An intelligent urban traffic planning and management system based on data analysis

Abstract

The present invention belongs to the field of traffic planning, involves data analysis technology, and is used to solve the problem that the existing urban traffic intelligent planning and management system cannot make decision analysis on the traffic planning direction by combining the congestion levels in the time dimension and the space dimension. Specifically, it is an urban traffic intelligent planning and management system based on data analysis, including a planning and management platform, which is communicatively connected to a traffic monitoring module, a load analysis module, an optimization analysis module, and a storage module; the urban traffic planning area is marked as a planning region, a monitoring period is generated, each natural day within the monitoring period is divided into several monitoring time periods, and the monitoring objects are marked as congested objects or unobstructed objects; the present invention can monitor and analyze the traffic facility carrying capacity of the urban traffic planning area, evaluate the overall optimization necessity of the planning region from the congestion time dimension, so as to improve the comprehensiveness of the urban traffic congestion analysis results.

Description

Technical Field

[0001] The present invention belongs to the field of traffic planning, involves data analysis technology, and specifically is an intelligent urban traffic planning and management system based on data analysis. Background Art

[0002] Urban traffic planning refers to the systematic planning and design of the development of the traffic system within and between cities to achieve efficient, convenient, safe, and sustainable development of urban traffic; urban traffic planning aims to solve traffic problems, improve the travel quality of urban residents, reduce traffic congestion and environmental pollution, and promote the sustainable development of cities.

[0003] The existing urban traffic intelligent planning and management systems can only monitor road congestion conditions, but cannot make decision - making analysis on traffic planning directions by combining the congestion levels in the time dimension and the space dimension, resulting in the urban traffic planning scheme being unable to prescribe the right medicine and solve urban congestion problems in the most efficient way.

[0004] In view of the above - mentioned technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent urban traffic planning and management system based on data analysis, which is used to solve the problem that the existing urban traffic intelligent planning and management systems cannot make decision - making analysis on traffic planning directions by combining the congestion levels in the time dimension and the space dimension;

[0006] The technical problem to be solved by the present invention is: how to provide an intelligent urban traffic planning and management system based on data analysis that can make decision - making analysis on traffic planning directions by combining the congestion levels in the time dimension and the space dimension.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] An intelligent urban traffic planning and management system based on data analysis includes a planning and management platform, and the planning and management platform is communicatively connected to a traffic monitoring module, a load analysis module, an optimization analysis module, and a storage module;

[0009] The traffic monitoring module is used to monitor and analyze the road vehicle traffic conditions in the urban traffic planning area: mark the urban traffic planning area as a planning region, generate a monitoring period, divide each natural day within the monitoring period into several monitoring time periods, mark the traffic roads within the planning region as monitoring objects, and mark the monitoring objects as congestion objects or unobstructed objects;

[0010] The described load analysis module is used to monitor and analyze the carrying capacity of traffic facilities in the urban traffic planning area: the ratio of the number of times a monitored object is marked as a congestion object during the monitoring period to the number of monitored objects is marked as the congestion coefficient of the monitoring period, and the monitoring period is marked as a congestion period or a smooth period through the congestion coefficient; the number of congestion periods within the monitoring cycle is marked as the optimization value of the monitoring cycle, and it is determined whether there is an overall optimization necessity for the planning area during the monitoring cycle through the optimization value; the load objects among the monitored objects are marked; the load objects are sent to the optimization analysis module through the planning management platform;

[0011] The described optimization analysis module is used to optimize the management of the road vehicle passing conditions in the urban traffic planning area.

[0012] As a preferred embodiment of the present invention, the specific process of marking the monitored object as a congestion object or a smooth object includes: obtaining the number of passing vehicles of the monitored object during the monitoring period and marking it as the passing value, obtaining the passing threshold through the storage module, and comparing the passing value with the passing threshold: if the passing value is less than the passing threshold, it is determined that the passing state of the monitored object during the monitoring period meets the requirements, and the corresponding monitored object is marked as a smooth object; if the passing value is greater than or equal to the passing threshold, the average speed of the passing vehicles of the monitored object during the monitoring period is marked as the passing speed value, obtaining the passing speed threshold through the storage module, and comparing the passing speed value with the passing speed threshold: if the passing speed value is greater than or equal to the passing speed threshold, it is determined that the passing state of the monitored object during the monitoring period meets the requirements, and the corresponding monitored object is marked as a smooth object; if the passing speed value is less than the passing speed threshold, it is determined that the passing state of the monitored object during the monitoring period does not meet the requirements, and the corresponding monitored object is marked as a congestion object.

[0013] As a preferred embodiment of the present invention, the specific process of marking the monitoring period as a congestion period or a smooth period includes: obtaining the congestion threshold through the storage module, and comparing the congestion coefficient with the congestion threshold: if the congestion coefficient is less than the congestion threshold, the corresponding monitoring period is marked as a smooth period; if the congestion coefficient is greater than or equal to the congestion threshold, the corresponding monitoring period is marked as a congestion period.

[0014] As a preferred embodiment of the present invention, the specific process for determining whether there is a need for overall optimization of the planned area within the monitoring period includes: obtaining the optimization threshold through the storage module, and comparing the optimization value with the optimization threshold. If the optimization value is less than the optimization threshold, it is determined that the traffic facility carrying capacity of the planned area within the monitoring period meets the requirements and there is no need for overall optimization. A local optimization signal is generated and sent to the planning management platform. After receiving the local optimization signal, the planning management platform sends the local optimization signal to the optimization analysis module. If the optimization value is greater than or equal to the optimization threshold, it is determined that the traffic facility carrying capacity of the planned area within the monitoring period does not meet the requirements and there is a need for overall optimization. An overall optimization signal is generated and sent to the planning management platform. After receiving the overall optimization signal, the planning management platform sends the overall optimization signal to the optimization analysis module.

[0015] As a preferred embodiment of the present invention, the specific process for marking the load object in the monitoring object includes: marking the ratio of the number of times the monitoring object is marked as a congested object within the monitoring period to the number of monitoring time periods within the monitoring period as the marking coefficient of the monitoring object. Obtain the marking threshold through the storage module, and compare the marking coefficient with the marking threshold. If the marking coefficient is less than the marking threshold, the corresponding monitoring object is marked as a normal object. If the marking coefficient is greater than or equal to the marking threshold, the corresponding monitoring object is marked as a load object.

[0016] As a preferred embodiment of the present invention, when the optimization analysis module receives the local optimization signal, it adopts the local optimization mode for optimization management: obtain the number of schools, the number of hospitals, and the number of shopping malls along the route of the load object and mark them as the school value XX, the hospital value YY, and the shopping mall value SC respectively. Calculate the concentration coefficient JZ of the load object by performing numerical calculations on the school value XX, the hospital value YY, and the shopping mall value SC. Obtain the concentration threshold JZmax through the storage module. If the concentration coefficient JZ is less than the concentration threshold JZmax, a road expansion signal is generated and sent to the mobile terminal of the management personnel through the planning management platform. If the concentration coefficient JZ is greater than or equal to the concentration threshold JZmax, a road condition optimization signal is generated and sent to the mobile terminal of the management personnel through the planning management platform.

[0017] As a preferred embodiment of the present invention, when the optimization analysis module receives the overall optimization signal, it adopts the overall optimization mode for optimization management: mark the number of load objects on the same main road as the coincidence value of the main road, obtain the coincidence threshold through the storage module, mark the ratio of the coincidence value of the main road to the total number of load objects as the coincidence coefficient, and compare the coincidence coefficients of all main roads with the coincidence threshold: if all coincidence coefficients are less than the coincidence threshold, generate a public optimization signal and send the public optimization signal to the mobile terminal of the management personnel through the planning management platform; otherwise, mark the main road with a coincidence coefficient not less than the coincidence threshold as the road to be built, send the road to be built to the mobile terminal of the management personnel through the planning management platform, and adopt the local optimization mode to optimize and manage the remaining load objects.

[0018] As a preferred embodiment of the present invention, the working method of the intelligent urban traffic planning management system based on data analysis includes the following steps:

[0019] Step 1: Monitor and analyze the road vehicle passing conditions in the urban traffic planning area: mark the urban traffic planning area as the planning area, generate the monitoring period, divide each natural day within the monitoring period into several monitoring time periods, mark the passing roads within the planning area as the monitoring objects, and mark the monitoring objects as unobstructed objects or congested objects through the passing value;

[0020] Step 2: Monitor and analyze the traffic facility carrying capacity in the urban traffic planning area: mark the ratio of the number of times marked as congested objects to the number of monitoring objects within the monitoring time period as the congestion coefficient of the monitoring time period, mark the monitoring time period as an unobstructed time period or a congested time period through the congestion coefficient, and determine whether there is a need for overall optimization of the planning area according to the proportion of the number of congested time periods within the monitoring period;

[0021] Step 3: Adopt the local optimization mode or the overall optimization mode to optimize and manage the road vehicle passing conditions in the urban traffic planning area.

[0022] The present invention has the following beneficial effects:

[0023] 1. Through the passing monitoring module, the road vehicle passing conditions in the urban traffic planning area can be monitored and analyzed, the congestion status of each sub-section can be monitored in the way of time segmentation and road segmentation, and then different marks are made on the sub-sections, providing data support for the analysis of traffic facility carrying capacity;

[0024] 2. Through the carrying analysis module, the traffic facility carrying capacity in the urban traffic planning area can be monitored and analyzed, the necessity of overall optimization of the planning area can be evaluated from the dimension of congestion time, and then the sub-sections that need to be optimized are marked from the spatial dimension, thereby improving the comprehensiveness of the urban traffic congestion analysis results;

[0025] 3. Through the optimization analysis module, the traffic conditions of roads in the urban traffic planning area can be optimized and managed. The planning areas with different congestion characteristics are optimized and managed in two optimization modes, and targeted optimization decisions are generated through data analysis to improve the effectiveness of urban traffic optimization management measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0027] Figure 1 It is the system block diagram of Embodiment 1 of the present invention;

[0028] Figure 2 It is the method flowchart of Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The following will clearly and completely describe the technical solutions of the present invention in combination with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0030] Embodiment 1

[0031] As Figure 1 shown, an intelligent urban traffic planning management system based on data analysis includes a planning management platform, which is communicatively connected to a traffic monitoring module, a bearing analysis module, an optimization analysis module, and a storage module.

[0032] The traffic monitoring module is used to monitor and analyze the traffic conditions of road vehicles in the urban traffic planning area: mark the urban traffic planning area as the planning region, generate a monitoring period, divide each natural day within the monitoring period into several monitoring time slots, mark the traffic roads within the planning region as monitoring objects, obtain the number of passing vehicles of the monitoring objects in the monitoring time slots and mark it as the passing value, obtain the passing threshold through the storage module, and compare the passing value with the passing threshold: if the passing value is less than the passing threshold, it is determined that the traffic state of the monitoring object in the monitoring time slot meets the requirements, and the corresponding monitoring object is marked as an unobstructed object; if the passing value is greater than or equal to the passing threshold, the average speed of the passing vehicles of the monitoring object in the monitoring time slot is marked as the speed value, obtain the speed threshold through the storage module, and compare the speed value with the speed threshold: if the speed value is greater than or equal to the speed threshold, it is determined that the traffic state of the monitoring object in the monitoring time slot meets the requirements, and the corresponding monitoring object is marked as an unobstructed object; if the speed value is less than the speed threshold, it is determined that the traffic state of the monitoring object in the monitoring time slot does not meet the requirements, and the corresponding monitoring object is marked as a congested object; monitor and analyze the traffic conditions of road vehicles in the urban traffic planning area, monitor the congestion state of each sub-road section in the way of time division and road division, and then make different marks on the sub-road sections to provide data support for the analysis of the bearing capacity of traffic facilities.

[0033] The load analysis module is used to monitor and analyze the carrying capacity of traffic facilities in the urban traffic planning area: the ratio of the number of times a monitored object is marked as a congestion object during the monitoring period to the number of monitored objects is marked as the congestion coefficient of the monitoring period. The congestion threshold is obtained through the storage module, and the congestion coefficient is compared with the congestion threshold: if the congestion coefficient is less than the congestion threshold, the corresponding monitoring period is marked as an unobstructed period; if the congestion coefficient is greater than or equal to the congestion threshold, the corresponding monitoring period is marked as a congested period; the number of congested periods within the monitoring cycle is marked as the optimization value of the monitoring cycle. The optimization threshold is obtained through the storage module, and the optimization value is compared with the optimization threshold: if the optimization value is less than the optimization threshold, it is determined that the carrying capacity of the traffic facilities in the planning area meets the requirements during the monitoring cycle, and there is no need for overall optimization. A local optimization signal is generated and sent to the planning management platform. After receiving the local optimization signal, the planning management platform sends the local optimization signal to the optimization analysis module; if the optimization value is greater than or equal to the optimization threshold, it is determined that the carrying capacity of the traffic facilities in the planning area does not meet the requirements during the monitoring cycle, and there is a need for overall optimization. An overall optimization signal is generated and sent to the planning management platform. After receiving the overall optimization signal, the planning management platform sends the overall optimization signal to the optimization analysis module; the ratio of the number of times a monitored object is marked as a congestion object within the monitoring cycle to the number of monitoring periods within the monitoring cycle is marked as the marking coefficient of the monitored object. The marking threshold is obtained through the storage module, and the marking coefficient is compared with the marking threshold: if the marking coefficient is less than the marking threshold, the corresponding monitored object is marked as a normal object; if the marking coefficient is greater than or equal to the marking threshold, the corresponding monitored object is marked as a loaded object; the normal objects and the loaded objects are sent to the optimization analysis module through the planning management platform; monitor and analyze the carrying capacity of the traffic facilities in the urban traffic planning area, evaluate the overall optimization necessity of the planning area from the congestion time dimension, and then mark the sub-sections that need to be optimized from the space dimension, so as to improve the comprehensiveness of the urban traffic congestion analysis results.

[0034] The optimization analysis module is used to optimize the management of the road vehicle traffic conditions in the urban traffic planning area: when the optimization analysis module receives a local optimization signal, it adopts the local optimization mode for optimization management: it obtains the number of schools, hospitals, and shopping malls along the load object's route and marks them as the school value XX, the hospital value YY, and the shopping mall value SC respectively, and obtains the concentration coefficient JZ of the load object through the formula JZ = α1*XX + α2*YY + α3*SC, where α1, α2, and α3 are all proportionality coefficients, and α1 > α2 > α3 > 1; it obtains the concentration threshold JZmax through the storage module. If the concentration coefficient JZ is less than the concentration threshold JZmax, it generates a road expansion signal and sends the road expansion signal to the mobile terminal of the management personnel through the planning management platform; if the concentration coefficient JZ is greater than or equal to the concentration threshold JZmax, it generates a road condition optimization signal and sends the road condition optimization signal to the mobile terminal of the management personnel through the planning management platform. The road condition optimization means measures such as building pedestrian overpasses and parking lots; when the optimization analysis module receives an overall optimization signal, it adopts the overall optimization mode for optimization management: it marks the number of load objects on the same main road as the coincidence value of the main road, obtains the coincidence threshold through the storage module, marks the ratio of the coincidence value of the main road to the total number of load objects as the coincidence coefficient, and compares the coincidence coefficients of all main roads with the coincidence threshold: if all coincidence coefficients are less than the coincidence threshold, it generates a public optimization signal and sends the public optimization signal to the mobile terminal of the management personnel through the planning management platform, and adds public transportation means such as buses and light rails; otherwise, it marks the main roads with coincidence coefficients not less than the coincidence threshold as roads to be built, and sends the roads to be built to the mobile terminal of the management personnel through the planning management platform, and builds viaducts and overpasses around the roads to be built; and adopts the local optimization mode to optimize the management of the remaining load objects; it optimizes the management of the road vehicle traffic conditions in the urban traffic planning area, optimizes the planning areas with different congestion characteristics in two optimization modes, and generates targeted optimization processing decisions through data analysis to improve the effectiveness of urban traffic optimization management measures.

[0035] Embodiment 2

[0036] As Figure 2 shown, a method for intelligent urban traffic planning management based on data analysis includes the following steps:

[0037] Step 1: Monitor and analyze the road vehicle traffic conditions in the urban traffic planning area: Mark the urban traffic planning area as the planning region, generate a monitoring period, divide each natural day within the monitoring period into several monitoring time periods, mark the passing roads within the planning region as the monitoring objects, and mark the monitoring objects as unobstructed objects or congested objects through the passing value;

[0038] Step 2: Monitor and analyze the traffic facility carrying capacity in the urban traffic planning area: Mark the ratio of the number of times marked as a congestion object to the number of monitoring objects within the monitoring period as the congestion coefficient of the monitoring period. Mark the monitoring period as an unobstructed period or a congestion period through the congestion coefficient, and determine whether there is a need for overall optimization of the planning area based on the proportion of the number of congestion periods within the monitoring cycle.

[0039] Step 3: Optimize and manage the road vehicle traffic conditions in the urban traffic planning area using a local optimization mode or an overall optimization mode.

[0040] An intelligent urban traffic planning management system based on data analysis. When working, mark the urban traffic planning area as the planning area, generate a monitoring cycle, divide each natural day within the monitoring cycle into several monitoring periods, mark the passing roads within the planning area as monitoring objects, and mark the monitoring objects as unobstructed objects or congestion objects through the passing value; Mark the ratio of the number of times marked as a congestion object to the number of monitoring objects within the monitoring period as the congestion coefficient of the monitoring period. Mark the monitoring period as an unobstructed period or a congestion period through the congestion coefficient, and determine whether there is a need for overall optimization of the planning area based on the proportion of the number of congestion periods within the monitoring cycle; Optimize and manage the road vehicle traffic conditions in the urban traffic planning area using a local optimization mode or an overall optimization mode.

[0041] The above content is only an example and explanation of the structure of the present invention. Those skilled in the art of this technology make various modifications or supplements to the described specific embodiments or use similar methods to replace them. As long as they do not deviate from the structure of the invention or exceed the scope defined by this claim book, they should all fall within the protection scope of the present invention.

[0042] The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The coefficients in the formula are set by those skilled in the art according to the actual situation; for example: the formula JZ = α1 * XX + α2 * YY + α3 * SC; Those skilled in the art collect multiple groups of sample data and set corresponding concentration coefficients for each group of sample data; Substitute the set concentration coefficients and the collected sample data into the formula. Any three formulas form a system of ternary linear equations. Screen and take the average value of the calculated coefficients to obtain the values of α1, α2, and α3 as 4.35, 3.87, and 2.26 respectively.

[0043] The magnitude of the coefficient is a specific value obtained by quantifying each parameter for subsequent comparison. Regarding the magnitude of the coefficient, it depends on the amount of sample data and the corresponding concentration coefficients initially set by those skilled in the art for each group of sample data; As long as it does not affect the proportional relationship between the parameter and the quantified value, such as the concentration coefficient is proportional to the value of the school.

[0044] In the description of this specification, the descriptions referring to the terms "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in a suitable manner in any one or more embodiments or examples.

[0045] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to only the specific implementation manners. Obviously, according to the content of this specification, many modifications and variations can be made. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An urban traffic intelligent planning and management system based on data analysis, characterized in that: It includes a planning management platform, which is communicatively connected to a traffic monitoring module, a load analysis module, an optimization analysis module and a storage module; The traffic monitoring module is used to monitor and analyze the traffic conditions of vehicles on roads in the urban traffic planning area: the urban traffic planning area is marked as a planning area, a monitoring cycle is generated, each natural day in the monitoring cycle is divided into a number of monitoring periods, the roads in the planning area are marked as monitoring objects, and the monitoring objects are marked as congested objects or unblocked objects; The load analysis module is used to monitor and analyze the carrying capacity of traffic facilities in the urban traffic planning area: the ratio of the number of times the objects are marked as congested objects to the number of monitored objects in the monitoring period is marked as the congestion coefficient of the monitoring period, and the monitoring period is marked as a congested period or a smooth period by the congestion coefficient; the number of congested periods in the monitoring period is marked as the optimization value of the monitoring period, and whether the planning area has the necessity of overall optimization in the monitoring period is determined by the optimization value; the load objects in the monitored objects are marked; Send the load object to the optimization analysis module through the planning management platform; The specific process of marking the load object in the monitoring object includes: marking the ratio of the number of times the monitoring object is marked as a congestion object in the monitoring period to the number of monitoring time periods in the monitoring period as the marking coefficient of the monitoring object, obtaining the marking threshold through the storage module, and comparing the marking coefficient with the marking threshold: if the marking coefficient is less than the marking threshold, the corresponding monitoring object is marked as a normal object; if the marking coefficient is greater than or equal to the marking threshold, the corresponding monitoring object is marked as a load object; The optimization analysis module is used to optimize the traffic conditions of vehicles on roads in urban traffic planning areas; When the optimization analysis module receives the local optimization signal, it adopts the local optimization mode for optimization management: the number of schools, hospitals and shopping malls along the load object is obtained and marked as school value XX, hospital value YY and shopping mall value SC respectively, and the concentration coefficient JZ of the load object is obtained by the formula JZ=α1*XX+α2*YY+α3*SC, where α1, α2 and α3 are all proportional coefficients, and α1>α2>α3>1; the concentration threshold JZmax is obtained through the storage module. If the concentration coefficient JZ is less than the concentration threshold JZmax, a road expansion signal is generated and sent to the mobile phone terminal of the manager through the planning management platform; if the concentration coefficient JZ is greater than or equal to the concentration threshold JZmax, a road condition optimization signal is generated and sent to the mobile phone terminal of the manager through the planning management platform; When the optimization analysis module receives the overall optimization signal, it adopts the overall optimization mode for optimization management: the number of load objects located on the same main road is marked as the overlap value of the main road, the overlap threshold is obtained through the storage module, and the ratio of the overlap value of the main road to the total number of load objects is marked as the overlap coefficient, and the overlap coefficients of all main roads are compared with the overlap threshold: if all overlap coefficients are less than the overlap threshold, a public optimization signal is generated and sent to the mobile phone terminal of the manager through the planning management platform; otherwise, the main road with an overlap coefficient not less than the overlap threshold is marked as an additional road, the additional road is sent to the mobile phone terminal of the manager through the planning management platform, and the local optimization mode is adopted to optimize the remaining load objects.

2. The urban traffic intelligent planning and management system based on data analysis according to claim 1 is characterized in that: The specific process of marking a monitored object as a congested object or a smooth object includes: obtaining the number of vehicles passing through the monitored object during the monitoring period and marking it as a pass value, obtaining the pass threshold through the storage module, and comparing the pass value with the pass threshold: if the pass value is less than the pass threshold, it is determined that the traffic status of the monitored object during the monitoring period meets the requirements, and the corresponding monitored object is marked as a smooth object; if the pass value is greater than or equal to the pass threshold, the average speed of vehicles passing through the monitored object during the monitoring period is marked as a speed value, obtaining the speed threshold through the storage module, and comparing the speed value with the speed threshold: if the speed value is greater than or equal to the speed threshold, it is determined that the traffic status of the monitored object during the monitoring period meets the requirements, and the corresponding monitored object is marked as a smooth object; if the speed value is less than the speed threshold, it is determined that the traffic status of the monitored object during the monitoring period does not meet the requirements, and the corresponding monitored object is marked as a congested object.

3. The urban traffic intelligent planning and management system based on data analysis according to claim 2 is characterized in that: The specific process of marking the monitored object as a congested period or a smooth period includes: obtaining the congestion threshold through the storage module, and comparing the congestion coefficient with the congestion threshold: if the congestion coefficient is less than the congestion threshold, the corresponding monitoring period is marked as a smooth period; if the congestion coefficient is greater than or equal to the congestion threshold, the corresponding monitoring period is marked as a congested period.

4. The urban traffic intelligent planning and management system based on data analysis according to claim 3 is characterized in that: The specific process of determining whether there is a necessity for overall optimization of the planning area within the monitoring period includes: obtaining the optimization threshold through the storage module, and comparing the optimization value with the optimization threshold: if the optimization value is less than the optimization threshold, it is determined that the carrying capacity of the transportation facilities in the planning area within the monitoring period meets the requirements, and there is no necessity for overall optimization, a local optimization signal is generated and sent to the planning management platform, and the planning management platform sends the local optimization signal to the optimization analysis module after receiving the local optimization signal; if the optimization value is greater than or equal to the optimization threshold, it is determined that the carrying capacity of the transportation facilities in the planning area within the monitoring period does not meet the requirements, and there is a necessity for overall optimization, an overall optimization signal is generated and sent to the planning management platform, and the planning management platform sends the overall optimization signal to the optimization analysis module after receiving the overall optimization signal.

5. An urban traffic intelligent planning and management system based on data analysis according to any one of claims 1 to 4, characterized in that: The working method of the urban traffic intelligent planning and management system based on data analysis includes the following steps: Step 1: Monitor and analyze the traffic conditions of vehicles on roads in the urban traffic planning area: mark the urban traffic planning area as a planning area, generate a monitoring cycle, divide each natural day in the monitoring cycle into several monitoring periods, mark the roads in the planning area as monitoring objects, and mark the monitoring objects as unblocked objects or congested objects according to the traffic values; Step 2: Monitor and analyze the carrying capacity of traffic facilities in the urban traffic planning area: mark the ratio of the number of objects marked as congested objects to the number of monitored objects during the monitoring period as the congestion coefficient of the monitoring period, mark the monitoring period as a smooth period or a congested period according to the congestion coefficient, and judge whether the planning area needs to be optimized as a whole according to the proportion of the number of congested periods during the monitoring period; Step 3: Use local optimization mode or overall optimization mode to optimize the management of road vehicle traffic conditions in urban traffic planning areas.

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