Urban commuting flow estimation method and device based on multi-source data, equipment and medium

Abstract

The invention discloses a city commuting flow estimation method and device based on multi-source data, equipment and a medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining population census, interest points, online recruitment and path planning multi-source city data, carrying out the preprocessing, calculating the resident worker scale, employment post scale and commuting time matrix of each region, and carrying out the calculation of the population census, interest points, online recruitment and path planning; and the three are input into a preset commuting flow estimation model to finally obtain a target city commuting flow estimation result, and the target city commuting flow estimation result is adaptive to small and medium-sized cities with limited data, so that the accuracy of commuting flow estimation among a plurality of regions is improved, the urban traffic management efficiency is improved, and the sustainable development of cities is promoted.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, device, and medium for estimating urban commuter traffic based on multi-source data. Background Technology

[0002] Currently, urban commuter traffic estimation methods are mainly based on travel survey data, such as household travel surveys and labor force surveys. These methods can obtain information such as individuals' socioeconomic attributes, travel purposes, commuting modes, and commuting times. However, these methods suffer from problems such as small sample size, limited coverage, and long update cycles, making it difficult to reflect the dynamic commuting characteristics at the urban scale. Furthermore, with the development of information technology, location-based big data (such as mobile phone signaling, BeiDou navigation trajectories, subway card swiping, and license plate recognition) is increasingly being applied to commuting research. This data possesses high spatiotemporal accuracy and continuity, and can reveal overall urban travel patterns. However, this data is limited by difficulties in acquisition, high costs, and privacy restrictions, hindering its widespread application and making it difficult to simultaneously reflect the spatial distribution of residence and employment, employment heterogeneity, and dynamic changes in commuting.

[0003] Therefore, there is an urgent need for a method to estimate urban commuter traffic flow in order to improve the accuracy of commuter traffic flow estimation across multiple regions. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, and medium for estimating urban commuter traffic based on multi-source data, aiming to solve the technical problem of how to improve the accuracy of commuter traffic estimation between multiple regions.

[0005] To achieve the above objectives, this application proposes a method for estimating urban commuter traffic based on multi-source data, including: Acquire multi-source city data, including census data, point-of-interest data, online recruitment data, and route planning data; The multi-source city data is preprocessed to obtain processed city data; Based on the processed urban data, calculations are performed to obtain a matrix of resident and worker scale, employment scale, and commuting time among different areas of the city. The population size of residents and workers, the employment size, and the commuting time matrix are input into a preset commuting flow estimation model for calculation to obtain the commuting flow estimation results for the target city.

[0006] In one embodiment, the step of calculating the resident-worker scale, employment scale, and commuting time matrix among different urban areas based on the processed urban data includes: Based on the census data in the processed urban data, the population and worker population size of each urban area is estimated. Based on the point of interest data and online recruitment data in the processed city data, the scale of employment positions in each area of ​​the city is estimated. Based on the route planning data in the processed urban data, a commuting time matrix between different areas of the city is constructed.

[0007] In one embodiment, the step of estimating the size of the resident workforce in each urban area based on census data from the processed urban data includes: Population census data is extracted from the processed urban data, wherein the population census data includes the total population of each region, age distribution information, and employment status information. The proportion of the working-age population in each region is determined based on the age distribution information, wherein the proportion of the working-age population is the ratio of the number of people in the preset working-age age group to the total population. The proportion of the working-age population is corrected by combining the employment status information to obtain the corrected proportion of the working-age population. Multiply the corrected proportion of the working-age population by the total population of each region to obtain the scale of resident workers in each region.

[0008] In one embodiment, the step of estimating the employment scale of each area of ​​the city based on the point-of-interest data and online recruitment data in the processed city data includes: Interest point data and online recruitment data are extracted from the processed city data. The interest point data includes the number of different types of interest points in each region and their corresponding geographic coordinates. The online recruitment data includes company size information, job descriptions and recruitment needs for each industry. The company size information in the online recruitment data is numerically processed to obtain the processed size information; By combining the interest point data and scale information, the average employment scale corresponding to different categories of interest points in each region is obtained; The employment scale of each region is estimated based on the number of different types of points of interest and the corresponding average employment scale within each region.

[0009] In one embodiment, the step of inputting the resident worker scale, the employment scale, and the commuting time matrix into a preset commuting flow estimation model for calculation to obtain the commuting flow estimation result of the target city includes: The scale of residents and workers, the scale of employment positions, and the commuting time matrix are input into a preset commuting flow estimation model for calculation to obtain the initial commuting flow between different areas of the city. The initial commuter flow is constrained and corrected to obtain the estimated commuter flow for the target city.

[0010] In one embodiment, the step of inputting the resident worker scale, the employment scale, and the commuting time matrix into a preset commuting flow estimation model for calculation to obtain the initial commuting flow between different areas of the city includes: Based on the scale of resident workers and the scale of employment positions, the potential commuter population at the departure point and the number of employment positions in the destination area are calculated, wherein the potential commuter population at the departure point is the scale of resident workers in each area, and the number of employment positions in the destination area is the scale of employment in each area. Based on the aforementioned commuting time matrix, the initial commuting flow between different areas of the city is calculated using an unconstrained gravity model. The specific formula is as follows: in, This represents the initial commuter flow between different areas of the city, i.e., from the point of origin. to destination area Initial commuter traffic, This indicates the potential commuter population at the point of origin. Indicates the number of jobs available in the destination area. Indicates the place of departure to destination area Commuting time, It is a proportionality constant. These are the parameters of the impedance function.

[0011] In one embodiment, the step of constraining and correcting the initial commuter flow to obtain the target city commuter flow estimation result includes: The percentage of commuter traffic from each origin to each destination area is calculated based on the initial commuter traffic. Based on the commuter traffic ratio, the commuter traffic from each origin is adjusted for regional constraints to obtain the first constrained commuter traffic, as shown in the following formula: in, This represents the first constraint, commuter traffic, i.e., residents living at the point of origin. Possibly in the destination area The number of people working Indicates the place of departure The size of the resident workers Indicates from the place of origin to destination area Initial commuter traffic, Indicates the place of departure The sum of commuter traffic to all other areas; Based on the commuter traffic ratio, the commuter traffic for each destination area is adjusted according to regional constraints to obtain the second constrained commuter traffic, as shown in the following formula: in, This represents the second constraint on commuter traffic, i.e., the destination area. Assigned to the place of origin Number of job positions Indicates the destination area The scale of employment opportunities, Indicates from the place of origin to destination area Initial commuter traffic, Indicates all other areas to the destination area The sum of commuter traffic; The commuter flow rate of the target city is estimated by comparing the first constrained commuter flow rate and the second constrained commuter flow rate, using the following formula: in, This represents the estimated commuter traffic flow in the target city. This represents a minimum value function, which selects the minimum value among multiple values.

[0012] Furthermore, to achieve the above objectives, this application also proposes an urban commuter traffic estimation device based on multi-source data, the urban commuter traffic estimation device based on multi-source data comprising: The acquisition module is used to acquire multi-source city data, which includes population census data, point of interest data, online recruitment data, and route planning data. The processing module is used to preprocess the multi-source city data to obtain processed city data; The calculation module is used to perform calculations based on the processed urban data to obtain a matrix of resident worker scale, employment scale and commuting time among different areas of the city. The results module is used to input the resident worker scale, the employment scale, and the commuting time matrix into a preset commuting flow estimation model for calculation, and obtain the commuting flow estimation results for the target city.

[0013] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the urban commuter traffic estimation method based on multi-source data as described above.

[0014] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the urban commuter traffic estimation method based on multi-source data as described above.

[0015] This application acquires multi-source urban data from population census, points of interest, online recruitment, and route planning. After preprocessing, it calculates the resident-worker scale, employment scale, and commuting time matrix for each region. These three data are then input into a preset commuting flow estimation model to obtain the commuting flow estimation results for the target city. This approach is suitable for small and medium-sized cities with limited data, improves the accuracy of commuting flow estimation across multiple regions, enhances urban traffic management efficiency, and promotes sustainable urban development. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the first embodiment of the urban commuter traffic estimation method based on multi-source data in this application; Figure 2 This is a flowchart illustrating the second embodiment of the urban commuter traffic estimation method based on multi-source data in this application; Figure 3 This is a flowchart illustrating the third embodiment of the urban commuter traffic estimation method based on multi-source data in this application; Figure 4 This is a schematic diagram of the module structure of the urban commuter traffic estimation device based on multi-source data in this application; Figure 5 This is a schematic diagram of the hardware operating environment involved in the urban commuter traffic estimation method based on multi-source data in the embodiments of this application.

[0018] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0020] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0021] Currently, urban commuter traffic estimation methods are mainly based on travel survey data, such as household travel surveys and labor force surveys. These methods can obtain information such as individuals' socioeconomic attributes, travel purposes, commuting modes, and commuting times. However, these methods suffer from problems such as small sample size, limited coverage, and long update cycles, making it difficult to reflect the dynamic commuting characteristics at the urban scale. Furthermore, with the development of information technology, location-based big data (such as mobile phone signaling, BeiDou navigation trajectories, subway card swiping, and license plate recognition) is increasingly being applied to commuting research. This data possesses high spatiotemporal accuracy and continuity, and can reveal overall urban travel patterns. However, this data is limited by difficulties in acquisition, high costs, and privacy restrictions, hindering its widespread application and making it difficult to simultaneously reflect the spatial distribution of residence and employment, employment heterogeneity, and dynamic changes in commuting.

[0022] Therefore, this application proposes a method for estimating urban commuter traffic based on multi-source data to solve the above-mentioned problems. The main solution of this application embodiment is as follows: acquiring multi-source urban data, wherein the multi-source urban data includes census data, point-of-interest data, online recruitment data, and route planning data; preprocessing the multi-source urban data to obtain processed urban data; calculating based on the processed urban data to obtain the resident-worker scale, employment scale, and commuting time matrix of each region of the city; inputting the resident-worker scale, employment scale, and commuting time matrix into a preset commuter traffic estimation model for calculation to obtain the commuter traffic estimation result of the target city.

[0023] Based on the above, this application also provides a method for estimating urban commuter traffic flow based on multi-source data, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the urban commuter traffic estimation method based on multi-source data according to this application. In this embodiment, the urban commuter traffic estimation method based on multi-source data includes steps S10 to S40: Step S10: Obtain multi-source city data.

[0024] It should be noted that the aforementioned multi-source urban data includes census data, points of interest (POI) data, online recruitment data, and route planning data. Specifically, census data provides key socioeconomic information such as resident distribution, age structure, gender ratio, education level, and family composition, which is crucial for estimating the size of the resident workforce in each region. Secondly, Points of Interest (POI) data typically includes geographical location and type information such as commercial facilities, office locations, schools, and hospitals, helping to identify and analyze the spatial distribution characteristics of employment and residence. Online recruitment data reflects the dynamics of the city's job market, including information on job types, required skills, company size, and industry distribution; this data can be used to estimate the size of job openings and industry characteristics in different regions. Route planning data, such as traffic flow, road conditions, and public transportation timetables, is essential for constructing a commuting time matrix, providing commuting time and route information between different areas, thereby helping to estimate commuting traffic.

[0025] Step S20: Preprocess the multi-source city data to obtain processed city data.

[0026] It should be noted that this process involves taking different processing measures for different types of data to ensure data quality and consistency.

[0027] Specifically, for census data, preprocessing includes removing duplicate records, filling in missing demographic information, correcting erroneous address information, and converting the data to a uniform format. Furthermore, census data needs to be segmented by administrative divisions to facilitate matching with Point of Interest (POI) data and other data sources. For POI data, preprocessing measures include cleaning erroneous or outdated POI entries, standardizing POI classifications, and geocoding or coordinate transformation of POIs as needed. Preprocessing of online recruitment data involves identifying and removing duplicate job advertisements, extracting key information such as job type, company size, and work location, and performing natural language processing on the text data to extract useful features. Preprocessing of route planning data includes correcting erroneous route information, updating traffic condition data, and converting the data to a format suitable for model input. Data fusion is also required across all these data sources to integrate data from different sources into a unified data framework, which involves resolving data conflicts, aligning timestamps, and merging similar geographical areas. Furthermore, data transformation and normalization are necessary, such as calculating average commute time and distance from residence to workplace, or estimating the potential number of jobs in each area from recruitment data. This is especially important when the data will be used in machine learning models, ensuring all features are on the same scale. Finally, the processed data undergoes validation, including statistical analysis, visualization checks, and comparisons with known data to verify accuracy and consistency. These detailed preprocessing measures yield accurate, reliable, and analytically suitable urban data, providing a solid foundation for subsequent urban commuter flow estimation and other related research.

[0028] Step S30: Based on the processed urban data, calculations are performed to obtain the matrix of resident worker scale, employment scale, and commuting time among different areas of the city.

[0029] It should be noted that calculating the size of the resident worker population requires extracting information on the working-age population from census data. This typically involves filtering the data to determine the population size within a specific age range. Next, by analyzing employment status information from the census data, the employed and unemployed populations can be identified, thereby estimating potential labor resources. Furthermore, considering that different regional economic development levels and industry distribution may affect labor participation rates, these factors need to be comprehensively considered to adjust the estimated size of the resident worker population.

[0030] Secondly, calculating the number of job openings requires identifying employment-related locations, such as office buildings, factories, and shops, from the POI data and counting their numbers. Simultaneously, by combining online recruitment data and analyzing company size and job requirements in job postings, the number of job openings in different industries within each region can be estimated. This step may also require utilizing natural language processing techniques to analyze job descriptions, thereby more accurately identifying and quantifying employment opportunities in specific industries.

[0031] Finally, the construction of the commuting time matrix relies on route planning data, requiring the analysis of commuting times and routes under different modes of transportation using traffic simulation software or real-time traffic data. This includes considering factors such as traffic congestion, road construction, and public transportation timetables to calculate the commuting time matrix between different areas of the city. Furthermore, utilizing Geographic Information System (GIS) technology to combine commuting time data with geospatial information can more intuitively display the spatial distribution characteristics of commuting traffic. After completing these calculations, the results need to be validated to ensure the accuracy of the estimates. This may involve comparison with actual observation data, such as actual commuting information collected through traffic surveys or smart card data. Through comparative analysis of this data, model parameters can be validated and adjusted, improving the accuracy and reliability of the commuting traffic estimation model. The entire calculation process requires the comprehensive application of statistical analysis, data mining, and GIS technologies to ensure that the obtained urban commuting traffic estimates are both scientific and practical. These detailed calculation steps and validation processes provide important data support for urban transportation planning and management, helping to optimize the allocation of transportation resources, improve urban traffic efficiency, reduce congestion, and promote sustainable urban development.

[0032] Step S40: Input the resident worker scale, employment scale and commuting time matrix into the preset commuting flow estimation model for calculation to obtain the commuting flow estimation result of the target city.

[0033] It is important to note that, first and foremost, ensuring the consistency and accuracy of all input data is crucial. The resident worker size data needs to be updated based on the latest census information to reflect the latest changes in the working-age population. This data typically includes information such as gender, age, education level, and occupation, which helps to more accurately estimate the potential labor force in each area. Job size data needs to be extracted from point-of-interest (POI) data and online recruitment data. POI data provides the distribution of commercial and industrial activity across areas, while online recruitment data provides specific job vacancy information. By analyzing this data, the number of jobs in each area can be estimated. Furthermore, the estimation of job size also needs to consider industry employment density, i.e., the number of employees per unit area or unit of capital per industry. The construction of the commuting time matrix relies on route planning data, which is typically obtained from the transportation sector or through GPS tracking technology. The commuting time matrix includes not only the average commuting time between different areas but may also include information such as traffic congestion, the availability and reliability of public transportation. This information is crucial for estimating commuting flow, as it directly affects people's commuting choices and commuting times.

[0034] Once all this data is ready, it can be input into a pre-defined commuter traffic estimation model. In this embodiment, the pre-defined commuter traffic estimation model is built based on a gravity model, a statistical model, to simulate and predict commuter traffic. The model's inputs include the size of the resident workforce, the size of employment positions, and a commuting time matrix; the output is an estimate of commuter traffic between different areas of the city. The model may also need to consider other factors, such as residents' commuting preferences, changes in transportation policies, and urban development plans, to improve the accuracy of the predictions. During the model calculation process, parameter tuning and model validation are required. Parameter tuning ensures that the model best fits the actual data, while model validation verifies the model's predictive ability by comparing it with actual observed data. This step may involve complex statistical analysis and machine learning techniques to ensure the robustness and reliability of the model.

[0035] Ultimately, the commuter flow estimates obtained through model calculations will provide valuable information for urban planners and traffic managers, helping them to better understand urban commuting patterns, optimize traffic planning, improve traffic efficiency, reduce congestion, and provide decision support for sustainable urban development.

[0036] This embodiment acquires multi-source urban data from population census, points of interest, online recruitment, and route planning. After preprocessing, it calculates the resident-worker scale, employment scale, and commuting time matrix for each region. These three data are then input into a preset commuting flow estimation model to obtain the commuting flow estimation results for the target city. This model is suitable for small and medium-sized cities with limited data, improves the accuracy of commuting flow estimation across multiple regions, enhances urban traffic management efficiency, and promotes sustainable urban development.

[0037] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 The urban commuter traffic estimation method based on multi-source data, step S30, further includes steps S201 to S203: Step S201: Based on the census data in the processed urban data, estimate the size of the resident workers in each area of ​​the city.

[0038] It should be noted that in commuting analysis, the size of the regional resident worker population is a key indicator for estimating commuting volume. Due to the lack of detailed employment statistics for each region, this embodiment uses the population structure ratio method to estimate the worker size of each region. Considering that the working-age population (usually defined in China as 18–60 years old) constitutes the main urban workforce, this embodiment multiplies the total regional population by the corresponding working-age population ratio to approximate the size of the regional resident worker population.

[0039] Specifically, step S201 includes: First, extracting census data from the processed urban data. The census data includes the total population of each region, age distribution information, and employment status information, providing a multi-dimensional reference for estimation. Then, determining the proportion of the working-age population in each region based on the age distribution information. Specifically, confirming the population within a preset working-age age group based on the age distribution information, and then calculating the proportion of the working-age population in each region based on the population within the preset working-age age group and the total population. The specific formula is as follows: The proportion of the working-age population is the ratio of the population within the pre-set working-age age group to the total population. This indicates the population within the pre-set working-age group. This represents the total population of each region.

[0040] Then, by combining employment status information with the adjusted proportion of the working-age population, we obtain the corrected proportion. Employment status information provides the proportion of the working-age population actually participating in the job market, which is crucial for adjusting the working-age population proportion. The specific formula is as follows: in, Indicates employment status information. This indicates the corrected proportion of the working-age population.

[0041] Multiplying the corrected proportion of the working-age population by the total population of each region yields the size of the resident worker population in each region, expressed by the following formula: in, Indicates the area The size of the resident workforce, i.e., the number of people in the region available for the labor market.

[0042] Step S202: Based on the point of interest data and online recruitment data in the processed city data, an estimate is made to obtain the employment scale of each region of the city.

[0043] It's important to note that, firstly, point-of-interest (POI) data and online recruitment data are extracted from the processed city data. POI data includes the number of different categories of POIs in each region (such as commercial, industrial, educational, and medical) and their corresponding geographic coordinates. This information helps identify potential employment areas and types within the city. Online recruitment data includes company size information, job descriptions, and recruitment needs across various industries. This data provides real-time dynamics of the job market, including which industries are expanding and which positions have high demand.

[0044] Next, the company size information in the online recruitment data is quantified to obtain processed size information. Specifically, descriptive information about company size (such as "small," "medium," and "large") is converted into quantifiable data, i.e., company size data. To facilitate calculation, the specific formula is as follows: in, This represents the lower limit of the company's size range. This represents the upper limit of the company's size range.

[0045] For example, a "small" company can be defined as having 1-50 employees, a "medium" company as having 51-500 employees, and a "large" company as having 501 or more employees, and a numerical value can be assigned to each company size accordingly.

[0046] Then, by combining the data on points of interest and scale information, the average employment scale corresponding to different categories of points of interest in each region is calculated. Specifically, a weighted average is calculated for the employment size of each interest category, where the weight can be the number of interest points in that category or the size of the companies in that category. The specific calculation formula is as follows: in, This represents the total number of companies. This indicates the current number of companies. This refers to the category of POI. For example, if there are multiple retail stores in an area, and each store has an average size of 20 employees, then the average size of the retail industry in that area is the sum of the sizes of all stores divided by the number of stores.

[0047] Finally, the employment scale of each region is estimated based on the number of different types of points of interest and the corresponding average employment size. The specific calculation formula is as follows: in, Indicates the area The scale of employment opportunities, Indicates the area The Number of POIs Indicates the first Average employment size for POI-like entities.

[0048] Step S203: Construct a commuting time matrix between different areas of the city based on the route planning data in the processed city data.

[0049] It should be noted that, firstly, the geometric center or representative location of each area in the city needs to be determined as the start and end point of the commute. Then, using route planning data, the commute time between areas is determined by simulating or calculating travel times under different modes of transportation, including driving, cycling, walking, and public transportation.

[0050] For each region pair, travel time during typical commuting periods (such as morning and evening rush hours) is calculated, taking into account the impact of factors such as traffic congestion, road construction, and weather conditions. This time data will be organized into a matrix, where rows and columns represent different regions, and each element in the matrix represents the commuting time between the corresponding region pairs.

[0051] This embodiment accurately estimates the size of the resident workforce and the number of jobs in various urban areas by integrating census, points of interest, and online recruitment data. Simultaneously, it constructs a commuting time matrix using route planning data, providing a foundation for urban commuting flow analysis.

[0052] Based on the first embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 The urban commuter traffic estimation method based on multi-source data, step S40, further includes steps S301 to S302: Step S301: Input the resident worker scale, employment scale and commuting time matrix into the preset commuting flow estimation model for calculation to obtain the initial commuting flow between different areas of the city.

[0053] It should be noted that the preset commuter flow estimation model calculates initial commuter flow by simulating the movement of commuters between their residences and workplaces. This model is based on a gravity model and takes into account commuters' sensitivity to commuting time and distance, as well as the distribution of employment and residence in the city.

[0054] Specifically, calculations are performed based on the size of the resident workforce and the number of available jobs to obtain the potential commuter population at the origin and the number of available jobs at the destination. The potential commuter population at the origin represents the size of the resident workforce in each region, reflecting the amount of labor available for commuting in each region and serving as the basis for commuter flow estimation. The number of available jobs at the destination represents the size of available jobs in each region, indicating the employment opportunities that each region can provide.

[0055] The initial commuter flow between different urban areas is calculated using an unconstrained gravity model, combining the commuter time matrix. Specifically, after obtaining the potential commuter population at the origin and the number of jobs at the destination, the unconstrained gravity model can be used for calculation, based on the commuter time matrix. The unconstrained gravity model is a widely used model in transportation planning and geographic information systems. It assumes that commuter flow is directly proportional to the potential commuter population at the origin and the number of jobs at the destination, and inversely proportional to the commuter time between the two locations. The specific formula is as follows: in, This represents the initial commuter flow between different areas of the city, i.e., from the point of origin. Arrive at the destination Initial commuter traffic, This indicates the potential commuter population at the point of origin. Indicates the number of jobs available at the destination. Indicates the place of departure Arrive at the destination Commuting time, It is a proportionality constant. The impedance function parameters are used. This model allows for the estimation of initial commuter traffic flow between different urban areas, providing fundamental data for further traffic planning and management. Estimating initial commuter traffic flow helps identify commuter hotspots in the city, assess the capacity of the transportation network, and predict future changes in traffic demand. Furthermore, this data can be used to optimize public transportation services, improve commuting efficiency, reduce congestion, and ultimately enhance the quality of life for urban residents.

[0056] Step S302: Constraint correction is applied to the initial commuter flow to obtain the commuter flow of the target city.

[0057] It should be noted that this process involves imposing practical constraints on the initial commuter flow data to simulate real-world commuting behavior. Constraint adjustments typically involve two main aspects: first, ensuring that commuter outflow from any area does not exceed the size of the area's resident workforce; and second, ensuring that commuter inflow into any area does not exceed the size of the area's employment positions.

[0058] Specifically, the percentage of commuter traffic from each origin to each destination is calculated based on initial commuter traffic. This is done by dividing the initial commuter traffic from each origin to its destination by the sum of commuter traffic from that origin to all other regions. This percentage reflects commuters' tendency to travel from a specific origin to a specific destination.

[0059] Then, based on the proportion of commuter traffic, the commuter traffic from each origin is adjusted according to regional constraints to obtain the first constraint commuter traffic, the specific formula of which is: in, This represents the first constraint, commuter traffic, i.e., residents living at the point of origin. Possibly at the destination The number of people working Indicates the place of departure The size of the resident workers Indicates from the place of origin Arrive at the destination Initial commuter traffic, Indicates the place of departure This correction, which sums commuter traffic to all other areas, ensures that commuter traffic estimates do not exceed the actual available labor resources.

[0060] Then, based on the commuter traffic ratio, the commuter traffic to each destination is adjusted according to regional constraints to obtain the second constraint commuter traffic, the specific formula of which is: in, This represents the second constraint, commuter flow, i.e., destination. Assigned to the place of origin Number of job positions Indicate destination The scale of employment opportunities, Indicates from the place of origin Arrive at the destination Initial commuter traffic, Indicates all other areas to the destination The sum of commuter traffic ensures that the estimate of commuter traffic matches the actual employment opportunities.

[0061] Finally, by comparing the commuter traffic under the first constraint and the commuter traffic under the second constraint, the estimated commuter traffic for the target city is obtained, using the following formula: in, This represents the estimated commuter traffic flow in the target city. This represents a minimum-value function, which selects the minimum value among multiple values. By taking the minimum of two values ​​to obtain the estimation result, it ensures that the estimated commuter traffic does not exceed the labor resources at the origin or the employment opportunities at the destination. This final commuter traffic estimate not only considers geographical location and industry distribution but also incorporates real-time online recruitment data and census data, making the estimate closer to reality.

[0062] This embodiment integrates the scale of residents and workers, the scale of employment positions, and the commuting time matrix, and uses a preset commuting flow estimation model to calculate the initial commuting flow between different areas of the city. It then applies constraints and corrections to these initial flows to obtain more realistic target city commuting flow, providing accurate commuting flow data for urban planning and traffic management. This helps optimize the allocation of traffic resources, alleviate traffic congestion, improve urban traffic efficiency, and promote sustainable urban development.

[0063] Based on the first embodiment of this application, this application also provides an urban commuter traffic estimation device based on multi-source data. Please refer to... Figure 4 The device includes: Module 10 is used to acquire multi-source city data, which includes population census data, point of interest data, online recruitment data, and route planning data.

[0064] Processing module 20 is used to preprocess multi-source city data to obtain processed city data.

[0065] The calculation module 30 is used to perform calculations based on the processed urban data to obtain the matrix of resident worker scale, employment scale and commuting time among different areas of the city.

[0066] The results module 40 is used to input the resident worker scale, employment scale and commuting time matrix into the preset commuting flow estimation model for calculation, and obtain the commuting flow estimation results of the target city.

[0067] The urban commuter traffic estimation device based on multi-source data provided in this application, employing the urban commuter traffic estimation method based on multi-source data in the above embodiments, can solve the technical problem of how to improve the accuracy of commuter traffic estimation between multiple regions. Compared with the prior art, the beneficial effects of the urban commuter traffic estimation device based on multi-source data provided in this application are the same as those of the urban commuter traffic estimation method based on multi-source data provided in the above embodiments, and other technical features in the urban commuter traffic estimation device based on multi-source data are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0068] In one embodiment, the calculation module 30 is further configured to estimate the scale of residents and workers in each area of ​​the city based on the census data in the processed urban data; estimate the scale of job openings in each area of ​​the city based on the point of interest data and online recruitment data in the processed urban data; and construct a commuting time matrix between different areas of the city based on the path planning data in the processed urban data.

[0069] In one embodiment, the calculation module 30 is further configured to extract census data from the processed urban data, wherein the census data includes the total population of each region, age distribution information, and employment status information; determine the proportion of the working-age population in each region based on the age distribution information, wherein the proportion of the working-age population is the ratio of the population within a preset working-age age group to the total population; correct the proportion of the working-age population by combining it with the employment status information to obtain a corrected proportion of the working-age population; and multiply the corrected proportion of the working-age population by the total population of each region to obtain the scale of resident workers in each region.

[0070] In one embodiment, the calculation module 30 is further configured to extract point-of-interest (POI) data and online recruitment data from the processed city data, wherein the POI data includes the number of different categories of POIs and their corresponding geographic coordinates in each region, and the online recruitment data includes company size information, job descriptions, and recruitment needs for each industry; classify the POI data by reclassifying the POIs into three levels according to preset standards to form an POI classification system; quantify the company size information in the online recruitment data to obtain processed size information; calculate the average employment scale corresponding to different categories of POIs in each region by combining the POI data and the size information; and estimate the employment scale of each region based on the number of different categories of POIs and their corresponding average employment scale.

[0071] In one embodiment, the result module 40 is further configured to input the resident worker scale, the employment scale and the commuting time matrix into a preset commuting flow estimation model for calculation to obtain the initial commuting flow between different areas of the city; and to perform constraint correction on the initial commuting flow to obtain the commuting flow estimation result of the target city.

[0072] In one embodiment, the result module 40 is further configured to calculate, based on the resident worker scale and the employment scale, the potential commuter population of the departure point and the number of employment positions in the destination area, wherein the potential commuter population of the departure point is the resident worker scale of each area and the number of employment positions in the destination area is the employment scale of each area; and combine the commuting time matrix with an unconstrained gravity model to calculate the initial commuting flow between different areas of the city.

[0073] In one embodiment, the result module 40 is further configured to calculate, based on the initial commuter traffic, the commuter traffic ratio from each departure point to each destination area; perform regional constraint correction on the commuter traffic of each departure point based on the commuter traffic ratio to obtain a first constrained commuter traffic; perform regional constraint correction on the commuter traffic of each destination area based on the commuter traffic ratio to obtain a second constrained commuter traffic; and compare the first constrained commuter traffic and the second constrained commuter traffic to obtain the target city commuter traffic estimation result.

[0074] This application provides a multi-source data-based urban commuter traffic estimation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the multi-source data-based urban commuter traffic estimation method in the first embodiment described above.

[0075] The following is for reference. Figure 5This document illustrates a structural schematic diagram of a multi-source data-based urban commuter traffic estimation device suitable for implementing embodiments of this application. The multi-source data-based urban commuter traffic estimation device in this application embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The urban commuter traffic estimation device based on multi-source data shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0076] like Figure 5 As shown, a multi-source data-based urban commuter traffic estimation device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the multi-source data-based urban commuter traffic estimation device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the multi-source data-based urban commuter traffic estimation device to wirelessly or wiredly communicate with other devices to exchange data. Although various multi-source data-based urban commuter traffic estimation devices are shown in the figures, it should be understood that implementation or possession of all of them is not required. More or fewer may be implemented alternatively.

[0077] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0078] The urban commuter traffic estimation device based on multi-source data provided in this application, employing the urban commuter traffic estimation method based on multi-source data in the above embodiments, can solve the technical problem of how to improve the accuracy of commuter traffic estimation between multiple regions. Compared with the prior art, the beneficial effects of the urban commuter traffic estimation device based on multi-source data provided in this application are the same as those of the urban commuter traffic estimation method based on multi-source data provided in the above embodiments, and other technical features in this urban commuter traffic estimation device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0079] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0080] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0081] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the urban commuter traffic estimation method based on multi-source data in the above embodiments.

[0082] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, 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 thereof. In this embodiment, the computer-readable storage medium may be any tangible storage medium containing or storing a program that can be executed by instructions, used by a device, or used in conjunction with it. The program code contained on the computer-readable storage medium may be transmitted using any suitable storage medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0083] The aforementioned computer-readable storage medium may be included in a multi-source data-based urban commuter traffic estimation device; or it may exist independently and not be assembled into a multi-source data-based urban commuter traffic estimation device.

[0084] The aforementioned computer-readable storage medium carries one or more programs that, when executed by a multi-source data-based urban commuter traffic estimation device, enable the device to write computer program code for performing the operations of this application in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and 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 can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0085] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of this application. 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 the 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 dedicated hardware-based implementations that perform the specified functions or operations, or can be implemented using a combination of dedicated hardware and computer instructions.

[0086] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0087] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described urban commuter traffic estimation method based on multi-source data. This addresses the technical problem of improving the accuracy of commuter traffic estimation between multiple regions. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the urban commuter traffic estimation method based on multi-source data provided in the above embodiments, and will not be repeated here.

[0088] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the urban commuter traffic estimation method based on multi-source data as described above.

[0089] The computer program product provided in this application solves the technical problem of how to improve the accuracy of commuter traffic estimation between multiple regions. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the urban commuter traffic estimation method based on multi-source data provided in the above embodiments, and will not be repeated here.

[0090] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for estimating urban commuter traffic flow based on multi-source data, characterized in that, include: Acquire multi-source city data, including census data, point-of-interest data, online recruitment data, and route planning data; The multi-source city data is preprocessed to obtain processed city data; Based on the processed urban data, calculations are performed to obtain a matrix of resident and worker scale, employment scale, and commuting time among different areas of the city. The population size of residents and workers, the employment size, and the commuting time matrix are input into a preset commuting flow estimation model for calculation to obtain the commuting flow estimation results for the target city.

2. The method as described in claim 1, characterized in that, The step of calculating the resident-worker scale, employment scale, and commuting time matrix for each urban area based on the processed urban data includes: Based on the census data in the processed urban data, the population and worker population size of each urban area is estimated. Based on the point of interest data and online recruitment data in the processed city data, the scale of employment positions in each area of ​​the city is estimated. Based on the route planning data in the processed urban data, a commuting time matrix between different areas of the city is constructed.

3. The method as described in claim 2, characterized in that, The step of estimating the size of the resident workforce in each area of ​​the city based on the census data in the processed urban data includes: Population census data is extracted from the processed urban data, wherein the population census data includes the total population of each region, age distribution information, and employment status information. The proportion of the working-age population in each region is determined based on the age distribution information, wherein the proportion of the working-age population is the ratio of the number of people in the preset working-age age group to the total population. The proportion of the working-age population is corrected by combining the employment status information to obtain the corrected proportion of the working-age population. Multiply the corrected proportion of the working-age population by the total population of each region to obtain the scale of resident workers in each region.

4. The method as described in claim 2, characterized in that, The step of estimating the employment scale of each area of ​​the city based on the point-of-interest data and online recruitment data in the processed city data includes: Interest point data and online recruitment data are extracted from the processed city data. The interest point data includes the number of different types of interest points in each region and their corresponding geographical coordinates. The online recruitment data includes company size information, job descriptions and recruitment needs for each industry. The company size information in the online recruitment data is numerically processed to obtain the processed size information; By combining the interest point data and scale information, the average employment scale corresponding to different categories of interest points in each region is obtained; The employment scale of each region is estimated based on the number of different types of points of interest and the corresponding average employment scale within each region.

5. The method as described in claim 1, characterized in that, The step of inputting the resident worker scale, the employment scale, and the commuting time matrix into a preset commuting flow estimation model for calculation to obtain the commuting flow estimation result of the target city includes: The scale of residents and workers, the scale of employment positions, and the commuting time matrix are input into a preset commuting flow estimation model for calculation to obtain the initial commuting flow between different areas of the city. The initial commuter flow is constrained and corrected to obtain the estimated commuter flow for the target city.

6. The method as described in claim 5, characterized in that, The step of inputting the resident worker scale, the employment scale, and the commuting time matrix into a preset commuting flow estimation model for calculation to obtain the initial commuting flow between different areas of the city includes: Based on the scale of resident workers and the scale of employment positions, the potential commuter population at the departure point and the number of employment positions in the destination area are calculated, wherein the potential commuter population at the departure point is the scale of resident workers in each area, and the number of employment positions in the destination area is the scale of employment in each area. Based on the aforementioned commuting time matrix, the initial commuting flow between different areas of the city is calculated using an unconstrained gravity model. The specific formula is as follows: in, This represents the initial commuter flow between different areas of the city, i.e., from the point of origin. to destination area Initial commuter traffic, This indicates the potential commuter population at the point of origin. Indicates the number of jobs in the destination area. Indicates the place of departure to destination area Commuting time, It is a proportionality constant. These are the parameters of the impedance function.

7. The method as described in claim 5, characterized in that, The step of constraining and correcting the initial commuter flow to obtain the target city commuter flow estimation result includes: The percentage of commuter traffic from each origin to each destination area is calculated based on the initial commuter traffic. Based on the commuter traffic ratio, the commuter traffic from each origin is adjusted for regional constraints to obtain the first constrained commuter traffic, as shown in the following formula: in, This represents the first constraint, commuter traffic, i.e., residents living at the point of origin. Possibly in the destination area The number of people working Indicates the place of departure The size of the resident workers Indicates from the place of origin to destination area Initial commuter traffic, Indicates the place of departure The sum of commuter traffic to all other areas; Based on the commuter traffic ratio, the commuter traffic for each destination area is adjusted according to regional constraints to obtain the second constrained commuter traffic, as shown in the following formula: in, This represents the second constraint on commuter traffic, i.e., the destination area. Assigned to the place of origin Number of job positions Indicates the destination area The scale of employment opportunities, Indicates from the place of origin to destination area Initial commuter traffic, Indicates all other areas to the destination area The sum of commuter traffic; The commuter flow rate of the target city is estimated by comparing the first constrained commuter flow rate and the second constrained commuter flow rate, using the following formula: in, This represents the estimated commuter traffic flow in the target city. This represents a minimum value function, which selects the minimum value among multiple values.

8. A device for estimating urban commuter traffic flow based on multi-source data, characterized in that, The device includes: The acquisition module is used to acquire multi-source city data, which includes population census data, point of interest data, online recruitment data, and route planning data. The processing module is used to preprocess the multi-source city data to obtain processed city data; The calculation module is used to perform calculations based on the processed urban data to obtain a matrix of resident worker scale, employment scale and commuting time among different areas of the city. The results module is used to input the resident worker scale, the employment scale, and the commuting time matrix into a preset commuting flow estimation model for calculation, and obtain the commuting flow estimation results for the target city.

9. A device for estimating urban commuter traffic flow based on multi-source data, characterized in that, The device includes: a memory, a processor, and a multi-source data-based urban commuter traffic estimation program stored on the memory and running on the processor, the multi-source data-based urban commuter traffic estimation program being configured to implement the steps of the multi-source data-based urban commuter traffic estimation method as described in any one of claims 1-7.

10. A storage medium, characterized in that, The storage medium stores a city commuter traffic estimation program based on multi-source data, which, when executed by a processor, implements the steps of the city commuter traffic estimation method based on multi-source data as described in any one of claims 1-7.

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