A road traffic noise crowd influence measurement method based on mobile phone signaling data

Abstract

The application discloses a road traffic noise crowd influence measurement method based on mobile phone signaling data, and the method comprises the following steps: acquiring mobile phone signaling data, determining the space-time distribution information of mobile phone signaling users, estimating the actual population space-time distribution according to the space-time distribution information of mobile phone signaling users and local population census data, calculating the residence time of users staying at each base station position according to the population age attribute in the mobile phone signaling data and the population activity characteristics in the actual population space-time distribution, and then determining a target data set; combining the hourly traffic noise map of the city to obtain the hourly traffic noise equivalent sound level of the coverage range of each base station, and calculating the road traffic noise continuous exposure intensity of the target crowd according to the target data set and the traffic noise equivalent sound level. The application has high accuracy and can be widely applied to the field of computer technology.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, and particularly relates to a road traffic noise crowd influence measurement method based on mobile phone signaling data. BACKGROUND

[0002] Urban road traffic pollution has a huge impact on the daily life, work, study and rest of residents. Long-term exposure to road traffic noise pollution can increase the risk of a series of diseases suffered by urban residents, and the impact on the sound environment vulnerable groups such as the elderly and children is particularly serious. According to the statistics of the World Health Organization, at least 1 million healthy life years are lost in Western Europe every year due to traffic noise. Road traffic noise pollution needs to be effectively controlled.

[0003] The demand for road traffic transportation and travel is difficult to compress, and the heavy traffic load of urban roads brings large-scale and all-weather traffic noise pollution. At the present stage, it is difficult to effectively implement the prevention and control of traffic noise pollution in the whole city, and the priority is to prevent and control noise pollution in the area where the crowd is dense and the noise pollution is serious, which is an effective measure to control traffic noise pollution at present.

[0004] However, the current measurement and evaluation method of road traffic noise pollution mainly reflects the equivalent sound level of traffic noise in the research area according to the traffic noise monitoring data or the calculation result of the traffic noise map, ignoring the impact of traffic noise on the crowd. Only according to the equivalent sound level of traffic noise, the noise pollution area is determined, which will lead to inaccurate pollution area determination. For example, the traffic noise along the highway has a high sound pressure level, but due to the sparseness of the population, the pollution caused by it is much lower than that of urban roads.

[0005] Therefore, it is necessary to establish a road traffic noise pollution measurement method considering the number of exposed crowds, age structure, residence time and polluted noise value, so as to screen out the areas in the city that most need to prioritize traffic noise pollution control and help gradually improve the quality of urban sound environment. SUMMARY

[0006] Therefore, the present application provides a road traffic noise crowd influence measurement method based on mobile phone signaling data with high accuracy.

[0007] One aspect of the present application provides a road traffic noise crowd influence measurement method based on mobile phone signaling data, which comprises:

[0008] Obtaining mobile phone signaling data, determining the space-time distribution information of mobile phone signaling users, and estimating the actual population space-time distribution according to the space-time distribution information of the mobile phone signaling users and local population census data;

[0009] According to the population age attribute in the mobile phone signaling data and the population activity characteristics in the actual population spatio-temporal distribution, the stay time of the user staying at each base station position is calculated, and then a target data set is determined, the target data set including the population quantity and stay time at different times in a day, the population quantity and stay time in the coverage range of each base station, and the population quantity and stay time of different age groups;

[0010] Combined with the hourly traffic noise map of the city, the hourly traffic noise equivalent sound level of the coverage range of each base station is obtained, and according to the target data set and the traffic noise equivalent sound level, the road traffic noise continuous exposure intensity of the target population is calculated.

[0011] Optionally, the mobile phone signaling data is obtained, the spatio-temporal distribution information of the mobile phone signaling user is determined, and according to the spatio-temporal distribution information of the mobile phone signaling user and the local population census data, the actual population spatio-temporal distribution is estimated, including:

[0012] The user number data, gender data, age data, timestamp data and base station longitude and latitude data connected by the mobile phone are obtained from the mobile phone signaling data;

[0013] The abnormal data in the mobile phone signaling data is cleaned, and the ping-pong data, drift data, error data and multiple position data at the same time in the mobile phone signaling data are screened out;

[0014] According to the base station longitude and latitude coordinate point, a Voronoi diagram is generated to determine the coverage range of the base station; when the signaling is connected with the base station, it is determined that the user is in the coverage range of the base station;

[0015] According to the time of the mobile phone signaling data of the user and the base station longitude and latitude data, the spatio-temporal distribution of the mobile phone signaling user is obtained;

[0016] The number and age structure of the active mobile phone signaling users in the research area are counted, the mobile phone signaling users are expanded in combination with the local population census data, and on the basis of the spatio-temporal distribution of the signaling users, the actual population quantity and the spatio-temporal distribution of the gender and age structure of the population are estimated according to the total population, gender and age structure of the population census.

[0017] Optionally, according to the population age attribute in the mobile phone signaling data and the population activity characteristics in the actual population spatio-temporal distribution, the stay time of the user staying at each base station position is calculated, and then a target data set is determined, including:

[0018] According to the signaling data record of the mobile phone signaling user, the difference between the time when the next signaling record of the user is generated and the time of the current signaling record is calculated as the stay time of the user in the coverage range of the current base station;

[0019] The mobile phone signaling users are classified according to age, and are divided into 0-12 years old, 13-18 years old, 18-40 years old, 40-60 years old and 60 years old and above, a total of 5 groups of people;

[0020] The time of a day is divided by hours, and the number of people of different ages and their corresponding residence time in each hour and each base station coverage range are counted.

[0021] The statistical results are expanded according to the population number and age structure of the region to estimate the residence time period and residence time of the actual population at each base station position.

[0022] Optionally, the hourly traffic noise equivalent sound level of each base station coverage range is obtained by combining the hourly traffic noise map of the city, and the road traffic noise continuous exposure intensity of the target population is calculated according to the target data set and the traffic noise equivalent sound level, including:

[0023] The road traffic noise map is divided according to the Voronoi diagram generated by the base station latitude and longitude coordinates, and the average equivalent sound level of each base station coverage range per hour is calculated, and the average equivalent sound level is used to represent the average value of the hourly equivalent sound level of all noise receiving points in the noise map in the base station coverage range.

[0024] The road traffic noise continuous exposure intensity of the target population in each base station range is calculated.

[0025] The calculation formula of the road traffic noise continuous exposure intensity is: continuous exposure intensity = average equivalent sound level * total exposure amount of urban population.

[0026] Optionally, the calculation formula of the total exposure amount of urban population is:

[0027] E total = ∫∫pdt

[0028] Wherein, E total represents the total exposure amount of urban population; p represents the population number; t represents the exposure time of the population.

[0029] Another aspect of the embodiment of the application also provides a road traffic noise population influence metering device based on mobile phone signaling data, comprising:

[0030] The first module is used for acquiring mobile phone signaling data, determining the space-time distribution information of mobile phone signaling users, and estimating the actual population space-time distribution according to the space-time distribution information of the mobile phone signaling users and local population census data.

[0031] The second module is used to calculate the dwell time of users staying at each base station location based on the population age attributes in the mobile phone signaling data and the population activity characteristics in the actual spatiotemporal distribution of the population, and then determine the target dataset. The target dataset includes the number of people and dwell time at different times of the day, the number of people and dwell time within the coverage area of ​​each base station, and the number of people and dwell time in different age groups.

[0032] The third module is used to combine the hourly traffic noise map of the city to obtain the hourly equivalent sound level of traffic noise within the coverage area of ​​each base station, and to calculate the continuous exposure intensity of road traffic noise to the target population based on the target dataset and the equivalent sound level of traffic noise.

[0033] Optionally, the first module includes:

[0034] The first unit is used to obtain user ID data, gender data, age data, timestamp data, and latitude and longitude data of the base station to which the mobile phone is connected from the mobile phone signaling data;

[0035] The second unit is used to clean the abnormal data in the mobile phone signaling data, and to filter out ping-pong data, drift data, erroneous data, and data with multiple locations at the same time in the mobile phone signaling data.

[0036] The third unit is used to generate a Voronoi diagram based on the latitude and longitude coordinates of the base station to determine the coverage area of ​​the base station; when the signaling is connected to the base station, it determines that the user is within the coverage area of ​​the base station.

[0037] The fourth unit is used to obtain the spatiotemporal distribution of mobile signaling users based on the time and base station latitude and longitude data of the user's mobile signaling data;

[0038] The fifth unit is used to statistically analyze the number and age structure of active mobile signaling users within the research area. By combining local census data, the sample of mobile signaling users is expanded. Based on the spatiotemporal distribution of signaling users, and according to the total population, gender, and age structure of the census, the actual local population and the spatiotemporal distribution of gender and age structure are estimated.

[0039] Another aspect of the present invention provides an electronic device, including a processor and a memory;

[0040] The memory is used to store programs;

[0041] The processor executes the program to implement the method described above.

[0042] Another aspect of this invention provides a computer-readable storage medium storing a program that is executed by a processor to implement the methods described above.

[0043] This invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned method.

[0044] Embodiments of this invention acquire mobile phone signaling data, determine the spatiotemporal distribution information of mobile phone signaling users, and estimate the actual spatiotemporal distribution of the population based on the spatiotemporal distribution information of the mobile phone signaling users and local census data. Based on the population age attributes in the mobile phone signaling data and the population activity characteristics in the actual spatiotemporal distribution of the population, the dwell time of users staying at each base station location is calculated, thereby determining a target dataset. The target dataset includes the number of people and their dwell time at different times of the day, the number of people and their dwell time within the coverage area of ​​each base station, and the number of people and their dwell time in different age groups. Combining this with hourly traffic noise maps of the city, the hourly equivalent sound level of traffic noise within the coverage area of ​​each base station is obtained. Based on the target dataset and the equivalent sound level of traffic noise, the continuous exposure intensity of road traffic noise for the target population is calculated. This invention has high accuracy; it can obtain residents' travel activity patterns based on urban residents' mobile phone signaling data, estimate the dwell time of urban residents in various areas, and, combined with urban traffic noise maps, calculate the traffic noise exposure intensity of different age groups in different areas. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is an overall flowchart of an embodiment of the present invention;

[0047] Figure 2 A diagram illustrating mobile signaling data cleaning and base station coverage.

[0048] Figure 3 This is a schematic diagram showing the spatiotemporal distribution of different age groups based on mobile signaling data amplification.

[0049] Figure 4 This is a schematic diagram illustrating the process of measuring the total number of people exposed to traffic noise by combining a road traffic noise map. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0051] To address the problems existing in the prior art, one aspect of the present invention provides a method for measuring the impact of road traffic noise on crowds based on mobile phone signaling data, comprising:

[0052] Obtain mobile signaling data, determine the spatiotemporal distribution information of mobile signaling users, and estimate the actual spatiotemporal distribution of the population based on the spatiotemporal distribution information of the mobile signaling users and local population census data;

[0053] Based on the population age attributes in the mobile signaling data and the population activity characteristics in the actual spatiotemporal distribution of the population, the dwell time of users staying at each base station location is calculated, and then the target dataset is determined. The target dataset includes the number of people and dwell time at different times of the day, the number of people and dwell time within the coverage area of ​​each base station, and the number of people and dwell time in different age groups.

[0054] By combining the hourly traffic noise map of the city, the hourly equivalent sound level of traffic noise within the coverage area of ​​each base station is obtained. Based on the target dataset and the equivalent sound level of traffic noise, the continuous exposure intensity of road traffic noise to the target population is calculated.

[0055] Optionally, the step of acquiring mobile signaling data, determining the spatiotemporal distribution information of mobile signaling users, and estimating the actual spatiotemporal distribution of the population based on the spatiotemporal distribution information of the mobile signaling users and local census data includes:

[0056] Obtain user ID data, gender data, age data, timestamp data, and latitude and longitude data of the base station connected to the mobile phone from the mobile phone signaling data;

[0057] Abnormal data in the mobile phone signaling data is cleaned to remove ping-pong data, drift data, erroneous data, and data with multiple locations at the same time.

[0058] A Voronoi diagram is generated based on the latitude and longitude coordinates of the base station to determine the coverage area of ​​the base station; when signaling is connected to the base station, it is determined that the user is within the coverage area of ​​the base station.

[0059] Based on the time and base station latitude and longitude data of the user's mobile signaling data, the spatiotemporal distribution of mobile signaling users can be obtained;

[0060] The study investigates the number and age structure of active mobile signaling users within the research area. Combined with local census data, the sample of mobile signaling users is expanded. Based on the spatiotemporal distribution of signaling users, and according to the total population, gender, and age structure of the census, the actual local population and the spatiotemporal distribution of gender and age structure are estimated.

[0061] Optionally, the step of calculating the dwell time of users at each base station location based on the population age attributes in the mobile signaling data and the population activity characteristics in the actual spatiotemporal distribution of the population, and then determining the target dataset, includes:

[0062] Based on the signaling data records of mobile signaling users, the time difference between the time of the next signaling record and the time of the current signaling record is calculated, which is taken as the time the user stays within the coverage area of ​​the current base station.

[0063] Mobile signaling users are categorized by age into five groups: 0-12 years old, 13-18 years old, 18-40 years old, 40-60 years old, and 60 years and above.

[0064] Divide the day into hours and count the number of people in different age groups and their corresponding dwell time within the coverage area of ​​each base station in each hour.

[0065] The statistical results were expanded according to the population size and age structure of the region to estimate the actual time period and duration of stay of the population at each base station location.

[0066] Optionally, the step of combining hourly traffic noise maps of the city to obtain hourly equivalent sound levels of traffic noise within the coverage area of ​​each base station, and calculating the continuous exposure intensity of road traffic noise to the target population based on the target dataset and the equivalent sound levels of traffic noise, includes:

[0067] The road traffic noise map is divided according to the Voronoi diagram generated by the latitude and longitude coordinates of the base station. The average equivalent sound level per hour is calculated for the coverage area of ​​each base station. The average equivalent sound level is used to characterize the average hourly equivalent sound level of all noise receiving points on the noise map within the coverage area of ​​the base station.

[0068] Calculate the sustained exposure intensity of road traffic noise to the target population within the range of each base station;

[0069] The formula for calculating the sustained exposure intensity of road traffic noise is: sustained exposure intensity = average equivalent sound level * total exposure of urban population.

[0070] Optionally, the formula for calculating the total exposure of the urban population is:

[0071] E total =∫∫pdt

[0072] Among them, E total p represents the total exposure of the urban population; t represents the population size; and t represents the duration of exposure.

[0073] Another aspect of this invention provides a road traffic noise crowd impact measurement device based on mobile phone signaling data, comprising:

[0074] The first module is used to acquire mobile phone signaling data, determine the spatiotemporal distribution information of mobile phone signaling users, and estimate the actual spatiotemporal distribution of the population based on the spatiotemporal distribution information of the mobile phone signaling users and local population census data.

[0075] The second module is used to calculate the dwell time of users staying at each base station location based on the population age attributes in the mobile phone signaling data and the population activity characteristics in the actual spatiotemporal distribution of the population, and then determine the target dataset. The target dataset includes the number of people and dwell time at different times of the day, the number of people and dwell time within the coverage area of ​​each base station, and the number of people and dwell time in different age groups.

[0076] The third module is used to combine the hourly traffic noise map of the city to obtain the hourly equivalent sound level of traffic noise within the coverage area of ​​each base station, and to calculate the continuous exposure intensity of road traffic noise to the target population based on the target dataset and the equivalent sound level of traffic noise.

[0077] Optionally, the first module includes:

[0078] The first unit is used to obtain user ID data, gender data, age data, timestamp data, and latitude and longitude data of the base station to which the mobile phone is connected from the mobile phone signaling data;

[0079] The second unit is used to clean the abnormal data in the mobile phone signaling data, and to filter out ping-pong data, drift data, erroneous data, and data with multiple locations at the same time in the mobile phone signaling data.

[0080] The third unit is used to generate a Voronoi diagram based on the latitude and longitude coordinates of the base station to determine the coverage area of ​​the base station; when the signaling is connected to the base station, it determines that the user is within the coverage area of ​​the base station.

[0081] The fourth unit is used to obtain the spatiotemporal distribution of mobile signaling users based on the time and base station latitude and longitude data of the user's mobile signaling data;

[0082] The fifth unit is used to statistically analyze the number and age structure of active mobile signaling users within the research area. By combining local census data, the sample of mobile signaling users is expanded. Based on the spatiotemporal distribution of signaling users, and according to the total population, gender, and age structure of the census, the actual local population and the spatiotemporal distribution of gender and age structure are estimated.

[0083] Another aspect of the present invention provides an electronic device, including a processor and a memory;

[0084] The memory is used to store programs;

[0085] The processor executes the program to implement the method described above.

[0086] Another aspect of this invention provides a computer-readable storage medium storing a program that is executed by a processor to implement the methods described above.

[0087] This invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned method.

[0088] The specific implementation process of the present invention will now be described in detail with reference to the accompanying drawings:

[0089] This invention discloses a method for measuring road traffic noise pollution based on mobile phone signaling data. It considers the number of people exposed to road traffic noise, their age structure, and the duration of their stay in urban areas. The method measures urban road traffic noise pollution based on the exposure intensity of the people exposed to traffic noise, thereby identifying the areas with the most severe road traffic noise pollution in the city and helping to gradually improve the quality of the urban sound environment.

[0090] refer to Figure 1 This invention discloses a method for measuring road traffic noise pollution based on mobile phone signaling data, comprising:

[0091] The mobile signaling data is processed to obtain the spatiotemporal distribution of mobile signaling users. Combined with local census data, the spatiotemporal distribution of the actual population is estimated.

[0092] Based on the demographic age attributes and activity characteristics of mobile phone signaling, the dwell time of users staying at each base station location is calculated, thereby obtaining the number and dwell time of people of different age groups within the coverage area of ​​each base station at different times of the day;

[0093] By combining hourly traffic noise maps of the city, the hourly equivalent sound level of traffic noise within the coverage area of ​​each base station is obtained, and the continuous exposure intensity of road traffic noise for different age groups within this time period and area is calculated.

[0094] Optionally, the process of processing mobile signaling data to obtain the spatiotemporal distribution of mobile signaling users, combined with local census data, estimates the spatiotemporal distribution of the actual population, including...

[0095] Mobile signaling data includes fields such as user ID, gender, age, record generation time (year, month, day, hour, minute, second), and the latitude and longitude of the connected base station. Abnormal data in the mobile signaling data is cleaned, filtering out ping-pong data, drifting data, erroneous data, and data from multiple locations at the same time. A Voronoi diagram is generated based on the base station's latitude and longitude coordinates to represent the base station's coverage area; when signaling connects to a base station, the user is considered to be within the base station's coverage area.

[0096] Based on the time and base station latitude and longitude data of user mobile signaling data, the spatiotemporal distribution of mobile signaling users is obtained. The number and age structure of active mobile signaling users in the study area are statistically analyzed. Combined with local census data, the sample of mobile signaling users is expanded. Based on the spatiotemporal distribution of signaling users, and according to the total population and gender and age structure of the census, the actual local population and the spatiotemporal distribution of gender and age structure are estimated.

[0097] Optionally, the step of calculating the dwell time of users at each base station location based on the demographic age attributes and activity characteristics of mobile phone signaling, thereby obtaining the number and dwell time of people of different age groups within the coverage area of ​​each base station at different times of the day, includes:

[0098] Based on the signaling data records of mobile signaling users, the time difference between the time of the next signaling record and the time of the current signaling record is calculated, which is taken as the time the user stays within the coverage area of ​​the current base station.

[0099] Mobile signaling users are categorized by age into five groups: 0-12 years old, 13-18 years old, 18-40 years old, 40-60 years old, and 60 years and above.

[0100] The day is divided into hours, and the number of people of different age groups and their corresponding dwell time are counted within the coverage area of ​​each base station for each hour. The statistical results are then expanded according to the population size and age structure of the area to estimate the actual dwell time and duration of the population at each base station location.

[0101] Optionally, the step of combining hourly traffic noise maps of the city to obtain hourly equivalent sound levels of traffic noise within the coverage area of ​​each base station, and calculating the continuous exposure intensity of road traffic noise for different age groups within that time period and area, includes:

[0102] The road traffic noise map is divided into Voronoi diagrams generated by the latitude and longitude coordinates of the base stations. The average equivalent sound level per hour within the coverage area of ​​each base station is calculated. This average equivalent sound level is the average of the hourly equivalent sound levels of all noise receiving points on the noise map within the coverage area of ​​the base station.

[0103] The sustained exposure intensity of road traffic noise within the coverage area of ​​each base station is calculated using the formula: Sustained Exposure Intensity = Average Equivalent Sound Level * Total Exposure of Urban Population. In other words, the total exposure of the urban population within the coverage area of ​​a specific base station is calculated cumulatively within the study hour. The formula for this total exposure is E. total =∫∫pdt, where p represents the population size, t represents the exposure duration of the population, and the total exposure is the sum of the products of the number of individuals staying in the area and their stay duration, expressed in (person-times * hours). The road traffic noise pollution experienced by the urban population in this hour and in this area is the continuous exposure intensity of road traffic noise.

[0104] In summary, the embodiments of this invention process mobile phone signaling data to obtain the spatiotemporal distribution of mobile phone signaling users. Combined with local census data, the spatiotemporal distribution of the actual population is estimated. Based on the age attributes and activity characteristics of the mobile phone signaling population, the dwell time of users at each base station location is calculated, thereby obtaining the number and dwell time of different age groups within the coverage area of ​​each base station at different times of the day. Combined with hourly traffic noise maps of the city, the hourly equivalent sound level of traffic noise within the coverage area of ​​each base station is obtained, and the continuous exposure intensity of road traffic noise to different age groups within that time period and area is calculated. This invention can consider the urban population size and the dwell time of different age groups in various areas, measure the pollution level of road traffic noise on the population, help identify areas in the city where road traffic noise has a severe impact on the population, and serve the precise prevention and control of traffic noise pollution.

[0105] The implementation process of this invention will be described in detail below using a specific application scenario as an example:

[0106] The method for measuring the crowd impact of road traffic noise based on mobile phone signaling data of the present invention mainly includes the following steps:

[0107] 1. Select a study area, acquire mobile signaling data from operators within that area, and clean the ping-pong data, drift data, erroneous data, and data from multiple locations at the same time, as shown in Table 1; generate a Voronoi diagram based on the latitude and longitude coordinates of the base stations to represent their coverage area. The results are as follows: Figure 2 As shown.

[0108] Table 1

[0109]

[0110]

[0111] 2. Based on the population census data of the study area, the population size of five groups was counted: 0-12 years old, 13-18 years old, 18-40 years old, 40-60 years old, and 60 years old and above. At the same time, the total number of mobile phone signaling users in the above five groups was counted, and the expansion coefficient of different age groups was calculated, as shown in Table 2 below.

[0112] Table 2

[0113] 0-12 years 13-18 years 18-40 years 40-60 years Over 60 years Census data N1 N2 N3 N4 N5 Mobile phone signalling data n1 n2 n3 n4 n5 Expansion factor N1 / n1 N2 / n2 N3 / n3 N4 / n4 N5 / n5

[0114] After obtaining the expansion coefficient, the number of users for all base stations is expanded. For example, if there are m2 people aged 13-18 within the coverage area of ​​base station A between 13:00 and 14:00, then after expansion, the number of people in that age group within the coverage area of ​​base station A during that time period should be m2*N2 / n2.

[0115] 3. Calculate the number of people in different age groups within the coverage area of ​​base station A multiplied by their dwell time. For example, if between 13:00 and 14:00, K residents aged 13-18 stayed within the coverage area of ​​base station A, and the number of people staying for t1 (hours) is k1, the number staying for t2 (hours) is k2, ..., the number staying for tn (hours) is kn, then the total number of people aged 13-18 staying within the coverage area of ​​base station A between 13:00 and 14:00 multiplied by their dwell time is t1*k1 + t2*k2 + ... + tn. n *k n Its effects are shown in Table 3 and... Figure 3 As shown.

[0116] Table 3

[0117]

[0118] 4. Obtain a road traffic noise map of the study area. Divide the area according to base station coverage, calculate the average noise value within each base station's coverage area, and then calculate the total noise exposure population for that area. For example, if the average noise value within the coverage area of ​​base station A is L1 between 13:00 and 14:00, then the total noise exposure for people aged 13-18 within the coverage area of ​​base station A during this period is L1*(t1*k1+t2*k2+…+t…). n *k n The calculation method for total exposure to other age groups is the same. Finally, by summing up the total noise exposure of all age groups, the total pollution level of the population exposed to traffic noise within the coverage area of ​​the base station during that time period can be obtained. The results are shown in Table 4. Figure 4 As shown.

[0119] Table 4

[0120]

[0121] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is altered and sub-operations described as part of a larger operation are executed independently.

[0122] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the described functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0123] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0124] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0125] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0126] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0127] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0128] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0129] The above is a detailed description of the preferred embodiments of the present invention, but the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A method for measuring the impact of road traffic noise on crowds based on mobile phone signaling data, characterized in that, include: Obtain mobile signaling data, determine the spatiotemporal distribution information of mobile signaling users, and estimate the actual spatiotemporal distribution of the population based on the spatiotemporal distribution information of the mobile signaling users and local population census data; Based on the population age attributes in the mobile signaling data and the population activity characteristics in the actual spatiotemporal distribution of the population, the dwell time of users staying at each base station location is calculated, and then the target dataset is determined. The target dataset includes the number of people and dwell time at different times of the day, the number of people and dwell time within the coverage area of ​​each base station, and the number of people and dwell time in different age groups. By combining the hourly traffic noise map of the city, the hourly equivalent sound level of traffic noise within the coverage area of ​​each base station is obtained. Based on the target dataset and the equivalent sound level of traffic noise, the continuous exposure intensity of road traffic noise to the target population is calculated. The formula for calculating the sustained exposure intensity of road traffic noise is: Sustained exposure intensity = Average equivalent sound level * Total exposure of urban population; the average equivalent sound level is used to characterize the average hourly equivalent sound level of all noise receiving points on the noise map within the base station coverage area; the formula for calculating the total exposure of urban population is: , Represents the total exposure of the urban population; Indicates population size; Indicates the duration of exposure in a population; The step of acquiring mobile phone signaling data, determining the spatiotemporal distribution information of mobile phone signaling users, and estimating the actual spatiotemporal distribution of the population based on the spatiotemporal distribution information of the mobile phone signaling users and local census data includes: Obtain user ID data, gender data, age data, timestamp data, and latitude and longitude data of the base station connected to the mobile phone from the mobile phone signaling data; Abnormal data in the mobile phone signaling data is cleaned to remove ping-pong data, drift data, erroneous data, and data with multiple locations at the same time. A Voronoi diagram is generated based on the latitude and longitude coordinates of the base station to determine the coverage area of ​​the base station; when signaling is connected to the base station, it is determined that the user is within the coverage area of ​​the base station. Based on the time and base station latitude and longitude data of the user's mobile signaling data, the spatiotemporal distribution of mobile signaling users can be obtained; The study investigates the number and age structure of active mobile signaling users within the research area. Combined with local census data, the sample of mobile signaling users is expanded. Based on the spatiotemporal distribution of signaling users, the actual local population and the spatiotemporal distribution of gender and age structure are estimated according to the total population, gender, and age structure of the census. The step of calculating the dwell time of users at each base station location based on the population age attributes in the mobile signaling data and the population activity characteristics in the actual spatiotemporal distribution of the population, and then determining the target dataset, includes: Based on the signaling data records of mobile signaling users, the time difference between the time of the next signaling record and the time of the current signaling record is calculated, which is taken as the time the user stays within the coverage area of ​​the current base station. Mobile signaling users are categorized by age into five groups: 0-12 years old, 13-18 years old, 18-40 years old, 40-60 years old, and 60 years and above. Divide the day into hours and count the number of people in different age groups and their corresponding dwell time within the coverage area of ​​each base station in each hour. The statistical results were expanded according to the population size and age structure of the area to estimate the actual time period and duration of stay of the population at each base station location; Specifically, the population size of each age group is calculated based on the census data, and the total number of each age group among the mobile signaling users is also calculated. The expansion coefficient for each age group is then calculated. The expansion coefficient for each age group is the ratio of the population size of each age group obtained from the census data to the total number of each age group among the mobile signaling users. The number of people in each age group within the expanded base station coverage area during the time period is the product of the expansion coefficient for each age group and the number of people in each age group within the base station coverage area during the time period.

2. The method for measuring the impact of road traffic noise on crowds based on mobile phone signaling data according to claim 1, characterized in that, The method involves combining hourly traffic noise maps of the city to obtain hourly equivalent sound levels of traffic noise within the coverage area of ​​each base station. Based on the target dataset and the equivalent sound levels of traffic noise, the method calculates the continuous exposure intensity of road traffic noise for the target population, including: The road traffic noise map is divided according to the Voronoi diagram generated by the latitude and longitude coordinates of the base stations, and the average equivalent sound level per hour within the coverage area of ​​each base station is calculated. Calculate the sustained exposure intensity of road traffic noise to the target population within the range of each base station.

3. A road traffic noise crowd impact measurement device based on mobile phone signaling data, characterized in that, include: The first module is used to acquire mobile phone signaling data, determine the spatiotemporal distribution information of mobile phone signaling users, and estimate the actual spatiotemporal distribution of the population based on the spatiotemporal distribution information of the mobile phone signaling users and local population census data. The second module is used to calculate the dwell time of users staying at each base station location based on the population age attributes in the mobile phone signaling data and the population activity characteristics in the actual spatiotemporal distribution of the population, and then determine the target dataset. The target dataset includes the number of people and dwell time at different times of the day, the number of people and dwell time within the coverage area of ​​each base station, and the number of people and dwell time in different age groups. The third module is used to combine the hourly traffic noise map of the city to obtain the hourly equivalent sound level of traffic noise within the coverage area of ​​each base station, and to calculate the continuous exposure intensity of road traffic noise to the target population based on the target dataset and the equivalent sound level of traffic noise. The formula for calculating the sustained exposure intensity of road traffic noise is: Sustained exposure intensity = Average equivalent sound level * Total exposure of urban population; the average equivalent sound level is used to characterize the average hourly equivalent sound level of all noise receiving points on the noise map within the base station coverage area; the formula for calculating the total exposure of urban population is: , Represents the total exposure of the urban population; Indicates population size; Indicates the duration of exposure in a population; The first module includes: The first unit is used to obtain user ID data, gender data, age data, timestamp data, and latitude and longitude data of the base station to which the mobile phone is connected from the mobile phone signaling data; The second unit is used to clean the abnormal data in the mobile phone signaling data, and to filter out ping-pong data, drift data, erroneous data, and data with multiple locations at the same time in the mobile phone signaling data. The third unit is used to generate a Voronoi diagram based on the latitude and longitude coordinates of the base station to determine the coverage area of ​​the base station; when the signaling is connected to the base station, it determines that the user is within the coverage area of ​​the base station. The fourth unit is used to obtain the spatiotemporal distribution of mobile signaling users based on the time and base station latitude and longitude data of the user's mobile signaling data; The fifth unit is used to statistically analyze the number and age structure of active mobile signaling users in the research area. Combined with local census data, the mobile signaling users are expanded to include a larger sample. Based on the spatiotemporal distribution of signaling users, the actual local population and the spatiotemporal distribution of gender and age structure are estimated according to the total population, gender, and age structure of the census. The step of calculating the dwell time of users at each base station location based on the population age attributes in the mobile signaling data and the population activity characteristics in the actual spatiotemporal distribution of the population, and then determining the target dataset, includes: Based on the signaling data records of mobile signaling users, the time difference between the time of the next signaling record and the time of the current signaling record is calculated, which is taken as the time the user stays within the coverage area of ​​the current base station. Mobile signaling users are categorized by age into five groups: 0-12 years old, 13-18 years old, 18-40 years old, 40-60 years old, and 60 years and above. Divide the day into hours and count the number of people in different age groups and their corresponding dwell time within the coverage area of ​​each base station in each hour. The statistical results were expanded according to the population size and age structure of the area to estimate the actual time period and duration of stay of the population at each base station location; Specifically, the population size of each age group is calculated based on the census data, and the total number of each age group among the mobile signaling users is also calculated. The expansion coefficient for each age group is then calculated. The expansion coefficient for each age group is the ratio of the population size of each age group obtained from the census data to the total number of each age group among the mobile signaling users. The number of people in each age group within the expanded base station coverage area during the time period is the product of the expansion coefficient for each age group and the number of people in each age group within the base station coverage area during the time period.

4. An electronic device, characterized in that, Including the processor and memory; The memory is used to store programs; The processor executes the program to implement the method as described in any one of claims 1 to 2.

5. A computer-readable storage medium, characterized in that, The storage medium stores a program that is executed by a processor to implement the method as described in any one of claims 1 to 2.

6. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 2.

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