A method and system for improving the public transportation sharing rate in a transportation community

Abstract

The present invention discloses a method and system for improving the public transportation share rate of a transportation community. The method comprises: obtaining public transportation travel parameters for all transportation communities based on multi-source data analysis; selecting target transportation communities that meet preset requirements based on filtering parameters; selecting travel directions from the target transportation communities to each other area that do not meet a first preset public transportation share rate as key public transportation passenger flow channels to be analyzed; performing a travel chain analysis on the key public transportation passenger flow channels to be analyzed to obtain corresponding travel parameters, and optimizing the key public transportation passenger flow channels that do not meet the travel parameter requirements using an expert knowledge base. The present invention analyzes the public transportation share rates of the selected target transportation communities, selects key public transportation passenger flow channels from them, and performs a travel chain analysis on them, thereby regularly analyzing the public transportation share rates of transportation communities in a geographically fine-grained manner and improving and optimizing key public transportation passenger flow channels with low public transportation share rates.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban traffic planning, and in particular relates to a method and system for improving the public transportation share rate in a traffic community. Background Art

[0002] The public transport motorized travel share, also known as the public transport share (also known as the urban public transport travel rate), refers to the ratio of trips by urban residents using public transport (including conventional buses, rail transit, and city ferries, but excluding bicycles and taxis) to total trips (including trips by vehicles other than public transport, such as cars, taxis, motorcycles, commuter buses, government vehicles, and school buses) during the statistical period. This indicator is a key measure of public transport development and the rationality of urban transport structures. The public transport motorized travel share is calculated as: total public transport trips / total trips * 100%. See "Survey and Statistical Methods for Urban Public Transport Travel Share" (JT / T1052-2016).

[0003] Currently, public transport share calculations are typically performed by local Planning and Natural Resources Bureaus (referred to as Planning and Natural Resources Bureaus, broadly referring to urban planning and travel statistics departments) through regular analysis of various data sets within a city. For example, in the applicant's city, the Bureau utilizes mobile phone signaling data from the three major operators, public transport passenger travel data, various types of connected vehicle data in the main city (video camera license plate recognition data, RFID data, taxi GPS data, bus GPS data, GPS data for "two passenger and one dangerous goods" vehicles, and on-board diagnostic system data), and public transport infrastructure data. This data is then integrated and correlated to understand the movement patterns of people across the main city. This is supplemented by a three- to five-year travel survey to revise indicators such as trip frequency and travel mode. This allows for multi-dimensional tracking and analysis of individual movements throughout the urban space, enabling large-scale perception of transportation within the main city, namely, identification of user travel activities and public transport share. Traditional methods for calculating public transport share rates involve multiple data sources, including camera data, residential data, and IC card swipe data. These methods, supplemented by traffic surveys and model inversion, take at least six months to produce a public transport share result for a city.

[0004] The aforementioned city-wide bus share ratio, as provided by the Bureau, is a macro metric that can be used to evaluate a city's public transportation service levels. However, public transportation operators are more concerned with understanding the specific bus share ratios within each transportation zone. For key travel directions with low bus share ratios ("key travel directions" are referred to as "passenger flow corridors"), the reasons for these low bus share ratios must be analyzed in detail to facilitate improvements and optimization efforts by urban planning, construction, and management departments, as well as public transportation operators. However, this analysis and optimization process typically relies on manual surveys and investigations to identify routes and areas with strong public feedback, leading to subsequent optimization. This is not only inaccurate but also extremely time-consuming.

[0005] It can be seen that there is currently no method at home or abroad that can analyze the reasons for the low traffic share rate in a geographically fine-grained manner (for example, analysis by 100m*100m grid of geographical areas) and regularly (monthly or weekly) and automatically provide solutions. Summary of the Invention

[0006] In response to the problem in the existing technology that the macro bus share rate cannot be analyzed and optimized in a geographically fine-grained and regular manner, the present invention provides a method and system that can analyze the bus share rate of the traffic community in a fine-grained and regular manner and improve the passenger flow channels with low bus share rates in the traffic community.

[0007] In order to achieve the above technical objectives, the technical solutions adopted by the present invention are as follows:

[0008] In a first aspect, the present application provides a method for increasing the public transportation share rate in a transportation community, comprising:

[0009] Obtaining public transportation travel parameters for all transportation zones based on multi-source data analysis, wherein the public transportation travel parameters at least include a public transportation share rate;

[0010] Filtering out target transportation areas that meet preset requirements based on filtering parameters, wherein the filtering parameters are filtering thresholds set for public transportation travel parameters;

[0011] Select the travel directions that do not meet the first preset bus share rate among the bus share rates of the target traffic area to each other area as key bus passenger flow channels;

[0012] A travel chain analysis is conducted on key bus passenger flow channels to obtain corresponding travel parameters, and key bus passenger flow channels that do not meet the travel parameter requirements are optimized through the expert knowledge base.

[0013] Furthermore, the public transportation travel parameters also include: the average total number of trips in the transportation area and the average number of trips per square kilometer in the transportation area, and the filtering parameters are the lower limit of the average total number of trips in the transportation area, the lower limit of the average number of trips per square kilometer in the transportation area, and the lower limit of the public transportation share rate in the transportation area.

[0014] Furthermore, the selection of travel directions whose bus share from the target transportation area to each other area is less than a preset bus share as key bus passenger flow channels specifically includes:

[0015] Obtain the total number of trips from the target transportation area to each other area and the total number of public transportation trips;

[0016] The travel directions with the highest total number of people are selected from the total number of people traveling from the target transportation area to each other area;

[0017] For the selected travel directions with the highest total number of travelers, determine the public transportation share of the travel directions in the target transportation area based on the corresponding total number of travelers and the total number of public transportation travelers;

[0018] The bus share rates of the selected travel directions of the target traffic areas will be ranked, and the travel directions that are lower than a certain proportion of the bus share rate of the entire city will be designated as key bus passenger flow channels.

[0019] Furthermore, the area is selected from administrative regions or groups divided by local planning and natural resources bureaus.

[0020] Furthermore, the total number of trips from the target traffic area to each other area and the total number of public transportation trips are obtained during the morning and evening peak hours on the attendance day.

[0021] Furthermore, performing a travel chain analysis on the key public transportation passenger flow channel to obtain corresponding travel parameters includes obtaining the following travel parameters based on the travel chain analysis:

[0022] Average walking or cycling time from the departure point to the nearest bus stop;

[0023] Average waiting time at the nearest bus rail station from the departure point;

[0024] Bus rail ride times and transfer waiting times;

[0025] The non-linear coefficient of the line from the bus track boarding station to the bus track alighting station;

[0026] Average walking or cycling time from the bus stop to the destination;

[0027] The average travel time of cars and public transportation among people traveling in key bus passenger flow channels.

[0028] Furthermore, the optimization of key public transportation passenger flow channels that do not meet travel parameter requirements through the expert knowledge base specifically includes:

[0029] If the average walking or cycling time from the departure point to the nearest bus stop exceeds a first preset value, pedestrian corridors and bicycle lanes will be added, and alley buses will be opened;

[0030] If the average waiting time at the nearest bus stop at the departure point exceeds a second preset value, the number of buses in the travel direction is increased, bus stops in the travel direction are increased, other bus routes in the same direction are allowed to stop at surrounding stops, and demand-responsive transportation services are increased;

[0031] If the non-linear coefficient of the route from the bus track boarding station to the bus track alighting station exceeds a third preset value, the relevant travel route is recorded for optimization by the urban public transportation planning and operation department;

[0032] If the average walking or cycling time from the bus stop to the destination exceeds a fourth preset value, pedestrian corridors and bicycle lanes will be added, and alley buses will be opened;

[0033] If the ratio of the average travel time of public transportation to the average travel time of car travel exceeds the fifth preset value, the cause of the delay in the key bus passenger flow channel will be analyzed. If there are too many stations or too many detours, point-to-point vehicles will be opened. If the route settings are unreasonable, special routes will be opened. If the bus routes are congested, bus lanes will be planned. If the connection is not smooth, connecting vehicles will be operated on the relevant sections.

[0034] In a second aspect, the present application also provides a system for improving the public transportation share rate in a transportation community, comprising:

[0035] A public transportation travel parameter calculation module, which obtains public transportation travel parameters of all traffic areas based on multi-source data analysis, wherein the public transportation travel parameters at least include a public transportation share rate;

[0036] A target transportation zone screening module is configured to screen target transportation zones that meet preset requirements based on filtering parameters, wherein the filtering parameters are filtering thresholds set for public transportation parameters;

[0037] A key public transport passenger flow channel screening module is used to select travel directions whose public transport share ratio from the target traffic area to each other area does not meet the first preset public transport share ratio as key public transport passenger flow channels;

[0038] The key bus passenger flow channel optimization module conducts travel chain analysis on key bus passenger flow channels to obtain corresponding travel parameters, and optimizes key bus passenger flow channels that do not meet the travel parameter requirements through the expert knowledge base.

[0039] Furthermore, the key public transportation passenger flow channel optimization module includes:

[0040] The travel parameter acquisition module obtains the following travel parameters based on the travel chain analysis:

[0041] Average walking or cycling time from the departure point to the nearest bus stop;

[0042] Average waiting time at the nearest bus rail station from the departure point;

[0043] Bus rail ride times and transfer waiting times;

[0044] The non-linear coefficient of the line from the bus track boarding station to the bus track alighting station;

[0045] Average walking or cycling time from the bus stop to the destination;

[0046] The average travel time of cars and public transportation among people traveling in key bus passenger flow channels.

[0047] On the third aspect, the present application also provides a public transportation management system, which includes the above-mentioned traffic community bus share rate improvement system.

[0048] Compared with the existing technology, the present invention first filters out target transportation areas based on the public transportation travel parameters of all transportation areas, then analyzes the bus share rate of travel routes from the target transportation areas to various areas, and then selects travel routes with low bus share rates as key bus passenger flow channels, and conducts travel chain analysis on them, thereby regularly analyzing the bus share rates of transportation areas in a geographically fine-grained manner and improving and optimizing key bus passenger flow channels with low bus share rates. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a flow chart of an embodiment of a method for increasing the public transportation share rate in a transportation community;

[0050] Figure 2 A flow chart to screen out key bus passenger flow channels in the target transportation area;

[0051] Figure 3 This is a module diagram of a system for improving the public transportation share rate in a transportation community. DETAILED DESCRIPTION

[0052] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to embodiments and drawings. The contents mentioned in the embodiments are not intended to limit the present invention.

[0053] Figure 1A flow chart of an embodiment of a method for increasing the public transportation share rate in a transportation community is shown, comprising the following steps:

[0054] S100 . Obtain public transportation travel parameters of all traffic zones based on multi-source data analysis, where the public transportation travel parameters at least include a public transportation share rate.

[0055] Public transport travel parameters should at least include the bus share rate or bus share data that can be used to obtain the bus share rate (for example, the ratio of the total number of public transport trips to the total number of total trips can be used to calculate the bus share rate). Of course, public transport travel parameters can also include other factors, such as the overall motorized travel situation, especially cars.

[0056] The technology for obtaining public transportation travel parameters for all traffic zones based on multi-source data can be found in the applicant's previous application for "Regional Public Transportation Share Rate Evaluation Method and Evaluation System Based on Multi-Source Data". Here, we will only briefly introduce the method for obtaining the public transportation share rate of a traffic zone based on multi-source data as an example. The steps include:

[0057] (1) Divide the target area into a plurality of minimum map units, where the minimum map units are determined by the map data provider;

[0058] (2) Obtaining the travel chain data of the users traveling in multiple minimum map units within the traffic zone;

[0059] (3) Aggregate the travel chain data of all users in the traffic area to form an overall travel profile of the travel population in the traffic area;

[0060] (4) Calculating the public transportation share data of the corresponding transportation area based on the overall travel portrait of the travel population in the transportation area, wherein the public transportation share data includes the total number of public transportation trips and the total number of trips;

[0061] (5) The bus share rate of the target area is obtained by summarizing the bus share data of each transportation zone in the target area.

[0062] Before obtaining the bus share rate of the target area based on the bus share data of each traffic zone in the target area, the method further includes: using bus share data provided by other suppliers that can obtain traffic travel data to correct the bus share data of the traffic zones obtained by the current map data supplier, wherein the other suppliers that can obtain traffic travel data include: mobile phone signaling data, population census data, and bus and rail card swiping data.

[0063] For other detailed processes on how to calculate the public transportation share rate of a transportation community, please refer to the above-mentioned patent documents, which will not be described in detail here.

[0064] The purpose of the invention of this application is to further explore the key traffic areas for analysis based on existing technologies, and to analyze and optimize the passenger flow channels in these traffic areas with low bus share rates.

[0065] S200 , selecting target transportation communities that meet preset requirements based on filtering parameters, where the filtering parameters are filtering thresholds set for public transportation parameters.

[0066] The filtering parameter is intended to screen out target transportation communities where the bus share rate needs to be increased. If the public transportation travel parameter is only the bus share rate, then the filtering parameter is the filtering threshold (i.e., the lower limit) set for the bus share rate.

[0067] Of course, other filtering parameters can be added to identify transportation areas that truly need to increase public transportation share. For example, filtering parameters can include the average total number of trips in the transportation area and the average number of trips per square kilometer in the transportation area. The corresponding filtering threshold is the lower limit of the average total number of trips in the transportation area and the average number of trips per square kilometer in the transportation area.

[0068] The preferred screening situation is to select the time of commuting day (non-holiday) to calculate the average total number of trips in the transportation area, the average number of trips per square kilometer in the transportation area, and the public transportation share rate.

[0069] For example, the filtering parameters can be filtered according to the following actual parameters (select the average number of commuting people per day, of course, you can also select statistics for a certain period, such as all commuting days per week): the average total number of trips in the transportation community is more than 3,000 people per day, the average number of trips per square kilometer in the transportation community is more than 2,000 people per day, and the public transportation share rate of the transportation community is less than 50%. The above filtering parameters are intended to screen out transportation communities with large travel flows but low public transportation share rates. Transportation communities that meet the above preset requirements will be screened out for improvement and optimization.

[0070] In addition to the conventional filtering parameters mentioned above, you can also add specific traffic areas as target traffic areas for analysis, such as areas with user hotline complaints, areas of focus after the opening of new rail lines, and areas of focus after bus route adjustments.

[0071] S300: Selecting travel directions whose public transportation share ratios from the target transportation area to other areas do not meet a first preset public transportation share ratio as key public transportation passenger flow channels.

[0072] The regions here are based on the city's clusters (determined by local planning authorities) or administrative regions (if no clusters are defined). Chongqing, for example, has approximately 30 clusters. To facilitate more detailed passenger flow analysis, it's best if the city or region being optimized contains at least 20 clusters or administrative regions.

[0073] For each target transportation zone selected for optimization in step 200, it is necessary to select the travel directions with a bus share rate lower than a certain proportion as key bus passenger flow channels through the above steps. Figure 2 The specific steps for selecting key bus passenger flow channels are as follows:

[0074] S301: Obtain the total number of trips from the target transportation area to each other area and the total number of trips by public transportation.

[0075] The total number of trips and the total number of public transportation trips here belong to public transportation sharing data, which can be obtained from the user's travel chain data.

[0076] Of course, in order to obtain more accurate bus share data, the bus share data of the traffic area obtained by the current map data supplier can also be used to correct the bus share data provided by other suppliers that can obtain traffic travel data. The other suppliers that can obtain traffic travel data include: mobile phone signaling data, census data and bus track card swiping data, etc.

[0077] S302: Filter out the travel directions with the highest total number of people from the total number of people traveling from the target traffic area to each other area.

[0078] This step first selects several travel directions with a relatively large total number of travelers for subsequent focused analysis, such as considering the total number of travelers in the top 10 travel directions.

[0079] S303: Determine the public transportation share of the target transportation area's travel direction based on the total number of travelers and the total number of public transportation travelers in the selected travel directions with the highest total number of travelers.

[0080] The public transport share rate is defined as: public transport share rate = total number of public transport trips / total number of trips*100%.

[0081] For each target transportation community, the total number of trips to other groups or administrative areas and the total number of public transportation trips (excluding trips to the area where the target transportation community is located) can be calculated. This step mainly targets the travel directions selected in step S302. The total number of trips in the corresponding travel directions and the total number of public transportation trips are used to determine the public transportation share of the travel directions that need to be analyzed in the target transportation community.

[0082] S304. Sort the bus share rates of the selected travel directions of the target traffic areas, and select the travel directions with a certain percentage lower than the bus share rate of the entire city as key bus passenger flow channels.

[0083] The bus share obtained in step S303 has highs and lows. To optimize travel directions with low bus share, it is necessary to statistically sort or filter the bus share of the target transportation area to each other area. Travel directions with a share below a certain level will be identified as key bus passenger flow channels that need to be analyzed and optimized.

[0084] Here, we can consider taking travel directions with a share of less than 10% of the entire city's public transportation as key public transportation passenger flow channels, preferably more than 20%.

[0085] The screening of the public transport sharing data in steps S301 to S304 is also preferably performed by considering the morning and evening peak hours on commuting days (non-holidays) for statistics, such as 7:00 to 9:00 in the morning and 16:00 to 19:00 in the evening.

[0086] S400: Perform travel chain analysis on key public transportation passenger flow channels to obtain corresponding travel parameters, and optimize key public transportation passenger flow channels that do not meet the travel parameter requirements through an expert knowledge base.

[0087] For the key public transport passenger flow channels selected in step S300, this step uses the travel chain data to analyze the travel parameters in the corresponding travel direction, so as to optimize and improve some key public transport passenger flow channels that do not meet the requirements.

[0088] The trip chain analysis of the key public transport passenger flow channel to obtain corresponding travel parameters includes obtaining the following travel parameters based on the trip chain analysis:

[0089] Average walking or cycling time from the departure point to the nearest bus stop;

[0090] Average waiting time at the nearest bus rail station from the departure point;

[0091] Bus rail ride times and transfer waiting times;

[0092] The non-linear coefficient of the line from the bus track boarding station to the bus track alighting station;

[0093] Average walking or cycling time from the bus stop to the destination;

[0094] The average travel time of cars and public transportation among people traveling in key bus passenger flow channels.

[0095] The optimization of key public transportation passenger flow channels that do not meet the travel parameter requirements through the expert knowledge base is specifically optimized for the above-mentioned situations that do not meet the travel parameter requirements, as follows:

[0096] If the average walking or cycling time from the departure point to the nearest bus stop exceeds the first preset value, connecting measures such as adding pedestrian corridors, bicycle lanes, and opening alley buses will be implemented. Here, the first preset value is set to 15 minutes.

[0097] If the average waiting time at the nearest bus station from the departure point exceeds the second preset value, which can be set to 10 minutes, then new public transportation methods such as increasing the bus frequency density in the travel direction, increasing the bus stops in the travel direction, allowing other bus lines in the same direction to stop at surrounding stops, and increasing demand-responsive transportation services will be adopted.

[0098] Demand-responsive transport (DRT) is a non-fixed-route public transportation system where passengers reserve their trips in real time using mobile phones, telephones, or computers. It represents a new form of public transportation, somewhere between traditional buses and taxis. It is particularly suitable for suburban residents commuting to the city for shopping, errands, medical care, and leisure activities. It also provides loose connections between suburban and rural areas, serves urban residents commuting to suburban jobs, and can provide connecting services to commuter trains and rail transit.

[0099] If the nonlinearity coefficient between the bus boarding station and the bus alighting station exceeds a third preset value, the relevant travel route is recorded for optimization by the urban public transportation planning and operation departments. The "Urban Road Traffic Planning and Design Specification GB50220-95" stipulates that the nonlinearity coefficient of public transportation routes should not exceed 1.4, and the average nonlinearity coefficient of the entire network is preferably 1.15-1.2. In this embodiment, the third preset value is preferably set to 1.2-1.4.

[0100] If the average walking or cycling time from the bus stop to the destination exceeds the fourth preset value, connecting measures such as adding pedestrian corridors, bicycle lanes and opening alley buses will be implemented. Here, the fourth preset value is set to 15 minutes.

[0101] If the ratio of the average travel time of public transportation to the average travel time of car travel exceeds the fifth preset value (for example, 1.5 times), the relevant situation will be recorded for analysis (the reasons for the delay will be analyzed as line problems, station setting problems, road congestion problems, poor connection problems, etc.), and then suggestions will be given separately. If there are too many stations or too many detours, point-to-point vehicles will be opened (customized buses, etc.).

[0102] For example, if the walking time at both ends exceeds 20% and exceeds 15 minutes on one side, the connection will be optimized; if the transfer time exceeds 15 minutes, the frequency will be increased; if the overall travel time of bus and rail exceeds that of car, the network needs to be optimized, such as opening special lines (such as planned rail lines, bus express buses at major stations), planning bus lanes if bus-related lines are congested, and running shuttle vehicles on relevant sections of the road if the connection is not smooth.

[0103] In actual situations, we can also evaluate the abnormal situations in the key bus passenger flow channels with the above-mentioned problems, such as 30% of the number of travelers in the morning and evening peak hours, 30% of the time ratio of more than 1.5 times that of car travel, 20% of abnormal walking time, 10% of public transportation connections, and 10% of the line network not meeting the non-linear coefficient requirements. Then, we can give each key bus passenger flow channel a score and evaluation.

[0104] like Figure 3 As shown, this embodiment also provides a system for improving the public transportation share rate in a transportation community, including:

[0105] Public transportation travel parameter calculation module 1, which obtains public transportation travel parameters of all traffic areas based on multi-source data analysis, and the public transportation travel parameters at least include the public transportation share rate;

[0106] Target traffic area screening module 2, which screens out target traffic areas that meet preset requirements based on filtering parameters, wherein the filtering parameters are filtering thresholds set for public transportation travel parameters;

[0107] A key public transport passenger flow channel screening module 3 is used to select travel directions whose public transport share ratio from the target transportation area to each other area does not meet the first preset public transport share ratio as key public transport passenger flow channels;

[0108] The key public transportation passenger flow channel optimization module 4 performs a travel chain analysis on the key public transportation passenger flow channels to obtain corresponding travel parameters, and optimizes the key public transportation passenger flow channels that do not meet the travel parameter requirements through the expert knowledge base.

[0109] Figure 3 It is also shown that the key public transportation passenger flow channel optimization module 4 includes a travel parameter acquisition module, which obtains the following travel parameters based on the travel chain analysis:

[0110] Average walking or cycling time from the departure point to the nearest bus stop;

[0111] Average waiting time at the nearest bus rail station from the departure point;

[0112] Bus rail ride times and transfer waiting times;

[0113] The non-linear coefficient of the line from the bus track boarding station to the bus track alighting station;

[0114] Average walking or cycling time from the bus stop to the destination;

[0115] The average travel time of cars and public transportation among people traveling in key bus passenger flow channels.

[0116] By analyzing the above travel parameters and combining them with the expert knowledge base, preliminary optimization suggestions can be obtained.

[0117] This embodiment also provides a public transportation management system, which includes any of the systems for increasing the public transportation share of a transportation community described in the aforementioned embodiments. Integrating the systems for increasing the public transportation share of a transportation community described in the aforementioned embodiments into a public transportation management system can enhance the public transportation management system's ability to optimize the city's public transportation share.

[0118] The above describes in detail the method and system for increasing the public transportation share rate in a transportation community provided by this application. The description of the specific embodiments is only intended to facilitate understanding of the method and core concept of this application. It should be noted that those skilled in the art may make various improvements and modifications to this application without departing from the principles of this application, and such improvements and modifications also fall within the scope of protection of the claims of this application.

Claims

1. A method for increasing the public transportation share rate in a transportation community, characterized in that: include: Obtaining public transportation travel parameters for all transportation zones based on multi-source data analysis, wherein the public transportation travel parameters at least include a public transportation share rate; Filtering out target transportation areas that meet preset requirements based on filtering parameters, wherein the filtering parameters are filtering thresholds set for public transportation travel parameters; The travel directions from the target transportation area to each other area that do not meet the first preset bus share ratio are selected as the key bus passenger flow channels to be analyzed; Conduct travel chain analysis on the key bus passenger flow channels to be analyzed to obtain corresponding travel parameters, and optimize the key bus passenger flow channels that do not meet the travel parameter requirements through the expert knowledge base; The method of selecting the travel directions whose bus share ratio from the target transportation area to each other area is less than the preset bus share ratio as the key bus passenger flow channels to be analyzed specifically includes: Obtain the total number of trips from the target transportation area to each other area and the total number of public transportation trips; The travel directions with the highest total number of people are selected from the total number of people traveling from the target transportation area to each other area; For the selected travel directions with the highest total number of travelers, determine the public transportation share of the travel directions in the target transportation area based on the corresponding total number of travelers and the total number of public transportation travelers; The bus share rates of the selected target transportation areas are ranked, and the travel directions with a certain percentage lower than the bus share rate of the entire city are selected as key bus passenger flow channels; The optimization of key public transportation passenger flow channels that do not meet travel parameter requirements through the expert knowledge base specifically includes: If the average walking or cycling time from the departure point to the nearest bus stop exceeds a first preset value, pedestrian corridors and bicycle lanes will be added, and alley buses will be opened; If the average waiting time at the nearest bus stop at the departure point exceeds a second preset value, the number of buses in the travel direction is increased, bus stops in the travel direction are increased, other bus routes in the same direction are allowed to stop at surrounding stops, and demand-responsive transportation services are increased; If the non-linear coefficient of the route from the bus track boarding station to the bus track alighting station exceeds a third preset value, the relevant travel route is recorded for optimization by the urban public transportation planning and operation department; If the average walking or cycling time from the bus stop to the destination exceeds a fourth preset value, pedestrian corridors and bicycle lanes will be added, and alley buses will be opened; If the ratio of the average travel time of public transportation to the average travel time of car travel exceeds the fifth preset value, the cause of the delay in the key bus passenger flow channel will be analyzed. If there are too many stations or too many detours, point-to-point vehicles will be opened. If the route settings are unreasonable, special routes will be opened. If the bus routes are congested, bus lanes will be planned. If the connection is not smooth, connecting vehicles will be operated on the relevant sections.

2. The method for increasing the public transportation share rate in a transportation community according to claim 1, characterized in that: The public transportation travel parameters also include: the average total number of trips in the transportation area and the average number of trips per square kilometer in the transportation area. The filtering parameters are the lower limit of the average total number of trips in the transportation area, the lower limit of the average number of trips per square kilometer in the transportation area, and the lower limit of the public transportation share rate in the transportation area.

3. The method for increasing the public transportation share rate in a transportation community according to claim 2, characterized in that: The area is selected from administrative regions or groups divided by local planning and natural resources bureaus.

4. The method for increasing the public transportation share rate in a transportation community according to claim 3, characterized in that: The total number of trips from the target traffic area to each other area and the total number of public transportation trips are obtained by selecting the morning and evening peak hours on the attendance day.

5. The method for increasing the public transportation share rate in a transportation community according to any one of claims 1 to 4, characterized in that: The corresponding travel parameters are obtained by performing a trip chain analysis on key bus passenger flow channels. The following travel parameters are obtained based on the trip chain analysis: Average walking or cycling time from the departure point to the nearest bus stop; Average waiting time at the nearest bus rail station from the departure point; Bus rail ride times and transfer waiting times; The non-linear coefficient of the line from the bus track boarding station to the bus track alighting station; Average walking or cycling time from the bus stop to the destination; The average travel time of cars and public transportation among people traveling in key bus passenger flow channels.

6. A system for improving the public transport share rate in a transportation community that implements the method for improving the public transport share rate in a transportation community as claimed in claim 1, characterized in that: include: A public transportation travel parameter calculation module, which obtains public transportation travel parameters of all traffic areas based on multi-source data analysis, wherein the public transportation travel parameters at least include a public transportation share rate; A target transportation zone screening module is configured to screen target transportation zones that meet preset requirements based on filtering parameters, wherein the filtering parameters are filtering thresholds set for public transportation parameters; A key public transport passenger flow channel screening module is used to select travel directions whose public transport share ratio from the target traffic area to each other area does not meet the first preset public transport share ratio as key public transport passenger flow channels; The key bus passenger flow channel optimization module conducts travel chain analysis on key bus passenger flow channels to obtain corresponding travel parameters, and optimizes key bus passenger flow channels that do not meet the travel parameter requirements through the expert knowledge base.

7. The system for improving the public transportation share rate in a transportation community according to claim 6, characterized in that: The key public transportation passenger flow channel optimization module includes: The travel parameter acquisition module obtains the following travel parameters based on the travel chain analysis: Average walking or cycling time from the departure point to the nearest bus stop; Average waiting time at the nearest bus rail station from the departure point; Bus rail ride times and transfer waiting times; The non-linear coefficient of the line from the bus track boarding station to the bus track alighting station; Average walking or cycling time from the bus stop to the destination; The average travel time of cars and public transportation among people traveling in key bus passenger flow channels.

8. A public transportation management system, characterized in that: Including the traffic community public transportation share rate improvement system as described in claim 6 or 7.

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