Public transport optimization method and device based on heterogeneous crowd

Abstract

The invention relates to the technical field of public transport planning and management, and particularly provides a public transport optimization method and device based on heterogeneous crowds. The method comprises the following steps: acquiring the number of heterogeneous crowds in each community in a target area, the total number of interest terminals, the number of reachable interest terminals based on public transport and a negative exponential decay factor; utilizing a reachability calculation model to calculate a reachability index based on the total number of interest end points, the number of reachable interest end points and the negative exponential decay factor; using a fairness calculation model to calculate a fairness index based on the number and the reachability index of each heterogeneous crowd in each community; using a stability calculation model to calculate a stability index based on the reachability index; and optimizing the public transportation of the plurality of communities based on the comprehensive optimization priority index of each community calculated by the accessibility index, the fairness index and the stability index. According to the invention, the public transport short boards of multiple communities can be positioned, and a scientific basis is provided for optimization.

Description

Technical Field

[0001] This disclosure relates to the field of public transportation planning and management technology, and in particular to a method and apparatus for optimizing public transportation based on heterogeneous populations. Background Technology

[0002] Accessibility of public transportation refers to people's ability to conveniently and efficiently use public transportation systems to reach their destinations. It is a core indicator for measuring the service level of public transportation systems and a key basis for optimizing public transportation networks and improving travel fairness.

[0003] In related technologies, public transportation accessibility evaluation methods are typically based on the assumption of a "homogeneous population," failing to adequately consider the differences among different groups (such as the elderly, children, and people with disabilities) in terms of walking ability, passenger load sensitivity, and travel purpose. This leads to discrepancies between the evaluation results and the actual travel experiences of heterogeneous populations. Furthermore, these methods often focus on a single travel segment, neglecting the dynamic changes in time throughout the entire process, including walking, waiting, and transfers, and failing to consider the differentiated accessibility needs of different travel purposes such as commuting, medical visits, and shopping. For example, the elderly are more sensitive to walking time when traveling for medical treatment, while commuters are more concerned with waiting and boarding efficiency; existing evaluation systems struggle to accurately identify these scenario-specific shortcomings. Summary of the Invention

[0004] This disclosure is made in view of the above-mentioned problems. This disclosure provides a method and apparatus for optimizing public transportation based on heterogeneous populations.

[0005] According to one aspect of this disclosure, a public transportation optimization method based on heterogeneous populations is provided, comprising: The system obtains the number of heterogeneous populations in each community of multiple communities in the target area, the total number of interest endpoints, the number of interest endpoints accessible by public transportation within a preset travel time from the target time, and the negative exponential decay factor, as well as the pre-constructed accessibility calculation model, fairness calculation model, and stability calculation model. Using the accessibility calculation model, based on the total number of interest endpoints, the number of reachable interest endpoints, and the negative exponential decay factor, the accessibility index of each heterogeneous population in each community at the target time is calculated. The fairness calculation model is used to calculate the fairness index of each community at the target time based on the number of heterogeneous groups in each community and the accessibility index of each heterogeneous group in each community at the target time. Using the stability calculation model, based on the target time and the accessibility index of each heterogeneous population in each community at the target time, the stability index of each heterogeneous population in each community at the target time is calculated. Public transportation in the multiple communities is optimized based on a comprehensive optimization priority index calculated from the accessibility index, the fairness index, and the stability index.

[0006] According to another aspect of this disclosure, a public transportation optimization device based on heterogeneous populations is provided, comprising: The acquisition module is used to acquire the number of heterogeneous populations in each community of multiple communities in the target area, the total number of interest endpoints, the number of interest endpoints accessible by public transportation within a preset travel time from the target time, the negative exponential decay factor, as well as the pre-built accessibility calculation model, fairness calculation model and stability calculation model. The processing module is used to calculate the accessibility index of each heterogeneous population in each community at the target time based on the total number of interest endpoints, the number of reachable interest endpoints, and the negative exponential decay factor using the accessibility calculation model. The processing module is further configured to use the fairness calculation model to calculate the fairness index of each community at the target time based on the number of each heterogeneous population in each community and the accessibility index of each heterogeneous population in each community at the target time; The processing module is further configured to use the stability calculation model to calculate the stability index of each heterogeneous population in each community at the target time based on the target time and the accessibility index of each heterogeneous population in each community at the target time; The processing module is also used to optimize public transportation in the multiple communities based on a comprehensive optimization priority index for each community calculated from the accessibility index, the fairness index, and the stability index.

[0007] In another aspect of exemplary embodiments of this disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory, the processor executing the computer program to implement the methods described in exemplary embodiments of this disclosure.

[0008] In another aspect of exemplary embodiments of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the methods described in exemplary embodiments of the present disclosure.

[0009] In another aspect of the exemplary embodiments of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the methods described in the exemplary embodiments of this disclosure.

[0010] As will be described in detail below, the public transportation optimization method based on heterogeneous populations according to embodiments of this disclosure involves obtaining the number of heterogeneous populations in each community of multiple communities in a target area, the total number of interest endpoints, the number of reachable interest endpoints based on public transportation within a preset travel time from the target time, and a negative exponential decay factor, as well as pre-constructed accessibility calculation models, fairness calculation models, and stability calculation models; using the accessibility calculation model based on the total number of interest endpoints, the number of reachable interest endpoints, and the negative exponential decay factor, calculating the accessibility index of each heterogeneous population in each community at the target time; and using the fairness calculation model based on the number of heterogeneous populations in each community and the number of heterogeneous populations within each community, calculating the accessibility index of each heterogeneous population in each community at the target time. The accessibility index of heterogeneous populations within the area is used to calculate the fairness index of each community at the target time. A stability calculation model is then used to calculate the stability index of each heterogeneous population within each community at the target time, based on the accessibility index and the stability index. Based on the comprehensive optimization priority index of each community calculated from the accessibility, fairness, and stability indices, public transportation in multiple communities is optimized. This approach considers the travel preferences and travel times of different heterogeneous populations. Based on the multi-dimensional requirements of accessibility, fairness, and stability, the optimization priority order of multiple communities within the target area is determined to pinpoint the shortcomings of public transportation in multiple communities within the target area, providing a scientific basis for the optimization of the public transportation system.

[0011] It should be understood that both the foregoing general description and the following detailed description are exemplary and intended to provide further illustration of the claimed technology. Attached Figure Description

[0012] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0013] Figure 1 A flowchart illustrating a public transportation optimization method based on heterogeneous populations provided by an exemplary embodiment of this disclosure is shown. Figure 2 A flowchart is shown for a method for evaluating the dynamic accessibility of public transportation considering heterogeneous populations, provided by an exemplary embodiment of this disclosure. Figure 3 A schematic diagram of the structure of a public transportation optimization device based on heterogeneous populations provided in an exemplary embodiment of this disclosure is shown. Figure 4 A schematic diagram of the structure of an electronic device provided in an exemplary embodiment of this disclosure is shown; Figure 5 A schematic diagram of the structure of a computer system provided in an exemplary embodiment of this disclosure is shown. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this disclosure more apparent, exemplary embodiments according to this disclosure will now be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this disclosure, and not all embodiments of this disclosure. It should be understood that this disclosure is not limited to the exemplary embodiments described herein.

[0015] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0016] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc., used in this disclosure are only used to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0017] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0018] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0019] In related technologies, public transportation accessibility assessments often neglect the travel characteristics of heterogeneous populations and fail to incorporate differentiated travel purposes and the dynamics of the entire journey.

[0020] With the widespread availability of multi-source data, the technological conditions for characterizing population heterogeneity and travel dynamics are now in place. However, a systematic evaluation method that integrates multi-source data and caters to the needs of diverse populations is currently lacking, failing to provide scientific support for the personalized optimization of public transportation systems. Therefore, there is an urgent need to construct a dynamic accessibility evaluation method for public transportation that considers the characteristics of heterogeneous populations and covers the entire travel chain.

[0021] To address the aforementioned problems in related technologies, this disclosure provides a public transportation optimization method based on heterogeneous populations. By integrating multi-source data and constructing a full-chain travel time model (i.e., a full-journey travel time calculation model), it enables dynamic accessibility assessment under different time periods and travel purposes, accurately identifies accessibility shortcomings of heterogeneous populations, and provides a scientific basis for public transportation system optimization.

[0022] This disclosure provides a public transportation optimization method based on heterogeneous populations, which can be executed by a terminal or by a chip applied to the terminal.

[0023] For example, the terminal may include one or more of the following: mobile phone, tablet computer, wearable device, in-vehicle device, laptop computer, ultra-mobile personal computer (UMPC), netbook, PDA, and wearable device based on augmented reality (AR) and / or virtual reality (VR) technology. The exemplary embodiments disclosed herein do not impose specific limitations on these.

[0024] Figure 1 This illustration shows a flowchart of a public transportation optimization method based on heterogeneous populations, provided by an exemplary embodiment of this disclosure. Figure 1 As shown, this public transportation optimization method based on heterogeneous populations includes: S101, obtain the number of heterogeneous populations in each community of multiple communities in the target area, the total number of interest endpoints, the number of interest endpoints accessible by public transportation within the preset travel time from the target time, and the negative exponential decay factor, as well as the pre-constructed accessibility calculation model, fairness calculation model and stability calculation model. S102, using the accessibility calculation model based on the total number of interest endpoints, the number of accessible interest endpoints, and the negative exponential decay factor, calculates the accessibility index of each heterogeneous population in each community at the target time. S103, using a fairness calculation model, calculates the fairness index of each community at the target time based on the number of heterogeneous groups in each community and the accessibility index of each heterogeneous group in each community; S104. Using a stability calculation model, based on the accessibility index of each heterogeneous population in each community at the target time and the target time, calculate the stability index of each heterogeneous population in each community at the target time. S105 optimizes public transportation in multiple communities based on a comprehensive optimization priority index calculated from accessibility, fairness, and stability indices.

[0025] Specifically, public transportation accessibility assessments in related technologies typically treat all residents of an area as a homogeneous group, calculating an overall, average accessibility index, which fails to accurately assess the differences in public transportation services among different social groups. However, the method in this disclosure achieves a fundamental breakthrough by introducing the concept of "heterogeneous population" and conducting a comprehensive assessment from three perspectives: community accessibility based on public transportation, community equity, and community stability.

[0026] This disclosure allows for the acquisition of heterogeneous population survey data, used to obtain information such as travel destinations and travel time preferences among different population groups. Based on travel preferences (i.e., daily activity needs, such as work, medical treatment, shopping, and entertainment), residents of various communities within the target area are categorized into multiple heterogeneous populations. For example, these heterogeneous populations may include "commuters," "carless elderly," "low-income groups," "families with children," and "people with disabilities," among others.

[0027] The total number of interest endpoints mentioned above can refer to the total number of facilities or locations within the target area that match the travel preferences of each heterogeneous group and can theoretically be considered as interest endpoints (i.e., facilities or locations of travel destinations). It can be the sum of all places that each heterogeneous group might want to go to within the spatial scope of the study.

[0028] The aforementioned preset total travel time is the maximum time budget set for a one-way trip. It represents the maximum time that a specific group of heterogeneous people is willing to spend on public transportation. If a trip exceeds this time, it is considered that the destination of interest is too far away or too inconvenient, or that the value and utility of the trip has become very low. In this case, the evaluation of public transportation accessibility for heterogeneous people that exceeds the preset total travel time will lose its practical significance.

[0029] The aforementioned number of reachable destinations based on public transportation within a preset travel time, starting from the target time, can be understood as: considering real-time or planned public transportation services, the total number of all reachable destinations for heterogeneous groups starting from a specific starting point (each community) using public transportation (such as buses, subways, trams, etc.), provided that the travel time does not exceed the preset travel time. Simply put, it represents the number of places that heterogeneous groups living in this community can actually reach using the public transportation system within a preset travel time at the target time. Furthermore, "starting from the target time" implies that the method provided in this embodiment is a dynamic, time-related assessment that reflects the real-time level of public transportation services. Here, the target time can be any departure time chosen by the heterogeneous groups; this embodiment does not specifically limit this.

[0030] The negative exponential decay factor based on public transportation within the preset total travel time can reflect the changes in travel intentions of heterogeneous groups related to the preset total travel time, making the calculation results of the accessibility index closer to people's actual feelings. Here, the changes in travel intentions of heterogeneous groups related to the preset total travel time can be understood as follows: travel intentions decrease more slowly within short travel times, decrease more rapidly within medium travel times, and decrease more slowly within long travel times, but the overall intention value is very low.

[0031] This disclosure also pre-constructs an accessibility calculation model, which, based on the total number of interest endpoints, the number of reachable interest endpoints, and a negative exponential decay factor, calculates the accessibility index for each heterogeneous population within each community at the target time. Here, the accessibility index can be understood as an accessibility index based on public transportation.

[0032] As a quantitative measure of the convenience of public transportation services, this accessibility index can fully adapt to the travel preferences and time sensitivity of different heterogeneous groups, thereby customizing the selection rules for interest destinations and the parameters of decay factors; it can also determine the target time of travel, and under the constraint of the preset total travel time, realize the dynamic accessibility assessment of multiple time periods, and locate the core shortcomings of heterogeneous groups in terms of spatial accessibility.

[0033] This disclosure also pre-constructs a fairness calculation model. This model takes as input the number of heterogeneous populations within each community (implicitly including the total number of different heterogeneous population types within the target area, the total number of each type of heterogeneous population, and the total number of all heterogeneous population types), and the accessibility index of each heterogeneous population within each community at the target time (implicitly including the average of the accessibility indices of all heterogeneous population types within each community at the target time). The final output is a fairness index between each community and the heterogeneous populations within the region. Using this index, the accessibility gap between different heterogeneous populations can be quantified, enabling a horizontal comparative evaluation of fairness from a regional perspective. Here, the fairness index can be understood as a fairness index based on public transportation.

[0034] This disclosure also pre-constructs a stability calculation model. This model calculates the stability index of each heterogeneous population within a community at a target time, based on the accessibility index of different heterogeneous populations within the community at different time periods (implicitly including the average of the accessibility indices of various heterogeneous populations within the community at different time periods). By quantifying the degree of fluctuation in accessibility at different time periods through this stability index, not only can the time period division standards and fluctuation calculation rules be determined based on the characteristics of public transport operations at different time periods, but it can also, in conjunction with long-term operational data, conduct a continuous reliability assessment of public transport services from a full-time perspective, accurately identifying time-specific weaknesses with drastic accessibility fluctuations. Here, the stability index can be understood as a stability index based on public transport.

[0035] Based on this, embodiments of this disclosure can optimize public transportation in multiple communities using a comprehensive optimization priority index calculated from accessibility, fairness, and stability indices. For example, the comprehensive optimization priority indices of multiple communities can be sorted in descending order, prioritizing the optimization of public transportation in communities with higher rankings to meet the public transportation needs of the majority.

[0036] In this optimization process, not only can the travel preferences of different heterogeneous groups be considered, but also the dynamic accessibility assessment of public transportation at different times can be carried out in combination with the target time of travel and under the limitation of the preset total travel time. This can accurately identify the accessibility shortcomings of heterogeneous groups, and at the same time consider the multi-dimensional needs of accessibility, fairness and stability, so as to provide a scientific basis for the optimization of public transportation system.

[0037] According to the technical solution of the exemplary embodiments of this disclosure, the following steps are taken: First, the number of heterogeneous populations within each community in multiple communities of a target area, the total number of interest endpoints, the number of reachable interest endpoints based on public transportation within a preset travel time from the target time, and the negative exponential decay factor are obtained. Second, a pre-constructed accessibility calculation model, a fairness calculation model, and a stability calculation model are used. Third, the accessibility calculation model is used to calculate the accessibility index of each heterogeneous population within each community at the target time based on the total number of interest endpoints, the number of reachable interest endpoints, and the negative exponential decay factor. Finally, the fairness calculation model is used to calculate the accessibility index of each heterogeneous population within each community based on the number of heterogeneous populations within each community and the accessibility index of each heterogeneous population within each community. The accessibility index calculates the fairness index of each community at the target time. A stability calculation model is used to calculate the stability index of each heterogeneous population within each community at the target time, based on the accessibility index and the stability index of each community. Based on the comprehensive optimization priority index of each community calculated from the accessibility, fairness, and stability indices, public transportation in multiple communities is optimized. This approach considers the travel preferences and travel times of different heterogeneous populations. Based on the multi-dimensional requirements of accessibility, fairness, and stability, the optimization priority order of multiple communities within the target area is determined to pinpoint the shortcomings of public transportation in multiple communities within the target area, providing a scientific basis for the optimization of the public transportation system.

[0038] In some embodiments, the reachability calculation model can be: (1) in, Indicates at the target time Community o Inner Accessibility index of heterogeneous populations; Indicates community o Inner Heterogeneous population from the target time The number of interest destinations reachable by public transportation within the preset total travel time; Indicates community o Inner Total number of interest endpoints in heterogeneous populations; This represents the service quality factor corresponding to each type of reachable interest endpoint; Indicates the preset total travel time Internal negative exponential decay factor based on public transportation; N greater than or equal to 1 and less than or equal to 1 N T integers, N T This indicates the preset total travel time. The total number.

[0039] Here, the service quality factor is the actual service capacity coefficient of different types of interest endpoints (such as medical, commercial, etc.), representing the actual service capacity of this type of interest endpoint to various heterogeneous groups (such as the proportion of remaining beds in medical institutions, the proportion of remaining passenger flow in shopping malls, etc.).

[0040] The fairness calculation model is as follows: (2) in, Indicates at the target time Community o Fairness index; This represents the total number of different heterogeneous population groups across multiple communities in the target area. Indicates at the target time Community o The average accessibility index of various heterogeneous populations within the group; Indicates the first in the target area The number of heterogeneous individuals is equal to the number of individuals within each community. The sum of the number of heterogeneous populations; It represents the total number of heterogeneous populations within the target area, which is equal to the sum of the number of heterogeneous populations within each community.

[0041] Here, The smaller the value, the faster the target time. Community o The better the fairness.

[0042] The stability calculation model is as follows: (3) in, Indicates at the target time Time period t Community o Inner Stability index of heterogeneous populations; Indicates at the target time Time period t Community o Inner Accessibility index of heterogeneous populations; express n Community in different time periods o Inner The average accessibility index of heterogeneous populations; n Indicates the total number of time periods.

[0043] Here, The smaller the value, the faster the target time. Time period t Community o The better the stability.

[0044] In some embodiments, the method may further include: Obtain the pre-built modified logistic decay function; Using the modified Logistic decay function based on the preset total travel time, the negative exponential decay factor based on public transportation is calculated for the preset total travel time.

[0045] Specifically, this embodiment of the disclosure also pre-constructs a modified Logistic decay function, which is used to calculate the negative exponential decay factor based on public transportation within a preset total travel time. This modified Logistic decay function can simulate the changes in travel intentions of heterogeneous groups related to the preset total travel time, making the calculation results of the accessibility index closer to people's real feelings.

[0046] The modified Logistic decay function can be: (4) in, Indicates the preset total travel time Internal negative exponential decay factor based on public transportation; k Indicates the decay rate coefficient; This indicates the inflection point time of decay, which is the critical time when "utility changes from slow decay to fast decay"; C This represents the basic utility value.

[0047] In some embodiments, the method may further include: The system acquires real-time card swipe information for public transportation, map information covering at least the target area, walking speeds of various heterogeneous groups, and a pre-built model for calculating the total travel time. The map information includes station information for public transportation within the target area and location information for each community. The full-journey travel time calculation model is used to allocate the preset full-journey travel time according to any travel route based on real-time card swiping information, map information and walking speed. This results in the boarding walking time, waiting time based on public transportation passenger load, boarding time and alighting walking time for each heterogeneous group from each community to the reachable destination of interest. This is used to determine the multiple reachable destinations of interest for each heterogeneous group in each community from the target time within the preset full-journey travel time based on public transportation. By counting the number of multiple reachable destinations of interest, we can obtain the number of reachable destinations of interest for each heterogeneous group in each community based on public transportation, starting from the target time and within the preset total travel time.

[0048] Specifically, Figure 2 A flowchart illustrating a method for evaluating the dynamic accessibility of public transportation considering heterogeneous populations, as provided in an exemplary embodiment of this disclosure, is shown. Figure 2 As shown, this embodiment of the disclosure can acquire real-time card swiping information for public transportation, map information covering at least the target area, walking speeds of various heterogeneous groups, and a pre-built model for calculating the entire journey time. The real-time card swiping information for public transportation may include IC card data, bus passenger flow data, etc., which can be used to estimate public transportation travel time and public transportation load rate; the map information may include, but is not limited to, station information for public transportation within the target area and location information of various communities, which can be used to calculate the walking network and basic accessibility.

[0049] The aforementioned full-journey travel time calculation model can also be called a full-journey travel time allocation model. It can allocate the preset full-journey travel time based on real-time card swipe information, map information, and walking speed according to any travel route, obtaining the boarding walking time, waiting time based on public transportation occupancy rate, travel time, and alighting walking time for each heterogeneous group from their respective communities to their reachable destination of interest. Based on this, the embodiments of this disclosure, when allocating the preset full-journey travel time, can utilize multi-source data including real-time public transportation card swipe information, map information, and survey data (travel preferences, walking speed, etc.) from each heterogeneous group. This addresses the issue of not considering the travel characteristics of heterogeneous groups and the lack of integration of travel purpose and full-journey dynamics when evaluating public transportation accessibility, thus better aligning with the actual needs of heterogeneous groups and providing a more objective and comprehensive approach, offering a scientific basis for optimizing the public transportation system.

[0050] Table 1 illustrates the walking time algorithm for boarding and alighting from public transportation provided by an exemplary embodiment of this disclosure. In the walking time algorithm provided in Table 1, the Mapbox Isochrone API is invoked to generate walking isochrones starting from each community center and public transportation stop, combining the walking speeds of heterogeneous groups. The isochrones are determined by identifying the destination isochrone. Here, the walking speed of each heterogeneous group is adjusted based on an adjustment coefficient set according to the characteristic of walking speed decreasing with age. This adjustment coefficient is used to correct the default walking speed parameters of the Mapbox Isochrone API, ensuring that the generated walking isochrones match the actual walking abilities of the heterogeneous groups.

[0051] This embodiment of the disclosure can calculate the boarding and alighting walking times of each heterogeneous population from each community to the reachable destination of interest according to the algorithm shown in Table 1, and generate boarding and alighting walking isochrones. Based on the boarding walking isochrones, the public transportation boarding stations that can be reached from each community within the boarding walking time are determined, and the population boards the public transportation at the boarding station, travels along any route to the public transportation alighting station, and then determines the reachable destination of interest that can be reached from the public transportation alighting station within the alighting walking time based on the alighting walking isochrones.

[0052] Table 1. Walking time calculation method for boarding and alighting.

[0053] When waiting at public transportation boarding stations, travel information is constructed based on real-time card swiping data and assigned a unique identifier. The number of buses passing each station within a specific time interval on each route is counted, and the departure interval is calculated. Half of this interval is taken as the average waiting time. The formula for calculating normal waiting time is: (5) in, Indicates from the target time Departure points for diverse groups of people at public transport boarding stations i Waiting for travel route l Normal waiting time; Indicates at the target time The specific time interval afterwards Domestic travel routes l Passing by public transportation boarding station i The number of train services.

[0054] The public transportation passenger load factor is calculated based on real-time card swipe data, summarizing the number of passengers boarding and alighting at each stop to determine the actual number of passengers on each route, and combining this with the rated passenger capacity of the public transportation system. The formula for calculating the public transportation passenger load factor is as follows: (6) in, This indicates the passenger load factor of public transportation on a specific route segment. This indicates the actual number of passengers on public transportation within the specified route section; This indicates the rated passenger capacity of public transportation.

[0055] In actual operation, when the public transportation occupancy rate reaches a certain threshold, passengers may face difficulties in boarding. The public transportation occupancy rate threshold is a critical value representing the maximum acceptable level of crowding for heterogeneous populations, determined through heterogeneous population survey data. When the public transportation occupancy rate exceeds this threshold, the waiting time is adjusted to a larger parameter value to reflect the inhibitory effect of high occupancy rates on the public transportation choices of heterogeneous populations, meaning that these heterogeneous populations cannot successfully board the bus. The adjusted waiting time is the waiting time based on the public transportation occupancy rate, and its calculation formula is as follows: (7) in, Indicates from the target time Departure points for diverse groups of people at public transport boarding stations i Waiting for travel route l Waiting time based on public transportation occupancy rate; L A collection representing travel routes; Indicates the target time Heterogeneous groups at public transportation boarding stations i Waiting for travel route l Public transport passenger load factor; M This represents a sufficiently large parameter value. Here, The public transport passenger load factor of a certain route segment can be calculated using formula (6), and used as a threshold for public transport passenger load factor in formula (7).

[0056] For direct routes, the travel time is estimated based on the boarding and alighting timestamps recorded on the IC card. This is done by aggregating passenger card swipe data between any two stops on different public transport routes at different times, using the following formula: (8) in, Indicates at the target time Starting with different groups of people on different travel routes l From public transportation boarding station i Public transport drop-off point j Travel time; Indicates at the target time Departure route lThe total number of public transportation trips; Indicates at the target time Departure route l Upper m Public transport arrives at public transport boarding station i Time; Indicates at the target time Departure route l Upper m Public transport arrives at the public transport stop j The time.

[0057] In practical applications, in addition to direct routes, there may be transfer routes between public transportation boarding and alighting points. When the route between public transportation boarding and alighting points is direct, the preset total travel time from each community to the reachable destination can be allocated as boarding walking time, waiting time based on public transportation occupancy rate, travel time, and alighting walking time.

[0058] When the route between a public transport boarding station and a public transport alighting station is a transfer route, this embodiment of the present disclosure can treat the transfer station as the starting point during the transfer, calculate the waiting time and travel time at the transfer station, and take into account the public transport passenger load factor during the transfer process to obtain the total transfer time. Based on this, the preset total travel time from each community to the reachable destination can be allocated as boarding walking time, first-segment waiting time, first-segment travel time, transfer waiting time, transfer travel time and alighting walking time. At this time, for the transfer route, the waiting time based on the public transport passenger load factor is equal to the sum of the first-segment waiting time and the transfer waiting time, and the travel time is equal to the sum of the first-segment travel time and the transfer travel time. Among them, the first-segment waiting time and the transfer waiting time can be calculated with reference to the above formulas (5) to (7), and the first-segment travel time and the transfer travel time can be calculated with reference to the above formula (8).

[0059] If there are both direct routes and transfer routes from a public transport boarding point to a public transport alighting point, the shortest travel time is the minimum of the travel times for the direct route and the transfer route. Here, the shortest travel time is the total time from each community to the reachable destination, excluding walking time after disembarking. The formula for calculating the shortest travel time is: (9) (10) (11) in, Indicates at the target time From various communities oDeparture, from the public transportation boarding station i Get off at public transport stop j travel routes l Excluding walking time after disembarking, the travel route here. l This is a direct route; U A collection of public transportation stops; O This represents a collection of multiple communities within the target area; Indicates at the target time From various communities o To public transport boarding station i Walking time to board the bus; Indicates from the target time Departure points for diverse groups of people at public transport boarding stations i Waiting for travel route l Waiting time based on public transportation occupancy rate; Indicates at the target time Starting with different groups of people on different travel routes l From public transportation boarding station i Get off at public transport stop j Travel time; Indicates at the target time From various communities o Departure, from the public transportation boarding station i Get off at public transport stop j The travel time for transfer routes excluding walking time after disembarking; Indicates from the target time Departure points for diverse groups of people at public transport boarding stations i Waiting for the first leg of the route l 1. The first waiting time based on public transport passenger load factor; Indicates at the target time Starting with different groups of people on different travel routes l From public transportation boarding station i to transfer station k The first leg of the journey; Indicates from the target time Different groups of people departing from the transfer station k Waiting for transfer routes l 2. Transfer waiting time based on public transport occupancy rate; Indicates at the target time Starting with different groups of people on different travel routes l From the transfer station k Get off at public transport stop j Transfer and travel time; Indicates at the target time Starting from various communities, heterogeneous groups of people... o Get off at public transport stop j The shortest travel time.

[0060] After calculating the shortest travel time from each community to the reachable destination, the walking time from the public transportation stop to the destination can be calculated by combining the preset total travel time from each community to the destination. The formula for calculating the walking time is as follows: (12) in, Indicates at the target time Heterogeneous groups from various communities o Departure at the preset total travel time T Inside, from the public transportation stop j Walking time to the destination of interest after getting off the bus; Indicates at the target time Starting from various communities, heterogeneous groups of people... o Get off at public transport stop j The shortest travel time.

[0061] Based on the full-journey travel time calculation model of the above formulas (5) to (12), the embodiments of this disclosure can consider the entire travel chain of public transportation, allocate the preset full-journey travel time according to any travel route, and determine the multiple reachable interest destinations of each heterogeneous group in each community from the target time within the preset full-journey travel time based on public transportation; and count the number of multiple reachable interest destinations to obtain the number of reachable interest destinations of each heterogeneous group in each community from the target time within the preset full-journey travel time based on public transportation.

[0062] In some embodiments, optimizing public transportation for multiple communities based on a comprehensive optimization priority index calculated from accessibility, fairness, and stability indices may include: Obtain a pre-built comprehensive optimization computational model; Using a comprehensive optimization calculation model based on accessibility index, fairness index, and stability index, the comprehensive optimization priority index of each community is calculated. Based on the overall optimization priorities of each community, public transportation in multiple communities was optimized. The comprehensive optimization calculation model is as follows: (13) (14) (15) (16) in, Indicates based on the first Heterogeneous population calculation at the target time Community o The overall optimization priority index; Indicated at the target time Community o Inner Accessibility index of heterogeneous populations; Indicated at the target time Community o Fairness index; This represents the total number of different heterogeneous population groups in multiple communities within the target area. Indicated at the target time Time period t Community o Inner Stability index of heterogeneous populations; The standardized value of the accessibility index; The standardized value representing the fairness index; The standardized value representing the stability index; The weighting coefficients of the accessibility index; The weighting coefficients representing the fairness index; The weighting coefficients representing the stability index; This represents the maximum value of the accessibility index; This represents the minimum value of the accessibility index; This represents the maximum value of the fairness index; This represents the minimum value of the fairness index; This represents the maximum value of the stability index; This represents the minimum value of the stability index.

[0063] Specifically, in this comprehensive optimization calculation model, the comprehensive optimization priority index of each community within the target area can be calculated by standardization followed by weight allocation, enabling accurate evaluation and optimization direction positioning of public transportation services. This comprehensive optimization priority index quantifies the urgency of optimizing the public transportation system. It not only considers multi-dimensional needs such as accessibility, fairness, and stability to determine the weight allocation of each indicator and the comprehensive scoring rules, but also, in conjunction with regional development planning, ranks the optimization priorities of different communities from an overall optimization perspective, accurately identifying the areas and directions most in need of improvement, thus providing a scientific basis for the optimization of the public transportation system.

[0064] Here, combining formulas (1) to (3) above, the embodiments of this disclosure can be based on the first... Heterogeneous population, calculated at the target time Community o Accessibility index Fairness Index and stability index Then, using formulas (13) to (16), the target time is calculated. Community o The comprehensive optimization priority index is used to analyze the comprehensive optimization priority of public transportation in various communities based on different heterogeneous populations, quantify the urgency of optimizing the public transportation system, accurately identify the areas and directions that need the most improvement, and provide a scientific basis for optimizing the public transportation system.

[0065] Meanwhile, in this embodiment of the present disclosure, when the comprehensive optimization priority index of a community at a target time is lower than a certain threshold, the public transportation of that community at that target time will be optimized so that the optimized public transportation can meet the travel needs of heterogeneous people in the community and realize their daily life needs.

[0066] In some embodiments, the method may further include: Obtain a pre-built regional accessibility calculation model; The regional accessibility calculation model is used to calculate the regional accessibility index of each heterogeneous population in the target area at the target time based on the accessibility index of each heterogeneous population in each community at the target time and the number of each heterogeneous population in each community. The regional accessibility index is used to evaluate the public transportation accessibility of the target area for each heterogeneous population.

[0067] Specifically, the target area can be a larger area composed of multiple communities, such as an administrative district, a city, or a planned new town.

[0068] This disclosure embodiment can pre-construct a regional accessibility calculation model, which is a mathematical model used to aggregate data from various communities. Its inputs are the "accessibility index" for each community and the "number of heterogeneous populations" within that community. The output is a "regional accessibility index" representing the overall level of the entire region. Here, the "number of heterogeneous populations" within each community can be derived from mobile signaling data, which is used to analyze the age and regional distribution of heterogeneous populations. As can be seen in the regional accessibility calculation model, the "number of heterogeneous populations" serves as a weight to determine the contribution of each community's "accessibility index" to the evaluation of the "regional accessibility index."

[0069] The accessibility index of each heterogeneous population in each community at the target time can be calculated separately according to the method provided in the embodiments of this disclosure. Then, using the regional accessibility calculation model, based on the accessibility index of each heterogeneous population in each community at the target time and the number of each heterogeneous population in each community, the regional accessibility index of each heterogeneous population in the target area at the target time is calculated.

[0070] At this point, the regional accessibility index serves as a comprehensive indicator to evaluate the overall level of public transportation accessibility for various heterogeneous populations in the entire target area, providing a scientific basis for optimizing the public transportation system in the target area and helping to create personalized public transportation services.

[0071] The regional accessibility calculation model can be: (17) in, Indicates at the target time The first in the target area Regional accessibility index for heterogeneous populations; Indicates at the target time Community o Inner Accessibility index of heterogeneous populations o greater than or equal to 1 and less than or equal to 1 O integers, O This indicates the total number of communities included in the target area; Indicates community o Inner The number of heterogeneous populations; Indicates the first in the target area The total number of heterogeneous populations is equal to the number of the first heterogeneous populations in each community. The sum of the number of heterogeneous populations.

[0072] Using this method, embodiments of this disclosure can determine the public transportation accessibility of heterogeneous populations in any region. For larger regions, a weighted average of the number of a certain type of heterogeneous population in multiple sub-regions is required. The weight is the proportion of the number of heterogeneous populations in each region to the total number of heterogeneous populations in the study region. This weighted average is then used to calculate the overall accessibility of the study region. The data on the number of a certain type of population is derived from mobile signaling data.

[0073] Based on this, the method provided in this disclosure can address the problems of existing public transportation accessibility evaluation neglecting the travel characteristics of heterogeneous groups, failing to cover the dynamics of the entire travel chain, and different travel purposes. By integrating multi-source data and constructing a full-chain travel time model, it can achieve dynamic accessibility assessment of public transportation considering heterogeneous groups at different times and for different travel purposes, accurately identify accessibility shortcomings of heterogeneous groups, and provide a scientific basis for optimizing public transportation systems.

[0074] The foregoing mainly describes the solutions provided by the embodiments of this disclosure. It is understood that, in order to achieve the above functions, the electronic device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this disclosure can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0075] This disclosure embodiment can divide the electronic device into functional units according to the above method example. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in this disclosure embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0076] By dividing each functional module according to its corresponding function, an exemplary embodiment of this disclosure provides a public transportation optimization device based on heterogeneous populations. This public transportation optimization device based on heterogeneous populations can be a terminal or a chip applied to the terminal. Figure 3 A schematic diagram of a public transportation optimization device based on heterogeneous populations, provided by an exemplary embodiment of this disclosure, is shown. Figure 3 As shown, the device 300 includes: The acquisition module 301 is used to acquire the number of heterogeneous populations in each community of multiple communities in the target area, the total number of interest endpoints, the number of interest endpoints accessible by public transportation within a preset travel time from the target time, the negative exponential decay factor, as well as the pre-built accessibility calculation model, fairness calculation model and stability calculation model. Processing module 302 is used to calculate the accessibility index of each heterogeneous population in each community at the target time based on the total number of interest endpoints, the number of reachable interest endpoints, and the negative exponential decay factor using the accessibility calculation model. The processing module 302 is further configured to use the fairness calculation model to calculate the fairness index of each community at the target time based on the number of each heterogeneous population in each community and the accessibility index of each heterogeneous population in each community at the target time; The processing module 302 is further configured to use the stability calculation model to calculate the stability index of each heterogeneous population in each community at the target time based on the target time and the accessibility index of each heterogeneous population in each community at the target time; The processing module 302 is further configured to optimize public transportation in the multiple communities based on a comprehensive optimization priority index for each community calculated from the accessibility index, the fairness index, and the stability index.

[0077] In some embodiments, the reachability calculation model is: (1) in, Indicated at the target time Community o Inner Accessibility index of heterogeneous populations; Indicates community o Inner Heterogeneous population from the target time The number of reachable destinations based on public transportation within the preset total travel time. Indicates community o Inner Total number of interest endpoints in heterogeneous populations; This represents the service quality factor corresponding to each type of reachable interest endpoint; This indicates the preset total travel time. The internal factor is based on the negative exponential decay factor of the public transportation system. N greater than or equal to 1 and less than or equal to 1 N T integers, N T This indicates the preset total travel time. The total number; The fairness calculation model is as follows: (2) in, Indicated at the target time Community o Fairness index; This represents the total number of different heterogeneous population groups in multiple communities within the target area. Indicated at the target time Community o The average accessibility index of various heterogeneous populations within the group; Indicates the first within the target area The total number of heterogeneous populations is equal to the number of the first populations in each of the aforementioned communities. The sum of the number of heterogeneous populations; The total number of heterogeneous populations in multiple communities within the target area is equal to the sum of the number of each heterogeneous population within each community. The stability calculation model is as follows: (3) in, Indicated at the target time Time period t Community o Inner Stability index of heterogeneous populations; Indicated at the target time Time period t Community o Inner Accessibility index of heterogeneous populations; express n Community in different time periods o Inner The average accessibility index of heterogeneous populations; n This indicates the total number of time periods.

[0078] In some embodiments, the acquisition module 301 is further configured to acquire a pre-constructed modified Logistic decay function; The processing module 302 is further configured to use the modified Logistic decay function to calculate the negative exponential decay factor based on the public transportation within the preset total travel time based on the preset total travel time; The modified Logistic decay function is as follows: (4) in, This indicates the preset total travel time. The internal factor is based on the negative exponential decay factor of the public transportation system. k Indicates the decay rate coefficient; The value of C represents the inflection point of decay, which is the critical time when "utility changes from slow decay to fast decay"; C represents the basic utility value.

[0079] In some embodiments, the acquisition module 301 is used to acquire real-time card swiping information of the public transportation, map information covering at least the target area, walking speed of each heterogeneous group, and a pre-built full-journey travel time calculation model; wherein, the map information includes station information of the public transportation within the target area and location information of each community; The processing module 302 is further configured to use the full-journey travel time calculation model to allocate the preset full-journey travel time according to any travel route based on the real-time card swiping information, the map information, and the walking speed, to obtain the boarding walking time, waiting time based on public transportation passenger capacity, boarding time, and alighting walking time for each heterogeneous group from each community to the reachable destination of interest, so as to determine the multiple reachable destinations of interest for each heterogeneous group in the community based on public transportation within the preset full-journey travel time starting from the target time; The processing module 302 is also used to count the number of the multiple reachable destinations of interest, and to obtain the number of reachable destinations of interest for each heterogeneous group in each community based on the public transportation within the preset total travel time, starting from the target time.

[0080] In some embodiments, the acquisition module 301 is used to acquire a pre-built comprehensive optimization calculation model; The processing module 302 is further configured to use the comprehensive optimization calculation model to calculate the comprehensive optimization priority index of each of the communities based on the accessibility index, the fairness index, and the stability index; and optimize the public transportation of the multiple communities based on the comprehensive optimization priority of each community. The comprehensive optimization calculation model is as follows: (13) (14) (15) (16) in, Indicates based on the first The heterogeneous population calculated at the target time Community o The overall optimization priority index; Indicated at the target time Community o Inner Accessibility index of heterogeneous populations; Indicated at the target time Community o Fairness index; This represents the total number of different heterogeneous population groups in multiple communities within the target area. Indicated at the target time Time period t Community o Inner Stability index of heterogeneous populations; This represents the standardized value of the accessibility index; This represents the standardized value of the fairness index; This represents the standardized value of the stability index; The weighting coefficients of the accessibility index are indicated. This represents the weighting coefficient of the fairness index; The weighting coefficients represent the stability index. This represents the maximum value of the accessibility index; This represents the minimum value of the reachability index; This represents the maximum value of the fairness index; This represents the minimum value of the fairness index; This represents the maximum value of the stability index; This represents the minimum value of the stability index.

[0081] In some embodiments, the acquisition module 301 is used to acquire a pre-built regional reachability calculation model; The processing module 302 is further configured to use the regional accessibility calculation model to calculate the regional accessibility index of each heterogeneous population in the target area at the target time based on the accessibility index of each heterogeneous population in each community at the target time and the number of each heterogeneous population in each community, so as to evaluate the public transportation accessibility of the target area for each heterogeneous population through the regional accessibility index. The regional accessibility calculation model is as follows: (17) in, Indicated at the target time Within the target area, the first Regional accessibility index for heterogeneous populations; Indicated at the target time Community o Inner Accessibility index of heterogeneous populations o greater than or equal to 1 and less than or equal to 1 O integers, O This indicates the total number of communities included in the target area; Indicates communityo Inner The number of heterogeneous populations; Indicates the first within the target area The total number of heterogeneous populations is equal to the number of the first populations in each of the aforementioned communities. The sum of the number of heterogeneous populations.

[0082] This disclosure also provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the methods disclosed in this disclosure.

[0083] Figure 4 A schematic diagram of the structure of an electronic device provided in an exemplary embodiment of this disclosure is shown. For example... Figure 4 As shown, the electronic device 400 includes at least one processor 401 and a memory 402 coupled to the processor 401, which can perform the corresponding steps in the methods disclosed in the embodiments of this disclosure.

[0084] The processor 401 described above can also be called a Central Processing Unit (CPU), which can be an integrated circuit chip with signal processing capabilities. Each step in the method disclosed in this embodiment can be implemented by the integrated logic circuitry in the hardware of the processor 401 or by instructions in software form. The processor 401 can be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this embodiment can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can be located in the memory 402, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor 401 reads information from the memory 402 and, in conjunction with its hardware, completes the steps of the method described above.

[0085] Furthermore, various operations / processes according to this disclosure, implemented via software and / or firmware, can be transmitted from a storage medium or network to a computer system with a dedicated hardware architecture, for example, Figure 5The computer system 500 shown is equipped with the programs that constitute the software. When various programs are installed, the computer system is able to perform various functions, including functions such as those described above. Figure 5 A schematic diagram of the structure of a computer system provided in an exemplary embodiment of this disclosure is shown.

[0086] Computer system 500 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of this disclosure described and / or claimed herein.

[0087] like Figure 5 As shown, the computer system 500 includes a computing unit 501, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. The RAM 503 may also store various programs and data required for the operation of the computer system 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0088] Multiple components in the computer system 500 are connected to the I / O interface 505, including: an input unit 506, an output unit 507, a storage unit 508, and a communication unit 509. The input unit 506 can be any type of device capable of inputting information into the computer system 500. The input unit 506 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 507 can be any type of device capable of presenting information and may include, but is not limited to, a monitor, speaker, video / audio output terminal, vibrator, and / or printer. The storage unit 508 may include, but is not limited to, a hard disk and an optical disk. The communication unit 509 allows the computer system 500 to exchange information / data with other devices via a network such as the Internet, and may include, but is not limited to, a modem, network card, infrared communication device, wireless communication transceiver, and / or chipset, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0089] The computing unit 501 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above. For example, in some embodiments, the methods disclosed in this disclosure can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via ROM 502 and / or communication unit 509. In some embodiments, the computing unit 501 can be configured to perform the methods disclosed in this disclosure by any other suitable means (e.g., by means of firmware).

[0090] This disclosure also provides a computer-readable storage medium, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is able to perform the methods disclosed in this disclosure.

[0091] The computer-readable storage medium in this disclosure can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. The aforementioned computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specifically, the aforementioned computer-readable storage medium may include electrical connections based on one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0092] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0093] This disclosure also provides a computer program product, including a computer program, wherein when the computer program is executed by a processor, it implements the methods disclosed in the embodiments of this disclosure.

[0094] In embodiments of this disclosure, computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include, but are not limited to, object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network (including a local area network (LAN) or a wide area network (WAN)), or it can be connected to an external computer.

[0095] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0096] The modules, components, or units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the modules, components, or units do not necessarily constitute a limitation on the module, component, or unit itself.

[0097] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary hardware logic components that can be used include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0098] The above description is merely an illustration of some embodiments of this disclosure and the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0099] While specific embodiments of this disclosure have been described in detail by way of example, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of this disclosure. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of this disclosure. The scope of this disclosure is defined by the appended claims.

Claims

1. A public transportation optimization method based on heterogeneous populations, characterized in that, include: The system obtains the number of heterogeneous populations in each community of multiple communities in the target area, the total number of interest endpoints, the number of interest endpoints accessible by public transportation within a preset travel time from the target time, and the negative exponential decay factor, as well as the pre-constructed accessibility calculation model, fairness calculation model, and stability calculation model. Using the accessibility calculation model, based on the total number of interest endpoints, the number of reachable interest endpoints, and the negative exponential decay factor, the accessibility index of each heterogeneous population in each community at the target time is calculated. The fairness calculation model is used to calculate the fairness index of each community at the target time based on the number of heterogeneous groups in each community and the accessibility index of each heterogeneous group in each community at the target time. Using the stability calculation model, based on the target time and the accessibility index of each heterogeneous population in each community at the target time, the stability index of each heterogeneous population in each community at the target time is calculated. Public transportation in the multiple communities is optimized based on a comprehensive optimization priority index calculated from the accessibility index, the fairness index, and the stability index.

2. The method as described in claim 1, characterized in that, The reachability calculation model is as follows: in, Indicated at the target time Community o Inner Accessibility index of heterogeneous populations; Indicates community o Inner Heterogeneous population from the target time The number of reachable destinations based on public transportation within the preset total travel time; Indicates community o Inner Total number of interest endpoints in heterogeneous populations; This represents the service quality factor corresponding to each type of reachable interest endpoint; This indicates the preset total travel time. The internal factor is based on the negative exponential decay factor of the public transportation system. N greater than or equal to 1 and less than or equal to 1 N T integers, N T This indicates the preset total travel time. The total number; The fairness calculation model is as follows: in, Indicated at the target time Community o Fairness index; This represents the total number of different heterogeneous population groups in multiple communities within the target area. Indicated at the target time Community o The average accessibility index of various heterogeneous populations within the group; Indicates the first within the target area The total number of heterogeneous populations is equal to the number of the first populations in each of the aforementioned communities. The sum of the number of heterogeneous populations; The total number of heterogeneous populations in multiple communities within the target area is equal to the sum of the number of each heterogeneous population within each community. The stability calculation model is as follows: in, Indicated at the target time Time period t Community o Inner Stability index of heterogeneous populations; Indicated at the target time Time period t Community o Inner Accessibility index of heterogeneous populations; express n Community in different time periods o Inner The average accessibility index of heterogeneous populations; n This indicates the total number of time periods.

3. The method as described in claim 1, characterized in that, The method further includes: Obtain the pre-built modified logistic decay function; Using the modified Logistic decay function based on the preset total travel time, calculate the negative exponential decay factor based on the public transportation within the preset total travel time; The modified Logistic decay function is as follows: in, This indicates the preset total travel time. The internal factor is based on the negative exponential decay factor of the public transportation system. k Indicates the decay rate coefficient; The value of C represents the inflection point of decay, which is the critical time when "utility changes from slow decay to fast decay"; C represents the basic utility value.

4. The method as described in claim 1, characterized in that, The method further includes: The system acquires real-time card swiping information of the public transportation, map information covering at least the target area, walking speeds of the heterogeneous populations, and a pre-built model for calculating the total travel time; wherein the map information includes station information of the public transportation within the target area and location information of each community. The full-journey travel time calculation model is used to allocate the preset full-journey travel time according to any travel route based on the real-time card swiping information, the map information, and the walking speed. This yields the boarding walking time, waiting time based on public transportation occupancy rate, travel time, and alighting walking time for each heterogeneous group from each community to the reachable destination of interest. This determines the multiple reachable destinations of interest for each heterogeneous group within the community from the target time within the preset full-journey travel time based on public transportation. By counting the number of the multiple reachable destinations of interest, the number of reachable destinations of interest for each heterogeneous population within each community, starting from the target time and within the preset total travel time, based on the public transportation system, is obtained.

5. The method as described in claim 1, characterized in that, The optimization of public transportation in the multiple communities, based on a comprehensive optimization priority index calculated from the accessibility index, the fairness index, and the stability index, includes: Obtain a pre-built comprehensive optimization computational model; Using the comprehensive optimization calculation model, based on the accessibility index, the fairness index, and the stability index, the comprehensive optimization priority index of each community is calculated; Based on the comprehensive optimization priority of each community, the public transportation of the multiple communities is optimized. The comprehensive optimization calculation model is as follows: in, Indicates based on the first The heterogeneous population calculated at the target time Community o The overall optimization priority index; Indicated at the target time Community o Inner Accessibility index of heterogeneous populations; Indicated at the target time Community o Fairness index; This represents the total number of different heterogeneous population groups in multiple communities within the target area. Indicated at the target time Time period t Community o Inner Stability index of heterogeneous populations; This represents the standardized value of the accessibility index; This represents the standardized value of the fairness index; This represents the standardized value of the stability index; The weighting coefficients of the accessibility index are indicated. This represents the weighting coefficient of the fairness index; The weighting coefficients represent the stability index. This represents the maximum value of the accessibility index; This represents the minimum value of the reachability index; This represents the maximum value of the fairness index; This represents the minimum value of the fairness index; This represents the maximum value of the stability index; This represents the minimum value of the stability index.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Obtain a pre-built regional accessibility calculation model; The regional accessibility calculation model is used to calculate the regional accessibility index of each heterogeneous population in the target area at the target time based on the accessibility index of each heterogeneous population in each community at the target time and the number of each heterogeneous population in each community. The regional accessibility index is used to evaluate the public transportation accessibility of the target area for each heterogeneous population. The regional accessibility calculation model is as follows: in, Indicated at the target time Within the target area, the first Regional accessibility index for heterogeneous populations; Indicated at the target time Community o Inner Accessibility index of heterogeneous populations o greater than or equal to 1 and less than or equal to 1 O integers, O This indicates the total number of communities included in the target area; Indicates community o Inner The number of heterogeneous populations; Indicates the first within the target area The total number of heterogeneous populations is equal to the number of the first populations in each of the aforementioned communities. The sum of the number of heterogeneous populations.

7. A public transportation optimization device based on heterogeneous populations, characterized in that, include: The acquisition module is used to acquire the number of heterogeneous populations in each community of multiple communities in the target area, the total number of interest endpoints, the number of interest endpoints accessible by public transportation within a preset travel time from the target time, the negative exponential decay factor, as well as the pre-built accessibility calculation model, fairness calculation model and stability calculation model. The processing module is used to calculate the accessibility index of each heterogeneous population in each community at the target time based on the total number of interest endpoints, the number of reachable interest endpoints, and the negative exponential decay factor using the accessibility calculation model. The processing module is further configured to use the fairness calculation model to calculate the fairness index of each community at the target time based on the number of each heterogeneous population in each community and the accessibility index of each heterogeneous population in each community at the target time; The processing module is further configured to use the stability calculation model to calculate the stability index of each heterogeneous population in each community at the target time based on the target time and the accessibility index of each heterogeneous population in each community at the target time; The processing module is also used to optimize public transportation in the multiple communities based on a comprehensive optimization priority index for each community calculated from the accessibility index, the fairness index, and the stability index.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method described in any one of claims 1 to 6.

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