Urban and rural passenger transport line network optimization system based on intelligent evaluation and scheduling

Abstract

The invention discloses an urban and rural passenger transport line network optimization system based on intelligent evaluation and scheduling, which relates to the technical field of urban and rural traffic and comprises a data acquisition module, a data processing module, a line network diagnosis module, a traffic supply and demand analysis module and a line network optimization module. The data acquisition module comprises a bus data acquisition unit, a road condition data acquisition unit and a travel data acquisition unit and is used for acquiring urban and rural bus operation data in real time; the data processing module is used for preprocessing the collected data. Technical research and development and product construction are carried out around an intelligent bus big data analysis system, Internet geographic information data and a big data analysis technology are fully utilized, the current situation of a bus network is evaluated, the bus travel demands of all county residents are actually mined and extracted, the travel demand change of people is analyzed, and the travel demand change of people is analyzed. The conditions of insufficient line coverage, unreasonable transport capacity configuration and inconvenience in resident trip are avoided, the resident trip time and cost are reduced, and convenience is provided.

Description

Technical Field

[0001] The present invention relates to the field of urban and rural transportation technology, and in particular to an urban and rural passenger transport line network optimization system based on intelligent evaluation and scheduling. Background Art

[0003] In the past, urban and rural passenger transport network planning relied on manual experience and simple survey data for route design. This approach makes it difficult to fully and accurately grasp the travel distribution patterns, travel time preferences, and population mobility characteristics of urban and rural residents. For example, in the passenger route planning of some emerging towns and surrounding villages, due to the lack of forward-looking analysis of future population growth and travel demand changes in newly developed areas, the route coverage is insufficient or the capacity allocation is unreasonable, making it inconvenient for some remote rural residents to travel. Some popular routes have tight capacity during peak hours, and passengers often miss the nearest bus when transferring, which increases travel time and cost. In this regard, we propose an urban and rural passenger transport network optimization system based on intelligent evaluation and scheduling. Summary of the invention

[0004] In order to solve the above technical problems, a rural-urban passenger transport line network optimization system based on intelligent evaluation and scheduling is provided. This technical solution solves the problems of insufficient line coverage and unreasonable configuration.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is: an urban and rural passenger transport line network optimization system based on intelligent evaluation and scheduling, including: a data acquisition module, a data processing module, a line network diagnosis module, a traffic supply and demand analysis module and a line network optimization module;

[0006] The data collection module includes a bus data collection unit, a road condition data collection unit and a travel data collection unit, which are used to collect urban and rural bus operation data in real time;

[0007] The data processing module is used to pre-process the collected data and classify and organize the data to form a data set;

[0008] The network diagnosis module obtains data from the data set, performs analysis and diagnosis, analyzes the current status of the traffic network, determines whether the current network is reasonable, and optimizes the current network if it is unreasonable;

[0009] The traffic supply and demand analysis module analyzes the local bus demand based on the data set, understands the flow distribution of passenger flow in the county, the popularity of travel between different areas, and calculates the popularity value;

[0010] The line network optimization module optimizes the results of the line network diagnosis module based on the heat value, adjusts the bus routes, establishes multiple groups of bus routes in areas with high heat values, and establishes at least one group of bus routes in areas with low heat values ​​to ensure travel.

[0011] Preferably, the bus data collection unit is based on a GPS sensor to record the location information of the bus, monitor whether it deviates from the normal operating route, collect the driving speed data of the bus based on the speed sensor, understand the driving conditions of the vehicle on different sections, whether it is speeding or underspeeding, analyze whether the vehicle runs and arrives on time, and provide speed information; based on infrared sensing and pressure sensing technology, the number of passengers getting on and off the bus is accurately counted, and the changes in passenger flow at each line and station are grasped in real time.

[0012] Preferably, the road condition data collection unit collects road conditions based on cameras, and cameras are set at major road intersections, key sections and locations prone to traffic congestion. Through image recognition and analysis technology, the density, driving speed, and queue length of vehicles on the road are determined, and the traffic volume and vehicle type distribution data on the road are obtained. The travel data collection unit records the travel data of passengers by setting up intelligent interactive terminal devices at urban and rural bus stops. The data reflects the distribution of residents' travel needs in different time periods and locations.

[0013] Preferably, the data processing module preprocesses the data, and the preprocessing includes data cleaning, data integration and standardization. The data classification and sorting are based on the decision tree algorithm, and the acquired data are classified and constructed into a data set.

[0014] Preferably, the line network diagnosis module screens and extracts the operation data of the public transportation vehicles, the passenger flow data of the stations, the line information and the road condition data from the database, and analyzes the current traffic line network situation by analyzing the passenger flow and the line load rate, wherein the passenger flow characteristic analysis formula is:

[0015]

[0016] Where Q is the current daily passenger flow of buses in the county, P i is the change in the number of passengers getting on and off the i-th bus on the day, n is the number of times recorded on the day, the overall passenger flow scale of the station is evaluated through the formula, the peak and trough periods of passenger flow at the station are determined, and the time periods with the largest and smallest passenger flow are found by analyzing the passenger flow data in different time periods;

[0017] The line load rate calculation formula is: Among them, R is the full load rate of the line, Qm is the actual number of bus passengers, and Qs is the rated passenger capacity of the vehicle. Through calculation, we can understand the operating conditions of urban and rural lines in different time periods. If the calculated full load rate value is too large, it is judged that the current transportation network is unreasonable. When the full load rate value is greater than 20%, it is judged to be too large. This is based on the expert analysis method.

[0018] Preferably, the traffic supply and demand analysis module sorts out the card swiping data of bus stops, counts the number of card swiping times at each stop in different time periods, and understands the passenger flow change trend of each stop. The number of card swiping times at the central area of ​​the county-level administrative area during the morning and evening rush hours on weekdays will increase significantly, indicating that there is a large flow of people in the area during the time period. Combined with the operation trajectory data of the bus, according to the vehicle's stop at different stations and the passengers getting on and off, the passengers' travel routes are tracked, and a schematic diagram of the flow direction of passenger flow in the entire county is drawn, which intuitively reflects the popularity range in urban and rural areas.

[0019] Preferably, the heat value calculation assumes that the travel heat value from A to B is H AB , where the calculation formula is:

[0020]

[0021] Where Q AB To collect passenger flow data from point A to point B within a certain period of time, Q Ai It is expressed as the total passenger flow from point A to all other areas, i represents the number of all stations that can be reached from point A; the formula expresses the ratio of the number of passengers from point A to point B to the total number of passengers from point A to all stations.

[0022] Preferably, the heat value formula is further optimized by adding the travel distance factor D AB and travel time factor T AB , where the distance factor calculates the travel distance from point A to point B on the bus line based on geographic coordinates, and the time factor optimizes the heat value calculation formula by counting the average time it takes passengers to travel from point A to point B. The optimized formula is:

[0023]

[0024] Among them, k is a normalized constant, which adjusts the heat value to a numerical range. The revised formula takes into account the situation that the longer the travel distance and the longer the time will reduce the willingness to travel, thus having a negative impact on the heat value. The heat value is calculated more reasonably, and the heat value threshold of the current county is obtained through expert analysis. The calculated heat value is compared with the threshold. If it is greater than the threshold, the current county is judged to be a region with a large passenger travel volume. If it is less than the threshold, the current county is judged to be a region with a small passenger travel volume.

[0025] Preferably, the line network optimization module is based on the calculated heat value and integrates the current status information obtained by the line network diagnosis module. In areas with high heat values, multiple groups of bus routes are established. Between the central area and the transportation hub within the county-level administrative area, multiple groups of bus routes are established according to the geographical spatial layout of the area and the traffic conditions of the surrounding roads to ensure that passengers can travel quickly.

[0026] Preferably, in areas with lower heat values, at least one group of bus routes will be established. Taking travel demand into consideration, urban and rural bus routes covering major schools and corporate parks will be planned in towns and administrative villages with low population density to meet the needs of rural students commuting from home to school and the commuting needs of employees of enterprises and institutions. The line network optimization module will continue to pay attention to the changing trend of its heat value. If there are signs of a significant increase in travel demand, it will start the planning of line expansion and new lines, and make arrangements in advance to adapt to future changes in travel demand.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] The present invention conducts technical research and development and product construction around the smart public transportation big data analysis system. It makes full use of Internet geographic information data and big data analysis technology, evaluates the current status of the public transportation network, and extracts the public transportation travel needs of residents in the county from the actual mining. It analyzes the changes in people's travel needs, avoids insufficient line coverage, unreasonable capacity allocation, and inconvenience in residents' travel, reduces the time and cost of residents' travel, and provides convenience. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a block diagram of the line network optimization system of the present invention. DETAILED DESCRIPTION

[0030] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0031] Reference Figure 1 As shown, an urban and rural passenger transport line network optimization system based on intelligent evaluation and scheduling includes: a data acquisition module, a data processing module, a line network diagnosis module, a traffic supply and demand analysis module and a line network optimization module;

[0032] The data collection module includes a bus data collection unit, a road condition data collection unit and a travel data collection unit, which are used to collect urban and rural bus operation data in real time;

[0033] The data processing module is used to pre-process the collected data and classify and organize the data to form a data set;

[0034] The network diagnosis module obtains data from the data set, performs analysis and diagnosis, analyzes the current status of the traffic network, determines whether the current network is reasonable, and optimizes the current network if it is unreasonable;

[0035] The traffic supply and demand analysis module analyzes the local bus demand based on the data set, understands the flow distribution of passenger flow in the county, the popularity of travel between different areas, and calculates the popularity value;

[0036] The line network optimization module optimizes the results of the line network diagnosis module based on the heat value, adjusts the bus routes, establishes multiple groups of bus routes in areas with high heat values, and establishes at least one group of bus routes in areas with low heat values ​​to ensure travel.

[0037] This application collects urban and rural bus operation data in real time through bus data collection units, road condition data collection units and travel data collection units, which can cover bus operations, road conditions and residents' travel information, providing a rich and first-hand data basis for subsequent accurate analysis and decision-making, and helping to fully and carefully understand the actual operation status of urban and rural buses; pre-processing and classifying the collected chaotic data to form a data set, making the data more standardized and organized, facilitating direct call and analysis by subsequent modules, improving the utilization efficiency of data in different analysis links, and avoiding inaccurate analysis results due to data confusion;

[0038] Based on the data set, we can deeply analyze the local bus demand situation, clearly grasp the flow distribution of passenger flow in the whole county, the popularity of travel between different areas and calculate the heat value, which will help the bus operation department understand the travel preferences of residents and the areas where demand is concentrated, so that we can reasonably allocate resources. For example, we can increase the capacity in the directions and time periods with large passenger flow, set up multiple groups of bus routes in areas with high heat values, which can fully meet the travel requirements of residents in areas with strong travel demand, improve travel convenience and reduce congestion; and set up at least one group of bus routes in areas with low heat values, while ensuring basic travel services, avoiding excessive investment of resources, realizing the scientific and reasonable allocation of bus resources, and comprehensively improving the comprehensive benefits of urban and rural bus operations, better serving the travel of the majority of residents, and promoting the healthy development of urban and rural transportation.

[0039] The bus data collection unit is based on GPS sensors to record the location information of buses and monitor whether they deviate from the normal operating route. It collects the speed data of buses based on speed sensors to understand the driving conditions of vehicles on different sections of the road, whether they are speeding or underspeeding, and analyze whether the vehicles run and arrive on time, and provide speed information. Based on infrared sensing and pressure sensing technology, it accurately counts the number of passengers getting on and off the bus, and grasps the changes in passenger flow at each line and station in real time.

[0040] This application records the location information of public buses through GPS sensors, and can monitor in real time whether the vehicles deviate from the normal operating routes. Based on infrared sensing and pressure sensing technology, it accurately counts the number of passengers getting on and off the bus, and grasps the changes in passenger flow at each line and station in real time.

[0041] The road condition data collection unit collects road conditions based on cameras. Cameras are set up at major road intersections, key sections and locations prone to traffic congestion. Through image recognition and analysis technology, the density, driving speed and queue length of vehicles on the road are determined, and the traffic volume and vehicle type distribution data on the road are obtained. The travel data collection unit records passengers' travel data by setting up intelligent interactive terminal devices at urban and rural bus stops. The data reflects the distribution of residents' travel needs in different time periods and locations.

[0042] This application can monitor the traffic conditions in these key areas in real time by installing cameras at major road intersections, key sections and locations prone to congestion, use image recognition and analysis technology to determine vehicle density, driving speed and queue length information, and promptly detect signs of traffic congestion. Intelligent interactive terminal devices are installed at urban and rural bus stops to record passengers' travel data, which can accurately reflect the distribution of residents' travel needs in different time periods and locations. The collection of travel data can also provide a basis for the future development of personalized bus services to meet personalized travel needs.

[0043] The data processing module preprocesses the data, which includes data cleaning, data integration and standardization. The data classification and organization are based on the decision tree algorithm, and the acquired data are classified into data sets.

[0044] The data in this application is disorganized and has a lot of problems, which will increase the difficulty and amount of calculation of data analysis, and consume more time and computing resources. The cleaned data is more concise and standardized, which can make subsequent links such as line network diagnosis and traffic supply and demand analysis more efficient, reduce unnecessary data processing steps, and quickly focus on valuable data content for in-depth mining. The decision tree algorithm is an existing technology, so I will not elaborate on it here.

[0045] The line network diagnosis module extracts the operation data of public buses, station passenger flow data, line information and road condition data from the database, and analyzes the current traffic line network situation by analyzing the passenger flow and line load rate. The passenger flow characteristic analysis formula is:

[0046]

[0047] Where Q is the current daily passenger flow of buses in the county, P i is the change in the number of passengers getting on and off the i-th bus on the day, n is the number of times recorded on the day, the overall passenger flow scale of the station is evaluated through the formula, the peak and trough periods of passenger flow at the station are determined, and the time periods with the largest and smallest passenger flow are found by analyzing the passenger flow data in different time periods;

[0048] The line load rate calculation formula is: Among them, R is the full load rate of the line, Qm is the actual number of bus passengers, and Qs is the rated passenger capacity of the vehicle. Through calculation, we can understand the operating conditions of urban and rural lines in different time periods. If the calculated full load rate value is too large, it is judged that the current transportation network is unreasonable. When the full load rate value is greater than 20%, it is judged to be too large. This is based on the expert analysis method.

[0049] This application calculates the daily passenger flow through the passenger flow characteristic analysis formula using the specific data on the change in the number of passengers getting on and off the bus, and can accurately evaluate the overall passenger flow scale of each station. Through the line load rate calculation formula, the operating conditions of urban and rural lines in different time periods are understood by the ratio of the actual number of bus passengers to the rated passenger capacity of the vehicle. This provides an objective and quantitative indicator for judging the busyness of the line. Compared with relying solely on subjective feelings or rough estimates, it can more accurately know which lines have tight capacity and which time periods, and which time periods have idle capacity.

[0050] The traffic supply and demand analysis module sorts out the card swiping data of bus stops, counts the number of card swiping times at each stop in different time periods, and understands the passenger flow trend of each stop. The number of card swiping times at the central area of ​​the county-level administrative region will increase significantly during the morning and evening rush hours on weekdays, indicating that there is a large flow of people in this area during the time period. Combined with the operation trajectory data of bus vehicles, according to the vehicle stops at different stations and the passengers getting on and off, the passengers' travel routes are tracked, and a schematic diagram of the flow direction of passenger flow in the entire county is drawn, which intuitively reflects the popularity range in urban and rural areas.

[0051] This application combs through the card swiping data at bus stops, counts the number of card swiping times at each stop in different time periods, and clearly understands the changing trends in passenger flow at each stop. This enables bus operating departments to accurately adjust the bus capacity deployment at corresponding stops based on the increase or decrease in passenger flow at specific stops in different time periods. Combined with bus vehicle operation trajectory data, it tracks passengers' travel routes and draws a schematic diagram of the passenger flow direction across the county. It can intuitively present the popularity range in urban and rural areas, that is, the popular areas and directions for passenger travel. Bus operating companies and transportation planning departments can optimize and adjust bus routes based on this information.

[0052] The heat value calculation assumes that the travel heat value from A to B is H AB , where the calculation formula is:

[0053]

[0054] Where Q AB To collect passenger flow data from point A to point B within a certain period of time, Q AiIt is expressed as the total passenger flow from point A to all other areas, i represents the number of all stations that can be reached from point A; the formula expresses the ratio of the number of passengers from point A to point B to the total number of passengers from point A to all stations.

[0055] Through this formula, the present application can present the travel popularity between regions in a quantified value. In the past, the popularity of people's travel between different regions may have been based on general feelings or vague cognition, but now it is calculated based on specific passenger flow data, which can accurately reflect the travel popularity from A to B and objectively measure the closeness of the connection between the two places. For example, it can be clearly known which regions have frequent personnel exchanges and which have relatively few, providing data support for further analysis of the economic and social connections between regions.

[0056] Further optimize the heat value formula and add the travel distance factor D AB and travel time factor T AB , where the distance factor calculates the travel distance from point A to point B on the bus line based on geographic coordinates, and the time factor optimizes the heat value calculation formula by counting the average time it takes passengers to travel from point A to point B. The optimized formula is:

[0057]

[0058] Among them, k is a normalized constant, which adjusts the heat value to a numerical range. The revised formula takes into account the situation that the longer the travel distance and the longer the time will reduce the willingness to travel, thus having a negative impact on the heat value. The heat value is calculated more reasonably, and the heat value threshold of the current county is obtained through expert analysis. The calculated heat value is compared with the threshold. If it is greater than the threshold, the current county is judged to be a region with a large passenger travel volume. If it is less than the threshold, the current county is judged to be a region with a small passenger travel volume.

[0059] This application applies the optimized heat value in the travel service platform, which can provide passengers with travel suggestions that are more in line with their actual needs. The platform can comprehensively consider the heat value, distance, and time factors based on the passengers' departure and destination, and recommend the best public transportation travel plan for passengers. It not only considers the passenger flow of the route, but also takes into account the convenience and timeliness of travel, helping passengers to better plan their itineraries and improve travel efficiency and satisfaction.

[0060] The line network optimization module is based on the calculated heat value and integrates the current status information obtained by the line network diagnosis module. In areas with high heat values, multiple groups of bus routes are established. Between the central area and transportation hubs within the county-level administrative area, multiple groups of bus routes are established based on the geographical spatial layout of the area and the traffic conditions of the surrounding roads to ensure that passengers can travel quickly.

[0061] This application sets up multiple groups of bus routes in areas with high popularity values, so that passengers have more route options. This means that during peak travel hours when the passenger flow is large, passengers can take suitable buses more conveniently without having to wait for a long time, greatly shortening the waiting time. At the same time, multiple groups of routes can also disperse passenger flow, avoiding the situation where a certain route is too crowded and causes slow driving, thereby reducing the time passengers spend on the road, improving travel efficiency as a whole, and facilitating people to quickly travel to and from these important areas with frequent personnel exchanges;

[0062] The setting up of multiple groups of bus routes increases the possibility of connections between different lines. For passengers who need to transfer, there are more flexible transfer options, which reduces waiting time and the number of transfers, further improving the convenience and overall efficiency of travel.

[0063] In areas with lower heat values, at least one group of bus routes will be set up. Taking travel demand into consideration, urban and rural bus routes covering major schools and corporate parks will be planned in towns and administrative villages with low population density to meet the needs of rural students for commuting from home to school and the commuting needs of employees of enterprises and institutions. The line network optimization module will continue to pay attention to the changing trend of its heat value. If there are signs of a significant increase in travel demand, it will start the planning of line expansion and new lines, and make arrangements in advance to adapt to future changes in travel demand.

[0064] This application sets up at least one group of bus routes in low-density, low-heat-value areas in remote suburban areas, which can effectively protect the basic travel rights and interests of local residents and corporate employees. Whether residents go to nearby schools and hospitals for medical treatment or employees commute to work, this bus route provides a reliable means of transportation, avoiding travel difficulties caused by lack of public transportation, enabling them to smoothly participate in social life and economic activities, and maintain normal life and production order in the region.

[0065] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention, and these changes and improvements fall within the scope of the present invention claimed.

Claims

1. An urban and rural passenger transport network optimization system based on intelligent evaluation and scheduling, characterized in that: include: Data acquisition module, data processing module, line network diagnosis module, traffic supply and demand analysis module and line network optimization module; The data collection module includes a bus data collection unit, a road condition data collection unit and a travel data collection unit, which are used to collect urban and rural bus operation data in real time; The data processing module is used to pre-process the collected data and classify and organize the data to form a data set; The network diagnosis module obtains data from the data set, performs analysis and diagnosis, analyzes the current status of the traffic network, determines whether the current network is reasonable, and optimizes the current network if it is unreasonable; The traffic supply and demand analysis module analyzes the local bus demand based on the data set, understands the flow distribution of passenger flow in the county, the popularity of travel between different areas, and calculates the popularity value; The line network optimization module optimizes the results of the line network diagnosis module based on the heat value, adjusts the bus routes, establishes multiple groups of bus routes in areas with high heat values, and establishes at least one group of bus routes in areas with low heat values ​​to ensure travel.

2. According to claim 1, a system for optimizing the urban and rural passenger transport network based on intelligent evaluation and scheduling is characterized in that: The bus data collection unit is based on GPS sensors to record the location information of buses, monitor whether they deviate from the normal operating route, and collect the driving speed data of buses based on the speed sensor to understand the driving conditions of vehicles on different sections of the road, whether they are speeding or underspeeding, analyze whether the vehicles are running and arriving on time, and provide speed information; Based on infrared sensing and pressure sensing technology, the number of passengers getting on and off the bus is accurately counted, and the changes in passenger flow at each line and station are grasped in real time.

3. According to claim 1, the urban and rural passenger transport network optimization system based on intelligent evaluation and scheduling is characterized in that: The road condition data collection unit collects road conditions based on cameras. Cameras are set up at major road intersections, key sections and locations prone to traffic congestion. Through image recognition and analysis technology, the density, driving speed and queue length of vehicles on the road are determined, and the traffic volume and vehicle type distribution data on the road are obtained. The travel data collection unit records passengers' travel data by setting up intelligent interactive terminal devices at urban and rural bus stops. The data reflects the distribution of residents' travel needs in different time periods and locations.

4. The urban and rural passenger transport network optimization system based on intelligent evaluation and scheduling according to claim 1 is characterized in that: The data processing module preprocesses the data, which includes data cleaning, data integration and standardization. The data classification and organization are based on the decision tree algorithm, and the acquired data are classified into data sets.

5. The urban and rural passenger transport network optimization system based on intelligent evaluation and scheduling according to claim 1 is characterized in that: The line network diagnosis module extracts the operation data of public buses, station passenger flow data, line information and road condition data from the database, and analyzes the current traffic line network situation by analyzing the passenger flow and line load rate. The passenger flow characteristic analysis formula is: Where Q is the current daily passenger flow of buses in the county, P i is the change in the number of passengers getting on and off the i-th bus on the day, n is the number of times recorded on the day, the overall passenger flow scale of the station is evaluated through the formula, the peak and trough periods of passenger flow at the station are determined, and the time periods with the largest and smallest passenger flow are found by analyzing the passenger flow data in different time periods; The line load rate calculation formula is: Among them, R is the full load rate of the line, Qm is the actual number of bus passengers, and Qs is the rated passenger capacity of the vehicle. Through calculation, we can understand the operating conditions of urban and rural lines in different time periods. If the calculated full load rate value is too large, it is judged that the current transportation network is unreasonable. When the full load rate value is greater than 20%, it is judged to be too large. This is based on the expert analysis method.

6. The urban and rural passenger transport network optimization system based on intelligent evaluation and scheduling according to claim 1 is characterized in that: The traffic supply and demand analysis module sorts out the card swiping data of bus stops, counts the number of card swiping times at each stop in different time periods, and understands the passenger flow trend of each stop. The number of card swiping times at the central area of ​​the county-level administrative area will increase significantly during the morning and evening rush hours on weekdays, indicating that there is a large flow of people in this area during the time period. Combined with the operation trajectory data of bus vehicles, according to the vehicle stops at different stations and the passengers getting on and off, the passengers' travel routes are tracked, and a schematic diagram of the flow direction of passenger flow in the entire county is drawn, which intuitively reflects the urban and rural popularity range.

7. The urban and rural passenger transport network optimization system based on intelligent evaluation and scheduling according to claim 6 is characterized in that: The heat value calculation assumes that the travel heat value from A to B is H AB , where the calculation formula is: Where Q AB To collect passenger flow data from point A to point B within a certain period of time, Q Ai It is expressed as the total passenger flow from point A to all other areas, i represents the number of all stations that can be reached from point A; the formula expresses the ratio of the number of passengers from point A to point B to the total number of passengers from point A to all stations.

8. The urban and rural passenger transport network optimization system based on intelligent evaluation and scheduling according to claim 7 is characterized in that: Further optimize the heat value formula and add the travel distance factor D AB and travel time factor T AB , where the distance factor calculates the travel distance from point A to point B on the bus line based on geographic coordinates, and the time factor optimizes the heat value calculation formula by counting the average time it takes passengers to travel from point A to point B. The optimized formula is: Among them, k is a normalized constant, which adjusts the heat value to a numerical range. The revised formula takes into account the situation that the longer the travel distance and the longer the time will reduce the willingness to travel, thus having a negative impact on the heat value. The heat value is calculated more reasonably, and the heat value threshold of the current county is obtained through expert analysis. The calculated heat value is compared with the threshold. If it is greater than the threshold, the current county is judged to be a region with a large passenger travel volume. If it is less than the threshold, the current county is judged to be a region with a small passenger travel volume.

9. The urban and rural passenger transport network optimization system based on intelligent evaluation and scheduling according to claim 1 is characterized in that: The line network optimization module is based on the calculated heat value and integrates the current status information obtained by the line network diagnosis module. In areas with high heat values, multiple groups of bus routes are established. Between the central area and transportation hubs within the county-level administrative area, multiple groups of bus routes are established based on the geographical spatial layout of the area and the traffic conditions of the surrounding roads to ensure that passengers can travel quickly.

10. The urban and rural passenger transport network optimization system based on intelligent evaluation and scheduling according to claim 9, characterized in that: In areas with lower heat values, at least one group of bus routes will be set up. Taking travel demand into consideration, urban and rural bus routes covering major schools and corporate parks will be planned in towns and administrative villages with low population density to meet the needs of rural students for commuting from home to school and the commuting needs of employees of enterprises and institutions. The line network optimization module will continue to pay attention to the changing trend of its heat value. If there are signs of a significant increase in travel demand, it will start the planning of line expansion and new lines, and make arrangements in advance to adapt to future changes in travel demand.

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