CN114090554A - Division method for position and live balance area of resident travel OD - Google Patents
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Abstract
The invention discloses a division method of a position and live balance area of a resident travel OD, which comprises the following specific steps: step 1: collecting resident trip data and traffic cell geographic space data which take a traffic cell as a unit, and step 2: cleaning the acquired data to remove abnormal data; and step 3: judging cross-region travel and intra-region travel; and 4, step 4: calculating the average resident travel distance of the starting point traffic cell in each travel direction; and 5: judging the travel distance type in each traffic direction of the ith traffic cell; step 6: calculating the travel intensity and independent index of each traffic cell; judging the area type and the area characteristics of the traffic cell; and 7: and judging whether each traffic cell is a balanced occupation area or not according to the area type and the area characteristics of each traffic cell. The invention better reflects the difference of travel distances of residents in various working space types, and can provide a new idea for solving the problem of unbalanced working and dwelling from the perspective of travel of residents in traffic.
Description
Technical Field
The invention belongs to the field of division of urban occupation space.
Background
The optimizing and adjusting speed of the urban spatial layout structure lags behind the constantly accelerated urbanization process, so that the phenomenon of unbalanced occupation is caused, the travel demand and the travel distance of residents are obviously increased, and the problems of traffic jam, environmental pollution and the like are caused. In order to relieve the job and live imbalance and the travel problem caused by the job and live imbalance, before a specific improvement measure aiming at the job and live imbalance is provided, an optimization measure is provided for the job and live imbalance area in a targeted manner by combining the area development degree, the area characteristics and the job and live balance degree. Traditional job-and-live balance research focuses on regional job population distribution characteristics, and population survey data, land utilization data and the like are used for development and analysis. The method consumes manpower, is slow in data updating and is difficult to timely and accurately update the current situation of urban job and live balance.
Disclosure of Invention
The purpose of the invention is as follows: in order to solve the problems in the prior art, the invention provides a division method of a position and live balance area of a resident travel OD.
The technical scheme is as follows: the invention provides a division method of a position balance area of a resident travel OD, which specifically comprises the following steps:
step 1: collecting resident travel data and traffic cell geographic space data which take a traffic cell as a unit, wherein the resident travel data comprises a traffic cell number of a resident travel origin-destination point and resident travel quantity in each travel direction; the traffic cell geographic space data comprise longitude and latitude coordinates of a central point of each traffic cell;
step 2: cleaning the acquired data to remove abnormal data;
and step 3: if the starting points and the end points of the resident trips are all in the same traffic cell, the trips are intra-area trips; if the origin-destination points of the resident trips are not in the same traffic cell, the trips are cross-district trips;
and 4, step 4: calculating the spatial distance between the origin-destination traffic cells in each travel direction according to the longitude and latitude coordinates of the central point of the origin-destination traffic cell in each travel direction; obtaining total travel distances of all residents in the traffic travel direction, and performing mean value calculation by using the population number of the residents in the starting point traffic cell to obtain the average resident travel distance of each starting point traffic cell in each travel direction;
and 5: judging the travel distance type of each travel direction of the ith traffic cell according to the preset range and the average resident travel distance in each travel direction of the ith traffic cell; the travel distance types include: near, medium and far distances; 1, 2, 3, n is the total number of traffic cells;
step 6: calculating an independent index of each traffic cell according to the number of in-zone trips and the number of cross-zone trips of each traffic cell, and calculating the trip intensity of each traffic cell according to the trip distance type of each trip direction of each traffic cell; judging the area type of the traffic cell according to the independent index of the traffic cell, and judging the area characteristics of the traffic cell according to the travel intensity of the traffic cell;
and 7: and judging whether each traffic cell is a balanced occupation area or not according to the area type and the area characteristics of each traffic cell.
Further, in the step 5, if the average resident travel distance in a certain travel direction of the ith transportation cell is [0, x ]1]Within the range of (2), the travel distance type of the travel direction is a short distance; if the average resident travel distance in a certain travel direction of the ith traffic cell is (x)1，x2]If so, the type of the travel distance in the travel direction is a medium distance; if the average resident travel distance in a certain travel direction of the ith traffic cell is more than x2Then the travel distance type in the travel direction is long distance, x1And x2Respectively a preset medium distance threshold and a preset long distance threshold.
Further, x1＝5；x2＝10。
Further, in the step 6, the independent index of each traffic cell is calculated according to the following formula:
wherein N isiIs an independent index of the ith traffic cell, aiThe number of trips, k, of residents in the area of the ith traffic celliFor the ith trafficThe total cross-district resident trip quantity of the residential district;
calculating the travel intensity of each traffic cell according to the following formula:
wherein k isijThe total resident trip quantity of j groups of trip distances in the cross-district trip of the ith traffic cell; when j is 1, 2, 3, j is 1, a short distance is indicated, when j is 2, a medium distance is indicated, and when j is 3, a long distance is indicated.
Further, the specific method for determining the area type of the traffic cell according to the independent index of the traffic cell in the step 6 is as follows: if the independent index N of the ith traffic celliIf the area type of the ith traffic cell is more than or equal to 1, the area type of the ith traffic cell is an in-zone travel leading type; otherwise, the area type of the ith traffic cell is a cross-area travel leading type;
in the step 6, the area characteristics of the traffic cell are determined according to the travel intensity of the traffic cell, and the specific determination method is as shown in the following table 1:
TABLE 1
Wherein x and delta are both preset threshold values, and x is larger than delta.
Further, in the step 7, a traffic cell having an area type of an intra-area travel dominant type or an area characteristic of a short-distance travel dominant type is used as a work and live balance area.
Has the advantages that: the invention provides a method for dividing urban position balance areas by combining travel data and geographic information characteristics of residents in traffic districts, wherein a travel characteristic index system is constructed by utilizing the travel quantity and the travel distance of residents in each traffic district, the independence of areas and the travel distance of residents are described, the travel characteristics of the areas are reflected, and the urban position balance subarea method is constructed by combining the travel characteristics and the distribution of district positions, so that the travel distance characteristics corresponding to each position subarea are reflected, and the corresponding urban traffic service, facility arrangement and other development plans can be carried out on the basis of the travel characteristics and the distribution of the district positions. The method has the advantages that optimization measures are provided for the position and residence unbalanced area in a targeted manner, the urban position and residence balanced area is divided from the aspect of resident travel characteristics, the regional position and residence balanced degree can be identified, and meanwhile, the resident travel characteristics of the position and residence unbalanced area can be analyzed, so that relevant urban planning and traffic development suggestions are provided; the invention can better reflect the difference of travel distances of residents in various working and living space types, and explain the working and living balance degree from the daily flow angle of population, thereby providing a new idea for solving the problem of unbalanced working and living from the perspective of the transportation and travel of residents.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of the present invention for determining traffic cell zone type and zone characteristics;
FIG. 3 is a schematic diagram of determining the primary equilibrium area according to the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention.
As shown in fig. 1, the present embodiment includes the following steps:
1) collecting and processing resident trip data;
collecting resident travel data and traffic cell geographic space data which take a traffic cell as a unit, wherein a resident travel data field comprises a data collection date, a travel starting point traffic cell number, a travel end point traffic cell number and the resident travel number in the travel direction; the traffic cell data includes the number of each traffic cell, the longitude and latitude coordinates of the center of the traffic cell, the length, width and area of the cell. In specific implementation, data are provided by hundredth companies, and travel data of residents in a traffic community cover travel OD data of residents in Nanjing city of which one working day and one non-working day are randomly selected from 9 months in 2018 to 8 months in 2019 every month. The traffic cell data is geospatial data.
The collected data are processed, Python programming is utilized to clean the data, data with the trip quantity being obviously larger than or smaller than the average trip quantity of each traffic district are deleted, the effective resident trip quantity (the number of people) of each traffic district is obtained, the trip quantity of the traffic district with the starting point in the resident trip data is integrated, and the intra-district trip quantity (namely the trip quantity of the traffic district with the same origin and destination) and the cross-district trip quantity (namely the trip quantity of the traffic districts with different origins and destinations) are respectively obtained.
2) Extracting geospatial information of the data;
the specific implementation process is realized by geographic information processing software Arcgis. And importing the shp. file of the traffic cell and the resident travel data file into Arcgis, and associating the resident travel data with the traffic cell area data by utilizing an association function. Calculating the spatial distance between the origin-destination traffic cells according to the coordinates of the central point of the origin-destination traffic cells in each traffic travel direction in resident travel data by using a spatial analysis tool; and multiplying the number of the residents in the traveling direction by the space distance to obtain the total traveling distance in the traveling direction, and obtaining the average traveling distance of the residents in the starting point traffic cell in the traveling direction by the population number of the residents in the starting point traffic cell according to the total traveling distance of the starting point traffic cell in the traveling direction.
3) Grouping travel distances;
in order to summarize the travel distance characteristics of residents, discrete travel distances of the residents need to be redefined according to a certain standard, and the travel distances (the performance is the average travel distance of the residents in each travel direction of a traffic cell of a starting point) are divided into three groups, namely a short distance travel group, a medium distance travel group and a long distance travel group. According to the definition of 'happiness and commute', the proportion of commute population with the distance less than 5 kilometers can be used as an index for measuring the balance of work and live and the commute happiness of the city, so that the trip in the distance range of 0-5 kilometers is defined as short-distance trip, the 5 kilometers are used as step length for equidistant division, the trip in the distance range of 5-10 kilometers is medium-distance trip, and the trip more than 10 kilometers is long-distance trip. And grouping the travel distances of the residents in each traffic cell in each traffic direction obtained in the previous step.
In the specific implementation process, statistical analysis is carried out on the travel distances of residents by utilizing stata statistical data processing software to verify the rationality of the distance grouping, the average travel distance of the residents in a certain travel direction in Nanjing city is calculated to be 8.1km and falls into the middle travel distance grouping by taking the travel quantity as weight, the cumulative percentage of the travel distances to people is shown in a table 1, the travel distance of 50% of the residents is within 5 km, the travel distance of 75% of the residents is within 10 km, a commuting distance people cumulative percentage distribution graph generated by the grouping method is in a normal distribution and is unlikely to generate biased distribution, and the method is proved to be feasible.
TABLE 1
| Cumulative percentage of people | 25％ | 50％ | 75％ | 85％ |
| Travel distance/km | 2.35 | 5.07 | 10.78 | 14.88 |
4) Constructing a dividing basis index of the urban position and occupation balance area;
the urban occupation balance area division comprises two contents according to indexes: and (4) calculating each index value of each traffic cell by utilizing the number of trips in the residential traffic cell, the number of trips across the residential traffic cell and the trip distance groups obtained by sorting in the steps 1-3.
(1) Independent index NiThe specific calculation method comprises the following steps:
in the formula, aiNumber of in-zone trips, k, for traffic cell iiThe number of total trans-regional trips of the traffic cell i is shown, and n is the number of the traffic cells.
(2) Intensity of travel fijThe specific calculation method comprises the following steps:
in the formula, kijThe number of people (also the number of resident trips) belonging to j groups is the trip distance in the cross-district trip of the traffic district i, and n is the number of the traffic districts; when j is 1, the short distance is indicated, when j is 2, the medium distance is indicated, and when j is 3, the long distance is indicated.
5) Building a city position balance partition model based on travel characteristics;
as shown in fig. 2, according to the travel quantity and distance characteristics of residents, a position and residence balance partition model based on the travel characteristics of the traffic cell is provided, and the specific partitioning steps are as follows:
(1) partitioning region types by independent indices
And dividing the traffic cells into two types according to the independence degree according to the calculation result of the independence index: the cross-region travel leading traffic cell and the intra-region travel leading traffic cell are shown in table 2.
TABLE 2
| Independent index | Region type | Degree of independence |
| Ni＞1 | Zone-inside travel leading type | Is preferably used |
| Ni＜1 | Cross-region travel leading type | Is poor |
(2) Travel intensity zoning features grouped by travel distance
Further comparing the travel intensity of each travel distance group aiming at the traffic cell with the cross-region travel leading, and obtaining the travel distance characteristics of the cell leading, wherein the travel distance characteristics are divided into 7 types: a short-distance travel dominant type, a medium-distance travel dominant type, a long-distance travel distance dominant type, a medium-short distance travel dominant type, a medium-long distance travel dominant type, a two-end dominant type, and a travel distance balanced type, as shown in table 3.
TABLE 3
(3) Division of job and live
And dividing the urban job and live balance partition types by combining the resident travel characteristics and the geographic spatial characteristics of the traffic community, and specifically dividing the urban job and live balance partition types into 5 types of balanced job and live, unbalanced job and live, seriously unbalanced job and live, unbalanced general type and live balance-separation mixture. The specific classification flow is shown in fig. 3.
According to the division method, the occupation balance area of Nanjing City is shown in Table 4.
TABLE 4
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. The invention is not described in detail in order to avoid unnecessary repetition.
Claims (6)
1. A division method for a position balance area of a resident travel OD is characterized by comprising the following steps:
step 1: collecting resident travel data and traffic cell geographic space data which take a traffic cell as a unit, wherein the resident travel data comprises a traffic cell number of a resident travel origin-destination point and resident travel quantity in each travel direction; the traffic cell geographic space data comprise longitude and latitude coordinates of a central point of each traffic cell;
step 2: cleaning the acquired data to remove abnormal data;
and step 3: if the starting points and the end points of the resident trips are all in the same traffic cell, the trips are intra-area trips; if the origin-destination points of the resident trips are not in the same traffic cell, the trips are cross-district trips;
and 4, step 4: calculating the spatial distance between the origin-destination traffic cells in the traffic travel direction according to the longitude and latitude coordinates of the central point of the origin-destination traffic cell in each travel direction, multiplying the spatial distance by the number of all residents in the travel direction to obtain the total travel distance of all the residents in the traffic travel direction, and then performing mean value calculation by using the population number of the residents in the traffic cell at the starting point to obtain the average resident travel distance of each traffic cell at the starting point in each travel direction;
and 5: judging the travel distance type of each travel direction of the ith traffic cell according to the average resident travel distance in each travel direction of the ith traffic cell and a preset range; the travel distance types include: near, medium and far distances; 1, 2, 3, n is the total number of traffic cells;
step 6: calculating an independent index of each traffic cell according to the number of in-zone trips and the number of cross-zone trips of each traffic cell, and calculating the trip intensity of each traffic cell according to the trip distance type of each trip direction of each traffic cell; judging the area type of the traffic cell according to the independent index of the traffic cell, and judging the area characteristics of the traffic cell according to the travel intensity of the traffic cell;
and 7: and judging whether each traffic cell is a balanced occupation area or not according to the area type and the area characteristics of each traffic cell.
2. The method as claimed in claim 1, wherein the average resident travel distance in the travel direction of the i-th transportation cell in the step 5 is [0, x ]1]Within the range of (2), the travel distance type of the travel direction is a short distance; if the average resident travel distance in a certain travel direction of the ith traffic cell is (x)1，x2]If so, the type of the travel distance in the travel direction is a medium distance; if the average resident travel distance in a certain travel direction of the ith traffic cell is more than x2Then the travel distance type in the travel direction is long distance, x1And x2Respectively a preset medium distance threshold and a preset long distance threshold.
3. The method as claimed in claim 2, wherein x is x1＝5；x2＝10。
4. The method as claimed in claim 1, wherein the step 6 is to calculate the independent index of each traffic cell according to the following formula:
wherein N isiIs an independent index of the ith traffic cell, aiThe number of trips, k, of residents in the area of the ith traffic celliThe total number of the residents in the i-th traffic cell traveling across the area;
calculating the travel intensity of each traffic cell according to the following formula:
wherein k isijThe total resident trip quantity of j groups of trip distances in the cross-district trip of the ith traffic cell; when j is 1, 2, 3, j is 1, a short distance is indicated, when j is 2, a medium distance is indicated, and when j is 3, a long distance is indicated.
5. The dividing method for the occupation balance area of the resident travel OD according to claim 1, wherein the specific method for determining the area type of the transportation cell according to the independent index of the transportation cell in the step 6 is as follows: if the independent index N of the ith traffic celliIf the area type of the ith traffic cell is more than or equal to 1, the area type of the ith traffic cell is an in-zone travel leading type; otherwise, the area type of the ith traffic cell is a cross-area travel leading type;
in the step 6, the area characteristics of the traffic cell are determined according to the travel intensity of the traffic cell, and the specific determination method is as shown in the following table 1:
TABLE 1
Wherein x and delta are both preset threshold values, and x is larger than delta.
6. The method as claimed in claim 5, wherein the transportation district with the district type of the intra-district travel dominant type or the district characteristic of the short-distance travel dominant type is used as the accommodation balance area in the step 7.
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| CN115222268A (en) * | 2022-07-25 | 2022-10-21 | 广州市城市规划勘测设计研究院 | Method, device, equipment and medium for evaluating space-time scale of balance of position and occupation of area |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115222268A (en) * | 2022-07-25 | 2022-10-21 | 广州市城市规划勘测设计研究院 | Method, device, equipment and medium for evaluating space-time scale of balance of position and occupation of area |
| CN115222268B (en) * | 2022-07-25 | 2023-04-18 | 广州市城市规划勘测设计研究院 | Method, device, equipment and medium for evaluating space-time scale of regional position balance |
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