of the monitoring section and the surrounding connection sections. Using the set of daily traffic flow mean values for calculation can capture the mutual influence of traffic flows between sections in the short term, and use the predicted traffic flow of the connection section to correct the monitoring section, further improving the prediction accuracy. No content in the original text, so it is not translated

[0103] No content in the original text, so it is not translated Embodiment 2, a big data-based intelligent traffic management method for travel, includes the following steps:

[0104] S1. According to the position of the monitoring section in the traffic route, obtain the connection sections having a vehicle import relationship with the monitoring section;

[0105] S2. According to the travel time, extract the historical traffic flow of each section, and construct sets of the maximum value, mean value, and minimum value of the traffic flow in three dimensions of year, month, and day;

[0106] S3. Obtain the trend change rate for each set to obtain the maximum value, mean value, and minimum value trend change rates in three dimensions of year, month, and day;

[0107] S4. Use the section traffic flow prediction neural network to process the sets of the maximum value, mean value, and minimum value of the traffic flow in three dimensions, and perform feature enhancement based on the maximum value, mean value, and minimum value trend change rates in three dimensions to obtain the predicted traffic flow of each section;

[0108] S5. Obtain the flow ratio of the monitoring section and the connecting section according to the set of average traffic flow under the daily dimension, and correct the predicted traffic flow of the monitoring section based on the predicted traffic flow of the connecting section to obtain the corrected predicted traffic flow of the monitoring section.

[0109] The specific implementation process of Embodiment 2 is the same as that of Embodiment 1.

[0110] Starting from the three dimensions of year, month, and day, the present invention obtains the sets of maximum, average, and minimum traffic flows under the three dimensions, which can comprehensively capture the periodicity and trend of traffic flow on different time scales. Then, the trend change rates under the three dimensions are extracted to deeply mine the dynamic evolution characteristics of traffic flow. The present invention further processes the sets of maximum, average, and minimum traffic flows under the three dimensions by using a section traffic flow prediction neural network, and improves the accuracy of predicting the traffic flow of each section based on the enhancement of the characteristics of the trend change rates under the three dimensions.

[0111] Vehicles will flow between different sections, and the change in the traffic flow of the connecting section will directly or indirectly affect the monitoring section. Calculating the flow ratio through the set of average traffic flow under the daily dimension can quantify this correlation relationship and reflect the proportional law of the traffic flow between the monitoring section and the connecting section under normal circumstances. Thus, the predicted traffic flow of the monitoring section is corrected based on the predicted traffic flow of the connecting section, and further improves the accuracy of traffic flow prediction.

[0112] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent transportation management system for travel based on big data, characterized in that, Including: A road section acquisition subsystem, a set construction subsystem, a change rate acquisition subsystem, a prediction subsystem, and a correction subsystem; The road section acquisition subsystem is used to obtain connection road sections that have a vehicle import relationship with the monitored road section according to the position of the monitored road section in the traffic route; The set construction subsystem is used to extract the historical traffic flow of each road section according to the travel time, and construct sets of the maximum, average, and minimum traffic flows in the three dimensions of year, month, and day; The change rate acquisition subsystem is used to obtain the trend change rate for each set, and obtain the maximum, average, and minimum trend change rates in the three dimensions of year, month, and day; The prediction subsystem is used to process the sets of the maximum, average, and minimum traffic flows in the three dimensions by using a road section flow prediction neural network, and perform feature enhancement based on the maximum, average, and minimum trend change rates in the three dimensions to obtain the predicted traffic flow of each road section; The correction subsystem is used to obtain the flow ratio between the monitored road section and the connection road sections according to the set of average traffic flows in the day dimension, and correct the predicted traffic flow of the monitored road section based on the predicted traffic flow of the connection road sections to obtain the corrected predicted traffic flow of the monitored road section; The specific process of the correction subsystem includes: Obtaining the flow ratio between the monitored road section and the connection road sections according to the set of average traffic flows in the day dimension; Calculating the average of the predicted traffic flows of each connection road section to obtain the average predicted traffic flow of the connection road sections; Multiplying the average predicted traffic flow of the connection road sections by the flow ratio to obtain the comparison flow; Taking the average of the comparison flow and the predicted traffic flow of the monitored road section to obtain the corrected predicted traffic flow of the monitored road section.

2. The intelligent transportation management system for travel based on big data according to claim 1, characterized in that The specific process of the set construction subsystem includes: Obtaining the travel time period according to the travel time; Extracting the maximum traffic flow, average traffic flow, and minimum traffic flow in the same travel time period of each adjacent historical year of each road section, and arranging them in chronological order to obtain the set of annual maximum traffic flows, the set of annual average traffic flows, and the set of annual minimum traffic flows; Extracting the maximum traffic flow, average traffic flow, and minimum traffic flow in the same travel time period of each adjacent historical month of each road section, and arranging them in chronological order to obtain the set of monthly maximum traffic flows, the set of monthly average traffic flows, and the set of monthly minimum traffic flows; Extracting the maximum traffic flow, average traffic flow, and minimum traffic flow at the same travel time of each adjacent historical day of each road section, and arranging them in chronological order to obtain the set of daily maximum traffic flows, the set of daily average traffic flows, and the set of daily minimum traffic flows.

3. The intelligent transportation management system for travel based on big data according to claim 1, wherein The specific process of the change rate acquisition subsystem includes: Respectively obtaining the trend change rate for the set of annual maximum traffic flows, the set of annual average traffic flows, and the set of annual minimum traffic flows to obtain the annual maximum trend change rate, the annual average trend change rate, and the annual minimum trend change rate; Respectively obtaining the trend change rate for the set of monthly maximum traffic flows, the set of monthly average traffic flows, and the set of monthly minimum traffic flows to obtain the monthly maximum trend change rate, the monthly average trend change rate, and the monthly minimum trend change rate; Obtain the trend change rates for the sets of daily traffic flow maximum values, daily traffic flow average values, and daily traffic flow minimum values respectively, to obtain the daily maximum value trend change rate, daily average value trend change rate, and daily minimum value trend change rate.

4. The intelligent transportation management system for travel based on big data according to claim 1, characterized in that The formula for obtaining the trend change rate is as follows: , where θ is the trend change rate, and v i is the traffic flow of the i-th vehicle in the set, and v i+1 is the traffic flow of the (i + 1)-th vehicle in the set, N is the number of traffic flows in the set, and i is a positive integer.

5. The intelligent transportation management system for travel based on big data according to claim 1, characterized in that The road section traffic flow prediction neural network includes: a first fully connected unit, a second fully connected unit, a third fully connected unit, a first splicing layer, a second splicing layer, a fusion layer E1, a first convolutional layer, a second convolutional layer, and an output layer; The first fully connected unit is used to process the sets of traffic flow maximum values, average values, and minimum values in the annual dimension to obtain annual maximum value features, annual average value features, and annual minimum value features; the second fully connected unit is used to process the sets of traffic flow maximum values, average values, and minimum values in the monthly dimension to obtain monthly maximum value features, monthly average value features, and monthly minimum value features; the third fully connected unit is used to process the sets of traffic flow maximum values, average values, and minimum values in the daily dimension to obtain daily maximum value features, daily average value features, and daily minimum value features; The first splicing layer is used to splice the output features of three fully-connected units into the feature matrix; The second splicing layer is used to splice the maximum value, average value, and minimum value trend change rates in three dimensions into the change rate matrix; The input end of the fusion layer E1 is respectively connected to the output end of the first splicing layer and the output end of the second splicing layer, and its output end is connected to the input end of the first convolutional layer; The output end of the first convolutional layer is connected to the input end of the second convolutional layer; The input end of the output layer is connected to the output end of the second convolutional layer, and its output end serves as the output end of the road section traffic flow prediction neural network.

6. The intelligent transportation management system for travel based on big data according to claim 5, characterized in that, The first fully connected unit, the second fully connected unit, and the third fully connected unit all include: a first fully connected layer, a second fully connected layer, and a third fully connected layer; in the first fully connected unit, the first fully connected layer processes the set of annual traffic flow maximum values to obtain annual maximum value features, the second fully connected layer processes the set of annual traffic flow average values to obtain annual average value features, and the third fully connected layer processes the set of annual traffic flow minimum values to obtain annual minimum value features; In the second fully connected unit, the first fully connected layer processes the set of monthly traffic flow maximum values to obtain monthly maximum value features, the second fully connected layer processes the set of monthly traffic flow average values to obtain monthly average value features, and the third fully connected layer processes the set of monthly traffic flow minimum values to obtain monthly minimum value features; In the third fully connected unit, the first fully connected layer processes the set of daily traffic flow maximum values to obtain daily maximum value features, the second fully connected layer processes the set of daily traffic flow average values to obtain daily average value features, and the third fully connected layer processes the set of daily traffic flow minimum values to obtain daily minimum value features.

7. The intelligent transportation management system for travel based on big data according to claim 5, characterized in that, The fusion layer E1 is used to multiply the feature matrix and the change rate matrix element by element; The convolution kernel size of the first convolutional layer is ; The convolution kernel size of the second convolutional layer is .

8. The intelligent transportation management system for travel based on big data according to claim 1, characterized in that The specific process of obtaining the traffic flow ratio of the monitoring road section and the connecting road section includes: calculating the average value of the traffic flows with the same number in the set of daily traffic flow average values of each connecting road section to obtain the average traffic flow, and taking the ratio of the traffic flow with the same number in the set of daily traffic flow average values of the monitoring road section to the average traffic flow with the same number as the traffic flow ratio of this number, and taking the average value of the traffic flow ratios of each number as the traffic flow ratio.

9. A travel intelligent traffic management method based on big data, implemented based on the travel intelligent traffic management system according to any one of claims 1 to 8, characterized in that, It includes the following steps: S1. According to the position of the monitoring road section in the traffic route, obtain the connecting road sections that have a vehicle import relationship with this monitoring road section; S2. Extract the historical traffic flow of each road section according to the travel time, and construct the sets of the maximum, average, and minimum traffic flows in the three dimensions of year, month, and day; S3. Obtain the trend change rate for each set to get the trend change rates of the maximum, average, and minimum traffic flows in the three dimensions of year, month, and day; S4. Use the road section traffic flow prediction neural network to process the sets of the maximum, average, and minimum traffic flows in the three dimensions, and perform feature enhancement based on the trend change rates of the maximum, average, and minimum traffic flows in the three dimensions to obtain the predicted traffic flow of each road section; S5. According to the set of the average traffic flow in the day dimension, obtain the flow ratio of the monitored road section and the connected road section, and correct the predicted traffic flow of the monitored road section based on the predicted traffic flow of the connected road section to obtain the corrected predicted traffic flow of the monitored road section.

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