Commuting corridor identification and problem diagnosis method and system for time-poor people

Abstract

The invention discloses a commuting corridor identification and problem diagnosis method and system for time-poor people, and the method comprises the steps: obtaining all data of a target city, screening effective users, and calculating the residence and employment places of the effective users; constructing an index system of the portraits of the time-poor crowds, and screening the time-poor crowds; performing spatial association on the time-poverty crowd and land data through the spatio-temporal data integration platform to obtain a time-poverty commuting corridor feature set of the target city; carrying out commuting peak identification on time-poor people in the target city; constructing a constraint index system and a suggestive index system; combining the time poverty commuting galleries to check the constraint index, and if the check is not passed, outputting a result; if the test is passed, suggestive index evaluation continues to be carried out, and a result is output; according to the method, the characteristic problem of the urban traffic status can be quickly identified, and a time dimension research result and a basis can be provided for urban planning.

Description

Technical Field

[0001] The present invention relates to a commuting corridor identification and problem diagnosis method and system, and in particular to a commuting corridor identification and problem diagnosis method and system for time-poor people, belonging to the field of urban transportation technology. Background Art

[0002] Work and residence are important core functions of a city, and residents' commuting behavior based on this is a necessary travel behavior. Commuting, as an important observation dimension of daily urban traffic behavior, directly reflects the effectiveness of the city's traffic construction and management. In actual work, the diagnosis of the current situation of urban commuting problems often focuses on the calculation of static indicators in a single dimension, which makes it difficult to cope with the dynamically changing urban commuting problems and fails to identify urban commuting corridors with potential for transformation.

[0003] On the other hand, existing urban commuting problem diagnosis methods mainly focus on discovering and solving universal urban commuting problems, but it is difficult to discover vulnerable commuting groups in cities and take targeted measures to deal with them, thereby promoting social equity and residents' happiness. Based on this, the present invention introduces the concept of time poverty. The concept of time poverty was first proposed by economist Vickery in the 1970s, referring to the minimum time a family needs in addition to income in order to maintain a non-poverty consumption level. Summary of the invention

[0004] Purpose of the invention: The purpose of the present invention is to provide a commuting corridor identification and problem diagnosis method and system for time-poor people that can improve the efficiency of identifying current urban traffic problems.

[0005] Technical solution: The present invention provides a commuting corridor identification and problem diagnosis method for time-poor people, comprising:

[0006] S1: Obtain the target city's commuting monitoring big data, location-based service LBS data, land use data, POI data, road network data and administrative division space data, perform data preprocessing on the LBS data, screen valid users, calculate the valid users' places of residence and places of employment, assign a plot number to each place of residence and place of employment, and uniformly import the preprocessed data and the longitude and latitude of the place of residence and place of employment into the target city's spatiotemporal data integration platform; the commuting monitoring big data includes the average vehicle running speed by section and time period, and the bus passenger flow and rated capacity by section and time period;

[0007] S2: Construct an indicator system for the portrait of time-poor people, calculate the indicator weights based on the analytic hierarchy process and construct the time poverty index formula; and combine the spatiotemporal data integration platform to calculate the time poverty index of effective users and screen the time-poor people;

[0008] S3: spatially associate the time poverty population with the land use data through the spatiotemporal data integration platform, calculate and generate the shortest commuting path based on the shortest path algorithm and generate the buffer zone of the shortest commuting path, and obtain the time poverty commuting corridor feature set of the target city;

[0009] S4: Identify the commuting peaks of the time-poor population in the target city, and store the identified commuting peak times in the spatiotemporal data integration platform; construct a binding indicator system and a suggested indicator system as assessment and evaluation criteria for diagnosing the time-poor commuting corridor problem; the suggested indicator system includes static indicators and dynamic indicators;

[0010] S5: Conduct a test on the binding indicator assessment in combination with the time poverty commuting corridor. If the test fails, output the result; if the test passes, continue with the recommended indicator evaluation, and output the binding indicator assessment results and the recommended indicator evaluation results on the spatiotemporal data integration platform.

[0011] Furthermore, the LBS data preprocessing method in step S1 is:

[0012] Identify the stay points, cluster the trajectory points that have not moved within a preset time or moved within a preset range in the LBS data, identify the average center of the cluster as the stay point, and generate the stay point information, the stay point information includes the user, longitude and latitude, stay duration and stay start and end time; the average center means that each stay will return a point to represent the average center of the stay in time and distance, and the stay point information has a time type interval;

[0013] Clean the data and filter out valid users, specifically: summarize all identified stay points, count the number of days each user stays, delete the user data whose stay days are less than half of the total number of days, mark the remaining users as valid users, and associate all original stay point data according to valid users to obtain valid user stay data.

[0014] Furthermore, the method for calculating the residence and employment place of the valid user in step S1 is:

[0015] Assume that the spatiotemporal anchorage of the stay point represents the number of days and duration of stay of the user's stay point within a period of time, and the property of the stay period being stable. Assume that the spatiotemporal anchorage index M is larger, and the stronger the spatiotemporal anchorage of the user's stay point is; the spatiotemporal anchorage index M of the jth land unit of user i in the land data is ij The calculation formula is as follows:

[0016]

[0017] Among them, D ijrepresents the total number of days that user i stays in the jth land unit, D i represents the total number of days that user i stays in all land use units, T ij represents the total length of time that user i stays in the jth land unit, T i represents the total length of time that user i stays in all land units, and the residence time entropy q ij It represents the discrete degree of the daily stay time period of user i in plot j. The specific formula is:

[0018]

[0019] When |x n_ij -x n_ij+1 |≤12 hours, |x n_ij -x n_ij+1 |>12 o'clock, When |y n_ij -y n_ij+1 |≤12 hours, |y n_ij -y n_ij+1 |>12 o'clock,

[0020] in, Indicates point P n_ij With P n+1_ij The Euclidean distance, P n_ij (x n_ij ,y n_ij ) represents the data discrete point of user i on the nth day of the jth plot unit, x n_ij is the starting time of the longest daily stay period of user i in the jth plot unit, y n_ij is the end time of the longest daily stay period of user i in the jth plot unit, x n_ij and n-ij After rounding, it is an integer in the set [1, 24], s_ij is the total number of days that user i stayed in the jth plot unit;

[0021] Identify the place of residence by calculating the spatiotemporal anchorage dataset M of all land units where user i has stayed alldays_i , take M alldays_i The maximum value is obtained and its corresponding land is marked as the user's residence;

[0022] Identify the work place by calculating the spatiotemporal anchorage dataset M of all the land units that user i has stayed at on the workday workdays_i , take M workdays_i The maximum value is used to check whether the corresponding land use mark is different from the residence. If it is different from the residence, the corresponding land use mark is marked as the user's work place; if it is the same, Mworkdays_i The land use corresponding to the second largest value is marked as the user's workplace.

[0023] Furthermore, the spatiotemporal data integration platform in step S1 includes basic spatial data and spatiotemporal trajectory data, wherein the basic spatial data is obtained from a public website, including land use data, POI data, road network data and administrative division spatial data; the user spatiotemporal trajectory data includes effective user stay data, user residence and user workplace data, and commuting monitoring big data obtained from a public website.

[0024] Furthermore, the indicator system for constructing a portrait of the time-poor population in step S2 includes:

[0025] S21: Calculate the basic indicators of the indicator system for the time poverty population portrait to form a time poverty population portrait indicator data set; the basic indicators include commuting cost dimension, working time dimension, and facility acquisition dimension;

[0026] The commuting cost dimension includes commuting time and commuting distance. The commuting time is obtained from the valid user stay data, and the average time for valid users to travel from their residence to their workplace is taken, denoted as T. C The commuting distance is obtained from the valid user's residence and work data and land use data. The straight-line distance between the valid user's residence and work place is taken and recorded as D rw ;

[0027] The working time dimension includes the daily working time and the number of working days in a week. The daily working time is obtained from the effective user stay data, and the average length of time that effective users stay at their workplace is taken, which is recorded as T w The number of working days per week is obtained from the valid user stay data. The average number of days in which the valid user has a work place stay record per week and the stay time is longer than the preset time is taken, and recorded as d w ;

[0028] The facility acquisition dimensions include the number of living facilities, accessibility of living facilities and diversity of living facilities. The number of living facilities is obtained from business POI data, land use data and user residence data; the living facilities are classified into three categories: health management, commercial services and sports and fitness. The extended boundary is extended outward by a preset distance from the boundary of the effective user's residence and work place, and the total number of various living facilities within the extended boundary is counted, which is recorded as n f ;

[0029] The accessibility of living facilities is obtained by business POI data, land use data, and user residential data. The specific formula is as follows:

[0030]

[0031] Among them, ki is the kth point in the i-th type of living facilities, is the distance between the center of the residence or workplace and the k point of the surrounding i-type facilities, n i is the total number of type i facilities that can cover this plot, t i D is the average straight-line distance between the geometric center of the residential or work area and the i-th type of facility. a The accessibility index of living facilities at the user's place of residence and work;

[0032] The diversity of living facilities is obtained from business POI data, land use data, and user residential data. The specific formula is as follows:

[0033]

[0034] Among them, P i n i The proportion of the total number of living facilities in the user's residence or workplace, s is the total type of living facilities covered in the user's residence or workplace, and H is the diversity index of living facilities in the user's residence and workplace;

[0035] S22: The indicator commuting time T C , commuting distance D rw , Single-day working hours T w , Number of working days per weekd w 、Number of living facilities n f , Living facilities accessibility index D a The living facilities diversity index H is normalized in the composition time poverty population portrait indicator data set, and the corresponding data after processing is recorded as T Cn , D rwn , T wn ,d wn 、n fn , D an and H n , the calculation formula of time poverty index TPI is:

[0036] TPI = x1T Cn +x2D rwn +x3T wn +x4d wn +x5n fn +x6D an +x7H n

[0037] Among them, x1, x2, ..., x7 are the weight coefficients corresponding to each index, which are obtained by the hierarchical analysis method.

[0038] Furthermore, step S3 includes selecting the Dijkstra algorithm to calculate the shortest path from the place of residence to the place of employment, setting the place of residence of the time poor people as the starting and ending points, setting the place of work of the time poor people as the end point, constructing a road network model in the shortest path algorithm based on the road network data in the spatiotemporal data integration platform, calculating the shortest path according to the Dijkstra algorithm, superimposing all the shortest paths into the same data file, and recording the content of the data file as the commuting corridor for the time poor people.

[0039] Furthermore, the step S4 comprises:

[0040] S41: Based on the effective user stay data in the spatiotemporal data integration platform, the start and end time of the user's commuting behavior and the morning and evening peak hours of the time-poor population are obtained, and verified based on the commuting monitoring big data, and the morning and evening peak hours of the time-poor population are stored in the target city's spatiotemporal data integration platform;

[0041] S42: Preset assessment standards, construct a binding indicator system and store it in the spatiotemporal data integration platform for assessment of target cities; the binding indicators include: average motor vehicle speed indicator and average regular public transportation speed indicator, the average motor vehicle speed indicator is that the average speed of expressways and trunk roads during morning and evening peaks is not less than 30km / h and 20km / h respectively; the regular public transportation speed indicator is that the average speed of regular public transportation on the commuting corridors of time-poor people during morning and evening peaks on weekdays is not less than 15km / h;

[0042] S43: Static indicators and dynamic indicators are used as suggested indicators and stored in the spatiotemporal data integration platform, under the time dimension of morning and evening rush hours of commuting; the static indicators refer to indicators related to time points, reflecting the commuting information of the target city at a certain moment and having temporal stability, including the commuting ratio covered by the rail station periphery and the one-way average commuting distance. The commuting ratio covered by the rail station periphery refers to the proportion of the number of residences and employment places within the preset radius of the rail station to the total number of residences and employment places in the entire city. The one-way average commuting distance refers to the one-way commuting distance among the time-poor population. The dynamic index refers to an index related to the time period, reflecting the commuting information of the target city in a certain time period and having timeliness, including bus congestion and the operating ratio of living facilities. The specific time period selected is the morning and evening commuting peak time of the target city obtained in step S41; wherein, the bus congestion refers to the ratio of bus passenger flow on the commuting corridor of time-poor people during the morning and evening peak hours on weekdays to the rated passenger capacity; the operating ratio of living facilities refers to the ratio of the number of living facilities in the open state contained in the characteristic set of the time-poor commuting corridor during the morning and evening peak hours on weekdays to the total number of facilities.

[0043] Furthermore, the step S41 includes:

[0044] S411: Based on the effective user stay point data and user residence and employment data in the spatiotemporal data integration platform, extract the time-divided commuting data set of each user in the time-poor population within each hour of the day

[0045] {P1,P2,P3,…,P 24}, where P = {user, residential plot number, work plot number, commuting behavior time under this time slice}, 24 is the time slice, and the slice length is one hour;

[0046] S412: Based on the time-divided commuting data set, the total commuting time of all time-poor people in each time slice is summarized, and the time corresponding to the slice with the longest total commuting time from 0:00 to 12:00 is marked as the morning peak, and the time corresponding to the slice with the longest total commuting time from 12:00 to 24:00 is marked as the evening peak;

[0047] S413: Obtain the average vehicle running speed in different time periods in the commuting corridor of the time-poor population from the spatiotemporal data integration platform, and extract the average vehicle running speed v1 during the morning peak period and the average vehicle running speed v2 during the evening peak period marked in step S412; if v1 and v2 belong to the last three digits of the average vehicle running speed in each time period, the test is deemed to have passed.

[0048] Furthermore, the step S5 comprises:

[0049] S51: Taking the administrative division vector boundary as the statistical unit, output the distribution area and number of residences and workplaces of the time poor people in each statistical unit, and mark the corresponding number of time poor people; count the mileage of the time poor commuting corridors in each statistical unit, and mark the time poor commuting corridors within the regional scope;

[0050] S52: Calculate the target city's binding indicators in the spatiotemporal data integration platform. If the binding indicator assessment test fails, mark the binding indicator item that fails the assessment test and the gap between the indicator item and the assessment standard, end the assessment and output the result; if the binding indicator assessment test passes, proceed to step S53;

[0051] S53: Calculate the target city's recommended indicator evaluation in the spatiotemporal data integration platform, and output the target city's binding indicator assessment results and recommended indicator evaluation results, where the recommended indicator evaluation results specifically include the indicator name and the indicator recommended value or indicator requirement.

[0052] Based on the same inventive concept, the present invention also provides a commuting corridor identification and problem diagnosis system for time-poor people, comprising:

[0053] The preprocessing module is used to obtain the target city's commuting monitoring big data, location-based service LBS data, land use data, POI data, road network data and administrative division space data, perform data preprocessing on the LBS data, screen valid users, calculate the valid users' residence and place of employment, assign a plot number to each residence and place of employment, and uniformly import the preprocessed data and the longitude and latitude of the residence and place of employment into the target city's spatiotemporal data integration platform; the commuting monitoring big data includes the average vehicle running speed by section and time period, and the bus passenger flow and rated capacity by section and time period;

[0054] The screening module is used to construct an indicator system for the portrait of the time-poor population, calculate the indicator weights based on the hierarchical analysis method and construct the time poverty index formula; and combine the spatiotemporal data integration platform to calculate the time poverty index of effective users and screen the time-poor population;

[0055] An identification module is used to spatially associate the time-poor population with the land use data through the spatiotemporal data integration platform, calculate and generate the shortest commuting path based on the shortest path algorithm and generate a buffer zone of the shortest commuting path, and obtain a feature set of the time-poor commuting corridor in the target city;

[0056] An indicator establishment module is used to identify the commuting peak of the time-poor population in the target city and store the identified commuting peak time in the spatiotemporal data integration platform; a binding indicator system and a suggested indicator system are constructed as the assessment and evaluation criteria for the diagnosis of the time-poor commuting corridor problem; the suggested indicator system includes static indicators and dynamic indicators;

[0057] The assessment and testing module is used to conduct a test on the binding indicator assessment in combination with the time poverty commuting corridor. If the test fails, the result is output; if the test passes, the recommended indicator evaluation is continued, and the binding indicator assessment results and the recommended indicator evaluation results are output on the spatiotemporal data integration platform.

[0058] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: 1. The present invention takes the identification and problem diagnosis of commuting corridors for time-poor people as the observation dimension of characteristic problems of the current urban traffic situation. Compared with the existing methods for identifying problems of the current urban traffic situation, the present method effectively reduces the number of observation samples, identifies problems of commuting corridors for time-poor people with potential problems, improves the efficiency of identifying problems of the current urban traffic situation, and can mark commuting corridors with optimization potential and output their indicator evaluation results and reference values, facilitating the subsequent optimization of commuting corridors; 2. The present invention proposes a method for diagnosing the problems of commuting corridors for time-poor people, which combines dynamic and static dimensions by first assessing and then evaluating, and divides the evaluation indicators into binding indicators and advisory indicators, forming an evaluation method combining rigidity and elasticity, which can respond more flexibly to actual situations. situation; 3. The present invention realizes the dynamic indicator evaluation in the time dimension of commuting peak period, which can effectively identify the potential commuting problems under the scenario of tight road traffic resources, realize the targeted physical examination under extreme commuting conditions, and greatly improve the physical examination accuracy at the urban commuting level; 4. The present invention proposes a residence and business identification method based on the spatiotemporal anchoring of the stay point. Compared with the existing commonly used methods, the method proposed by the present invention does not require land use data, but pays more attention to the stay duration and stay stability of the user's stay point, while reducing the steps of residence and business identification, it also significantly improves the accuracy of identification; 5. The present invention constructs a spatiotemporal data integration platform with data storage, data analysis, indicator interaction and result output, which effectively solves the problems of data information chaos and difficulty in connecting data information processes in the current work, and greatly reduces the difficulty of data management. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a flow chart of the method of the present invention;

[0060] Figure 2 A schematic diagram of a time poverty commuting corridor evaluation process according to an embodiment of the present invention;

[0061] Figure 3 This is a schematic diagram of time poverty commuting corridor identification according to an embodiment of the present invention;

[0062] Figure 4 A schematic diagram of the commuting ratio of the periphery of a rail station according to an embodiment of the present invention;

[0063] Figure 5 A schematic diagram of conventional public transportation congestion according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in combination with the accompanying drawings and embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] This embodiment uses the commuting corridor identification and problem diagnosis of time-poor people in a certain city to explain the technical solution of the present invention in detail: Figure 1 As shown, the commuting corridor identification and problem diagnosis method for time-poor people in this embodiment includes:

[0066] S1: Obtain the target city's commuting monitoring big data, location-based service LBS data, land use data, POI data, road network data and administrative division space data, perform data preprocessing on the LBS data, screen valid users, calculate the valid users' places of residence and places of employment, assign a plot number to each place of residence and place of employment, and uniformly import the preprocessed data and the longitude and latitude of the place of residence and place of employment into the target city's spatiotemporal data integration platform; the commuting monitoring big data includes the average vehicle running speed by section and time period, and the bus passenger flow and rated capacity by section and time period;

[0067] S2: Construct an indicator system for the portrait of time-poor people, calculate the indicator weights based on the analytic hierarchy process and construct the time poverty index formula; and combine the spatiotemporal data integration platform to calculate the time poverty index of effective users and screen the time-poor people;

[0068] S3: spatially associate the time poverty population with the land use data through the spatiotemporal data integration platform, calculate and generate the shortest commuting path based on the shortest path algorithm and generate the buffer zone of the shortest commuting path, and obtain the time poverty commuting corridor feature set of the target city;

[0069] S4: Identify the commuting peaks of the time-poor population in the target city, and store the identified commuting peak times in the spatiotemporal data integration platform; construct a binding indicator system and a suggested indicator system as assessment and evaluation criteria for diagnosing the time-poor commuting corridor problem; the suggested indicator system includes static indicators and dynamic indicators;

[0070] S5: Conduct a test on the binding indicator assessment in combination with the time poverty commuting corridor. If the test fails, output the result; if the test passes, continue with the recommended indicator evaluation, and output the binding indicator assessment results and the recommended indicator evaluation results on the spatiotemporal data integration platform.

[0071] Specifically, the detailed process of step S1 is as follows:

[0072] Step S11: pre-processing the LBS data.

[0073] Step S111: Identify the stay points. Cluster the trajectory points that have not moved within 10 minutes or moved within 100 meters in the LBS data, identify their average centers as stay points, and generate information about the stay points (including user ID, longitude and latitude, stay duration, start and end time of stay). The average center means that each stay will return a point to represent the average center of the stay in time and distance, and the generated elements will have time type intervals.

[0074] Step S112: Data cleaning and effective user screening. Summarize all identified stay points, count the number of days of stay for each user ID, delete the user ID data with a stay number less than half of the total number of days, mark the remaining user IDs as effective users, screen out effective user IDs, and associate all original stay point data by effective user ID to obtain effective user stay data.

[0075] Step S12: Identify the residence and workplace of the user ID in the target city.

[0076] Step S121: Calculation of the spatiotemporal anchorage index of the stay point. The spatiotemporal anchorage of the stay point represents the property that the number of days and duration of stay at the user's stay point within a period of time are stable and persistent, and the stay period is stable. The strength of the spatiotemporal anchorage of the stay point is represented by the spatiotemporal anchorage index M. The larger the index M, the stronger the spatiotemporal anchorage of the user's stay point. M is calculated based on the stay time entropy q, the stay duration T, and the number of stay days D. The spatiotemporal anchorage index M is proportional to the number of stay days and the stay duration, and inversely proportional to the stay time entropy q. The specific formula is:

[0077]

[0078] Among them, D ij represents the total number of days that user i stays in the jth land unit, D i represents the total number of days that user i stays in all land use units, T ij represents the total length of time that user i stays in the jth land unit, T i represents the total length of time that user i stays in all land units, and the length of stay entropy q ij It represents the discrete degree of the daily stay time period of user i in plot j. The specific formula is:

[0079]

[0080] When |x n_ij -x n_ij+1 |≤12 hours, |x n_ij -x n_ij+1 |>12 o'clock, When |y n_ij -y n_ij+1 |≤12 hours, |y n_ij -y n_ij+1 |>12 o'clock,

[0081] in, Indicates point P n_ij With P n+1_ij The Euclidean distance, P n_ij (x n_ij ,y n_ij ) represents the data discrete point of user i on the nth day of the jth plot unit, x n_ij is the starting time of the longest daily stay period of user i in the jth plot unit, y n_ij is the end time of the longest daily stay period of user i in the jth plot unit, x n_ij and n_ij After rounding, it is an integer in the set [1, 24], and s_ij is the total number of days that user i stays in the jth plot unit.

[0082] Step S122: Identify the place of residence. The place of residence refers to a specific land unit that satisfies high spatiotemporal anchorage on both working days and non-working days. The specific identification method is: calculate the spatiotemporal anchorage dataset M of all land units that user i has stayed in the observation data. alldays_i , take M alldays_i The maximum value is found and its corresponding land is marked as the user’s residence.

[0083] Step S123: Workplace identification. The workplace refers to a specific land unit that satisfies high spatiotemporal anchorage during the workday and is different from the residence. The specific identification method is: calculate the spatiotemporal anchorage dataset M of all land units that user i has stayed in during the workday in the observation data workdays_i , take M workdays_i The maximum value is used to check whether the corresponding land use mark is different from the residence. If it is different from the residence, the corresponding land use mark is marked as the user's work place; if it is the same, M workdays_i The land use corresponding to the second largest value is marked as the user's workplace.

[0084] The detailed process of step S2 is as follows:

[0085] Step S21: Construct an indicator system for profiling the time-poor population.

[0086] Calculation of basic indicators. The basic indicators of the index system for the time poverty population portrait include three dimensions: commuting cost dimension, working time dimension, and facility acquisition dimension, forming a time poverty population portrait indicator data set. The specific indicator information is shown in Table 1:

[0087] Table 1 Time poverty population portrait indicator system

[0088]

[0089]

[0090] Among them, the buffer zone is processed by expanding the boundaries of the user's residence and workplace outward according to the 15-minute living circle radius of 800m determined in the "Notice on Issuing Guidelines for the Construction of Complete Residential Communities" issued by the Ministry of Housing and Urban-Rural Development.

[0091] Step S22: Calculate the time poverty index. C , commuting distance D rw , Single-day working hours T w , Number of working days per weekd w 、Number of living facilities n f , Living facilities accessibility index D a , the living facilities diversity index H is normalized in the time poverty population portrait indicator data set and is denoted as T Cn , D rwn , T wn ,d wn 、n fn , D an , H n , the calculation formula of time poverty index TPI is:

[0092] TPI = x1T Cn +x2D rwn +x3T wn +x4d wn +x5n fn +x6D an +x7H n

[0093] Among them, x1, x2, …, x7 are the weight coefficients corresponding to the index, which are obtained by the hierarchical analysis method. The specific method is: randomly select 100 residents in the study area to evaluate the importance of the basic indicators of the index system of the time poverty population portrait, build a hierarchical evaluation structure model of the time poverty index, then construct and calculate the judgment matrix, and calculate the weight coefficient after normalization.

[0094] The detailed process of step S3 is as follows:

[0095] Calculate the shortest path from the residence to the employment point. Select the Dijkstra algorithm to calculate the shortest path from the residence to the employment point, set the residence of the research user as the starting and ending points, and set the work place of the research user as the end point. Use the road network data in the spatiotemporal data integration platform described in step S1 to build the road network model in the shortest path algorithm, calculate the shortest path according to the Dijkstra algorithm, and generate the commuting corridor for the time-poor population by superimposing all the paths, such as Figure 3 shown.

[0096] The detailed process of step S4 is as follows:

[0097] Step S41: Identify the commuting peaks of the time-poor population. Based on the effective user stay data in the spatiotemporal data integration platform, obtain the start and end time of the user's commuting behavior, obtain the morning and evening peak times of the commuting of the time-poor population, and verify it based on the commuting monitoring big data. Store the morning and evening peak times of the commuting of the time-poor population in the target city's spatiotemporal data integration platform.

[0098] Step S411: Extracting the time-divided commuting data set. Based on the effective user stay data and user residence data in the spatiotemporal data integration platform, extract the time-divided commuting data set {P1, P2, P3, ..., P 24}, where P = {user ID, residential plot number, work plot number, commuting time in this time slice}, 24 is the time slice, and the slice length is one hour.

[0099] Step S412 summarizes the commuting time by time period. Based on the commuting data set by time period, the total commuting time of all time-poor people in each time slice is summarized, and the time corresponding to the slice with the longest commuting time from 0:00 to 12:00 is marked as the morning peak, and the time corresponding to the slice with the longest commuting time from 12:00 to 24:00 is marked as the evening peak.

[0100] Step S413: Commute peak identification test. The average vehicle speed in the commuting corridor of the time-poor population is obtained from the spatiotemporal data integration platform, and the average vehicle speed v1 in the morning peak period and the average vehicle speed v2 in the evening peak period marked in step S4-12 are extracted. If v1 and v2 belong to the last three digits of the average vehicle speed in each period, the test is considered to have passed.

[0101] Step S42: Binding indicator assessment: Check whether the binding indicators of the target city meet the requirements. The selected indicators are determined according to the "Basic Indicator System for Urban Physical Examination (Trial)" and "Commuting Monitoring Report of Major Chinese Cities", and the assessment results are stored in the target city spatiotemporal data integration platform.

[0102] Refer to the "Basic Indicator System for Urban Physical Examination (Trial)" to build a binding indicator system and store it in the spatiotemporal data integration platform to assess whether the indicators of the target cities meet the thresholds in the standards, and mark the cities that meet the standards for all assessment items as binding indicator compliance types. The assessment indicators include: the average speed indicator of motor vehicles and the average speed indicator of conventional public transportation. The average speed indicator of motor vehicles is specifically the average speed of expressways and trunk roads during the morning and evening peaks of not less than 30km / h and 20km / h respectively; the conventional public transportation speed indicator is specifically the average speed of conventional public transportation on the commuting corridors for time-poor people during the morning and evening peaks on weekdays, which is not less than 15km / h.

[0103] Step S43: Recommended indicator evaluation. Based on the two dimensions of static indicators and dynamic indicators, in the time dimension of the commuting peak period, the problem diagnosis is carried out for the cities whose binding indicators meet the requirements. The selected indicators are determined by referring to the "Green Travel Creation Action Assessment and Evaluation Standards" and the "National Public Transportation City Construction Demonstration Project Management Measures" and combined with the current situation of the target city, and the evaluation results are stored in the target city spatiotemporal data integration platform.

[0104] Construct a recommended indicator system and store it in the spatiotemporal data integration platform. The recommended indicator system specifically includes static indicators and dynamic indicators. The static indicators refer to indicators related to a specific time point, reflecting the commuting information of the target city at a certain moment and having temporal stability, including the commuting ratio covered by the rail station periphery and the average one-way commuting distance. Among them, the commuting ratio covered by the rail station periphery refers to the ratio of the number of residences and employment places within the preset radius of the rail station to the total number of residences and employment places in the entire city. The preset distance is the 15-minute living circle radius of 800m determined in the "Notice on Issuing the Guidelines for the Construction of Complete Residential Communities" issued by the Ministry of Housing and Urban-Rural Development. Figure 4 As shown; the one-way average commuting distance refers to the average distance of the shortest path for one-way commuting among the time-poor population; the dynamic index refers to an index related to a specific time period, reflecting the commuting information of the target city in a certain time period and having timeliness, including the congestion of conventional public transportation and the operating ratio of living facilities. The specific time period selected is the morning and evening commuting peaks of the target city obtained in step S41; wherein, the congestion of conventional public transportation refers to the ratio of the passenger flow of conventional public transportation on the commuting corridor of the time-poor population during the morning and evening peaks on weekdays to the rated passenger capacity, such as Figure 5 As shown; the operating ratio of living facilities refers to the ratio of the number of living facilities in an open state to the total number included in the time-poverty commuting corridor characteristic set during the morning and evening rush hours on weekdays.

[0105] like Figure 2 As shown, the detailed process of step S5 is as follows:

[0106] S51: Taking the administrative division vector boundary as the statistical unit, output the distribution area and number of residences and workplaces of the time poor people in each statistical unit, and mark the corresponding number of time poor people; count the mileage of the time poor commuting corridors in each statistical unit, and mark the time poor commuting corridors within the region.

[0107] S52: Calculate the target city's binding indicators in the spatiotemporal data integration platform. If the binding indicator assessment test fails, mark the binding indicator item that fails the assessment test and the gap between the indicator item and the assessment standard, and end the result output; if the binding indicator assessment test passes, proceed to step S53;

[0108] S53: Calculate the target city's recommended indicator evaluation in the spatiotemporal data integration platform, and output the target city's binding indicator assessment results and recommended indicator evaluation results, where the recommended indicator evaluation results specifically include the indicator name and the indicator recommended value or indicator requirement.

[0109] Based on the same inventive concept, this embodiment also provides a commuting corridor identification and problem diagnosis system for time-poor people, including:

[0110] The preprocessing module is used to obtain the target city's commuting monitoring big data, location-based service LBS data, land use data, POI data, road network data and administrative division space data, perform data preprocessing on the LBS data, screen valid users, calculate the valid users' residence and place of employment, assign a plot number to each residence and place of employment, and uniformly import the preprocessed data and the longitude and latitude of the residence and place of employment into the target city's spatiotemporal data integration platform; the commuting monitoring big data includes the average vehicle running speed by section and time period, and the bus passenger flow and rated capacity by section and time period;

[0111] The screening module is used to construct an indicator system for the portrait of the time-poor population, calculate the indicator weights based on the hierarchical analysis method and construct the time poverty index formula; and combine the spatiotemporal data integration platform to calculate the time poverty index of effective users and screen the time-poor population;

[0112] An identification module is used to spatially associate the time-poor population with the land use data through the spatiotemporal data integration platform, calculate and generate the shortest commuting path based on the shortest path algorithm and generate a buffer zone of the shortest commuting path, and obtain a feature set of the time-poor commuting corridor in the target city;

[0113] An indicator establishment module is used to identify the commuting peak of the time-poor population in the target city and store the identified commuting peak time in the spatiotemporal data integration platform; a binding indicator system and a suggested indicator system are constructed as the assessment and evaluation criteria for the diagnosis of the time-poor commuting corridor problem; the suggested indicator system includes static indicators and dynamic indicators;

[0114] The assessment and testing module is used to conduct a test on the binding indicator assessment in combination with the time poverty commuting corridor. If the test fails, the result is output; if the test passes, the recommended indicator evaluation is continued, and the binding indicator assessment results and the recommended indicator evaluation results are output on the spatiotemporal data integration platform.

Claims

1. A commuting corridor identification and problem diagnosis method for time-poor people, characterized by: include: S1: Obtain the target city's commuting monitoring big data, location-based service LBS data, land use data, POI data, road network data and administrative division space data, perform data preprocessing on the LBS data, screen valid users, calculate the valid users' places of residence and places of employment, assign a plot number to each place of residence and place of employment, and uniformly import the preprocessed data and the longitude and latitude of the place of residence and place of employment into the target city's spatiotemporal data integration platform; the commuting monitoring big data includes the average vehicle running speed by section and time period, and the bus passenger flow and rated capacity by section and time period; S2: Construct an indicator system for the portrait of time-poor people, calculate the indicator weights based on the analytic hierarchy process and construct the time poverty index formula; and combine the spatiotemporal data integration platform to calculate the time poverty index of effective users and screen the time-poor people; S3: spatially associate the time poverty population with the land use data through the spatiotemporal data integration platform, calculate and generate the shortest commuting path based on the shortest path algorithm and generate the buffer zone of the shortest commuting path, and obtain the time poverty commuting corridor feature set of the target city; S4: Identify the commuting peaks of the time-poor population in the target city, and store the identified commuting peak times in the spatiotemporal data integration platform; construct a binding indicator system and a suggested indicator system as assessment and evaluation criteria for diagnosing the time-poor commuting corridor problem; the suggested indicator system includes static indicators and dynamic indicators; S5: Conduct a test on the binding indicator assessment in combination with the time poverty commuting corridor. If the test fails, output the result; if the test passes, continue with the recommended indicator evaluation, and output the binding indicator assessment results and the recommended indicator evaluation results on the spatiotemporal data integration platform.

2. The commuting corridor identification and problem diagnosis method for time-poor people according to claim 1 is characterized in that: The LBS data preprocessing method in step S1 is: Identify the stay points, cluster the trajectory points that have not moved within a preset time or moved within a preset range in the LBS data, identify the average center of the cluster as the stay point, and generate the stay point information, the stay point information includes the user, longitude and latitude, stay duration and stay start and end time; the average center means that each stay will return a point to represent the average center of the stay in time and distance, and the stay point information has a time type interval; Clean the data and filter out valid users, specifically: summarize all identified stay points, count the number of days each user stays, delete the user data whose stay days are less than half of the total number of days, mark the remaining users as valid users, and associate all original stay point data according to valid users to obtain valid user stay data.

3. The commuting corridor identification and problem diagnosis method for time-poor people according to claim 1 is characterized in that: The method for calculating the residence and employment place of the valid user in step S1 is: Assume that the spatiotemporal anchorage of the stay point represents the number of days and duration of stay of the user's stay point within a period of time, and the property of the stay period being stable. Assume that the spatiotemporal anchorage index M is larger, and the stronger the spatiotemporal anchorage of the user's stay point is; the spatiotemporal anchorage index M of the jth land unit of user i in the land data is ij The calculation formula is as follows: Among them, D ij represents the total number of days that user i stays in the jth land unit, D i represents the total number of days that user i stays in all land use units, T ij represents the total length of time that user i stays in the jth land unit, T i represents the total length of time that user i stays in all land units, and the length of stay entropy q ij It represents the discrete degree of the daily stay time period of user i in plot j. The specific formula is: When |x n_ij -x n_ij+1 | ≤ 12, |x n_ij -x n_ij+1 | > 12, When |y n_ij -y n_ij+1 | ≤ 12, dy n_ij = y n_ij -y n_ij+1 ; |y n_ij -y n_ij+1 | > 12, in, Indicates point P n_ij With P n+1_ij The Euclidean distance, P n_ij (x n_ij ,y n_ij ) represents the data discrete point of user i on the nth day of the jth plot unit, x n_ij is the starting time of the longest daily stay period of user i in the jth plot unit, y n_ij is the end time of the longest daily stay period of user i in the jth plot unit, x n_ij and n_ij After rounding, it is an integer in the set [1, 24], s_ij is the total number of days that user i stayed in the jth plot unit; Identify the place of residence by calculating the spatiotemporal anchorage dataset M of all land units where user i has stayed alldays_i , take M alldays_i The maximum value is obtained and its corresponding land is marked as the user's residence; Identify the work place by calculating the spatiotemporal anchorage dataset M of all the land units that user i has stayed at on the workday workddys_i , take M workddys_i The maximum value is used to check whether the corresponding land use mark is different from the residence. If it is different from the residence, the corresponding land use mark is marked as the user's work place; if it is the same, M workddys_i The land use corresponding to the second largest value is marked as the user's workplace.

4. The commuting corridor identification and problem diagnosis method for time-poor people according to claim 1 is characterized in that: The spatiotemporal data integration platform in step S1 includes basic spatial data and spatiotemporal trajectory data. The basic spatial data is obtained from public websites, including land use data, POI data, road network data and administrative division spatial data; the user spatiotemporal trajectory data includes valid user stay data, user residence and user work place data, and commuting monitoring big data obtained from public websites.

5. The commuting corridor identification and problem diagnosis method for time-poor people according to claim 1 is characterized in that: The indicator system for constructing a portrait of the time-poor population in step S2 includes: S21: Calculate the basic indicators of the indicator system for the time poverty population portrait to form a time poverty population portrait indicator data set; the basic indicators include commuting cost dimension, working time dimension, and facility acquisition dimension; The commuting cost dimension includes commuting time and commuting distance. The commuting time is obtained from the valid user stay data, and the average time for valid users to travel from their residence to their workplace is taken, denoted as T. C The commuting distance is obtained from the valid user's residence and work data and land use data. The straight-line distance between the valid user's residence and work place is taken and recorded as D rw ; The working time dimension includes the daily working time and the number of working days in a week. The daily working time is obtained from the effective user stay data, and the average length of time that effective users stay at their workplace is taken, which is recorded as T w The number of working days per week is obtained from the valid user stay data. The average number of days in which the valid user has a work place stay record per week and the stay time is longer than the preset time is taken, and recorded as d w ; The facility acquisition dimensions include the number of living facilities, accessibility of living facilities and diversity of living facilities. The number of living facilities is obtained from business POI data, land use data and user residence data; the living facilities are classified into three categories: health management, commercial services and sports and fitness. The extended boundary is extended outward by a preset distance from the boundary of the effective user's residence and work place, and the total number of various living facilities within the extended boundary is counted, which is recorded as n f ; The accessibility of living facilities is obtained by business POI data, land use data, and user residential data. The specific formula is as follows: Among them, k i is the kth point in the i-th type of living facilities, is the distance between the center of the residence or workplace and the k point of the surrounding i-type facilities, n i is the total number of type i facilities that can cover this plot, t i D is the average straight-line distance between the geometric center of the residential or work area and the i-th type of facility. a The accessibility index of living facilities at the user's place of residence and work; The diversity of living facilities is obtained from business POI data, land use data, and user residential data. The specific formula is as follows: Among them, P i n i The proportion of the total number of living facilities in the user's residence or workplace, s is the total type of living facilities covered in the user's residence or workplace, and H is the diversity index of living facilities in the user's residence and workplace; S22: The indicator commuting time T C , commuting distance D rw , Single-day working hours T w , Number of working days per weekd w 、Number of living facilities n f , Living facilities accessibility index D a The living facilities diversity index H is normalized in the composition time poverty population portrait indicator data set, and the corresponding data after processing is recorded as T Cn , D rwn 、T wn ,d wn 、n fn , D an and H n , the calculation formula of time poverty index TPI is: TPI=x1T Cn +x2D rwn +x3T wn +x4d wn +x5n fn +x6D an +x7H n Among them, x1, x2, ..., x7 are the weight coefficients corresponding to each index, which are obtained by the hierarchical analysis method.

6. The commuting corridor identification and problem diagnosis method for time-poor people according to claim 1 is characterized in that: The step S3 includes selecting the Dijkstra algorithm to calculate the shortest path from the place of residence to the place of employment, setting the place of residence of the time poor as the starting and ending points, setting the place of work of the time poor as the end point, constructing a road network model in the shortest path algorithm based on the road network data in the spatiotemporal data integration platform, calculating the shortest path according to the Dijkstra algorithm, superimposing all the shortest paths into the same data file, and recording the content of the data file as the commuting corridor for the time poor.

7. The commuting corridor identification and problem diagnosis method for time-poor people according to claim 1 is characterized in that: The step S4 comprises: S41: Based on the effective user stay data in the spatiotemporal data integration platform, the start and end time of the user's commuting behavior and the morning and evening peak hours of the time-poor population are obtained, and verified based on the commuting monitoring big data, and the morning and evening peak hours of the time-poor population are stored in the target city's spatiotemporal data integration platform; S42: Preset assessment standards, construct a binding indicator system and store it in the spatiotemporal data integration platform for assessment of target cities; the binding indicators include: average motor vehicle speed indicator and average regular public transportation speed indicator, the average motor vehicle speed indicator is that the average speed of expressways and trunk roads during morning and evening peaks is not less than 30km / h and 20km / h respectively; the regular public transportation speed indicator is that the average speed of regular public transportation on the commuting corridors of time-poor people during morning and evening peaks on weekdays is not less than 15km / h; S43: Static indicators and dynamic indicators are used as suggested indicators and stored in the spatiotemporal data integration platform, under the time dimension of morning and evening rush hours of commuting; the static indicators refer to indicators related to time points, reflecting the commuting information of the target city at a certain moment and having temporal stability, including the commuting ratio covered by the rail station periphery and the one-way average commuting distance. The commuting ratio covered by the rail station periphery refers to the proportion of the number of residences and employment places within the preset radius of the rail station to the total number of residences and employment places in the entire city. The one-way average commuting distance refers to the one-way commuting distance among the time-poor population. The dynamic index refers to an index related to the time period, reflecting the commuting information of the target city in a certain time period and having timeliness, including bus congestion and the operating ratio of living facilities. The specific time period selected is the morning and evening commuting peak time of the target city obtained in step S41; wherein, the bus congestion refers to the ratio of bus passenger flow on the commuting corridor of time-poor people during the morning and evening peak hours on weekdays to the rated passenger capacity; the operating ratio of living facilities refers to the ratio of the number of living facilities in the open state contained in the characteristic set of the time-poor commuting corridor during the morning and evening peak hours on weekdays to the total number of facilities.

8. The commuting corridor identification and problem diagnosis method for time-poor people according to claim 7, characterized in that: The step S41 comprises: S411: Based on the effective user stay point data and user residence and employment data in the spatiotemporal data integration platform, extract the time-divided commuting data set {P1, P2, P3, …, P 24 }, where P = {user, residential plot number, work plot number, commuting behavior time under this time slice}, 24 is the time slice, and the slice length is one hour; S412: Based on the time-divided commuting data set, the total commuting time of all time-poor people in each time slice is summarized, and the time corresponding to the slice with the longest total commuting time from 0:00 to 12:00 is marked as the morning peak, and the time corresponding to the slice with the longest total commuting time from 12:00 to 24:00 is marked as the evening peak; S413: Obtain the average vehicle running speed in different time periods in the commuting corridor of the time-poor population from the spatiotemporal data integration platform, and extract the average vehicle running speed v1 during the morning peak period and the average vehicle running speed v2 during the evening peak period marked in step S412; if v1 and v2 belong to the last three digits of the average vehicle running speed in each time period, the test is deemed to have passed.

9. The commuting corridor identification and problem diagnosis method for time-poor people according to claim 1, characterized in that: The step S5 comprises: S51: Taking the administrative division vector boundary as the statistical unit, output the distribution area and number of residences and workplaces of the time poor people in each statistical unit, and mark the corresponding number of time poor people; count the mileage of the time poor commuting corridors in each statistical unit, and mark the time poor commuting corridors within the regional scope; S52: Calculate the target city's binding indicators in the spatiotemporal data integration platform. If the binding indicator assessment test fails, mark the binding indicator item that fails the assessment test and the gap between the indicator item and the assessment standard, end the assessment and output the result; if the binding indicator assessment test passes, proceed to step S53; S53: Calculate the target city's recommended indicator evaluation in the spatiotemporal data integration platform, and output the target city's binding indicator assessment results and recommended indicator evaluation results, where the recommended indicator evaluation results specifically include the indicator name and the indicator recommended value or indicator requirement.

10. A commuting corridor identification and problem diagnosis system for time-poor people, characterized by: include: The preprocessing module is used to obtain the target city's commuting monitoring big data, location-based service LBS data, land use data, POI data, road network data and administrative division space data, perform data preprocessing on the LBS data, screen valid users, calculate the valid users' residence and place of employment, assign a plot number to each residence and place of employment, and uniformly import the preprocessed data and the longitude and latitude of the residence and place of employment into the target city's spatiotemporal data integration platform; the commuting monitoring big data includes the average vehicle running speed by section and time period, and the bus passenger flow and rated capacity by section and time period; The screening module is used to construct an indicator system for the portrait of the time-poor population, calculate the indicator weights based on the hierarchical analysis method and construct the time poverty index formula; and combine the spatiotemporal data integration platform to calculate the time poverty index of effective users and screen the time-poor population; An identification module is used to spatially associate the time-poor population with the land use data through the spatiotemporal data integration platform, calculate and generate the shortest commuting path based on the shortest path algorithm and generate a buffer zone of the shortest commuting path, and obtain a feature set of the time-poor commuting corridor in the target city; An indicator establishment module is used to identify the commuting peak of the time-poor population in the target city and store the identified commuting peak time in the spatiotemporal data integration platform; a binding indicator system and a suggested indicator system are constructed as the assessment and evaluation criteria for the diagnosis of the time-poor commuting corridor problem; the suggested indicator system includes static indicators and dynamic indicators; An assessment and testing module, used to test the assessment of binding indicators in combination with the time poverty commuting corridor, and output the result if the test fails; If the test passes, the recommended indicator evaluation will continue, and the binding indicator assessment results and recommended indicator evaluation results will be output on the spatiotemporal data integration platform.

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