A bus route congestion point reminding system based on big data

Abstract

The present application belongs to the field of traffic guidance, and relates to a data analysis technique, which is used to solve the problem that the existing bus route congestion point reminding system cannot predict and analyze the congestion situation of the remaining complete path of the bus route, and specifically relates to a bus route congestion point reminding system based on big data, which comprises a congestion reminding platform, wherein a road condition analysis module, a congestion analysis module, a grade evaluation module and a storage module are communicatively connected to the congestion reminding platform; the road condition analysis module is used to detect and analyze the driving road condition of the bus route; a bus in operation is marked as a detection object, and an untraveled section in the driving line of the detection object is marked as an analysis line; the present application can detect and analyze the driving road condition of the bus route, obtain a congestion coefficient by comprehensively analyzing multiple parameters such as the carrying vehicles of the road and the carrying capacity of the vehicles, and then feed back the driving road condition state of the bus route through the congestion coefficient.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of traffic guidance, and relates to a data analysis technique, in particular to a bus route congestion point reminding system based on big data. BACKGROUND

[0002] Urban traffic congestion refers to a phenomenon of slow speed due to too many vehicles, which usually occurs at holidays or rush hours. This phenomenon often occurs in metropolitan areas, highways connecting two cities, and areas with high car usage.

[0003] The existing bus route congestion point reminding system can only detect and remind the congestion of the current driving section, and cannot predict and analyze the congestion of the remaining complete path of the bus route, nor can it evaluate the congestion level of the bus in combination with the congestion of the complete path and the distribution of the congestion section, so that the bus driver cannot obtain real and comprehensive road congestion data.

[0004] In view of the above technical problems, the application provides a solution. SUMMARY

[0005] The application aims to provide a bus route congestion point reminding system based on big data, which can solve the problem that the existing bus route congestion point reminding system cannot predict and analyze the congestion of the remaining complete path of the bus route.

[0006] The application needs to solve the technical problem of how to provide a bus route congestion point reminding system based on big data, which can predict and analyze the congestion of the remaining complete path of the bus route.

[0007] The application can be achieved by the following technical scheme:

[0008] A bus route congestion point reminding system based on big data comprises a congestion reminding platform, which is communicatively connected with a road condition analysis module, a congestion analysis module, a level evaluation module, and a storage module.

[0009] The road condition analysis module is used to detect and analyze the driving conditions of the bus route: mark the bus in the running state as a detection object, obtain the driving route of the detection object, mark the untraveled section in the driving route of the detection object as an analysis route, divide the analysis route into a plurality of analysis sections, and obtain the congestion coefficient YD of the analysis sections; send the congestion coefficient YD of the analysis sections to the congestion reminding platform, and send the congestion coefficient YD to the congestion analysis module after the congestion reminding platform receives the congestion coefficient YD.

[0010] The congestion analysis module is used for detecting and analyzing the congestion of the bus line: marking the top L1 analysis road sections as direct road sections, and marking the remaining analysis road sections as comprehensive road sections; performing congestion prediction analysis on the comprehensive road sections, and replacing the congestion coefficient YD of the comprehensive road sections according to the congestion prediction analysis result; obtaining the congestion threshold YDmax through the storage module, comparing the congestion coefficient YD of the analysis road section with the congestion threshold YDmax, and marking the analysis road section as a normal road section or a congestion road section according to the comparison result; and sending the congestion road section to the congestion reminding platform, which sends the congestion road section to the grade evaluation module after receiving the congestion road section.

[0011] The grade evaluation module is used for evaluating and analyzing the congestion grade of the bus line.

[0012] As a preferred embodiment of the present application, the process of obtaining the congestion coefficient YD of the analysis road section comprises: sorting and numbering the analysis road section according to the driving direction of the driving line; obtaining the lane data CD, the traffic flow data CL and the red-green data HL of the analysis road section, the lane data CD being the number of lanes of the analysis road section in the driving direction of the detection object, the traffic flow data CL being the number of vehicles of the analysis road section in the driving direction of the detection object, and the red-green data HL being the number of traffic lights in the analysis road section; and obtaining the congestion coefficient YD of the analysis road section by numerically calculating the lane data CD, the traffic flow data CL and the red-green data HL of the analysis road section.

[0013] As a preferred embodiment of the present application, the specific process of performing congestion prediction analysis on the comprehensive road section comprises: taking the center point of the comprehensive road section as the center and r1 as the radius to draw a circle, marking the obtained circular region as an analysis region, obtaining the number of schools, hospitals and shopping malls in the analysis region and marking them as XS, YS and SS respectively, and obtaining the prediction coefficient YC of the comprehensive road section by numerically calculating XS, YS and SS.

[0014] As a preferred embodiment of the present application, the specific process of comparing the prediction coefficient YC with the prediction threshold YCmax comprises: if the prediction coefficient YC is less than the prediction threshold YCmax, it is determined that the prediction state of the comprehensive road section meets the requirements; if the prediction coefficient YC is greater than or equal to the prediction threshold YCmax, it is determined that the prediction state of the comprehensive road section does not meet the requirements, a new congestion coefficient YDn of the comprehensive road section is obtained through the formula YDn=t1*YD, wherein t1 is a proportionality coefficient, and 1.15≤t1≤1.25; and the new congestion coefficient YDn is used to replace the congestion coefficient YD of the comprehensive road section.

[0015] As a preferred embodiment of the present application, the specific process of comparing the congestion coefficient YD with the congestion threshold YDmax includes: if the congestion coefficient YD is less than the congestion threshold YDmax, it is determined that the congestion state of the analysis road section meets the requirements, and the corresponding analysis road section is marked as a normal road section; if the congestion coefficient YD is greater than or equal to the congestion threshold YDmax, it is determined that the congestion state of the analysis road section does not meet the requirements, and the corresponding analysis road section is marked as a congestion road section.

[0016] As a preferred embodiment of the present application, the specific process of the grade evaluation module evaluating and analyzing the congestion grade of the bus route includes: obtaining the number of congestion road sections in the detection object driving route and marking it as LD, obtaining the number of congestion road sections in the detection object driving route and summing and averaging to obtain BH, obtaining the grade coefficient DJ of the detection object through the formula DJ=γ1*LD / BH, obtaining the grade threshold DJmin and DJmax through the storage module, comparing the grade coefficient DJ of the detection object with the grade threshold DJmin and DJmax, and marking the congestion grade of the detection object through the comparison result; sending the congestion grade of the detection object to the congestion reminding platform, and the congestion reminding platform sends the congestion grade to the mobile terminal of the driver of the detection object after receiving the congestion grade.

[0017] As a preferred embodiment of the present application, the specific process of comparing the grade coefficient DJ of the detection object with the grade threshold DJmin and DJmax includes: if DJ≤DJmin, the congestion grade of the detection object is marked as three levels; if DJmin<DJ<DJmax, the congestion grade of the detection object is marked as two levels; and if DJ≥DJmax, the congestion grade of the detection object is marked as one level.

[0018] The present application has the following advantages:

[0019] 1. The road condition analysis module can detect and analyze the driving road conditions of the bus route, obtain the congestion coefficient by comprehensively analyzing multiple parameters such as the carrying capacity of the road and the carrying capacity of the vehicle, and then feed back the driving road condition state of the bus route through the congestion coefficient;

[0020] 2. The congestion analysis module can detect and analyze the congestion of the bus route, realize congestion prediction of the whole path by using different detection and analysis methods for different analysis road sections, and update and replace the congestion coefficient value of the comprehensive road section through the congestion prediction analysis result, thereby improving the accuracy of the whole path congestion prediction result;

[0021] 3、Through the grade evaluation module, the congestion grade of the bus route can be evaluated and analyzed, the grade coefficient is obtained through data calculation on the number and distribution state of the congestion sections, and the congestion condition of the whole bus route can be intuitively fed back through the value of the grade coefficient, so that the real congestion state of the whole path can be directly obtained by the bus driver. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained from these drawings without creative labor.

[0023] Figure 1 The system block diagram of the present application. DETAILED DESCRIPTION

[0024] The technical solutions of the present application will be described in detail below in combination with the embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0025] As shown in Figure 1 A bus route congestion point reminding system based on big data, which comprises a congestion reminding platform, the congestion reminding platform is communicatively connected with a road condition analysis module, a congestion analysis module, a grade evaluation module and a storage module.

[0026] The road condition analysis module is used for detecting and analyzing the driving road condition of the bus route: marking the bus in the running state as a detection object, obtaining the driving route of the detection object, marking the non-driven road section in the driving route of the detection object as an analysis route, dividing the analysis route into a plurality of analysis road sections, and sorting and numbering the analysis road sections according to the driving direction of the driving route; obtaining lane data CD, traffic flow data CL, and red-green data HL of the analysis road section, the lane data CD being the number of lanes of the analysis road section in the driving direction of the detection object, the traffic flow data CL being the number of vehicles of the analysis road section in the driving direction of the detection object, and the red-green data HL being the number of traffic lights in the analysis road section, obtaining the congestion coefficient YD of the analysis road section through the formula YD=(α1*HL+α2*CL) / (α3*CD), wherein α1, α2, and α3 are proportional coefficients, and α1>α2>α3>1; sending the congestion coefficient YD of the analysis road section to the congestion reminding platform, and sending the congestion coefficient YD to the congestion analysis module after the congestion reminding platform receives the congestion coefficient YD; detecting and analyzing the driving road condition of the bus route by comprehensively analyzing a plurality of parameters such as the carrying vehicles of the road and the carrying capacity of the vehicles to obtain the congestion coefficient, and then feeding back the driving road condition state of the bus route through the congestion coefficient.

[0027] The congestion analysis module is used for detecting and analyzing the congestion of the bus line: the top L1 analysis road sections are marked as direct road sections, and the remaining analysis road sections are marked as comprehensive road sections; the congestion prediction analysis is performed on the comprehensive road sections: taking the center point of the comprehensive road section as the center and r1 as the radius to draw a circle, r1 is a numerical constant, and the value of r1 is set by the management personnel; the obtained circular region is marked as an analysis region, the number of schools, hospitals and shopping malls in the analysis region are obtained and marked as XS, YS and SS respectively, and the prediction coefficient YC of the comprehensive road section is obtained through the formula YC=β1*XS+β2*YS+β3*SS, wherein β1, β2 and β3 are proportional coefficients, and β1>β2>β3>1; the prediction threshold YCmax is obtained through the storage module, and the prediction coefficient YC is compared with the prediction threshold YCmax: if the prediction coefficient YC is less than the prediction threshold YCmax, it is determined that the prediction state of the comprehensive road section meets the requirements; if the prediction coefficient YC is greater than or equal to the prediction threshold YCmax, it is determined that the prediction state of the comprehensive road section does not meet the requirements, and the new congestion coefficient YDn of the comprehensive road section is obtained through the formula YDn=t1*YD, wherein t1 is a proportional coefficient, and 1.15≤t1≤1.25; the new congestion coefficient YDn replaces the congestion coefficient YD of the comprehensive road section, the congestion threshold YDmax is obtained through the storage module, and the congestion coefficient YD of the analysis road section is compared with the congestion threshold YDmax: if the congestion coefficient YD is less than the congestion threshold YDmax, it is determined that the congestion state of the analysis road section meets the requirements, and the corresponding analysis road section is marked as a normal road section; if the congestion coefficient YD is greater than or equal to the congestion threshold YDmax, it is determined that the congestion state of the analysis road section does not meet the requirements, and the corresponding analysis road section is marked as a congestion road section; the congestion road section is sent to the congestion reminding platform, and the congestion reminding platform sends the congestion road section to the grade evaluation module after receiving the congestion road section; the congestion of the bus line is detected and analyzed, the congestion prediction of the whole path is realized by using different detection and analysis methods for different analysis road sections, and the numerical value of the congestion coefficient of the comprehensive road section is updated and replaced through the congestion prediction analysis result, thereby improving the accuracy of the whole path congestion prediction result.

[0028] The grade evaluation module is used for evaluating and analyzing the congestion grade of the bus route: the number of congestion sections in the driving route of the detection object is obtained and marked as LD, the number of congestion sections in the driving route of the detection object is obtained and summed to obtain an average value BH, the grade coefficient DJ of the detection object is obtained through the formula DJ = γ1*LD / BH, the grade threshold DJmin and DJmax are obtained through the storage module, and the grade coefficient DJ of the detection object is compared with the grade threshold DJmin and DJmax: if DJ≤DJmin, the congestion grade of the detection object is marked as three grades; if DJmin<DJ<DJmax, the congestion grade of the detection object is marked as two grades; if DJ≥DJmax, the congestion grade of the detection object is marked as one grade; the congestion grade of the detection object is sent to the congestion reminding platform, and after receiving the congestion grade, the congestion reminding platform sends the congestion grade to the mobile terminal of the driver of the detection object; the congestion grade of the bus route is evaluated and analyzed, the grade coefficient is obtained through data calculation of the number and distribution state of the congestion sections, and the congestion state of the whole bus route can be intuitively fed back through the numerical value of the grade coefficient, so that the real congestion state of the whole path can be directly obtained by the bus driver.

[0029] A bus route congestion point reminding system based on big data, when working, a bus in a running state is marked as a detection object, a driving route of the detection object is obtained, a non-driven section in the driving route of the detection object is marked as an analysis route, the analysis route is divided into a plurality of analysis sections, and a congestion coefficient YD of the analysis sections is obtained; the first L1 analysis sections in the order are marked as direct sections, L1 is a numerical constant, and the specific value of L1 is set by the management personnel; the remaining analysis sections are marked as comprehensive sections; the comprehensive sections are subjected to congestion prediction analysis, the congestion coefficient YD of the comprehensive sections is replaced with a numerical value according to the congestion prediction analysis result, the congestion threshold YDmax is obtained through the storage module, the congestion coefficient YD of the analysis sections is compared with the congestion threshold YDmax, and the analysis sections are marked as normal sections or congestion sections according to the comparison result.

[0030] The above content is only an example and description of the structure of the application, and those skilled in the art can make various modifications or supplements or use similar ways to replace the described specific embodiments, as long as the modifications or supplements do not deviate from the structure of the application or exceed the scope defined by the claims, and should belong to the protection scope of the application.

[0031] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value, and the coefficients in the formula are set by a person skilled in the art according to the actual situation; for example: formula YD=(a1*HL+a2*CL) / (a3*CD); a plurality of sample data are collected by a person skilled in the art, and a corresponding congestion coefficient is set for each sample data; the set congestion coefficient and the collected sample data are substituted into the formula, any three formulas constitute a ternary linear equation group, the calculated coefficients are screened and the mean value is taken, and the values of a1, a2 and a3 are 3.74, 2.97 and 2.65 respectively;

[0032] The size of the coefficient is a specific value obtained by quantifying each parameter for subsequent comparison, and the size of the coefficient depends on the number of sample data and the preliminary setting of the corresponding congestion coefficient by a person skilled in the art for each sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value, such as the congestion coefficient and the value of the traffic data.

[0033] In the description of the present specification, the description of the terms "one embodiment", "example", "specific example" and the like means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0034] The preferred embodiments of the application disclosed above are only used to help explain the application. The preferred embodiments do not describe all the details and limit the application to the specific embodiments. Obviously, many modifications and changes can be made according to the content of the present specification. The present specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the application, so that those skilled in the art can well understand and utilize the application. The application is limited by the claims and their entire scope and equivalents.

Claims

1. A bus route congestion alert system based on big data, characterized in that, This includes a congestion alert platform, which is communicatively connected to a traffic condition analysis module, a congestion analysis module, a level assessment module, and a storage module; The traffic analysis module is used to detect and analyze the traffic conditions of bus routes: it marks buses in operation as detection objects, obtains the routes of the detection objects, marks the untraveled sections of the routes of the detection objects as analysis routes, divides the analysis routes into several analysis segments, and obtains the congestion coefficient YD of the analysis segments. The congestion coefficient YD of the analyzed road segment is sent to the congestion alert platform. After receiving the congestion coefficient YD, the congestion alert platform sends the congestion coefficient YD to the congestion analysis module. The congestion analysis module is used to detect and analyze the congestion of bus routes: the top L1 analysis segments are marked as direct segments, and the remaining analysis segments are marked as comprehensive segments; Congestion prediction analysis is performed on the comprehensive road segment, and the congestion coefficient YD of the comprehensive road segment is replaced with a numerical value based on the congestion prediction analysis results. The congestion threshold YDmax is obtained through the storage module. The congestion coefficient YD of the analyzed road segment is compared with the congestion threshold YDmax, and the analyzed road segment is marked as a normal road segment or a congested road segment based on the comparison results. The congested road sections are sent to the congestion alert platform, which then sends them to the rating module. The rating module is used to assess and analyze the congestion level of bus routes. The process of obtaining the congestion coefficient YD of the analyzed road segment includes: sorting and numbering the analyzed road segments according to the direction of travel of the driving route; obtaining the lane data CD, traffic flow data CL, and traffic light data HL of the analyzed road segment. The lane data CD is the number of lanes in the direction of travel of the analyzed road segment, the traffic flow data CL is the number of vehicles in the direction of travel of the analyzed road segment, and the traffic light data HL is the number of traffic lights in the analyzed road segment. The congestion coefficient YD of the analyzed road segment is obtained by numerically calculating the lane data CD, traffic flow data CL, and traffic light data HL of the analyzed road segment. The specific process of congestion prediction analysis for comprehensive road sections includes: drawing a circle with the center point of the comprehensive road section as the center and r1 as the radius, marking the resulting circular area as the analysis area, obtaining the number of schools, hospitals, and shopping malls within the analysis area and marking them as XS, YS, and SS respectively, obtaining the prediction coefficient YC of the comprehensive road section by numerical calculation of XS, YS, and SS; obtaining the prediction threshold YCmax through the storage module, comparing the prediction coefficient YC with the prediction threshold YCmax, and replacing the numerical value of the congestion coefficient YD of the comprehensive road section based on the comparison result; The specific process of comparing the prediction coefficient YC with the prediction threshold YCmax includes: if the prediction coefficient YC is less than the prediction threshold YCmax, the predicted state of the comprehensive road segment is determined to meet the requirements; if the prediction coefficient YC is greater than or equal to the prediction threshold YCmax, the predicted state of the comprehensive road segment is determined to not meet the requirements, and the new congestion coefficient YDn of the comprehensive road segment is obtained by the formula YDn=t1*YD, where t1 is the proportional coefficient, and 1.15≤t1≤1.25; the new congestion coefficient YDn is used to replace the congestion coefficient YD of the comprehensive road segment.

2. The bus route congestion alert system based on big data according to claim 1, characterized in that, The specific process of comparing the congestion coefficient YD with the congestion threshold YDmax includes: if the congestion coefficient YD is less than the congestion threshold YDmax, the congestion status of the analyzed road segment is determined to meet the requirements, and the corresponding analyzed road segment is marked as a normal road segment; if the congestion coefficient YD is greater than or equal to the congestion threshold YDmax, the congestion status of the analyzed road segment is determined to not meet the requirements, and the corresponding analyzed road segment is marked as a congested road segment.

3. The bus route congestion alert system based on big data according to claim 2, characterized in that, The specific process of the congestion assessment module for evaluating and analyzing the congestion level of bus routes includes: obtaining the number of congested road segments in the route of the test subject and marking them as LD; obtaining the numbers of the congested road segments in the route of the test subject and summing them to obtain the average value to get BH; obtaining the level coefficient DJ of the test subject through the formula DJ=γ1*LD / BH; obtaining the level thresholds DJmin and DJmax through the storage module; comparing the level coefficient DJ of the test subject with the level thresholds DJmin and DJmax and marking the congestion level of the test subject based on the comparison result; sending the congestion level of the test subject to the congestion reminder platform; and the congestion reminder platform sending the congestion level to the mobile terminal of the driver of the test subject after receiving the congestion level.

4. The bus route congestion alert system based on big data according to claim 3, characterized in that, The specific process of comparing the level coefficient DJ of the detected object with the level thresholds DJmin and DJmax includes: if DJ≤DJmin, the congestion level of the detected object is marked as level three; if DJmin<DJ<DJmax, the congestion level of the detected object is marked as level two; if DJ≥DJmax, the congestion level of the detected object is marked as level one.

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