Rail transit mode competitiveness calculation method based on Internet map data

Abstract

The invention provides a rail transit mode competitiveness calculation method, device and equipment based on Internet map data and a medium, and the method comprises the steps: determining reference points from a target area, and the reference points are determined based on an urban space structure, a transportation junction and a passenger flow attraction point, and represent a trip starting point and a trip ending point; determining influence factors such as travel time and cost, and constructing a quantitative travel mode utility function; based on Internet map information data, actual travel data between each rail station and each datum point under different travel modes are determined, a travel utility result is calculated in combination with a utility function, and the mode competitiveness of a traveler for selecting a rail transit mode is calculated based on the travel utility result so as to evaluate the competitiveness of the rail transit mode. According to the method and the system, traffic planning, management, policy making and traffic mode optimization can be scientifically guided by quantifying the travel utility and the competitiveness of the rail traffic mode, and the efficiency of an urban traffic system and the satisfaction degree of travelers are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of transportation, and in particular to a method, device, equipment and medium for calculating the competitiveness of rail transportation modes based on Internet map data. Background Art

[0002] With the acceleration of urbanization and increasing population mobility, urban traffic issues are becoming increasingly prominent, becoming a major factor restricting urban development. Traditional traffic planning and management methods, often based on empirical judgment or qualitative analysis, fail to accurately reflect the actual needs and behavioral characteristics of travelers, leading to frequent traffic congestion and low travel efficiency.

[0003] To address these challenges, researchers and practitioners in the transportation field have begun exploring more scientific and data-driven methods for calculating the competitiveness of transportation modes in recent years. These methods aim to quantify the utility provided by different travel modes to travelers by collecting and analyzing large amounts of travel data, and use this information to assess the competitiveness and attractiveness of various transportation modes. However, existing calculation methods still have many shortcomings in terms of data dynamics, cost, and cycle time. First, there are sampling bias and data quality issues. Second, there are insufficient timeliness and dynamism, as residents' transportation behavior may change rapidly over time. Third, the survey costs are high, requiring significant human, material, and financial resources, making it difficult to meet the needs of urban transportation planning. Summary of the Invention

[0004] In view of this, the embodiments of the present application provide a method, device, electronic device and storage medium for calculating the competitiveness of rail transit modes based on Internet map data, which can scientifically guide traffic planning, management, policy formulation and traffic mode optimization by quantifying travel utility and the competitiveness of selecting rail transit modes, and significantly improve the efficiency of urban transportation systems and traveler satisfaction.

[0005] The technical solution of the embodiment of the present application is implemented as follows:

[0006] In a first aspect, an embodiment of the present application provides a method for calculating rail transit competitiveness based on internet map data, the method comprising:

[0007] Determining a plurality of reference points in the target area; wherein the reference points represent the starting and ending points of travel in the target area, and the reference points are determined based on at least one of the urban spatial structure, transportation hubs, and passenger flow attraction points of the target area;

[0008] Determine the factors affecting the travel mode; wherein the factors affecting the travel mode include at least travel time and travel cost;

[0009] Constructing a travel mode utility function based on the influencing factors; wherein the utility function is used to quantify the attractiveness or utility value of different travel modes to travelers;

[0010] Determining actual travel data between each rail station and each reference point under different travel modes based on the internet map information data, and determining a travel utility result based on the actual travel data and the utility function;

[0011] The mode competitiveness of the rail transit mode selected by the traveler is calculated based on the travel utility result; wherein the mode competitiveness of the rail transit mode represents the competitiveness of the rail transit mode relative to other modes of transportation.

[0012] In a second aspect, an embodiment of the present application further provides a rail transit mode competitiveness calculation device based on Internet map data, the device comprising:

[0013] A first determination module is configured to determine a plurality of reference points in a target area; wherein the reference points represent the starting and ending points of trips in the target area, and the reference points are determined based on at least one of the urban spatial structure, transportation hubs, and passenger flow attraction points of the target area;

[0014] The second determining module is used to determine the influencing factors of the travel mode; wherein the influencing factors include at least travel time and travel cost;

[0015] A construction module is used to construct a travel mode utility function based on the influencing factors; wherein the utility function is used to quantify the attractiveness or utility value of different travel modes to travelers;

[0016] a third determining module, configured to determine actual travel data between each rail station and each reference point under different travel modes based on the internet map information data, and determine a travel utility result based on the actual travel data and the utility function;

[0017] A calculation module is used to calculate the competitiveness of the rail transit mode of travel chosen by the traveler based on the travel utility result; wherein the competitiveness of the rail transit mode of travel represents the competitiveness of the rail transit mode relative to other modes of transportation.

[0018] In a third aspect, an embodiment of the present application further provides an electronic device comprising: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to execute the rail transit mode competitiveness calculation method based on Internet map data as described in any one of the first aspects.

[0019] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for calculating the competitiveness of rail transit modes based on Internet map data as described in any one of the first aspects is executed.

[0020] The embodiments of the present application have the following beneficial effects:

[0021] (1) Improving the scientific nature of transportation planning: This embodiment of the application first identifies benchmark points in the target area. These benchmark points accurately reflect the characteristics of the region's travel origins and destinations, providing clear spatial positioning for subsequent transportation planning. By constructing a travel mode utility function and substituting actual travel data, the utility of different travel modes can be quantified, thereby more scientifically evaluating the competitiveness and attractiveness of various transportation modes.

[0022] (2) Improve the accuracy and timeliness of travel data analysis: By utilizing Internet map information data, the embodiments of the present application can collect and process a large amount of travel-related data in real time or near real time, including travel time, cost, mode, etc., thereby greatly improving the accuracy and timeliness of travel data analysis. Compared with traditional data collection methods, such as questionnaires or manual statistics, Internet map information data has a higher coverage rate and update frequency, and can more comprehensively reflect the actual travel situation of travelers. It can more accurately understand the needs and behavior patterns of travelers, and provide strong support for formulating more reasonable traffic policies, optimizing traffic resource allocation, and providing personalized travel services.

[0023] (3) Enhance the targeted nature of traffic management: Based on the competitiveness of the modes of travel chosen by travelers, it is possible to identify which modes of transportation are more popular with travelers and which modes need to be improved or optimized, providing a clear direction for urban traffic management and helping managers to formulate effective management strategies and measures for specific problems.

[0024] (4) Data-based support for policy making: The competitiveness of travel modes obtained through the implementation of this application provides strong data support for relevant agencies to formulate transportation policies. Based on this data, policymakers can more accurately predict the effects of policy implementation and thus formulate transportation policies that better meet actual needs.

[0025] (5) Promote the optimization and upgrading of transportation modes: By comparing the utility and competitiveness of different travel modes, we can discover the advantages and disadvantages of different transportation modes, provide clear guidance for the optimization and upgrading of transportation modes, and help promote innovation and development in the transportation industry.

[0026] (6) Improving travelers’ satisfaction: The travel mode utility function obtained by implementing this application can reflect the degree to which travelers attach importance to different factors, thereby providing more personalized travel suggestions, which helps to improve travelers’ satisfaction and promote the development of urban transportation.

[0027] (7) Enhance the applicability and flexibility of the method: The implementation of this application is not only applicable to urban transportation planning and management, but can also be extended to other areas of transportation research, such as regional transportation, intercity transportation, etc. At the same time, with the continuous accumulation of data and the continuous advancement of technology, the method can also be continuously optimized and improved to adapt to the ever-changing transportation needs and traveler behavior.

[0028] In summary, the rail transit competitiveness calculation method based on Internet map data has shown significant beneficial effects in improving the scientific nature of transportation planning, enhancing the targeted nature of transportation management, supporting the data-based policy making, promoting the optimization and upgrading of transportation modes, and improving travelers' satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0030] Figure 1 10 is a flow chart of steps S101-S105 provided in an embodiment of the present application;

[0031] Figure 2 This is a flow chart of steps S201-S202 provided in an embodiment of the present application;

[0032] Figure 3 3 is a flow chart of steps S301-S303 provided in an embodiment of the present application;

[0033] Figure 4 It is a flowchart of steps S401-S402 provided in an embodiment of the present application;

[0034] Figure 5 This is a schematic diagram of the structure of a rail transit mode competitiveness calculation device based on Internet map data provided by an embodiment of the present application;

[0035] Figure 6 It is a schematic diagram of the composition structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of illustration and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowcharts can be implemented out of sequence, and steps without logical context can be reversed or implemented simultaneously. In addition, those skilled in the art, under the guidance of the contents of this application, can add one or more other operations to the flowchart, or remove one or more operations from the flowchart.

[0037] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0038] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present application.

[0039] In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0040] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0042] See also Figure 1 , Figure 1This is a flow chart of steps S101-S105 of the method for calculating the competitiveness of rail transit modes based on Internet map data provided in an embodiment of the present application, which will be combined with Figure 1 Steps S101-S105 are shown for explanation.

[0043] In step S101, a plurality of reference points are determined in the target area; wherein the reference points represent the starting and ending points of travel in the target area, and the reference points are determined based on at least one of the urban spatial structure, transportation hub, and passenger flow attraction points of the target area.

[0044] First, multiple benchmark points are selected within the target area. These benchmark points represent the starting and ending points for travelers. The selection of benchmark points is based on the target area's urban spatial structure (e.g., old city center, new district center), transportation hubs (e.g., subway transfer stations, train stations, airports), and passenger flow attractions (e.g., commercial centers, business office centers).

[0045] In step S102, influencing factors of the travel mode are determined; wherein the influencing factors include at least travel time and travel cost.

[0046] Next, identify the main factors influencing travel mode choice. These factors should include, at a minimum, travel time and travel cost. Travel time refers to the time required to travel from your starting point to your destination, while travel cost covers transportation costs and other possible costs (such as parking fees, tolls, etc.).

[0047] In step S103, a travel mode utility function is constructed based on the influencing factors; wherein the utility function is used to quantify the attractiveness or utility value of different travel modes to travelers.

[0048] Here, based on the above influencing factors, a travel mode utility function can be constructed. This utility function aims to quantify the attractiveness or utility value of different travel modes to travelers.

[0049] In step S104, actual travel data between each rail station and each reference point under different travel modes is determined based on the Internet map information data, and travel utility results are determined based on the actual travel data and the utility function.

[0050] Here, we use internet map data, such as real-time route planning services for cars and public transportation, to obtain actual travel data between each rail station and each benchmark point under different travel modes. This actual travel data is input into the travel mode utility function to calculate the utility results of each travel mode between each benchmark point pair.

[0051] In step S105, the mode competitiveness of the traveler's choice of rail transit is calculated based on the travel utility result; wherein the mode competitiveness of the rail transit mode represents the competitiveness of the rail transit mode relative to other modes of transportation.

[0052] Finally, based on the travel utility results, the mode competitiveness of each travel mode is calculated. These mode competitiveness reflect the likelihood of travelers choosing a certain travel mode under given conditions, thus obtaining the mode competitiveness of rail transit.

[0053] This approach provides a quantitative means of evaluating the competitiveness of different transportation modes by comprehensively considering factors such as the target area's urban spatial structure, transportation hubs, passenger flow attraction points, and travel time and cost. By constructing a travel mode utility function and collecting actual travel data, it is possible to calculate travelers' preferences for different modes, providing a scientific basis for transportation planning and policy formulation.

[0054] In some embodiments, constructing a travel mode utility function based on the influencing factors includes:

[0055] Quantifying the utility brought by different travel modes to travelers based on the utility function to obtain the travel mode utility function;

[0056] Among them, the travel mode utility function is:

[0057] U=a×T+b×C;

[0058] Wherein, U represents utility, T represents the travel time, C represents the travel cost, a is the first weight coefficient, and b is the second weight coefficient.

[0059] The weight coefficients a and b are determined based on factors such as the research context, data characteristics, and traveler preferences. They reflect the relative importance travelers place on time and cost. For example, if the absolute value of a is greater than that of b, travel time has a greater influence on the traveler's choice; conversely, if the absolute value of b is greater, travel cost has a greater influence on the choice.

[0060] In addition, the weight coefficients a and b in the model usually need to be determined through statistical methods (such as regression analysis, survey data fitting, etc.). These methods can be based on actual travel data and use calculations and analysis to obtain the weight coefficient values ​​that best suit the actual situation.

[0061] In some embodiments, see Figure 2 , Figure 2This is a flow chart of steps S201-S202 provided in an embodiment of the present application. The first weight coefficient represents the degree of influence on the utility when the travel time changes by one unit, and the second weight coefficient represents the degree of influence on the utility when the travel cost changes by one unit. The method also includes steps S201-S202, which will be explained in combination with each step.

[0062] In step S201 , traveler data is collected; wherein the traveler data represents the degree to which travelers attach importance to different factors, and the collection method includes a questionnaire survey.

[0063] In step S202 , the first weight coefficient and the second weight coefficient are determined based on the traveler data.

[0064] Here, the first weight coefficient (a) represents the degree of influence on the utility (U) when the travel time changes by one unit, and the second weight coefficient (b) represents the degree of influence on the utility (U) when the travel cost changes by one unit.

[0065] In addition, traveler data can be collected to determine these weight coefficients. This traveler data reflects the importance that travelers place on different factors and is the key to determining the weight coefficients. Methods for collecting this data can include questionnaires.

[0066] Questionnaire surveys are a direct way to elicit traveler preferences. A well-designed questionnaire can be used to ask travelers how much they prioritize time and cost when choosing a transportation mode. The questionnaire can include multiple questions covering personal characteristics (such as age, gender, and occupation), travel purpose, travel distance, travel time preferences, and cost sensitivity. Statistical analysis of the questionnaire data can reveal the degree to which different factors influence traveler choice, thereby determining weighting coefficients.

[0067] Once the traveler data is collected, an appropriate method is needed to determine the weight coefficient. Common methods include:

[0068] Regression analysis: Using the collected data, a regression model can be constructed to estimate the weight coefficients. The regression model can be linear or nonlinear, depending on the characteristics of the data and the purpose of the study.

[0069] Discrete choice models: Discrete choice models (such as the Logit model and the Probit model) are statistical models specifically designed to process choice behavior data. These models can describe travelers' choice behavior based on a utility function and estimate weight coefficients by maximizing the likelihood function.

[0070] Machine learning methods: If the data volume is large and the relationships are complex, you can consider using machine learning methods to estimate the weight coefficients. Machine learning methods (such as neural networks and support vector machines) can automatically learn the complex relationships in the data and provide estimated values ​​for the weight coefficients.

[0071] It should be noted that the method for determining the weight coefficient should be selected based on the characteristics of the data and the purpose of the research. Different methods may produce different results, so it is necessary to comprehensively consider various factors to determine the final weight coefficient.

[0072] In some embodiments, see Figure 3 , Figure 3 This is a flow chart of steps S301-S302 provided in an embodiment of the present application, which determines the actual travel data between each rail station and each benchmark point under different travel modes based on Internet map information data, and determines the travel utility result based on the actual travel data and the travel mode utility function. This can be achieved through steps S301-S303, which will be explained in conjunction with each step.

[0073] In step S301, travel-related data is collected from the Internet map information data, and the travel-related data is cleaned; wherein the Internet map information includes at least one of an Internet map service and a real-time route planning service for cars and public transportation.

[0074] In step S302, the travel-related data is matched with the travel routes between each rail station and each reference point, and classified according to the travel mode to obtain the actual travel data between each rail station and each reference point under different travel modes.

[0075] In step S303, the actual travel data is substituted into the travel mode utility function to obtain the travel utility result. The travel utility result U i =a×T+b×C, where U i The utility function for a traveler to choose travel mode i, where i represents the travel mode.

[0076] Here, travel-related data is collected from at least one channel such as Internet map services and real-time route planning services for cars and public transportation. The collected data is preprocessed to remove duplicate, invalid and noisy data to ensure the accuracy and consistency of the data.

[0077] The travel data is then matched to the travel routes between each rail station and each benchmark point to determine the starting and ending points of each trip record. Based on the characteristics of the travel data (such as vehicle type and travel trajectory), travel modes are classified into different categories (such as bus, subway, and car). For each travel mode, the actual travel time and cost between each rail station and each benchmark point are calculated. Finally, the calculated travel time and cost data are integrated to form a complete actual travel data set.

[0078] Next, we will substitute the collected actual travel data into the travel mode utility function constructed previously. The specific form of the model is:

[0079] U i =a×T+b×C

[0080] in:

[0081] U i Represents the utility function of the traveler choosing travel mode i.

[0082] a and b are weight coefficients previously determined by statistical methods or expert evaluation, and they respectively represent the degree of influence of travel time and travel cost on utility.

[0083] T represents the travel time required for the traveler to choose travel mode i.

[0084] C represents the travel cost required for the traveler to choose travel mode i.

[0085] By inputting specific travel data (such as a traveler's actual travel time and cost), we can calculate the utility value of the traveler choosing different travel modes. These utility values ​​can be used to compare the attractiveness of different travel modes or to further calculate the competitiveness of each travel mode chosen by the traveler.

[0086] In summary, by utilizing internet map data, we can collect and process large amounts of travel-related data in real time or near real time, ensuring its accuracy and timeliness. By substituting this data into the travel mode utility function, we can obtain the utility results of travelers choosing different travel modes. These results not only help us understand travelers' choice behavior but also provide useful references for transportation planning and policy making.

[0087] In some embodiments, see Figure 4 , Figure 4 It is a flow chart of steps S401-S402 provided in an embodiment of the present application. The competitiveness of the travel mode selected by the traveler based on the travel utility result can be calculated through steps S401-S402, which will be explained in combination with each step.

[0088] In step S401 , it is assumed that the traveler's choice is based on the utility result.

[0089] In step S402 , the competitiveness of the travel mode selected by the traveler is calculated based on the Logit model and the utility results.

[0090] Here, utility indicates that when choosing a travel mode, travelers tend to choose those with the lowest cost and highest utility. Utility here is a comprehensive concept that may include a comprehensive consideration of multiple factors, such as travel time, travel cost, comfort, and safety. In the previous step, we calculated the utility values ​​of different travel modes using the travel mode utility function.

[0091] The logit model is a discrete choice model widely used in analyzing choice behavior. It predicts mode competitiveness by calculating the relative utility (i.e., utility difference) of different options based on utility results. In the logit model, the mode competitiveness of a traveler's choice of rail transit represents the competitiveness of rail transit relative to other modes of transportation. The mode competitiveness of a traveler's choice of rail transit is expressed as:

[0092]

[0093] in:

[0094] P i It represents the competitiveness of the traveler's choice of travel mode i.

[0095] U i It represents the utility value of the traveler choosing travel mode i. This value is usually calculated through the travel mode utility function, which integrates multiple factors such as travel time, travel cost, comfort, and safety.

[0096] It represents the exponential sum of the utility values ​​of all optional travel modes for the traveler. This summation operation is performed on all possible travel modes i.

[0097] A key feature of this formula is that, by converting utility values ​​into modal competitiveness, it ensures that the sum of the modal competitiveness of all travel modes is 1. This conforms to the basic principles of modal competitiveness theory and allows us to conduct further analysis and decision-making based on these modal competitiveness values.

[0098] In practical applications, we usually calculate the utility value U for each possible travel mode i of the traveler. i , and then substitute it into the above formula to calculate the competitiveness P of the selected method iThese mode competitiveness values ​​can be used to evaluate the competitiveness of different transportation modes, predict traffic flow, and optimize transportation network layout.

[0099] It’s important to note that while the Logit model offers high flexibility and accuracy in calculating the competitiveness of alternatives, its effectiveness also relies on the accuracy of the utility function and the reliability of the data. Therefore, when applying the Logit model, we need to ensure that the utility function truly reflects travelers’ preferences and behaviors and that the collected data is accurate and complete.

[0100] In summary, the embodiments of the present application have the following beneficial effects:

[0101] (1) Improving the scientific nature of transportation planning: This embodiment of the application first identifies benchmark points in the target area. These benchmark points accurately reflect the characteristics of the region's travel origins and destinations, providing clear spatial positioning for subsequent transportation planning. By constructing a travel mode utility function and substituting actual travel data, the utility of different travel modes can be quantified, thereby more scientifically evaluating the competitiveness and attractiveness of various transportation modes.

[0102] (2) Improve the accuracy and timeliness of travel data analysis: By utilizing Internet map information data, the embodiments of the present application can collect and process a large amount of travel-related data in real time or near real time, including travel time, cost, mode, etc., thereby greatly improving the accuracy and timeliness of travel data analysis. Compared with traditional data collection methods, such as questionnaires or manual statistics, Internet map information data has a higher coverage rate and update frequency, and can more comprehensively reflect the actual travel situation of travelers. It can more accurately understand the needs and behavior patterns of travelers, and provide strong support for formulating more reasonable traffic policies, optimizing traffic resource allocation, and providing personalized travel services.

[0103] (3) Enhance the targeted nature of traffic management: Based on the competitiveness of the modes of travel chosen by travelers, it is possible to identify which modes of transportation are more popular with travelers and which modes need to be improved or optimized, providing a clear direction for urban traffic management and helping managers to formulate effective management strategies and measures for specific problems.

[0104] (4) Data-based support for policy making: The competitiveness of travel modes obtained through the implementation of this application provides strong data support for relevant agencies to formulate transportation policies. Based on this data, policymakers can more accurately predict the effects of policy implementation and thus formulate transportation policies that better meet actual needs.

[0105] (5) Promote the optimization and upgrading of transportation modes: By comparing the utility and competitiveness of different travel modes, we can discover the advantages and disadvantages of different transportation modes, provide clear guidance for the optimization and upgrading of transportation modes, and help promote innovation and development in the transportation industry.

[0106] (6) Improving travelers’ satisfaction: The travel mode utility function obtained by implementing this application can reflect the degree to which travelers attach importance to different factors, thereby providing more personalized travel suggestions, which helps to improve travelers’ satisfaction and promote the development of urban transportation.

[0107] (7) Enhance the applicability and flexibility of the method: The implementation of this application is not only applicable to urban transportation planning and management, but can also be extended to other areas of transportation research, such as regional transportation, intercity transportation, etc. At the same time, with the continuous accumulation of data and the continuous advancement of technology, the method can also be continuously optimized and improved to adapt to the ever-changing transportation needs and traveler behavior.

[0108] In summary, the rail transit competitiveness calculation method based on Internet map data has shown significant beneficial effects in improving the scientific nature of transportation planning, enhancing the targeted nature of transportation management, supporting the data-based policy making, promoting the optimization and upgrading of transportation modes, and improving travelers' satisfaction.

[0109] Based on the same inventive concept, the embodiments of the present application also provide a rail transit mode competitiveness calculation device based on Internet map data corresponding to the rail transit mode competitiveness calculation method based on Internet map data in the first embodiment. Since the principle of solving the problem by the device in the embodiments of the present application is similar to the above-mentioned rail transit mode competitiveness calculation method based on Internet map data, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0110] like Figure 5 As shown, Figure 5 : is a schematic diagram of the structure of a rail transit mode competitiveness calculation device 500 based on Internet map data provided by an embodiment of the present application. The rail transit mode competitiveness calculation device 500 based on Internet map data includes:

[0111] A first determination module 501 is configured to determine a plurality of reference points in a target area; wherein the reference points represent the starting and ending points of trips in the target area, and the reference points are determined based on at least one of the urban spatial structure, transportation hubs, and passenger flow attraction points of the target area;

[0112] The second determining module 502 is used to determine the influencing factors of the travel mode; wherein the influencing factors include at least travel time and travel cost;

[0113] A construction module 503 is configured to construct a travel mode utility function based on the influencing factors; wherein the utility function is used to quantify the attractiveness or utility value of different travel modes to travelers;

[0114] A third determining module 504 is configured to determine actual travel data between each rail station and each reference point under different travel modes based on the Internet map information data, and determine a travel utility result based on the actual travel data and the utility function;

[0115] The calculation module 505 is used to calculate the mode competitiveness of the traveler's choice of rail transit based on the travel utility result; wherein the mode competitiveness of the rail transit represents the competitiveness of the rail transit mode relative to other modes of transportation.

[0116] It should be understood by those skilled in the art that Figure 5 The implementation functions of each unit in the rail transit mode competitiveness calculation device 500 based on Internet map data can be understood by referring to the relevant description of the rail transit mode competitiveness calculation method based on Internet map data mentioned above. Figure 5 The functions of the various units in the rail transit mode competitiveness calculation device 500 based on Internet map data shown can be implemented by a program running on a processor, or can be implemented by a specific logic circuit.

[0117] In a possible implementation, the construction module 503 constructs a travel mode utility function based on the influencing factors, including:

[0118] Quantifying the utility brought by different travel modes to travelers based on the utility function to obtain the travel mode utility function;

[0119] Among them, the travel mode utility function is:

[0120] U=a×T+b×C;

[0121] Wherein, U represents utility, T represents the travel time, C represents the travel cost, a is the first weight coefficient, and b is the second weight coefficient.

[0122] In a possible implementation, the first weight coefficient represents the degree of influence on the utility when the travel time changes by one unit, and the second weight coefficient represents the degree of influence on the utility when the travel cost changes by one unit. The construction module 503 further includes:

[0123] Collecting traveler data; wherein the traveler data represents the degree to which travelers attach importance to different factors, and the collection method includes a questionnaire survey;

[0124] The first weighting factor and the second weighting factor are determined based on the traveler data.

[0125] In one possible implementation, the third determination module 504 determines actual travel data between each rail station and each reference point under different travel modes based on the internet map information data, and determines a travel utility result based on the actual travel data and the travel mode utility function, including:

[0126] collecting travel-related data from the internet map information data and performing data cleaning on the travel-related data; wherein the internet map information includes at least one of an internet map service and a real-time route planning service for cars and public transportation;

[0127] Matching the travel-related data with the travel routes between each rail station and each reference point, and classifying them according to travel modes to obtain actual travel data between each rail station and each reference point under different travel modes;

[0128] Substitute the actual travel data into the travel mode utility function to obtain the travel utility result, which is U i =a×T+b×C, where U i The utility function for a traveler to choose travel mode i, where i represents the travel mode.

[0129] In one possible implementation, the calculation module 505

[0130] The mode competitiveness of the traveler's choice of rail transit mode is calculated based on the travel utility results, including:

[0131] Assume that travelers’ choices are based on utility outcomes;

[0132] The competitiveness of travelers' travel modes is calculated based on the Logit model and utility results.

[0133] In a possible implementation, the competitiveness of rail transit chosen by travelers represents the competitiveness of rail transit relative to other transportation modes. The competitiveness of rail transit chosen by travelers is expressed as:

[0134]

[0135] Among them, P i It represents the competitiveness of the traveler's choice of travel mode i.

[0136] In a possible implementation, the first determining module 501 determines at least one reference point in the target area, including:

[0137] determining a representative score of a target point in the target area;

[0138] Target points that are higher than the scoring threshold are determined as benchmark points; wherein, the benchmark points include urban center points, transportation hubs, rail transit two-line or multi-line transfer stations and passenger flow attraction points.

[0139] The above-mentioned rail transit mode competitiveness calculation device based on Internet map data has the following beneficial effects:

[0140] (1) Improving the scientific nature of transportation planning: This embodiment of the application first identifies benchmark points in the target area. These benchmark points accurately reflect the characteristics of the region's travel origins and destinations, providing clear spatial positioning for subsequent transportation planning. By constructing a travel mode utility function and substituting actual travel data, the utility of different travel modes can be quantified, thereby more scientifically evaluating the competitiveness and attractiveness of various transportation modes.

[0141] (2) Improve the accuracy and timeliness of travel data analysis: By utilizing Internet map information data, the embodiments of the present application can collect and process a large amount of travel-related data in real time or near real time, including travel time, cost, mode, etc., thereby greatly improving the accuracy and timeliness of travel data analysis. Compared with traditional data collection methods, such as questionnaires or manual statistics, Internet map information data has a higher coverage rate and update frequency, and can more comprehensively reflect the actual travel situation of travelers. It can more accurately understand the needs and behavior patterns of travelers, and provide strong support for formulating more reasonable traffic policies, optimizing traffic resource allocation, and providing personalized travel services.

[0142] (3) Enhance the targeted nature of traffic management: Based on the competitiveness of the modes of travel chosen by travelers, it is possible to identify which modes of transportation are more popular with travelers and which modes need to be improved or optimized, providing a clear direction for urban traffic management and helping managers to formulate effective management strategies and measures for specific problems.

[0143] (4) Data-based support for policy making: The competitiveness of travel modes obtained through the implementation of this application provides strong data support for relevant agencies to formulate transportation policies. Based on this data, policymakers can more accurately predict the effects of policy implementation and thus formulate transportation policies that better meet actual needs.

[0144] (5) Promote the optimization and upgrading of transportation modes: By comparing the utility and competitiveness of different travel modes, we can discover the advantages and disadvantages of different transportation modes, provide clear guidance for the optimization and upgrading of transportation modes, and help promote innovation and development in the transportation industry.

[0145] (6) Improving travelers’ satisfaction: The travel mode utility function obtained by implementing this application can reflect the degree to which travelers attach importance to different factors, thereby providing more personalized travel suggestions, which helps to improve travelers’ satisfaction and promote the development of urban transportation.

[0146] (7) Enhance the applicability and flexibility of the method: The implementation of this application is not only applicable to urban transportation planning and management, but can also be extended to other areas of transportation research, such as regional transportation, intercity transportation, etc. At the same time, with the continuous accumulation of data and the continuous advancement of technology, the method can also be continuously optimized and improved to adapt to the ever-changing transportation needs and traveler behavior.

[0147] In summary, the rail transit competitiveness calculation method based on Internet map data has shown significant beneficial effects in improving the scientific nature of transportation planning, enhancing the targeted nature of transportation management, supporting the data-based policy making, promoting the optimization and upgrading of transportation modes, and improving travelers' satisfaction.

[0148] like Figure 6 As shown, Figure 6 This is a schematic diagram of the structure of an electronic device 600 provided in an embodiment of the present application. The electronic device 600 includes:

[0149] A processor 601, a storage medium 602 and a bus 603, wherein the storage medium 602 stores machine-readable instructions executable by the processor 601. When the electronic device 600 is running, the processor 601 communicates with the storage medium 602 via the bus 603, and the processor 601 executes the machine-readable instructions to execute the steps of the method for calculating the competitiveness of rail transit modes based on Internet map data as described in the embodiment of the present application.

[0150] In actual application, the various components in the electronic device 600 are coupled together via bus 603. It is understood that bus 603 is used to realize the connection and communication between these components. In addition to the data bus, bus 603 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Figure 6 Various buses are labeled as bus 603.

[0151] The electronic device has the following beneficial effects:

[0152] (1) Improving the scientific nature of transportation planning: This embodiment of the application first identifies benchmark points in the target area. These benchmark points accurately reflect the characteristics of the region's travel origins and destinations, providing clear spatial positioning for subsequent transportation planning. By constructing a travel mode utility function and substituting actual travel data, the utility of different travel modes can be quantified, thereby more scientifically evaluating the competitiveness and attractiveness of various transportation modes.

[0153] (2) Improve the accuracy and timeliness of travel data analysis: By utilizing Internet map information data, the embodiments of the present application can collect and process a large amount of travel-related data in real time or near real time, including travel time, cost, mode, etc., thereby greatly improving the accuracy and timeliness of travel data analysis. Compared with traditional data collection methods, such as questionnaires or manual statistics, Internet map information data has a higher coverage rate and update frequency, and can more comprehensively reflect the actual travel situation of travelers. It can more accurately understand the needs and behavior patterns of travelers, and provide strong support for formulating more reasonable traffic policies, optimizing traffic resource allocation, and providing personalized travel services.

[0154] (3) Enhance the targeted nature of traffic management: Based on the competitiveness of the modes of travel chosen by travelers, it is possible to identify which modes of transportation are more popular with travelers and which modes need to be improved or optimized, providing a clear direction for urban traffic management and helping managers to formulate effective management strategies and measures for specific problems.

[0155] (4) Data-based support for policy making: The competitiveness of travel modes obtained through the implementation of this application provides strong data support for relevant agencies to formulate transportation policies. Based on this data, policymakers can more accurately predict the effects of policy implementation and thus formulate transportation policies that better meet actual needs.

[0156] (5) Promote the optimization and upgrading of transportation modes: By comparing the utility and competitiveness of different travel modes, we can discover the advantages and disadvantages of different transportation modes, provide clear guidance for the optimization and upgrading of transportation modes, and help promote innovation and development in the transportation industry.

[0157] (6) Improving travelers’ satisfaction: The travel mode utility function obtained by implementing this application can reflect the degree to which travelers attach importance to different factors, thereby providing more personalized travel suggestions, which helps to improve travelers’ satisfaction and promote the development of urban transportation.

[0158] (7) Enhance the applicability and flexibility of the method: The implementation of this application is not only applicable to urban transportation planning and management, but can also be extended to other areas of transportation research, such as regional transportation, intercity transportation, etc. At the same time, with the continuous accumulation of data and the continuous advancement of technology, the method can also be continuously optimized and improved to adapt to the ever-changing transportation needs and traveler behavior.

[0159] In summary, the rail transit competitiveness calculation method based on Internet map data has shown significant beneficial effects in improving the scientific nature of transportation planning, enhancing the targeted nature of transportation management, supporting the data-based policy making, promoting the optimization and upgrading of transportation modes, and improving travelers' satisfaction.

[0160] An embodiment of the present application also provides a computer-readable storage medium, which stores executable instructions. When the executable instructions are executed by at least one processor 601, the method for calculating the competitiveness of rail transit modes based on Internet map data described in an embodiment of the present application is implemented.

[0161] In some embodiments, the storage medium can be a magnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface storage, an optical disc, or a compact disc read-only memory (CDROM); it can also be various devices including one or any combination of the above memories.

[0162] In some embodiments, executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0163] As an example, executable instructions may, but do not necessarily, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, for example, in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (for example, files storing one or more modules, subroutines, or code portions).

[0164] By way of example, executable instructions may be deployed to be executed on one computing device, or on multiple computing devices at one site, or on multiple computing devices distributed across multiple sites and interconnected by a communication network.

[0165] The computer-readable storage medium has the following advantages:

[0166] (1) Improving the scientific nature of transportation planning: This embodiment of the application first identifies benchmark points in the target area. These benchmark points accurately reflect the characteristics of the region's travel origins and destinations, providing clear spatial positioning for subsequent transportation planning. By constructing a travel mode utility function and substituting actual travel data, the utility of different travel modes can be quantified, thereby more scientifically evaluating the competitiveness and attractiveness of various transportation modes.

[0167] (2) Improve the accuracy and timeliness of travel data analysis: By utilizing Internet map information data, the embodiments of the present application can collect and process a large amount of travel-related data in real time or near real time, including travel time, cost, mode, etc., thereby greatly improving the accuracy and timeliness of travel data analysis. Compared with traditional data collection methods, such as questionnaires or manual statistics, Internet map information data has a higher coverage rate and update frequency, and can more comprehensively reflect the actual travel situation of travelers. It can more accurately understand the needs and behavior patterns of travelers, and provide strong support for formulating more reasonable traffic policies, optimizing traffic resource allocation, and providing personalized travel services.

[0168] (3) Enhance the targeted nature of traffic management: Based on the competitiveness of the modes of travel chosen by travelers, it is possible to identify which modes of transportation are more popular with travelers and which modes need to be improved or optimized, providing a clear direction for urban traffic management and helping managers to formulate effective management strategies and measures for specific problems.

[0169] (4) Data-based support for policy making: The competitiveness of travel modes obtained through the implementation of this application provides strong data support for relevant agencies to formulate transportation policies. Based on this data, policymakers can more accurately predict the effects of policy implementation and thus formulate transportation policies that better meet actual needs.

[0170] (5) Promote the optimization and upgrading of transportation modes: By comparing the utility and competitiveness of different travel modes, we can discover the advantages and disadvantages of different transportation modes, provide clear guidance for the optimization and upgrading of transportation modes, and help promote innovation and development in the transportation industry.

[0171] (6) Improving travelers’ satisfaction: The travel mode utility function obtained by implementing this application can reflect the degree to which travelers attach importance to different factors, thereby providing more personalized travel suggestions, which helps to improve travelers’ satisfaction and promote the development of urban transportation.

[0172] (7) Enhance the applicability and flexibility of the method: The implementation of this application is not only applicable to urban transportation planning and management, but can also be extended to other areas of transportation research, such as regional transportation, intercity transportation, etc. At the same time, with the continuous accumulation of data and the continuous advancement of technology, the method can also be continuously optimized and improved to adapt to the ever-changing transportation needs and traveler behavior.

[0173] In summary, the rail transit competitiveness calculation method based on Internet map data has shown significant beneficial effects in improving the scientific nature of transportation planning, enhancing the targeted nature of transportation management, supporting the data-based policy making, promoting the optimization and upgrading of transportation modes, and improving travelers' satisfaction.

[0174] In the several embodiments provided in this application, it should be understood that the disclosed methods and electronic devices can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0175] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0176] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0177] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, platform server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0178] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for calculating the competitiveness of rail transit modes based on Internet map data, characterized in that: The method comprises: Determining a plurality of reference points in the target area; wherein the reference points represent the starting and ending points of travel in the target area, and the reference points are determined based on at least one of the urban spatial structure, transportation hubs, and passenger flow attraction points of the target area; Determine the factors affecting the travel mode; wherein the factors affecting the travel mode include at least travel time and travel cost; Constructing a travel mode utility function based on the influencing factors; wherein the utility function is used to quantify the attractiveness or utility value of different travel modes to travelers; Determining actual travel data between each rail station and each reference point under different travel modes based on the internet map information data, and determining a travel utility result based on the actual travel data and the utility function; The mode competitiveness of the rail transit mode selected by the traveler is calculated based on the travel utility result; wherein the mode competitiveness of the rail transit mode represents the competitiveness of the rail transit mode relative to other modes of transportation.

2. The method according to claim 1, characterized in that The constructing of a travel mode utility function based on the influencing factors includes: Quantifying the utility brought by different travel modes to travelers based on the utility function to obtain the travel mode utility function; Among them, the travel mode utility function is: U=a×T+b×C; Wherein, U represents utility, T represents the travel time, C represents the travel cost, a is the first weight coefficient, and b is the second weight coefficient.

3. The method according to claim 2, characterized in that The first weight coefficient represents the degree of influence on the utility when the travel time changes by one unit, and the second weight coefficient represents the degree of influence on the utility when the travel cost changes by one unit. The method further includes: Collecting traveler data; wherein the traveler data represents the degree to which travelers attach importance to different factors, and the collection method includes a questionnaire survey; The first weighting factor and the second weighting factor are determined based on the traveler data.

4. The method according to claim 2, characterized in that The determining, based on the internet map information data, actual travel data between each rail station and each reference point under different travel modes, and determining a travel utility result based on the actual travel data and the travel mode utility function, includes: collecting travel-related data from the internet map information data and performing data cleaning on the travel-related data; wherein the internet map information includes at least one of an internet map service and a real-time route planning service for cars and public transportation; Matching the travel-related data with the travel routes between each rail station and each reference point, and classifying them according to travel modes to obtain actual travel data between each rail station and each reference point under different travel modes; Substitute the actual travel data into the travel mode utility function to obtain the travel utility result, which is U i =a×T+b×C, where U i The utility function for a traveler to choose travel mode i, where i represents the travel mode.

5. The method according to claim 4, characterized in that The calculating of the competitiveness of the rail transit mode selected by the traveler based on the travel utility result includes: Assume that travelers’ choices are based on utility outcomes; The competitiveness of travelers' travel modes is calculated based on the Logit model and utility results.

6. The method according to claim 5, characterized in that The competitiveness of rail transit mode chosen by travelers represents the competitiveness of rail transit mode relative to other modes of transportation. The competitiveness of rail transit mode chosen by travelers is expressed as: Among them, P i It represents the competitiveness of the traveler's choice of travel mode i.

7. A rail transit mode competitiveness calculation device based on Internet map data, characterized in that: The device comprises: A first determination module is configured to determine a plurality of reference points in a target area; wherein the reference points represent the starting and ending points of trips in the target area, and the reference points are determined based on at least one of the urban spatial structure, transportation hubs, and passenger flow attraction points of the target area; The second determining module is used to determine the influencing factors of the travel mode; wherein the influencing factors include at least travel time and travel cost; A construction module is used to construct a travel mode utility function based on the influencing factors; wherein the utility function is used to quantify the attractiveness or utility value of different travel modes to travelers; a third determining module, configured to determine actual travel data between each rail station and each reference point under different travel modes based on the internet map information data, and determine a travel utility result based on the actual travel data and the utility function; A calculation module is used to calculate the competitiveness of the rail transit mode of travel chosen by the traveler based on the travel utility result; wherein the competitiveness of the rail transit mode of travel represents the competitiveness of the rail transit mode relative to other modes of transportation.

8. An electronic device, characterized in that: include: A processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the storage medium communicate through the bus, and the processor executes the machine-readable instructions to execute the rail transit mode competitiveness calculation method based on Internet map data as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, executes the rail transit mode competitiveness calculation method based on Internet map data as described in any one of claims 1 to 6.

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