CN109978224B - Method for analyzing and acquiring traffic trip rates of buildings with different properties - Google Patents
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Abstract
The invention discloses a method for analyzing and acquiring traffic trip rates of buildings with different properties, which comprises the following steps: firstly, processing mobile phone signaling data to obtain the traffic volume, counting land survey data in a research area, and storing the traffic volume and the total building area of buildings with different properties in a list form. And secondly, determining the research time, different travel purposes and traffic travel rates of different building types according to the research purposes. Then, the total building area of buildings with different properties in different traffic districts and traffic travel volume obtained based on mobile phone signaling data are counted, clustering analysis is carried out on the different traffic districts according to commuting population density and traffic accessibility, finally multivariate statistical regression analysis is carried out on each type of traffic districts, and traffic travel rates with different characteristics and different building types are determined. The invention realizes the acquisition of the traffic travel rate of buildings with different properties, and predicts the traffic travel condition of the planning year according to the acquired traffic travel rate.
Description
Technical Field
The invention relates to the technical field of traffic planning and travel investigation, in particular to a method for acquiring a traffic travel rate.
Background
The traffic planning can not be separated from the urban land utilization planning, which requires a scientific theoretical system as a basis in the traffic planning process. The land utilization form is used as the basis of travel prediction, so that the planning meets the future traffic demand. The interactive relationship between urban traffic and urban land utilization determines that social activities of different types and strengths can be generated by different land utilization layout forms and strengths, so that the traffic distribution amount and distribution conditions in different areas are determined. Correspondingly, the functional efficiency of the traffic system directly influences the price, rents and gas of surrounding land and influences the realization of the functions of the surrounding land. Therefore, the interrelationship between urban land utilization and traffic needs to be deeply researched in traffic planning, and the traffic trip rate is one of the important indexes for intuitively reflecting the interrelationship.
The traffic cell is called an OD (origin-destination) region (OD node), and the traffic flow is divided into traffic flows among a plurality of OD points through traffic cell division. The OD node is actually a fuzzy clustering process, and a fuzzy clustering method is needed to be adopted to divide the traffic flow into specific traffic cells according to a certain membership degree. However, in actual work, due to various reasons such as difficulty in collecting statistical data, administrative districts such as province (autonomous district), city, county (county, district) traffic hub centers and the like are generally adopted as units or highway entrances and exits are adopted as traffic districts, and specific traffic district division is determined according to requirements such as the depth of feasibility study on specific highway construction projects. For example, a national road can be used as a traffic district in county and city, a provincial highway can be used as a traffic district in city, county and county (town); a county level road can use counties, villages (towns) and villages as traffic districts. Meanwhile, when traffic cells are divided, an economic technology development area, a new technology industry area, a tourism area, an important mine or a large unit location, an important port or a transit distribution point and the like are also taken into consideration.
The travel rate model is a relation between a decision index describing the change of the travel generation amount of each land utilization and the travel generation amount thereof, and describes a quantitative rule between the self attribute of the research object and the traffic generation amount. Meanwhile, with the coming of the information age, the working and learning time system changes, and the travel production amount also changes. Therefore, traffic planners need to closely combine social development, closely pay attention to relevant system factors influencing travel, and timely correct and generate a prediction model so as to be closer to reality. Therefore, the urban traffic problem is solved, and the research on the traffic behavior of people living in the city, namely the travel characteristics of residents, cannot be conducted. Big data has been developed in various fields, and new opportunities and challenges are provided for travel rate prediction in traditional traffic planning.
According to the existing method, the travel amount is predicted through resident survey data, due to the characteristics of low sampling rate, high cost and the like of resident travel survey, the travel data is small in sample size and low in research precision, the travel rate with high precision is difficult to return through the resident travel survey data, and meanwhile due to the fact that the travel rates of different land types are different, the survey data is difficult to cover different lands in large quantity, so that the traditional travel rate survey method has obvious limitations. For example, the trip rate of a commercial building can only cover a few commercial buildings in a city in survey sampling, so the obtained trip rate cannot reflect the actual situation, and the accuracy of the trip amount prediction in the next year is obviously limited. In short, the accuracy of estimating the amount of travel by the resident survey data is not high.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method for acquiring the traffic trip rates of buildings with different properties based on mobile phone signaling data and land survey data.
The invention adopts the following technical scheme for solving the technical problems:
the invention provides a method for analyzing and acquiring traffic trip rates of buildings with different properties, which comprises the following steps:
step 1), counting the total area of buildings of different land types in different traffic districts;
step 2), acquiring traffic volume of each traffic cell based on the mobile phone signaling data;
step 3), carrying out cluster analysis on different traffic districts according to the commuting population density and the traffic accessibility;
and 4) performing multivariate statistical regression analysis on each type of traffic cell according to the analysis result of the step 3) to determine the traffic travel rates with different characteristics and different residential building types.
Further, in the method for analyzing and acquiring the travel rates of buildings with different properties, the total areas of the buildings with different land types in different traffic cells are counted in the step 1), and the method specifically comprises the following steps:
step 1.1), determining a building classification standard;
step 1.2), dividing the land in the research area range according to the boundary of the traffic cells according to the building classification standard to obtain a map layer for each traffic cell;
step 1.3), opening the map layer attribute table used in the step 1.2), sequentially adding text fields and naming numbers, inputting the numbers of corresponding traffic cells, and combining all the traffic cell layers into one layer;
and step 1.4), acquiring the total area of various buildings by taking the boundary of the traffic cell as a statistical unit.
Further, in the method for analyzing and acquiring traffic travel rates of buildings with different properties, the traffic travel amount is acquired based on the mobile phone signaling data in the step 2), and the method specifically includes the following steps:
step 2.1), creating a Thiessen polygon in the ARCGIS according to the service range of the base station, giving the trip data of the base station to the Thiessen polygon, opening an attribute list of the Thiessen polygon, and associating according to a spatial relationship;
step 2.2), after the trip population data are obtained, dividing the trip population of each Thiessen polygon by the area of each Thiessen polygon to obtain the trip population density;
step 2.3), carrying out superposition analysis INTERSECT on the traffic cell and the Thiessen polygon of the base station, calculating a generated superposition result, opening an attribute table, adding double-precision fields, and calculating the number of people going out by using a field calculator;
and step 2.4), finally carrying out spatial statistics to obtain the traffic volume of each traffic cell.
Further, in the method for analyzing and acquiring the traffic travel rates of buildings with different properties, the clustering analysis is performed on different traffic cells according to the commuting population density and the traffic reachability in the step 3), which is specifically as follows:
step 3.1), selecting classification factors according to research purposes: determining the traffic accessibility and the commuting population density of each traffic cell in the urban area as classification factors;
step 3.2), determining a clustering method: performing K-Means clustering according to the classification factors determined in the step 3.1), and finally determining the traffic cells classified into two, namely the traffic cells with high traffic accessibility and high commuting density, and the traffic cells with poor traffic accessibility and poor commuting density.
Further, in the method for analyzing and acquiring the travel rates of buildings with different properties, the step 4) is specifically as follows:
dividing the traffic subdistricts into several categories according to the result of the cluster analysis, and performing multivariate statistical regression according to the statistical result respectively to obtain the traffic generation rate; the method specifically comprises the following steps:
(1) constructing a multi-element linear equation for each traffic cell to form a plurality of equations for simultaneous solution,
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 : traffic volume of each traffic cell; beta is a n : the travel rate of each land type; x n : total area of buildings for each land type; b n : a constant term; n represents the number of traffic cells;
(2) according to the traffic cell regrouping by the control rule units, counting the traffic volume of each control rule unit, wherein the specific calculation formula is as follows:
in the formula:
B i : the output of the gauge control unit i; d ik : the kth building area of the gauge control unit i; w is a ik : the trip rate of each kth building of the control and regulation unit i;
(3) and finally, obtaining the traffic travel rate of each land type.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
(1) firstly, the invention counts the traffic volume based on the service range of the mobile phone base station and the traffic cell range. By the method, the travel amount of each traffic cell in each time period every day can be reflected, the travel characteristics of each traffic cell are analyzed, effective decision support is provided for work such as traffic planning and city planning compilation, and the method is high in applicability because various cities in the country can be analyzed.
(2) Secondly, the method for calculating the classified travel rate according to the characteristics of different traffic cells can be used for judging the specific characteristics of different traffic cells, so that a basis is provided for long-term traffic travel prediction, and the change conditions of the travel volume of various traffic cells can be calculated quickly; meanwhile, the trip rates of different traffic districts are obtained, the accuracy of the traffic trip rates is improved, and a technical guarantee is provided for accurately predicting the trip amount.
(3) Furthermore, the invention can accurately process the peak time in urban traffic. Due to the fact that the accuracy of the time attribute of the mobile phone data is high and can be accurate to seconds, after the urban trip characteristics are judged by using big data, the trip peak is specifically judged for each city, then the trip rate in the peak time period is accurately calculated, and effective judgment basis is provided for urban congestion.
(4) Finally, compared with the traditional method for acquiring the small samples based on the individual resident survey data, the method has the advantages that the sample size is large, the travel characteristics of urban residents can be comprehensively reflected, the traffic travel rate based on different building types can be acquired, and the method plays an important role in urban planning land layout and traffic planning flow prediction.
Drawings
FIG. 1 is a schematic diagram of the multivariate statistical regression statistical method provided by the present invention.
Fig. 2 is a schematic overall flow diagram of the present invention.
Fig. 3 is a schematic diagram of the total area of buildings for counting different land types of traffic cells.
Fig. 4 is a schematic diagram of traffic acquisition based on the mobile phone signaling data.
Fig. 5 is a schematic diagram showing traffic cell traffic volume.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the drawings as follows:
because the sample amount of the resident survey data is small, but the sample amount of the trip data based on the mobile phone signaling data is large, the data can be cleaned through machine learning, and the spatial distribution of the trip data can be finally obtained. Therefore, the travel rate calculation of different land areas by using the mobile phone signaling data and the land survey data is a feasible method. The key point of the invention is to establish a multivariate statistical regression model through mobile phone signaling data and land survey data to carry out relevant analysis.
And acquiring the mobile phone signaling data through a mobile phone service provider company. Through careful analysis and investigation and consultation of mobile phone service providers, the obtained mobile phone signaling data has the following three characteristics:
passive, wide coverage and non-random. The mobile phone signaling data is the position information of the mobile phone user when the network is active recorded by the operator, belongs to involuntary passive acquisition data, and when the mobile phone is started, shut down, called, receives and sends short messages, switches base stations, a mobile switching center or updates the position, the mobile phone identification number, the signaling time and the number of the cell base station at the moment are all stored in the mobile phone signaling data.
Timeliness, dynamic property and continuity. The mobile phone signaling data records daily behaviors of each user and the using mode of the urban space, can reflect the characteristics of the activity rules of the time-space behaviors of the users, realizes real-time dynamic continuous tracking and visual expression, and provides a new way for describing the time-space dynamic characteristics of activities such as urban living, employment, rest, traffic and the like.
And reflecting functional relation inside and outside the city. On the regional level, the daily places and the travel destinations can be identified from the space-time trajectory data of residents who travel across cities, and the functional relation among the cities is reflected by measuring and calculating the people flow relation among the cities. At the urban interior level, the connections between habitats, workplaces, and recreational grounds may be identified from the individual's spatio-temporal trajectories.
From the second characteristic of the mobile phone signaling data, it can be found that: determining the travel characteristics of each person is a key point. Meanwhile, only the base station where people switch services is recorded for one trip. Therefore, according to the characteristics of the mobile phone signaling data, the actual traffic travel amount needs to be screened from the mass data, and then the specific travel characteristics of each person are searched.
Next, traffic travel quantity models based on different building types are established to study specific travel characteristics: referring to fig. 1, wherein: a is public management and public service building area, B is commercial service industry facility building area, G is green land and square building area, M is industrial building area, R is residential building area, S is road and traffic facility building area, U is public facility building area, W is logistics storage building area, H is urban and rural construction building area, and traffic trip rate is expressed by beta n And expressing the trip rate of each land. The core of the algorithm for calculating the travel rates of different land is as follows: based on the characteristics of the second and third mobile phone signaling data, two times of records can be obtained for one-time effective travel, the two times of travel time can be inquired, and the travel characteristics of each person in the whole day can be respectively recorded. Then, determining the travel rate rules of different building types needs to combine the building areas X of different types of buildings n Analyzing, and obtaining the building areas of different types of buildings through land survey of a planning office to obtain the traffic trip rate beta n Building area X of different types of buildings n The amount of going of different statistical units Y n And performing multivariate statistical regression.
It should be noted that: the traffic trip rate beta n Different cities, different time periods and different building types are different, specific analysis needs to be carried out specifically for each city, the inferred trend conclusion is observed through long-term data, and the selected observation time period has generality, namely the interference of factors such as holidays and the like on passenger flow traveling is eliminated. Since the judgment is a trend judgment, certain obtained conclusions are characterized by probability judgment, although the judgment is not an absolute judgment, the method can provide thought reference and decision-making deep research for related researchers, and the thought reference and decision-making deep research and the direct conclusion of big data generally tend to be along withThe potential and appearance conclusions are consistent.
In summary, the traffic travel rate β is given n The specific meanings are as follows: if beta is n The larger the value, the larger the traffic travel per unit area.
Referring to fig. 2, the method of the present invention specifically comprises the following steps:
step 1), referring to the attached figure 3, counting the total area of buildings of different land types in different traffic districts, for example, selecting a Kun mountain central urban area as a research range, and specifically counting the building area of each building type.
The total area of the buildings in different land types of different traffic districts is counted, and the method specifically comprises the following steps:
step 1.1), determining a building classification standard: administrative office buildings, commercial buildings, residential buildings, logistics storage buildings, industrial buildings, transportation facility buildings, public facility buildings and green space square matching buildings.
Step 1.2), inquiring, dividing SPLIT (division) according to the boundary of the traffic cell in the research area range in the step 1.1), selecting a central urban area of the Kun-shan city as the research area, and dividing after determining the boundary of the traffic cell.
Step 1.3), opening the attribute table in the step 1.2), adding text fields and naming numbers, inputting the numbers of the corresponding traffic cells, and combining all the traffic cell layers into one layer MERGE. Traffic districts in urban areas in the center of Kun shan city are numbered from 1.
And step 1.4), acquiring the total area of various buildings by taking the boundary of the traffic cell as a statistical unit. In particular to the building area of various buildings in each traffic district in the urban area of the Kun shan city.
And step 2), referring to an attached figure 4, acquiring all-day mobile phone signaling data of 6 days of 2017 of Kunzhan based on the traffic volume acquired by the mobile phone signaling data, and performing data processing to acquire the actual traffic volume.
The traffic volume obtained based on the mobile phone signaling data is as follows:
step 2.1), creating a Thiessen polygon in the ARCGIS according to the service range of the base station, giving the trip data of the base station to the Thiessen polygon, opening an attribute list of the Thiessen polygon, and associating according to a spatial relationship; and (3) counting the trip data of No. 6/6 in 2017 of Kunshan city, obtaining a trip amount statistical table of each base station, and then performing spatial association with urban base stations in the center of the Kunshan city to obtain the trip amount of each base station.
Step 2.2), after the trip population data are obtained, dividing the trip population of each Thiessen polygon by the area of each Thiessen polygon to obtain the trip population density; and dividing the travel volume of each base station in the urban area of the Kun-shan city by the service range of each base station to obtain the travel population density of each base station.
Step 2.3), then carrying out superposition analysis INTERSECT (intersection) on the traffic cell and the Thiessen polygon of the base station, calculating a generated superposition result, opening an attribute table, adding a double-precision field, and calculating the number of people going out by using a field calculator; and intersecting the boundary of the traffic cell in the Kun shan city with the boundary of the service range of the base station, and numbering the base station again.
Step 2.4), and finally carrying out spatial statistics. And performing space statistics based on renumbering of urban base stations in the Kun-shan city center to obtain the traffic volume of each traffic cell. Fig. 5 is a schematic diagram showing traffic cell traffic volume.
Step 3), carrying out cluster analysis on different traffic districts according to the commuting population density and the traffic accessibility,
the method comprises the following steps of clustering and analyzing different traffic districts according to commuting population density and traffic accessibility, wherein the clustering and analyzing steps are as follows:
step 3.1), selecting a classification factor, and specifically determining according to the research purpose; and selecting the accessibility and the commuting density of each traffic cell in the urban area of the Kun shan city as classification factors.
Step 3.2), determining a clustering method. And performing K-Means (K mean value) clustering on the accessibility and the commuting density of each traffic cell in the urban area of the centre of the Kunshan city in the SPSS, and most definitely classifying the traffic cells into two, namely the traffic cells with high traffic accessibility, high commuting density, poor traffic accessibility and poor commuting density.
And 4) performing multivariate statistical regression analysis on each type of traffic cell according to the step 3) to determine the travel of different features and different residential building types.
The traffic subdistricts are divided into several types, and multivariate statistical regression is carried out according to statistical results respectively to obtain traffic generation rate. And performing multivariate statistical regression aiming at two traffic districts in the central urban area of the Kun-shan city to obtain the value of the specific traffic volume.
The following is a schematic diagram of the urban trip rate regression result in the center of Kun shan city; wherein, table 1 is the return situation of the trip rate of different land used in the central urban area, and table 2 is the return situation of the trip rate of different land used in the peripheral urban area.
TABLE 1
| Type of land used | Start at early peak | Early peak arrival | Go out all day | All day to day |
| Public management and public service land | 1.134 | 3.8075 | 7.903 ** | 8.075 ** |
| Place for business service facilities | 1.057 | 3.7685 | 16.472 ** | 17.315 ** |
| G green land and square land | 5.7805 | 2.05 | 8.551 | 2.303 |
| H land for urban and rural construction | 3.8615 | 0.8745 | 9.905 | 9.617 |
| M industrial land | 2.7885 ** | 4.5315 ** | 4.977 ** | 5.211 ** |
| Residential land for R | 1.986 ** | 0.7015 ** | 4.932 ** | 4.630 ** |
| Land for S road and traffic facilities | 14.4195 | 12.2035 | 47.872 * | 52.070 * |
| U public facility land | 3.267 | 13.18 | 9.848 | 15.112 |
| Land for W logistics storage | 1.634 | 1.468 | 3.121 | 3.596 |
Note: denotes a p value of less than 0.01, denotes a p value of less than 0.05, and the remaining p values are between 0.05 and 0.15.
TABLE 2
| Type of land used | Starting at early peak | Early peak arrival | Starting all day long | All day to day |
| Public management and public service land | 2.019 * | 1.060 * | 5.243 * | 5.099 * |
| Place for business service facilities | 1.049 ** | 0.285 ** | 1.488 * | 1.349 * |
| G green land and square land | 1.186 | 0.624 | 2.456 | 2.108 |
| H land for urban and rural construction | 0.181 | 0.140 | 0.605 | 0.538 |
| M industrial ground | 0.588 ** | 1.075 ** | 1.448 ** | 1.552 ** |
| Residential land for R | 2.439 ** | 1.129 ** | 4.461 ** | 4.232 ** |
| Land for S road and traffic facilities | 1.216 | 1.405 | 5.245 | 5.595 |
| U public facility land | 1.515 | 0.716 | 8.860 | 8.275 |
| Land for W logistics storage | 0.557 | 0.036 | 3.294 | 2.916 |
Note: denotes a p value of less than 0.01, denotes a p value of less than 0.05, and the remaining p values are between 0.05 and 0.15.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all should be considered as belonging to the protection scope of the invention.
Claims (5)
1. A method for analyzing and acquiring traffic trip rates of buildings with different properties is characterized by comprising the following steps:
step 1), counting the total area of buildings of different land types in different traffic districts;
step 2), acquiring the traffic of each traffic cell based on the mobile phone signaling data;
step 3), carrying out cluster analysis on different traffic districts according to the commuting population density and the traffic accessibility;
and 4) performing multivariate statistical regression analysis on each type of traffic cell according to the analysis result of the step 3) to determine the traffic trip rates of different residential building types.
2. The method for analyzing and acquiring the traffic travel rates of buildings with different properties according to claim 1, wherein the total area of the buildings with different land types in different traffic cells is counted in the step 1), and specifically, the method comprises the following steps:
step 1.1), determining building classification standards;
step 1.2), dividing the land in the research area range according to the boundary of the traffic cells according to the building classification standard to obtain a map layer for each traffic cell;
step 1.3), opening the attribute table of the map layer used in the step 1.2), sequentially adding text fields and name numbers, inputting the number of the corresponding traffic cell, and combining all the traffic cell layers into one layer;
and step 1.4) acquiring the total area of various buildings by taking the boundary of the traffic cell as a statistical unit.
3. The method for analyzing and acquiring traffic trip rates of buildings with different properties according to claim 2, wherein the traffic trip amount is acquired based on mobile phone signaling data in the step 2), and the method comprises the following specific steps:
step 2.1), creating a Thiessen polygon in the ARCGIS according to the service range of the base station, giving the trip data of the base station to the Thiessen polygon, opening an attribute list of the Thiessen polygon, and associating according to a spatial relationship;
step 2.2), after the trip population data are obtained, dividing the trip population of each Thiessen polygon by the area of each Thiessen polygon to obtain the trip population density;
step 2.3), carrying out superposition analysis INTERSECT on the traffic cell and the Thiessen polygon of the base station, calculating a generated superposition result, opening an attribute table, adding double-precision fields, and calculating the number of people going out by using a field calculator;
and 2.4) finally, carrying out spatial statistics to obtain the traffic volume of each traffic cell.
4. The method for analyzing and acquiring the traffic travel rates of buildings with different properties according to claim 3, wherein the clustering analysis is performed on different traffic cells according to the commute population density and the traffic accessibility in the step 3), and specifically, the method comprises the following steps:
step 3.1), selecting classification factors according to research purposes: determining traffic accessibility and commuting population density of each traffic cell in an urban area as classification factors;
step 3.2), determining a clustering method: performing K-Means clustering according to the classification factors determined in the step 3.1), and finally determining the traffic cells classified into two, namely the traffic cells with high traffic accessibility and high commuting density, and the traffic cells with poor traffic accessibility and poor commuting density.
5. The method for analyzing and acquiring the traffic travel rate of buildings with different properties according to claim 4, wherein the step 4) is as follows:
dividing the traffic subdistricts into two categories according to the result of the cluster analysis, and performing multivariate statistical regression according to the statistical result respectively to obtain the traffic trip rate; the method specifically comprises the following steps: constructing a multi-element linear equation for each traffic cell to form a plurality of equations for simultaneous solution,
in the formula (I), the compound is shown in the specification,
: traffic volume of each traffic cell;
: the travel rate of each land type;
: total area of buildings of each land type;
: a constant term; n represents the number of traffic cells.
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