CN109978224A - A method of analysis obtains the Trip Generation Rate of heterogeneity building - Google Patents

Abstract

The invention discloses a method for analyzing and obtaining the traffic travel rate of buildings of different natures, comprising the steps of: first, processing mobile phone signaling data to obtain travel volume, making statistics of land survey data in a research area, and comparing the travel volume with the traffic travel rate of buildings of different natures. The gross floor area is saved as a list. Secondly, according to the research purpose, determine the research time, different travel purposes, and the traffic travel rate of different building types. Then, by calculating the total building area of buildings of different natures in different traffic areas and the traffic trip volume obtained based on mobile phone signaling data, the different traffic areas are clustered according to the commuter population density and traffic accessibility, and finally for each traffic area. A multivariate statistical regression analysis was carried out on the traffic-like districts to determine the traffic travel rate of different characteristics and different building types. The invention realizes the acquisition of the traffic travel rate of buildings with different properties, and predicts the traffic travel situation in the planning year based on this.

Description

A method to analyze and obtain the traffic travel rate of buildings with different properties

technical field

The invention relates to the technical field of traffic planning travel survey, in particular to a method for obtaining a traffic travel rate.

Background technique

Traffic planning cannot be separated from urban land use planning, which requires a scientific theoretical system as a basis in the process of traffic planning. Using land use patterns as the basis for travel forecasting enables planning to meet future traffic demands. The interactive relationship between urban traffic and urban land use determines that different types and intensities of land use layouts will produce different types and intensities of social activities, thus determining the volume and distribution of traffic in different regions. Correspondingly, the functional efficiency of the transportation system also directly affects the surrounding land price, land rent and popularity, and affects whether the realization of the surrounding land functions is sufficient or not. Therefore, it is necessary to deeply study the relationship between urban land use and traffic in traffic planning, and the traffic trip rate is one of the important indicators to directly reflect this relationship.

The traffic cells are called OD zones (OD nodes), and the traffic flow is divided into traffic flows between several OD points through the division of the traffic cells. The division of OD nodes is actually a process of fuzzy clustering. It is necessary to adopt the method of fuzzy clustering to divide the traffic flow into specific traffic cells according to a certain degree of membership. However, in actual work, due to various reasons such as the difficulty in collecting statistical data, administrative districts such as provincial (autonomous region), city, county (township, district) transportation hub centers are generally used as units, or highway entrances and exits are used as transportation districts. For specific highway construction projects, according to the requirements of the depth of the feasibility study, determine the specific traffic area division. For example, for a national highway, counties and cities can be used as traffic areas; for a provincial highway, cities, counties, and townships (towns) can be used as traffic areas; for a county-level road, counties, townships (towns) and villages can be used as traffic areas. . At the same time, when dividing traffic areas, economic and technological development zones, new technology industrial zones, tourist areas, the location of important mines or large units, and important ports or transit distribution points should also be taken into account.

The trip rate model is to describe the relationship between the determinant index of the change of the trip generation amount of each land use and its trip generation amount, and to describe the quantitative law between the research object's own attributes and the traffic generation amount. At the same time, with the advent of the information age, the time system for work and school changes, and the amount of travel generation will also change. This requires that traffic planners should closely integrate social development, pay close attention to the relevant institutional factors that affect travel, and timely revise the generated prediction model to make it closer to reality. Therefore, to solve the urban traffic problem, it is inseparable from the research on the traffic behavior of the people living in the city, that is, the travel characteristics of the residents. Big data has developed in various fields, so it also brings new opportunities and challenges to the prediction of travel rate in traditional transportation planning.

In the existing method, the travel volume is predicted through the data of the residents' travel survey. Due to the low sampling rate and high cost of the residents' travel survey, the sample size of the travel data is small and the research accuracy is low. It is difficult to return the travel rate with high accuracy through the residents' travel survey data At the same time, due to the different travel rates of different land types, it is difficult for the survey data to cover a large number of different land uses, so the traditional travel rate survey method has its obvious limitations. For example, the travel rate of commercial buildings can only cover very few commercial buildings in the city in the survey sampling, so the obtained travel rate cannot reflect the actual situation, which has obvious restrictions on the accuracy of the travel volume forecast in the future years. In short, the accuracy of travel estimates based on resident survey data is not high.

SUMMARY OF THE INVENTION

The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art, and to provide a method for obtaining the traffic travel rate of buildings of different properties based on mobile phone signaling data and land survey data. Traffic trip rates and planning year land survey data for trip volume forecasts.

The present invention adopts the following technical solutions for solving the above-mentioned technical problems:

The present invention proposes a method for analyzing and obtaining the traffic travel rate of buildings of different properties, comprising the following steps:

Step 1), count the total building area of different land use types in different traffic districts;

Step 2), obtain the traffic travel volume of each traffic cell based on mobile phone signaling data;

Step 3), according to the commuter population density and traffic accessibility, perform cluster analysis on different traffic communities;

Step 4), according to the analysis result of step 3), perform multivariate statistical regression analysis for each type of traffic community, and determine the traffic travel rate of different characteristics and different residential building types.

Further, in the method for analyzing and obtaining the traffic travel rate of buildings of different natures proposed by the present invention, in the step 1), the total building area of different traffic districts and different land types is counted, and the details are as follows:

Step 1.1), determine the building classification standard;

Step 1.2), according to the building classification standard, divide the land in the study area according to the boundary of the traffic area, and obtain the map layer of each traffic area;

Step 1.3), open the attribute table of the map layer in step 1.2), add text fields in turn, name the number, enter the corresponding traffic area number, and combine all traffic area layers into one layer;

In step 1.4), the total area of various buildings is obtained by taking the boundary of the traffic area as the statistical unit.

Further, in the method for analyzing and obtaining the traffic travel rate of buildings of different properties proposed by the present invention, in the step 2), the traffic travel amount is obtained based on the mobile phone signaling data, and the details are as follows:

Step 2.1), create a Thiessen polygon in ARCGIS according to the service range of the base station, assign the trip data of the base station to the Thiessen polygon, open the attribute list of the Thiessen polygon, and associate according to the spatial relationship;

Step 2.2), after obtaining the travel population data, divide the travel population of each Thiessen polygon by the area of each Thiessen polygon to obtain the travel population density;

Step 2.3), carry out the superposition analysis INTERSECT of the Thiessen polygon of the traffic cell and the base station, and calculate the result of the superposition generated, open the attribute table, add a double field, and use the field calculator to calculate the number of travelers;

Step 2.4), and finally perform spatial statistics to obtain the travel volume of each traffic area.

Further, in the method for analyzing and obtaining the traffic travel rate of buildings with different properties proposed by the present invention, in the step 3), different traffic cells are clustered according to the commuter population density and traffic accessibility, and the details are as follows:

Step 3.1), select the classification factor according to the research purpose: determine the traffic accessibility and commuter population density of each traffic district in the urban area as the classification factor;

Step 3.2), determine the clustering method: perform K-Means clustering according to the classification factor determined in step 3.1), and finally determine the classification into two, that is, those with high traffic accessibility and high commuting density, and poor traffic accessibility and poor commuting density. traffic area.

Further, the method for analyzing and obtaining the traffic travel rate of buildings with different properties proposed by the present invention, the step 4) is as follows:

According to the results of cluster analysis, the traffic districts are divided into several categories, and the traffic generation rate is obtained by performing multivariate statistical regression according to the statistical results respectively; the details are as follows:

(1) Construct a multivariate linear equation for each traffic area, and form multiple equations to solve simultaneously,

Y ₁ =β ₁ x ₁ +β ₂ x ₂ +β ₃ x ₃ +…+β _n x _n +b ₁

Y ₂ =β ₁ x ₁ +β ₂ x ₂ +β ₃ x ₃ +…+β _n x _n +b ₂

...

Y _n =β ₁ x ₁ +β ₂ x ₂ +β ₃ x ₃ +…+β _n x _n +b _n

In the formula, Y _n : the traffic trip volume of each traffic area; β _n : the traffic trip rate of each land use type; X _n : the total building area of each land use type; b _n : constant term; n represents the traffic area of the traffic area. quantity;

(2) Regroup the traffic cells according to the control and regulation unit, and count the travel volume of each control and regulation unit. The specific calculation formula is as follows:

where:

B _i : the trip volume of the control unit i; d _ik : the building area of the kth class of the control unit i; w _ik : the trip rate of each k-th building of the control unit i;

(3) Finally, the traffic travel rate of each land use type is obtained.

Compared with the prior art, the present invention adopts the above technical scheme, and has the following technical effects:

(1) First, the present invention counts the travel volume based on the service range of the mobile phone base station and the traffic cell range. Through this method, the travel volume of each traffic area at each time of day can be reflected, and then the travel characteristics of each traffic area can be analyzed, which can provide effective decision support for traffic planning, urban planning and other work. Therefore, the applicability of the method of the present invention is stronger.

(2) Secondly, the present invention can classify the travel rate calculation method according to the characteristics of different traffic cells, and determine the specific characteristics of different traffic cells, which provides a basis for long-term traffic travel prediction, and is also conducive to quickly calculating various At the same time, this scheme obtains the travel rate of different traffic areas, improves the accuracy of the traffic travel rate, and provides a technical guarantee for accurately predicting the travel amount.

(3) Furthermore, the present invention can accurately handle the peak hours in urban traffic. Due to the high precision of the time attribute of mobile phone data, it can be accurate to the second. Therefore, after using big data to determine the characteristics of urban travel, we can determine the travel peak for each city, and then accurately calculate the travel rate during peak hours. Congestion provides an effective basis for judgment.

(4) Finally, the sample size of the present invention is huge. Compared with the small sample obtained from the traditional individual-based resident survey data, it can more comprehensively reflect the travel characteristics of urban residents, and at the same time, it can obtain the traffic travel rate based on different building types, which is very useful for urban planning. Both the land use layout and the traffic planning and traffic forecasting play an important role.

Description of drawings

FIG. 1 is a schematic diagram of the multivariate statistical regression statistical method proposed by the present invention.

FIG. 2 is a schematic diagram of the overall flow of the present invention.

Figure 3 is a schematic diagram of the total building area of different land use types of the traffic area.

FIG. 4 is a schematic diagram of obtaining travel volume based on mobile phone signaling data.

Figure 5 is a schematic diagram of the meaning of the trip volume in a traffic area.

Detailed ways

Below in conjunction with accompanying drawing, the technical scheme of the present invention is described in further detail:

Due to the small sample size of the resident survey data, but the large sample size of travel data based on mobile phone signaling data, machine learning can be used to clean the data, and finally the spatial distribution of the travel amount can be obtained. Therefore, it will be a practical method to use the mobile phone signaling data and land survey data to calculate the travel rate of different land use. The key point of the present invention is to establish a multivariate statistical regression model through mobile phone signaling data and land survey data to carry out relevant analysis.

Obtain mobile phone signaling data through the mobile phone service provider company. After careful analysis and research and consultation of mobile phone service providers, it is concluded that mobile phone signaling data has the following three characteristics:

①Passive, wide coverage, non-random. Mobile phone signaling data is the location information of mobile phone users during network activities recorded by operators. It belongs to involuntary passive collection of data. When updating, the mobile phone identification number, signaling time, and cell base station number at that time are all stored in the mobile phone signaling data.

②Timeliness, dynamics and continuity. The mobile phone signaling data records the daily behavior of each user and the way they use the urban space, which can reflect the characteristics of the user's time and space behavior and activities, and realize real-time dynamic continuous tracking and visual expression. The spatiotemporal dynamics of activities provide new avenues.

③ Reflect the functional connection inside and outside the city. At the regional level, resident places and travel destinations can be identified from the spatiotemporal trajectory data of residents traveling across cities, and the functional connections between cities can be reflected by measuring the flow of people between cities. At the inner city level, the connections between places of residence, places of work, and places of recreation can be identified from the temporal and spatial trajectories of individuals.

From the second characteristic of mobile phone signaling data, it can be found that determining the travel characteristics of each person is a key point. At the same time, only the base station where people switch services will be recorded as a trip. Therefore, according to the characteristics of mobile phone signaling data, it is first necessary to screen out the actual traffic travel volume from the massive data, and then check the specific travel characteristics of each person.

Next, establish a traffic travel volume model based on different building types to study specific travel characteristics: see Figure 1, in the figure: A: building area of public management and public services, B: building area of commercial service facilities, G: green space and square construction area, M: industrial construction area, R: residential construction area, S: construction area of roads and transportation facilities, U: construction area of public facilities, W: construction area of logistics and storage, H: construction area of urban and rural construction, traffic travel The rate is denoted by β _n , which is the travel rate of each land use. The core of the algorithm for calculating the travel rate of different land use is: based on the characteristics of the second and third characteristics of the mobile phone signaling data, an effective trip will be recorded twice, and the travel time of the two trips can be queried, and the travel characteristics of each person throughout the day can be recorded separately. Then, to determine the travel rate rules of different building types, it _is necessary to analyze the building area X _n of different types of buildings. The building areas of different types of buildings are obtained through the land survey of the Planning Bureau. Multivariate statistical regression is performed on X _n , and the travel volume Y _n of different statistical units.

It should be pointed out that: the above-mentioned traffic travel rate β _n will be different in different cities, different time periods, and different building types. It is necessary to carry out specific analysis for each city, and to infer the trend conclusions through long-term data observation, and The selected observation time period is general, that is, the interference of factors such as holidays and other factors on passenger flow is excluded. Since this is a trend judgment, some conclusions obtained have the characteristics of probabilistic judgment. Although this is not an absolute judgment, it can provide relevant researchers with reference for ideas and in-depth research on decision-making, which is also similar to the direct conclusions of big data. The characteristics of trend and appearance conclusion are consistent.

To sum up, the specific meaning of the traffic trip rate β _n is given: the larger the value of β _n , the greater the traffic trip volume per unit area.

Referring to Fig. 2, the method flow of the present invention is as follows:

Step 1), referring to Figure 3, count the total building area of different land use types in different traffic districts, for example, select the central urban area of Kunshan as the research scope, and specifically count the building area of each building type.

Among them, the total building area of different land use types in different traffic districts is calculated as follows:

Step 1.1), determine the building classification standards: administrative office buildings, commercial buildings, residential buildings, logistics and storage buildings, industrial buildings, transportation facilities buildings, public facilities buildings, green space square supporting buildings.

Step 1.2), query In step 1.1), the land in the study area is divided into SPLIT (segment) according to the boundary of the traffic area, and the central urban area of Kunshan is selected as the research area, and the boundary of the traffic area is determined before segmentation.

Step 1.3), open the attribute table in step 1.2), add a text field, name the number, enter the corresponding traffic area number, and merge all traffic area layers into one layer MERGE (merge). The traffic districts in the central urban area of Kunshan are numbered from 1.

In step 1.4), the total area of various buildings is obtained by taking the boundary of the traffic area as the statistical unit. Specifically, the construction area of various buildings in each traffic district in the central urban area of Kunshan.

Step 2), referring to FIG. 4, based on the traffic trip volume obtained from the mobile phone signaling data, obtain the all-day mobile phone signaling data of Kunshan on June 6, 2017, perform data processing, and obtain the actual trip volume.

Among them, the traffic travel volume obtained based on the mobile phone signaling data is as follows:

Step 2.1), create a Thiessen polygon in ARCGIS according to the service range of the base station, assign the trip data of the base station to the Thiessen polygon, open the attribute list of the Thiessen polygon, and associate according to the spatial relationship; The travel data of each base station is calculated, and then the travel volume statistics table of each base station is obtained, and the spatial correlation with the base station in the central urban area of Kunshan is carried out to obtain the travel volume of each base station.

Step 2.2), after obtaining the travel population data, divide the travel population of each Thiessen polygon by the area of each Thiessen polygon to obtain the travel population density; divide the travel volume of each base station in the central urban area of Kunshan by each After the service range of the base station, the travel population density of each base station is obtained.

Step 2.3), then superimpose the traffic cell and the Thiessen polygon of the base station to analyze the INTERSECT (intersection), and calculate the generated superimposed results, open the attribute table, add a double field, and use the field calculator to calculate the number of passengers ; Intersect the boundary of the traffic cell in Kunshan with the boundary of the service area of the base station, and renumber the base station.

Step 2.4), and finally perform spatial statistics. Based on the renumbering of base stations in the central urban area of Kunshan, spatial statistics are performed to obtain the travel volume of each traffic cell. Figure 5 is a schematic diagram of the meaning of the trip volume in a traffic area.

Step 3), according to the commuter population density and traffic accessibility, cluster analysis of different traffic districts,

Among them, according to the commuter population density and traffic accessibility, different traffic districts are clustered and analyzed, as follows:

Step 3.1), select the classification factor, which is determined according to the research purpose; select the accessibility and commuting density of each traffic area in the central urban area of Kunshan as the classification factor.

Step 3.2), determine the clustering method. In SPSS, the accessibility and commuting density of each traffic area in the central city of Kunshan are used as classification factors to perform K-Means (K-means) clustering. Traffic districts with poor accessibility and poor commuting density.

In step 4), according to step 3), multivariate statistical regression analysis is finally carried out for each type of traffic community to determine traffic travel with different characteristics and different residential building types.

Among them, the traffic districts are divided into several categories, and the traffic generation rate is obtained by performing multivariate statistical regression according to the statistical results. Multivariate statistical regression was carried out for the two types of traffic districts in the central urban area of Kunshan, and the value of specific traffic trips was obtained.

The following is a schematic diagram of the regression results of the travel rate in the central urban area of Kunshan. Among them, Table 1 is the regression of the travel rate of different land use in the central urban area, and Table 2 is the regression of the travel rate of different land use in the peripheral urban area.

Table 1

land type Morning peak departure Arrival in the morning peak Departure all day Arrival all day A public administration and public service land 1.134 3.8075 7.903^** 8.075^** B commercial service industry facility land 1.057 3.7685 16.472^** 17.315^** G green space and square land 5.7805 2.05 8.551 2.303 H urban and rural construction land 3.8615 0.8745 9.905 9.617 M industrial land 2.7885^** 4.5315^** 4.977^** 5.211 ^** R residence 1.986^** 0.7015^** 4.932^** 4.630^** S road and transportation facilities land 14.4195 12.2035 47.872^* 52.070^* U utility land 3.267 13.18 9.848 15.112 W logistics warehousing land 1.634 1.468 3.121 3.596

Note: ** means p-value is less than 0.01, * means p-value is less than 0.05, and the rest p-values are between 0.05-0.15.

Table 2

land type Morning peak departure Arrival in the morning peak Departure all day Arrival all day A public administration and public service land 2.019^* 1.060^* 5.243 ^* 5.099^* B commercial service industry facility land 1.049^** 0.285^** 1.488^* 1.349 ^* G green space and square land 1.186 0.624 2.456 2.108 H urban and rural construction land 0.181 0.140 0.605 0.538 M industrial land 0.588^** 1.075^** 1.448^** 1.552^** R residence 2.439^** 1.129 ^** 4.461^** 4.232 ^** S road and transportation facilities land 1.216 1.405 5.245 5.595 U utility land 1.515 0.716 8.860 8.275 W logistics warehousing land 0.557 0.036 3.294 2.916

Note: ** means p-value is less than 0.01, * means p-value is less than 0.05, and the rest p-values are between 0.05-0.15.

The above content is a further detailed description of the present invention in combination with specific preferred embodiments, and it cannot be considered that the specific implementation of the present invention is limited to these descriptions. For those of ordinary skill in the technical field of the present invention, without departing from the concept of the present invention, some simple deductions or substitutions can be made, which should be regarded as belonging to the protection scope of the present invention.

Claims (5)

1. a method for analyzing and obtaining the traffic travel rate of buildings of different natures, is characterized in that, comprises the following steps: Step 1), count the total building area of different land use types in different traffic districts; Step 2), obtain the traffic travel volume of each traffic cell based on mobile phone signaling data; Step 3), according to the commuter population density and traffic accessibility, perform cluster analysis on different traffic communities; Step 4), according to the analysis result of step 3), perform multivariate statistical regression analysis for each type of traffic community, and determine the traffic travel rate of different characteristics and different residential building types. 2. a kind of method according to claim 1 that analyzes and obtains the traffic trip rate of buildings of different natures, it is characterized in that, in described step 1), the total building area of different traffic districts and different land use types is counted, and is specifically as follows: Step 1.1), determine the building classification standard; Step 1.2), according to the building classification standard, divide the land in the study area according to the boundary of the traffic area, and obtain the map layer of each traffic area; Step 1.3), open the attribute table of the map layer in step 1.2), add text fields in turn, name the number, enter the corresponding traffic area number, and combine all traffic area layers into one layer; In step 1.4), the total area of various buildings is obtained with the boundary of the traffic area as the statistical unit. 3. a kind of method that analyzes and obtains the traffic trip rate of buildings of different properties according to claim 2, it is characterized in that, in described step 2), obtain traffic trip amount based on mobile phone signaling data, be specific as follows: Step 2.1), create a Thiessen polygon in ARCGIS according to the service range of the base station, assign the trip data of the base station to the Thiessen polygon, open the attribute list of the Thiessen polygon, and associate according to the spatial relationship; Step 2.2), after obtaining the travel population data, divide the travel population of each Thiessen polygon by the area of each Thiessen polygon to obtain the travel population density; Step 2.3), carry out the superposition analysis INTERSECT of the Thiessen polygon of the traffic cell and the base station, and calculate the result of the superposition generated, open the attribute table, add a double field, and use the field calculator to calculate the number of travelers; Step 2.4), and finally perform spatial statistics to obtain the travel volume of each traffic area. 4. The method according to claim 3, wherein in the step 3), different traffic cells are clustered according to commuter population density and traffic accessibility. The analysis is as follows: Step 3.1), select the classification factor according to the research purpose: determine the traffic accessibility and commuter population density of each traffic district in the urban area as the classification factor; Step 3.2), determine the clustering method: perform K-Means clustering according to the classification factor determined in step 3.1), and finally determine the classification into two, that is, those with high traffic accessibility and high commuting density, and poor traffic accessibility and poor commuting density. traffic area. 5. a kind of method according to claim 4, it is characterized in that, described step 4) is specifically as follows: According to the results of cluster analysis, the traffic districts are divided into several categories, and the traffic generation rate is obtained by performing multivariate statistical regression according to the statistical results respectively; the details are as follows: (1) Construct a multivariate linear equation for each traffic area, and form multiple equations to solve simultaneously, Y ₁ =β ₁ x ₁ +β ₂ x ₂ +β ₃ x ₃ +…+β _n x _n +b ₁ Y ₂ =β ₁ x ₁ +β ₂ x ₂ +β ₃ x ₃ +…+β _n x _n +b ₂ ... Y _n =β ₁ x ₁ +β ₂ x ₂ +β ₃ x ₃ +…+β _n x _n +b _n In the formula, Y _n : the traffic trip volume of each traffic area; β _n : the traffic trip rate of each land use type; X _n : the total building area of each land use type; b _n : constant term; n represents the traffic area of the traffic area. quantity; (2) Regroup the traffic cells according to the control and regulation unit, and count the travel volume of each control and regulation unit. The specific calculation formula is as follows: where: B _i : the trip volume of the control unit i; d _ik : the building area of the kth class of the control unit i; w _ik : the trip rate of each k-th building of the control unit i; (3) Finally, the traffic travel rate of each land use type is obtained.