An urban low-carbon passenger transport structure evaluation system based on data analysis

Abstract

The present invention discloses a city low-carbon passenger transportation structure evaluation system based on data analysis, which specifically relates to the field of urban passenger transportation planning and is used to solve the problem that public transportation city construction is often constantly updated, resulting in inaccurate evaluation of the overall transportation structure throughout the year; the system comprises a transportation evaluation platform, which is communicatively connected with a data acquisition module, a data processing module, a transportation evaluation module and a result feedback module; the present invention includes that the newly opened public transportation construction in the evaluation year is included in the annual transportation structure, so that the overall evaluation of the city's annual transportation structure can be more accurately performed, the reliability and effectiveness of the evaluation results can be improved, and the current development status of the city's low-carbon passenger transportation structure can be effectively evaluated accordingly, so as to provide a decision-making basis for urban passenger transportation planning and management, which is of great significance to optimizing the urban passenger transportation structure and reducing urban transportation carbon emissions.

Description

Technical Field

[0001] The present invention relates to the technical field of urban passenger transportation planning, and more specifically, to an urban low-carbon passenger transportation structure evaluation system based on data analysis. Background Art

[0002] As one of the development modes of transportation, low-carbon transportation is mainly about improving the energy efficiency of transportation. It has the four characteristics of high energy efficiency, low pollution, low energy consumption and low emissions. It improves the energy structure and development mode of transportation. Its goal is to reduce the burning of fossil fuels consumed by the public transportation system and infrastructure.

[0003] Most of the existing evaluation studies on urban passenger transportation structure are based on the city's traffic data in previous years, and the weighting of different influencing factors is studied. However, for some developing cities, public transportation city construction is often constantly updated, resulting in inaccurate evaluation of the overall transportation structure throughout the year, and there is room for further optimization.

[0004] Therefore, in order to solve the above problems, the present invention proposes an urban low-carbon passenger transportation structure evaluation system based on data analysis. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an urban low-carbon passenger transportation structure evaluation system based on data analysis. By classifying the newly opened public transportation construction in the evaluation year into the annual transportation structure, it is possible to more accurately evaluate the overall transportation structure of the city throughout the year, thereby improving the reliability and effectiveness of the evaluation results, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An urban low-carbon passenger transportation structure evaluation system based on data analysis includes a transportation evaluation platform, wherein the transportation evaluation platform is communicatively connected with a data acquisition module, a data processing module, a transportation evaluation module and a result feedback module;

[0008] The data acquisition module is used to acquire newly added public transportation projects, the operation dates of newly added public transportation projects and the city's traffic data over the years, and send the acquired data to the data processing module for data processing;

[0009] The data processing module is used to calculate the average daily mileage carbon emissions of various types of vehicles and the annual carbon emissions of the city based on the information sent by the data acquisition module, and send the calculated data to the traffic evaluation module;

[0010] The traffic evaluation module generates the city traffic structure evaluation signal according to the signal sent by the data processing module, and sends it to the traffic evaluation platform;

[0011] The result feedback module generates a traffic structure optimization strategy according to the urban traffic structure evaluation signal.

[0012] In a preferred embodiment, the traffic data over the years include the types of vehicles in the city, the fuel consumption per 100 kilometers of each type of vehicle, the number of each type of vehicle in use, and the mileage of each type of vehicle.

[0013] In a preferred embodiment, the data processing module marks the received newly added public transportation projects as P, the number of which is n, where n is a positive integer; and divides the whole year into n+1 analysis intervals according to the operation dates of the newly added public transportation projects;

[0014] The data processing module uses a BP neural network based on time series analysis to perform prediction analysis based on the mileage of various types of vehicles over the years. The specific prediction process is as follows:

[0015] The annual mileage of various types of vehicles in the city over the years is received as neural network training samples for neural network learning to obtain the predicted annual mileage of various types of vehicles.

[0016] In a preferred embodiment, after the data processing module calculates the predicted average annual mileage of each type of vehicle in that year, it divides the average annual mileage of each type of vehicle by the number of days to obtain the average daily mileage of each type of vehicle in that year, and calculates the carbon emissions of the city in the relevant days according to the formula based on the traffic mileage. The specific formula is as follows:

[0017]

[0018] Where i is the fuel type of urban transportation, such as diesel, gasoline, natural gas, liquefied petroleum gas, etc.; j is the vehicle type of urban transportation, such as public transportation vehicles, cars, etc.; M ij The number of vehicles of type j using fuel i, in units of vehicles; L ij The average daily mileage of a vehicle of type j using fuel i, in km; EF i is the CO2 emission factor of fuel i, in kg / J; F ij is the average consumption of j-type vehicles using i-fuel, L / km; d is the number of calculation days; E CO2d is the CO2 emission of urban transport in a specified number of days.

[0019] In a preferred embodiment, the data processing module sends the calculated carbon emission information of each analysis interval to the traffic evaluation module for evaluation and analysis, and simultaneously sends the calculated carbon emission information of each analysis interval and each traffic information to the traffic evaluation platform for storage.

[0020] In a preferred embodiment, the traffic evaluation module specifically analyzes the traffic structure of the city as follows:

[0021] Assume that the annual carbon emission gradient reference value is E CO2 1 With E CO2 2 , E CO2 1 <E CO2 2 ; Compare E CO2 With E CO2 1 、E CO2 2 The size between;

[0022] If E CO2 <E CO2 1 ; This means that the city’s traffic structure is good this year and meets the low-carbon emission indicators. At this time, the traffic evaluation module generates a good traffic structure signal;

[0023] If E CO2 1 ≤E CO2 <E CO2 2 ; This means that the city has not met the low-carbon requirements this year, but has not exceeded carbon dioxide emissions. At this time, the traffic evaluation module generates a general signal of traffic structure;

[0024] If E CO2 2 ≤E CO2 , it means that the city’s carbon emissions have exceeded the standard this year, and the traffic evaluation module generates a signal of poor traffic structure.

[0025] In a preferred embodiment, when the traffic evaluation module generates a general traffic structure signal or a poor traffic structure signal, the data processing module sends the calculated carbon emission information of each vehicle type to the result feedback module, and the result feedback module performs analysis accordingly. The specific analysis process is as follows:

[0026] Compare the daily average mileage carbon emissions generated by each type of vehicle with the daily average mileage carbon standard threshold emissions of each type of vehicle;

[0027] Determine whether the carbon emissions per day of various types of public transportation vehicles are greater than the standard threshold emissions. If greater, it indicates that the number of such public transportation vehicles meets the emission requirements; otherwise, it indicates that the number of such public transportation vehicles put into use does not meet the requirements.

[0028] Determine whether the carbon emissions per day of various types of private vehicles are less than the standard threshold emissions. If less, it indicates that the number of such private vehicles meets the emission requirements; otherwise, it indicates that the number of such private vehicles is greater than the emission requirements.

[0029] For public transportation vehicles that do not meet the requirements for the number put into use, the result feedback module generates a vehicle placement prompt message for such vehicles; for private vehicles with a number greater than the emission requirements, the result feedback module generates a vehicle traffic restriction prompt message for such vehicles.

[0030] In a preferred embodiment, the data acquisition module divides the city into k regions according to the vehicle-intensive areas of the city, where k is a positive integer greater than or equal to 1. It respectively obtains the private vehicle traffic volume and the number of public transportation trips in each region, calibrates them as Q and G respectively, and conducts a formulaic analysis on them. According to the formula, the public transportation sharing coefficient T of each region is obtained. The specific formula is as follows:

[0031] Tk = a1Gk / a2Qk

[0032] In the formula, a1 and a2 are the weight coefficients of the number of public transportation trips and the private vehicle traffic volume respectively, and a1 > a2. Tk represents the public transportation sharing coefficient of each region, Gk is the number of public transportation trips in each region, and Qk is the private vehicle traffic volume in each region.

[0033] In a preferred embodiment, the result feedback module sets the minimum public transportation sharing coefficient threshold Tk* for each vehicle-intensive area of the city, and judges the size of the public transportation sharing coefficient of each region and the minimum public transportation sharing coefficient threshold:

[0034] If Tk ≥ Tk*, it indicates that the number of public transportation trips in this region meets the requirements; when placing public transportation vehicles, this region is not considered, and when restricting private vehicles, this region is also not considered;

[0035] If Tk < Tk*, it indicates that the number of public transportation trips in this region does not meet the requirements; when placing public transportation vehicles, this region is given priority, and when restricting private vehicles, this region is also given priority.

[0036] The technical effects and advantages of the present invention:

[0037] The present invention classifies the newly opened public transportation construction in the evaluation year into the annual transportation structure, so as to more accurately evaluate the overall transportation structure of the city throughout the year, improve the reliability and effectiveness of the evaluation results, and thereby effectively evaluate the development status of the city's low-carbon passenger transportation structure, provide a decision-making basis for urban passenger transportation planning and management, which is of great significance to optimizing the urban passenger transportation structure and reducing urban transportation carbon emissions, and is conducive to promoting the high-efficiency, low-carbon development and digital transformation of urban transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a structural schematic diagram of a city low-carbon passenger transportation structure evaluation system based on data analysis of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Example 1

[0041] The present invention discloses an urban low-carbon passenger transportation structure evaluation system based on data analysis. By classifying the newly opened public transportation construction in the evaluation year into the annual transportation structure, the overall evaluation of the city's annual transportation structure can be more accurately performed, thereby improving the reliability and effectiveness of the evaluation results. Based on this, the current development status of the urban low-carbon passenger transportation structure can be effectively evaluated, and a decision-making basis can be provided for urban passenger transportation planning and management. This is of great significance for optimizing the urban passenger transportation structure and reducing urban transportation carbon emissions, and is conducive to promoting the energy-efficient, low-carbon development and digital transformation of urban transportation.

[0042] In order to solve the problem that when evaluating the current transportation structure of the city, there are new public transportation projects that affect the overall structure, the present invention defaults to evaluating the urban transportation structure when there are new public transportation projects in the city. Public transportation projects include new bus routes, new rail transportation, new passenger flights, etc.

[0043] Specifically, Figure 1 As shown, the present invention provides an urban low-carbon passenger transportation structure evaluation system based on data analysis, including a transportation evaluation platform, which is communicatively connected with a data acquisition module, a data processing module, a transportation evaluation module and a result feedback module.

[0044] The data acquisition module acquires newly added public transportation projects, the operation dates of newly added public transportation projects and the city's traffic data over the years, and sends the acquired data to the data processing module for data processing.

[0045] Among them, the city’s traffic data over the years include the types of vehicles in the city, the fuel consumption per 100 kilometers of each type of vehicle, the number of each type of vehicle in use, and the mileage of each type of vehicle.

[0046] It should be noted that the vehicle types mentioned in this embodiment refer to the following buses, rail transit, taxis and private cars, among which buses, rail transit and taxis are all public transportation. The higher their proportion in the travel choices of urban residents, the better the low-carbon effect of the overall passenger transportation structure of the city. The fuel consumption per 100 kilometers of various types of vehicles and the number of various types of vehicles in the city can be directly obtained from the National Statistical Yearbook or other published data, among which the number of various types of vehicles adopts the published data at the beginning of the year. The mileage of various types of vehicles is the mileage of various types of vehicles in the city over the years, which can also be directly obtained from the National Statistical Yearbook or other published data.

[0047] The data processing module marks the newly added public transportation projects received as P, and the number thereof is n, where n is a positive integer; and divides the whole year into n+1 analysis intervals according to the operation dates of the newly added public transportation projects, so as to facilitate segmented targeted analysis of the city's traffic structure throughout the year.

[0048] For the mileage of various types of vehicles in the city that year, since there are missing values ​​in the annual average mileage of various modes of transportation, it is necessary to use certain methods to predict the missing values. The neural network model has great advantages in the prediction field due to its good nonlinear quality, fully distributed storage structure and flexible and effective learning method.

[0049] The data processing module of the present invention uses a BP neural network based on time series analysis to perform prediction analysis based on the mileage of various types of vehicles over the years. The specific prediction process is as follows:

[0050] The mileage of various types of vehicles in the city over the years is received as neural network training samples for neural network learning, so as to obtain the annual average mileage of various modes of transportation in that year.

[0051] It should be noted that the specific years of the mileage of various types of vehicles in the city over the years can be selected according to actual needs, such as selecting the data published in the last ten years, so as to carry out neural network learning. In addition, the data processing module can actually use SPSS software for prediction, and the input network parameters are automatically matched to the best by SPSS software. There is a covariate, a hidden layer and an output result in the neural network model.

[0052] After obtaining the predicted average annual mileage of each type of vehicle in that year, divide the average annual mileage of each type of vehicle by the number of days to obtain the average daily mileage of each type of vehicle in that year, and then calculate the carbon emissions of the city in the relevant days based on the traffic mileage according to the formula. The specific formula is as follows:

[0053]

[0054] Where i is the fuel type of urban transportation, such as diesel, gasoline, natural gas, liquefied petroleum gas, etc.; j is the vehicle type of urban transportation, such as public transportation vehicles, cars, etc.; M ij The number of vehicles of type j using fuel i, in units of vehicles; L ij The average daily mileage of a vehicle of type j using fuel i, in km; EF i is the CO2 emission factor of fuel i, in kg / J; F ij is the average consumption of j-type vehicles using i-fuel, L / km; d is the number of calculation days; E CO2d is the CO2 emission of urban transport in a specified number of days.

[0055] Therefore, when relevant public transportation projects are opened, the types of vehicles in urban transportation can be increased in time, which changes the M in the above formula. ij With L ij , so that the city’s annual carbon emissions can be accurately calculated.

[0056] For example, a total of ten public transportation projects were added throughout the year, that is, n is 10, and the public transportation projects are {P1, P2, ..., P 10}, and the corresponding interval days are {d1, d2, ..., d 10}, then the annual E CO2 =E CO2d1 +E CO2d2 +…+E CO2d10 .

[0057] The data processing module sends the calculated carbon emission information of each analysis interval to the traffic evaluation module for evaluation and analysis, and at the same time sends the calculated carbon emission information of each analysis interval and each traffic information to the traffic evaluation platform for storage.

[0058] The specific analysis process of the traffic evaluation module for the city's traffic structure is as follows:

[0059] Assume that the annual carbon emission gradient reference value is E CO2 1 With E CO2 2 , E CO2 1 <E CO22 ; Compare E CO2 With E CO2 1 、E CO2 2 The size between;

[0060] If E CO2 <E CO2 1 ; This means that the city’s traffic structure is good this year and meets the low-carbon emission indicators. At this time, the traffic evaluation module generates a good traffic structure signal;

[0061] If E CO2 1 ≤E CO2 <E CO2 2 ; This means that the city has not met the low-carbon requirements this year, but has not exceeded carbon dioxide emissions. At this time, the traffic evaluation module generates a general signal of traffic structure;

[0062] If E CO2 2 ≤E CO2 , it means that the city’s carbon emissions have exceeded the standard this year, and the traffic evaluation module generates a signal of poor traffic structure.

[0063] The traffic evaluation module sends the generated traffic structure evaluation signal to the traffic evaluation platform for display.

[0064] When the traffic evaluation module generates a general traffic structure signal or a poor traffic structure signal, the data processing module sends the calculated carbon emission information of each vehicle type to the result feedback module, and the result feedback module analyzes it accordingly. The specific analysis process is as follows:

[0065] A. Compare the daily average mileage carbon emissions generated by each type of vehicle with the daily average mileage carbon standard threshold emissions of each type of vehicle;

[0066] B. Determine whether the carbon emissions of the average daily mileage of various types of public transportation vehicles are greater than the standard threshold emissions. If so, it means that the number of such public transportation vehicles meets the emission requirements; otherwise, it means that the number of such public transportation vehicles does not meet the requirements. Determine whether the carbon emissions of the average daily mileage of various types of private vehicles are less than the standard threshold emissions. If so, it means that the number of such private vehicles meets the emission requirements; otherwise, it means that the probability of the number of private vehicles exceeding the emission requirements is greater.

[0067] C. For public transport vehicles that do not meet the deployment quantity requirements, the result feedback module generates deployment reminder information for such vehicles; for private vehicles with a quantity greater than the emission requirements, the result feedback module generates restriction reminder information for such vehicles.

[0068] Furthermore, the average daily driving mileage carbon emissions generated by various types of vehicles are all the average daily driving mileage carbon emissions of that year, rather than the average daily driving mileage carbon emissions of a certain analysis interval, to avoid the distortion of the average daily driving mileage carbon emissions caused by the increase in new public transportation projects, and to better plan the traffic structure of this year.

[0069] Example 2

[0070] The difference between Example 2 and Example 1 of the present invention is that in the above Example 1, the result feedback module generated corresponding feedback signals for the increase and decrease of traffic restrictions on various types of vehicles according to the standard carbon emission indicators of various types of vehicles, but did not specifically explain where to set the placement.

[0071] In this Example 2, according to the differences in traffic-intensive areas within the city, after restricting the private vehicles, in order to meet the traffic demand, new public transportation projects are added in the corresponding areas, so as to further optimize the overall traffic structure according to the traffic structure evaluation.

[0072] Specifically, the data acquisition module divides the city into k regions according to the vehicle-intensive areas of the city, where k is a positive integer greater than or equal to 1, respectively obtains the private car traffic flow and the number of public transportation trips in each region, and respectively calibrates them as Q and G, and performs a formulaic analysis on them. According to the formula, the public transportation sharing coefficient T of each region is obtained. The specific formula is as follows:

[0073] Tk = a1Gk / a2Qk

[0074] In the formula, a1 and a2 are the weight coefficients of the number of public transportation trips and the private car traffic flow respectively, and a1 > a2. Tk represents the public transportation sharing coefficient of each region, Gk is the number of public transportation trips in each region, and Qk is the private car traffic flow in each region.

[0075] The result feedback module sets the minimum public transportation sharing coefficient threshold Tk* for each vehicle-intensive area of the city, and judges the size of the public transportation sharing coefficient of each region and the minimum public transportation sharing coefficient threshold.

[0076] If Tk ≥ Tk*, it means that the operation frequency of public transportation in this region meets the requirements; when putting public transportation vehicles, this region is not considered, and when restricting private vehicles, this region is also not considered.

[0077] If Tk < Tk*, it means that the operation frequency of public transportation in this region does not meet the requirements; when putting public transportation vehicles, this region is given priority, and when restricting private vehicles, this region is also given priority.

[0078] Furthermore, the public transport sharing coefficients of each region that are less than the minimum public transport sharing coefficient threshold are sorted to facilitate the subsequent deployment of public transport routes or restrictions on private vehicles.

[0079] Formula of the present invention:

[0080] Tk=a1Gk / a2Qk

[0081] The technical personnel in this field collect multiple groups of sample data and set corresponding weight factor coefficients for each group of sample data. The size of the coefficient is to quantify each parameter to obtain a specific numerical value, which is convenient for subsequent comparison. The size of the coefficient depends on the amount of sample data and the technical personnel in this field preliminarily set the corresponding weight factor coefficients for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized numerical value.

[0082] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0083] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0084] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0085] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0086] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory. The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0087] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0088] Several points need to be explained: first, in the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0089] Second: The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media including storage devices.

[0090] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0091] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An urban low-carbon passenger transport structure evaluation system based on data analysis, characterized by: It includes a traffic evaluation platform, which is connected to a data acquisition module, a data processing module, a traffic evaluation module and a result feedback module; The data acquisition module is used to acquire newly added public transportation projects, the operation dates of newly added public transportation projects and the city's traffic data over the years, and send the acquired data to the data processing module for data processing; The data processing module is used to calculate the average daily mileage carbon emissions of various types of vehicles and the annual carbon emissions of the city based on the information sent by the data acquisition module, and send the calculated data to the traffic evaluation module; The traffic evaluation module generates the city traffic structure evaluation signal according to the signal sent by the data processing module, and sends it to the traffic evaluation platform; The result feedback module generates a traffic structure optimization strategy according to the urban traffic structure evaluation signal; The traffic evaluation module specifically analyzes the traffic structure of the city as follows: Assume that the annual carbon emission gradient reference value is E CO2 1 With E CO2 2 , E CO2 1 <E CO2 2 ; Compare E CO2 With E CO2 1 、E CO2 2 The size between; If E CO2 <E CO2 1 ; This means that the city’s traffic structure is good this year and meets the low-carbon emission indicators. At this time, the traffic evaluation module generates a good traffic structure signal; If E CO2 1 ≤E CO2 <E CO2 2 ; This means that the city has not met the low-carbon requirements this year, but has not exceeded carbon dioxide emissions. At this time, the traffic evaluation module generates a general signal of traffic structure; If E CO2 2 ≤E CO2 , it means that the city's carbon emissions have exceeded the standard this year, and the traffic evaluation module generates a signal of poor traffic structure; When the traffic evaluation module generates a general traffic structure signal or a poor traffic structure signal, the data processing module sends the calculated carbon emission information of each vehicle type to the result feedback module, and the result feedback module analyzes it accordingly. The specific analysis process is as follows: Compare the daily average mileage carbon emissions generated by each type of vehicle with the daily average mileage carbon standard threshold emissions of each type of vehicle; Determine whether the average daily mileage carbon emissions of various types of public transport vehicles are greater than the standard threshold emissions. If so, it means that the number of such public transport vehicles meets the emission requirements; otherwise, it means that the number of such public transport vehicles does not meet the requirements; Determine whether the average daily mileage carbon emissions of various types of private vehicles are less than the standard threshold emissions. If so, it means that the number of private vehicles of this type meets the emission requirements. Otherwise, it means that the probability of the number of private vehicles exceeds the emission requirements. For public transport vehicles that do not meet the deployment quantity requirements, the result feedback module generates deployment reminder information for such vehicles; for private vehicles with a quantity greater than the emission requirements, the result feedback module generates restriction reminder information for such vehicles; The data acquisition module divides the city into k areas according to the densely populated areas of the city, where k is a positive integer greater than or equal to 1, obtains the private car flow and the number of public transportation shifts in each area, and marks them as Q and G respectively, and performs a formula analysis to obtain the public transportation sharing coefficient T of each area according to the formula. The specific formula is as follows: Tk=a1Gk / a2Qk In the formula, a1 and a2 are the weight coefficients of the number of public transportation routes and the private car flow, respectively, and a1>a2, Tk is the public transportation sharing coefficient of each region, Gk is the number of public transportation routes in each region, and Qk is the private car flow in each region.

2. The urban low-carbon passenger transport structure evaluation system based on data analysis according to claim 1 is characterized by: The traffic data over the years include the types of vehicles in the city, the fuel consumption per 100 kilometers of each type of vehicle, the number of each type of vehicle in use, and the mileage of each type of vehicle.

3. The urban low-carbon passenger transport structure evaluation system based on data analysis according to claim 2 is characterized by: The data processing module marks the received newly added public transportation projects as P, and the number thereof is n, where n is a positive integer; and divides the whole year into n+1 analysis intervals according to the operation dates of the newly added public transportation projects; The data processing module uses a BP neural network based on time series analysis to perform predictive analysis on the basis of the annual driving mileage of various types of vehicles over the years. The specific prediction process is as follows: The annual driving mileage of various types of vehicles in this city received respectively is used as the neural network training sample for neural network learning to obtain the predicted annual driving mileage of various types of vehicles.

4. The urban low-carbon passenger transport structure evaluation system based on data analysis according to claim 3 is characterized by: After the data processing module calculates the average annual driving mileage of various types of vehicles in that year, it divides the average annual driving mileage of various types of vehicles by the number of days to obtain the average daily driving mileage of various types of vehicles in that year, and calculates the carbon emissions within the relevant number of days in this city based on the traffic driving mileage according to the formula. The specific formula is as follows: In the formula, i is the fuel type of urban transportation industry, such as diesel, gasoline, natural gas, liquefied petroleum gas; j is the vehicle type of urban transportation, public transportation vehicles, cars; M ij The number of vehicles of type j using fuel i, in units of vehicles; L ij The average daily mileage of a vehicle of type j using fuel i, in km; EF i is the CO2 emission factor of fuel i, in kg / J; F ij is the average consumption of j-type vehicles using i-fuel, L / km; d is the number of calculation days; E CO2d is the CO2 emission of urban transport in a specified number of days.

5. The urban low-carbon passenger transport structure evaluation system based on data analysis according to claim 4 is characterized by: The data processing module sends the carbon emission information of each analysis interval calculated to the traffic evaluation module for evaluation and analysis, and at the same time sends the carbon emission information of each analysis interval calculated and each traffic information to the traffic evaluation platform for storage.

6. The urban low-carbon passenger transport structure evaluation system based on data analysis according to claim 1 is characterized by: The result feedback module sets the threshold Tk* of the minimum public transport sharing coefficient for each vehicle-intensive area in this city, and judges the size of the public transport sharing coefficient of each area and the threshold of the minimum public transport sharing coefficient; If Tk≥Tk*, it indicates that the operation frequency of public transport in this area meets the requirements; when deploying public transport vehicles, this area is not considered, and when restricting the travel of private vehicles, this area is also not considered; If Tk<Tk*, it indicates that the operation frequency of public transport in this area does not meet the requirements; when deploying public transport vehicles, this area is given priority, and when restricting the travel of private vehicles, this area is also given priority.

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