CN111105130A - Optimal employment post layout site selection decision-making system based on commuting model - Google Patents
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Abstract
The invention provides an optimal employment post layout site selection decision-making system based on a commuting model, which is used for generating an employment post layout suggestion so as to help an analyst to complete the layout site selection decision of recent employment posts in city planning, and is characterized by comprising the following steps: the system comprises a commuting model storage part, a city data storage part, a simulation range acquisition part, a simulation space retrieval acquisition part, a commuting cost calculation part, a commuting numerical value calculation part and a planning suggestion generation output part, wherein the commuting model storage part is used for storing residual commuting models and unit commuting models which respectively correspond to all city space units in a city, the commuting cost calculation part is used for calculating commuting cost according to the residual commuting models or the unit commuting models, the commuting numerical value calculation part is used for calculating commuting evaluation values of all the simulation space units before and after a post simulation value is added according to all the commuting costs corresponding to each simulation space unit in sequence, and the planning suggestion generation output part is used for generating corresponding recent employment post layout suggestions according to the commuting evaluation values and outputting.
Description
Technical Field
The invention belongs to the field of urban planning, and particularly relates to an optimal employment post layout site selection decision-making system based on a commuting model.
Background
Urban planning is a combined problem-oriented and target-oriented process, and in domestic planning practice, the position of target-oriented (goal-oriented) is often more prominent. Some target vision is often set in the planning, which is achieved by certain strategies. However, whether the implemented strategy can achieve the expected effect is difficult to answer by planners in the traditional planning practice, and quantitative judgment can be realized only by means of a model. At present, in the field of domestic planning practice, few model systems for assisting planning decisions exist, and the conditions which may appear in the future are usually judged based on the experience of decision makers such as planners.
Under the traditional planning method, the planning layout of the employment space roughly comprises two steps: firstly, determining the approximate scale of employment posts according to the macroscopic economic development indexes of cities; then, the specific layout of employment posts is designed by means of the professional knowledge of the planner. This practice is in fact highly subjective. Planning is essentially future-oriented, and knowledge and experience of planners are effective in a short time, so that planning practice can be guided to a certain extent, but uncertainty increases in a long time; meanwhile, qualitative judgment based on experience can achieve certain effect in the aspect of a macroscopic urban development direction and an urban spatial structure, but under the situation of finer scale and more complex, the experience judgment is often unable to do.
In current urban planning practice, with popularization of big data, application of big data for urban research is more and more common. However, the application of the big data is basically limited in the aspect of the current state description of the city, and the prediction and simulation of the city space are performed by further applying the big data. Particularly, on the aspect of urban employment space planning, the big data is used for analyzing the current residence and employment distribution of the city, the average commuting time and the commuting distance of residents, the judgment of the separation condition of jobs and the like, and based on the current situation analysis, the requirements of which areas on employment posts are more urgent can be qualitatively sensed. However, the situation is often unknown after the number of employment opportunities increases. In fact, the change of employment space layout directly results in the change of resident commute distribution, and further results in the change of resident commute time and commute distance, thereby having influence on urban traffic, and the influence which cannot be calculated easily makes the planning and actual effect of analysts (such as planners and government-related decision makers and the like) have errors.
Disclosure of Invention
In order to solve the problems, the invention provides a refined commuting model system so as to assist an analyst in making a site selection decision for the arrangement of employment posts in urban planning, and the invention adopts the following technical scheme:
the invention provides an optimal employment post layout site selection decision-making system based on a commuting model, which is used for generating an employment post layout suggestion so as to help an analyst to complete the layout site selection decision of a recent employment post in urban planning, and is characterized by comprising the following steps: a commuting model storage part for storing residual commuting models respectively corresponding to each city space unit in the city; a city data storage part for storing unit information of each city space unit and corresponding unit commuting data; a simulation range acquisition unit for acquiring a commute simulation range input by an analyst; the simulated space retrieval acquisition part is used for retrieving the city data storage part according to the commuting simulation range and acquiring a corresponding city space unit as a simulated space unit according to corresponding unit information; the commute cost calculation part is used for calculating the commute data of the units and the pre-acquired post analog values according to the residual commute model so as to obtain the commute cost of other analog space units corresponding to each analog space unit before and after the post analog values are increased; the commute value calculating part is used for calculating commute evaluation values of each simulation space unit before and after the post simulation value is added according to all commute costs corresponding to each simulation space unit in sequence; and the planning suggestion generation output part is used for generating a corresponding recent employment position layout suggestion according to the commuting evaluation value and outputting the suggestion to the analyst.
The optimal employment post layout site selection decision-making system based on the commuting model provided by the invention can also have the technical characteristics that the formula of the residual commuting model is as follows: lnTij＝ κi+αilnNj+βilndij+∑kαkD_SEk+εijIn the formula, TijFor the sky of a cityThe commute amount between the cells, i represents the ith departure place cell, j represents the jth employment place cell, NjNumber of employment posts for jth employment unit, dijFor the commute cost between the ith origin unit and the jth employment unit, αiEmployment position influence coefficient for the ith departure location unit, βiIs the cost decay factor, κ, of the ith origin unitiIs a constant term of the ith departure location, epsilonijIs the residual error of the commute amount between the ith departure place unit and the jth employment place unit, D _ SEkIs a residual virtual variable corresponding to the k-th class of cluster type, k being taken to be [0,1,2, 3]]，D_SEkIs taken as value of [0,1]，αkAre the corresponding residual coefficients.
The invention also provides an optimal employment post layout site selection decision system based on the commuting model, which is used for generating an employment post layout suggestion so as to help an analyst to complete the layout site selection decision of a distant employment post in city planning, and is characterized by comprising the following steps: a commuting model storage part for storing unit commuting models respectively corresponding to each city space unit in the city; a city data storage part for storing unit information of each city space unit and corresponding unit commuting data; a simulation range acquisition unit for acquiring a commute simulation range input by an analyst; the simulation space retrieval acquisition part is used for retrieving the city data storage part according to the commuting simulation range and acquiring a corresponding city space unit as a simulation space unit according to the unit information; the commute cost calculation part is used for calculating the unit commute data and the pre-acquired post analog numerical value according to the unit commute model so as to obtain the commute cost of other analog space units corresponding to each analog space unit before and after the post analog numerical value is increased; the commute numerical value calculation part is used for calculating commute evaluation numerical values of each simulation space unit before and after the post simulation numerical value is added according to all commute costs corresponding to each simulation space unit in sequence; and the planning and construction generation output part is used for generating a corresponding remote employment position layout suggestion according to the commuting evaluation value and outputting the layout suggestion to an analyst.
The optimal employment post layout site selection decision-making system based on the commuting model provided by the invention can also have the technical characteristics that the formula of the unit commuting model is as follows: lnTij＝ κi+αilnNj+βilndij+εijIn the formula, TijFor the commute amount between city space units, i represents the ith departure place unit, j represents the jth employment place unit, NjNumber of employment posts for jth employment unit, dijFor the commute cost between the ith origin unit and the jth employment unit, αiEmployment position influence coefficient for the ith departure location unit, βiIs the cost decay factor, κ, of the ith origin unitiIs a constant term of the ith origin unit, epsilonijIs the residual of the commute volume between the ith origin unit and the jth employment unit.
The optimal employment post layout site selection decision-making system based on the commuting model provided by the invention can also have the technical characteristics that: a signaling data storage part, an allocation weight storage part, a commuting data analysis and acquisition part and a commuting data allocation part, wherein the signaling data storage part stores mobile phone signaling data of each mobile phone base station in a city acquired in advance, the distribution weight storage part stores distribution weights of each mobile phone base station corresponding to a predetermined number of city space units nearest to each periphery, the commute data analysis acquisition part analyzes the mobile phone signaling data to acquire resident commute data of a departure place base station and a employment place base station including residents, and the commute data distribution part sequentially distributes the departure foundation station and the employment place base station in the resident commute data to the surrounding city space units according to the distribution weights, the city data storage unit stores the unit commute data in correspondence with the unit information.
The optimal employment post layout site selection decision system based on the commuting model provided by the invention can also have the technical characteristics that the commuting cost is the commuting distance or the commuting time, and the commuting evaluation value is the mean value, the standard deviation, the quantile, the kurtosis or the skewness calculated according to the commuting cost.
The optimal employment post layout site selection decision-making system based on the commuting model provided by the invention can also have the technical characteristics that the city space unit is a living and committee unit consisting of living and committee ranges divided in a city.
Action and Effect of the invention
According to the optimal employment post layout site selection decision system based on the commuting model, the construction of the sub-unit model is realized through the mobile phone signaling data, the commuting cost of each urban space unit corresponding to other urban space units is calculated through the increase of the post simulation value, and the commuting evaluation values of each urban space unit before and after the increase of the post simulation value are further obtained according to the commuting cost, so that the commuting effect which can be generated by respectively increasing the post number of each urban space unit is obtained through the change of the commuting evaluation value, and an analyst is helped to complete the layout site selection decision of employment posts in urban planning, and the greatest employment post layout utility is easier to select. Meanwhile, the adopted model is a residual commuting model, so that the fitting goodness is higher, and the accuracy and the reliability are higher in short-term planning.
In addition, the optimal employment post layout site selection decision system based on the commuting model can also adopt a unit commuting model, and although the fitting goodness of the model is lower relative to the residual commuting model, the optimal employment post layout site selection decision system can provide more stable commuting data calculation in long-term planning and has a certain accuracy rate, so that the system can also realize long-term planning of city planning layout based on the unit commuting model.
Drawings
FIG. 1 is a block diagram of an optimal employment position layout addressing decision system in an embodiment of the present invention;
FIG. 2 is a schematic diagram of a commute data set in an embodiment of the present invention;
FIG. 3 is a schematic diagram of the effect of employment opportunities on commuting flow in an embodiment of the present invention;
FIG. 4 is a histogram of average commute time variation values of affected units after a target unit has increased its position in an embodiment of the present invention;
FIG. 5 is a schematic diagram of the Shanghai city employment space layout _ near term with the most reduction in commuting distance in an embodiment of the present invention;
FIG. 6 is a schematic diagram of the offshore employment space placement _ forward phase with the most reduction in commuting distance in an embodiment of the present invention; and
fig. 7 is a flow chart of a commute simulation process in an embodiment of the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the following describes the optimal job site layout location decision system based on the commuting model in detail with reference to the embodiments and the attached drawings.
< example >
Fig. 1 is a block diagram of a location selection decision system for an optimal employment post layout in an embodiment of the present invention.
As shown in fig. 1, the optimal employment post layout site selection decision system 100 includes a signaling data storage unit 11, a city data storage unit 12, an allocation weight storage unit 13, a unit calculation control unit 14, a commuting data analysis acquisition unit 15, a commuting data allocation unit 16, a commuting model storage unit 17, a simulation range acquisition unit 18, a simulation space search acquisition unit 19, a commuting cost calculation unit 20, a commuting numerical value calculation unit 21, a planning advice generation output unit 22, a screen storage unit 23, an input display unit 24, a system communication unit 25, and a system control unit 26 that controls the above-described respective units.
The system communication unit 25 exchanges data between the components of the optimal employment post placement and addressing decision system 100 and between the optimal employment post placement and addressing decision system 100 and other systems, and the system control unit 26 stores a computer program for controlling the operation of each component of the optimal employment post placement and addressing decision system 100.
The signaling data storage unit 11 stores base station information of each mobile phone base station in a city and corresponding mobile phone signaling data. The base station information is the ID number of the mobile phone base station, and the base station information and the mobile phone signaling data are acquired in advance through connection with the mobile phone base station database and stored in the signaling data storage portion 11, or are imported by the user and stored in the signaling data storage portion 11.
In this embodiment, the mobile phone signaling data includes mobile phone information (for example, a mobile phone number) of the mobile phone, time information (that is, time when communication occurs), and base station information (for example, a base station number) during communication. The mobile phone signaling data is mobile phone signaling generated when each mobile phone base station in a city communicates with mobile phones of each resident (namely, a mobile phone holder). When the resident mobile phone is turned on or off, receives and sends short messages and receives and calls, the resident mobile phone and the base station carry out 'information exchange', namely, the mobile phone is recorded with a point by the surrounding specific base station (space position) at a specific time (action occurrence time) to obtain the space-time information. If the resident has no action, the position of the mobile phone is updated periodically, namely, the periodic position updating is carried out every 2 hours, namely, even if the mobile phone of the resident is not used, a point is recorded every 2 hours.
In this embodiment, the base station information and the mobile phone signaling data stored in the signaling data storage unit 11 are collected and stored by an analyst in advance.
The city data storage unit 12 stores city information of a city, unit information of each city space unit corresponding to the city, commuting cost between each city space unit, and unit commuting data of each city space unit.
In this embodiment, the city space unit is a living committee unit configured by living committee ranges divided in a city, and the city information, the unit information, and the commute cost are acquired in advance by public city planning data (for example, city information published by the sixth national census). The city information is a name of a city, and the unit information is a name of a living committee unit (or identification information such as an ID number) and a division range of each living committee unit. The commute cost is the commute distance or the commute time required by the residents to commute between two urban space units.
In this embodiment, the unit commute data is calculated by the unit calculation control unit 14 controlling the relevant components according to the mobile phone signaling data of each mobile phone base station in the city, which will be described in detail below.
The distribution weight storage unit 13 stores distribution weights for each mobile base station, the distribution weights being a weight ratio of distribution of the resident commute data of one mobile base station to 30 nearest resident units.
In this embodiment, the allocation weight is calculated in advance based on the adjacent distance between the cell phone base station and the surrounding residence committee unit, and the calculation method includes:
simultaneously, the following requirements are met:
in the formula (I), the compound is shown in the specification,
is the assigned weight of the cell phone base station with number i obtained by the residence committee space unit with number k, d(i)kIs the adjacent distance between the base station i and the centroid of the living committee unit k, and θ is the bandwidth distance. Equation (2) ensures that the sum of the assigned weights calculated by equation (1) is 1.
In this embodiment, the bandwidth distance is 4km, and the bandwidth distance is the maximum value of the adjacent distance (i.e., the bandwidth distance is 4km when the adjacent distance is greater than 4km), and the allocation weights of each mobile phone base station and the 30 peripheral residence committee units corresponding to the mobile phone base station can be obtained through the formulas (1) and (2).
The unit calculation control unit 14 is configured to control component operations related to the unit commute data generation process, that is, to: when the signaling data is acquired, the commute data analyzing and acquiring part 15 is controlled to analyze the mobile phone signaling data so as to sequentially acquire resident commute data corresponding to the mobile phone base station, and the commute data distributing part 16 is further controlled to distribute the resident commute data corresponding to the mobile phone base station to the urban space units according to the distribution weight so as to obtain unit commute data corresponding to the urban space units.
In this embodiment, the commute data analysis and acquisition unit 15 first counts the mobile phone signaling data according to the mobile phone information, thereby obtaining the mobile phone signaling data of each resident in two weeks. Furthermore, for the mobile phone signaling data of each resident, the resident life track is analyzed sequentially according to the time information in the mobile phone signaling data, so that the resident residence place (namely, the base station corresponding to the departure place) and the working place (namely, the base station corresponding to the employment place) are identified.
Specifically, if the point at which the resident is recorded at night (8 pm to 6 pm) is relatively fixed (referred to as "nighttime high-frequency recording point"), it can be considered that this point (i.e., the location information corresponding to the cellular phone base station) is the place where the resident resides. Similarly, if the point at which the resident is recorded during the day (9 am to 6 pm) is relatively fixed (referred to as "daytime high frequency recording point"), it is highly likely that this point is the place where the resident works. Namely: the night high-frequency recording point represents a residence, and the day high-frequency recording point represents a work place.
Therefore, the commute data analysis and acquisition unit 15 can obtain the departure base station and the employment base station from the mobile phone signaling data analysis, and further generate the resident commute data. The resident commute data comprises departure place base station information corresponding to a departure place, corresponding departure time, employment foundation station information corresponding to an employment place, corresponding employment time and mobile phone information.
In the present embodiment, the commute data allocation unit 16 allocates the departure base station and the employment base station to the corresponding place of residence committee units, that is, the place of departure residence committee unit and the employment place of residence committee unit, at the same time when allocating the resident commute data. As shown in fig. 2, the finally obtained unit commute data includes a departure place committee element number pcq _ O (i.e., unit information of the departure place committee element) corresponding to the departure place and a place of employment corresponding to the place of employmentThe unit number pcq _ D of the place of employment unit (i.e., unit information of the place of employment unit), the total resident population num _ home _ O of the place of departure unit (obtained from the number of pieces of mobile phone information assigned to the place of departure unit), and the total employment position num _ work _ D of the place of employment unit (obtained from the number of pieces of mobile phone information assigned to the place of employment unit, hereinafter abbreviated as N)j) The commute amount num between the departure place and the work place (obtained from the number of mobile phone messages between the current departure place and destination place and committee unit, hereinafter abbreviated as T)ij) Commuting distance dist (obtained from the distance between the origin base station and the place of employment base station, hereinafter abbreviated as d)ij) The system comprises the following steps of (1) automobile commuting time (namely automobile commuting) dura _ car and bus commuting time dura _ bus (the commuting time is obtained through distance and preset commuting speed conversion). The residual error commuting model of this embodiment adopts the distance of commuting, and in other embodiments, the residual error commuting model still can adopt car commuting time or public transit commuting time as the cost of commuting. In addition, in this embodiment, the data of the commute time and the commute distance are obtained by batch capturing from the map service software (such as a high-grade map) through the web crawler.
The commute model storage unit 17 stores a unit commute model and a residual commute model for each city.
In this embodiment, both the residual commute model and the unit commute model may be used to make a site selection decision on job site layout, and preferably, the residual commute model is more suitable for performing near-term prediction, and the unit commute model is more suitable for performing long-term prediction.
Specifically, the residual commute model is of the form:
lnTij＝κi+αilnNj+βilndij+∑kαkD_SEk+εij(3)
in the formula, TijFor the commute amount between city space units, i represents the ith departure place unit, j represents the jth employment place unit, NjFor the jth employment unitNumber of job sites, dijFor the commute cost between the ith origin unit and the jth employment unit (the commute distance is used in the present embodiment), αiIs the employment position influence coefficient of the ith departure place unit, the coefficient is positive under the normal condition, βiIs the distance attenuation coefficient of the ith origin unit, normally the coefficient is negative, kiIs a constant term of the ith origin unit, epsilonijIs the residual of the commute amount between the ith departure location unit and the jth employment location unit. D _ SEkIs a residual virtual variable corresponding to the k-th class of cluster type, k is taken to be [0,1,2, 3]]，D_SEkIs taken as value of [0,1]， αkAre the corresponding residual coefficients. (wherein ln represents taking logarithm of corresponding variable, which is equivalent to making a numerical transformation of original variable, and making a transformation of commute amount, resident population, employment post and commute distance in the formula.)
In this embodiment, the residual virtual variable D _ SEkThe unit commute model is obtained by calculating the unit commute model for multiple times in advance, and has the following form:
lnTij＝κi+αilnNj+βilndij+εij(4)
in the formula, the meaning of the parameters is similar to that in the residual commute model of formula (3).
Meanwhile, it is worth noting that the employment position coefficient α in the formulas (3) and (4) of each city space unit is divided into units due to the fact that the unit commuting model and the residual commuting model are constructediAnd distance attenuation coefficient βiAll are different, and subscript i corresponds to each cell, respectively.
Calculating the commute amount between a specific urban space unit (which can be any one urban space unit in a city) and other urban space units according to a unit commute model of formula (4) in advance, and obtaining a residual error { R ] by combining the actual commute amount calculation difference valuen，Xn，YnIn which R isnRepresenting the corresponding nth city spaceAbsolute number of residual errors of a unit, XnAnd YnRepresenting the plane coordinates (i.e., latitude and longitude) of the nth city space unit. Taking an arbitrary place between two places as an example, A is a starting place, B is a destination, and an actual commuting amount, abbreviated as T, exists between the two places1Second, for A, the predicted commute amount, abbreviated as T, can be calculated by the unit-by-unit basis model2Residual namely (T)1- T2) Abbreviated as R. For the same departure point A, to a different destination B (B)1、B2、 B3……Bn) Are different in residual error and are respectively marked as R1、R2、R3……RnSince the spatial position of each destination is different, each residual has a spatial position attribute, and the value of the residual and the spatial position attribute are denoted as { Rn，Xn，Yn}。
Further, the residual { R }n，Xn，YnClustering is carried out in a spatial clustering mode (specifically, a local spatial autocorrelation computing tool built in ArcGIS is adopted, and a python writing cyclic algorithm is adopted to sequentially compute about 5000 living committee units in Shanghai city), so that 4 types of clustering results, namely high-high clustering (HH cluster), low-low clustering (LL cluster), high-low clustering (HL cluster) and low-high clustering (LH cluster), are obtained according to the height of two residuals of each city space unit (namely, residuals from residents starting from a specific city space unit to employment of one city space unit and residuals of all units around the city space unit). For example, a space unit is located along a subway, so that most of the employment of residents in the space unit is likely to be located along the subway, and the residual error along the subway may be high, and the subway is a high-level cluster. Finally, carrying out variable quantization processing on the 4 types of clustering results to obtain a residual virtual variable D _ SEk。
In this embodiment, the unit commute model and the residual commute model stored in the commute model storage unit 17 are as follows, compared with the conventional global commute model:
TABLE 1 comparison of conventional Global model, base model and residual model
As shown in Table 1, the average goodness of fit of the residual model reaches 0.92, which is far higher than 0.76 of the unit commuting model and 0.65 of the global model, and the improvement of the goodness of fit means that the prediction effect is greatly improved. The residual model also has certain limitations. First, the residual model can basically be considered "unexplained", but because these residuals have certain characteristics in their spatial distribution, and the space can correspond to the actual things (variables), the result has a certain reliability. Second, the "special link" characterized by a spatially significant set of residuals is stable only in the short term, but changes in the long term, so the prediction horizon of the residual model is limited to the near term. In contrast, the unit commute model is more stable in the long term range, although the average goodness of fit is lower, and therefore can be used as a prediction term for the long term. It is worth mentioning that no definite boundary exists between the near term and the long term, and the actual application can be adjusted according to the requirement. For example, 1-5 years may be considered recent, and 5-15 years may be considered long-term.
The simulation range acquisition unit 18 is configured to acquire a commute simulation range input by the analyst.
In actual use, when an analyst uses the system to perform analysis, a certain research area is usually selected according to actual needs. In the present embodiment, the simulation range acquisition unit 18 can acquire the commute simulation range input by the analyst through the input display unit 24, and the simulation space search acquisition unit 19 can determine the simulation range in the system operation.
The simulated space search acquisition unit 19 is configured to search the city data storage unit 12 according to the commute simulation range, and thereby acquire all the city space units within the commute simulation range as simulated space units according to the division range of each city space unit in the city data storage unit 12.
In the present embodiment, the optimal employment position layout space of the shanghai city is calculated as an example, and therefore the commuting simulation range selects the entire shanghai city area, that is, the simulation space unit is all the living committee units of the entire shanghai city.
In other embodiments, different spatial ranges may be selected by the analyst depending on the planning horizon. For example, for employment planning of the populus area, a spatial unit of the full populus area can be selected for simulation; when site selection comparison of employment posts is carried out on a plurality of industrial parks, the spatial units of the industrial parks can be independently selected for simulation.
The commute cost calculation unit 20 is configured to calculate the unit commute data and the post simulation value obtained in advance based on the residual commute model (or the unit commute model), and obtain the commute cost of another simulated space unit (hereinafter referred to as an affected unit) corresponding to each simulated space unit (hereinafter referred to as a target unit) before and after the post simulation value is added.
In this embodiment, the commute cost calculation unit 20 calculates the unit commute data based on the residual commute model (or the unit commute model) to obtain the original commute cost (i.e., the commute cost before adding the station simulation value, which is hereinafter referred to as the original commute cost).
In this embodiment, the post simulation value is a value pre-stored in the computer, and in this embodiment, the value is "+ 10000", that is, 10000 work posts are added to a certain city space unit (similarly, it may be also pre-set to "+ 10000", that is, the comparison condition is set to reduce 1 ten thousand employment posts, and the subsequent principle is similar). Further, when generating the commuting cost, the system only operates one of the simulated space units (namely, the target unit) during each simulation, namely, the number of posts of the unit is increased by 10000, while the number of posts of other units is unchanged, and the model calculation result is substituted into the result to obtain the commuting cost (namely, the commuting cost after increasing the post simulation value, which is hereinafter referred to as the changed commuting cost) of each other urban space unit (namely, affected unit) after the unit is increased; in this manner, the commute cost calculation unit 20 successively simulates 4991 space simulator units in Shanghai city in order.
In another embodiment, the analyst may input the station value as the station simulation value used by the commute cost calculation unit 20 through the input display unit 24 according to actual needs.
The commute numerical value calculation unit 21 is configured to calculate commute evaluation numerical values before and after the post simulation numerical value is added for each simulation space unit in turn based on all commute costs corresponding to each simulation space unit.
The commute evaluation value is a mean, a standard deviation, a quantile, a kurtosis or a skewness calculated according to the commute cost. For convenience, the commute evaluation value of this embodiment is averaged, i.e. the average commute time, and the calculation formula of the average commute time of the ith simulated space unit is as follows:
wherein the content of the first and second substances,
is the initial average commute time, T, of the i unitijIs the amount of commuting, dijThe commuting time (commuting distance is the same), j is the employment place, j is equal to (1,2, … …, n), when the employment position of any j unit is increased, the corresponding commuting amount is also increased, and T is changed into Tij+ΔTijThe average commuting time will also become
By calculating the difference between the two
A probability distribution is obtained.
In this embodiment, the commute value calculation unit 21 obtains the original average commute time and the changed average commute time corresponding to 4991 target units in order from the original commute cost and the changed commute cost obtained by 4991 simulations performed by the commute cost calculation unit 20.
In the calculation process of the commuting cost calculation unit 20 and the commuting cost calculation unit 20, in order to solve the problem of "which unit or units with 1 ten thousand new posts distributed in the sea have the best performance", it is necessary to determine the performance evaluation criteria of each unit after the post addition. Taking the average commute time as an example, if the new posts are distributed in certain units to shorten the average commute time of the whole city to the maximum extent, the performance of the units is the best. Theoretically, if a certain number of employment sites are added to any unit (i.e., target unit), the commuting amount of the unit (i.e., affected unit) currently in commuting connection with the unit changes, and because the residual commuting model and the unit commuting model of the embodiment are sub-unit models capable of corresponding to each unit, even if the same variation amount is generated in each unit, the influence magnitude is different, which is the key point that the sub-unit model is superior to the conventional global model.
It is noted that for each affected unit, there is only one affected commute flow, i.e., the commute flow with the target unit varies, and the commute flow with the other units is constant. For example, as shown in FIG. 3, after target unit A has increased position, the commute flow of x, y, z, etc. units to A changes, while the commute flow to other units B, C, D does not change.
Further, to compare the amount of commuting impact on other units before and after each target unit increases employment opportunities, it is necessary to aggregate the performance of all affected units to the target unit to form the "performance" of the target unit (i.e., calculate the commute rating value). Thus, the present embodiment shows a basic idea, namely: the average commute time before each unit change is first calculated from the model, totaling 4991 units meaning a total of 4991 commute times, referred to as the initial average commute time. After the employment position number of a certain unit (hereinafter referred to as a target unit A) is increased, calculating each affected unit to be in 'new' commuting connection with the affected unit, recalculating new average commuting time of each unit, comparing the new average commuting time with an initial value, obtaining the change value of the average commuting time of each affected unit after the position of the target unit A is increased, wherein the change values can be understood as a distribution (distribution) in probability, and the performance of the position increase of the target unit A can be quantitatively judged by examining the distribution of the change values. As shown in fig. 4, in order to increase the distribution of the time variation values of other city space units after 1 ten thousand employment sites are added to the target unit, it can be seen from fig. 4 that most of the average commute time of the units does not change much, and the average commute time of 90% of the units is concentrated in the range of [ -0.03,0.03], which means that the influence of changing the number of employment sites of only one unit is very limited, but the comparison between different target units is meaningful.
The planning suggestion generation output unit 22 is used for generating a near-term (or far-term) employment position layout suggestion corresponding to each simulated space unit (i.e. target space) according to the commute evaluation values (i.e. the original average commute time and the changed average commute time) and outputting the suggestion to the analyst.
In this embodiment, the recent (or distant) employment position layout suggestion includes each simulation space unit and a performance amplitude after the employment position is correspondingly added, where the performance amplitude may be a numerical value capable of reflecting a change degree of the commuting evaluation value, such as an average commuting time change amplitude, an average commuting distance change rate, a position demand degree, and the like.
For example, the recent simulation result (residual commuting model) of this embodiment is shown in fig. 5, where the Bay-pentagon field, Jinqiao, Zhang jiang-Chuansha and Shenzhuang-Qibao all belong to the city sub-center level, and Chuansha and Shenzhuang belong to the new main city region of Shanghai city (additionally, the hong qiao and Baoshan new city, which are called "main city slice region"), for this region, the Shanghai general rule (2017 + 2035) requires to accelerate the industry transformation and spatial adjustment, and increase employment opportunities appropriately to promote the product city integration. In addition, the Luoshan-Gucun, Jinqiao, Nanting-Jing, Cao Lu, Cokang-Daling, Gao Qing Lu-Yu Qiao and the like all belong to the central level of the region, and the requirement of the overall planning for the purpose is to realize the balanced layout of public service and employment posts according to the population scale and development requirements of the region, and mainly serve the peripheral region. Other areas basically belong to the current major employment centers, including areas such as Minjing-Kongxin and outer Gao bridge, and other areas, and the employment posts in areas such as Pujiang and Zhopu can improve the commuting status of the whole city.
Meanwhile, the employment posts are very centralized in the inner ring core belt, including the mouth of the continental family, the east road of Nanjing and the west road of Nanjing, and the inner ring core belt is the highest-level employment center in the whole market. However, the results of the model calculations show that these regions do not have the need to further increase employment opportunities, as too many employment opportunities will attract residents in more distant regions to come into employment, resulting in a further increase in the average commuting distance across the city.
Similarly, the long-term simulation result (unit commute model) of the present embodiment is shown in fig. 6, and the calculation result of the long term is similar to that of the near term, and the priority region is almost the same, but there is a difference in specific values.
In addition, the result obtained through the model calculation has higher matching degree with the area with the key development proposed in the Shanghai city overall planning, which can explain the rationality of the model computer calculation result and also means that the constructed model can better guide the city planning practice.
In summary, the planning advice generation output unit 22 generates, based on the commuting evaluation value calculated by the model, the following table, taking the employment position layout plan of the maritime city as an example:
TABLE 2 employment post layout optimization suggestions for Shanghai cities
As can be seen from table 2, the areas zhangjiang-chuansha and jinqiao are areas that should be developed in the upper sea in a future period of time because they belong to the city center, have more comprehensive functions, and have a large need for employment position increment from the calculation result. Secondly, the employment post increment requirements of south station-Jing and Luo shop-Gumural areas are large, the average commuting distance of residents nearby the areas is long, and therefore the commuting condition of the employment post to residents in peripheral areas is improved greatly.
The screen storage unit 23 stores a simulation range input screen and a employment position layout suggestion display screen.
The simulation range input screen is used for displaying when the analyst starts the analysis process of the system, so that the analyst can input the commuting simulation range.
In this embodiment, the simulation range input screen completes the display of the map corresponding to the city based on the city information and the unit information stored in the city data storage unit 12, so that the analyst can complete the selection of the commute simulation range by framing the boundary on the map. In other embodiments, the simulation range input screen may also display a text input box, so that the analyst may input information such as the area name or the range coordinates of the commuting simulation range as the commuting simulation range.
Meanwhile, the simulation range input picture also displays a planning period input box, so that an analyst can select whether to carry out near-term or far-term planning. In another embodiment, the analyst can select the near-term plan and the far-term plan at the same time, and at this time, the commute cost calculation unit 20 and the commute number calculation unit 21 perform calculations using the residual commute model and the unit commute model, respectively, and the plan-advice generation output unit 22 simultaneously outputs the near-term and far-term employment-position layout advice.
The employment position layout suggestion display screen is displayed for the analyst to view when the planning suggestion generation output unit 22 outputs the employment position layout suggestion.
In this embodiment, the employment location layout suggestion display screen displays a suggestion table as shown in table 2. In other embodiments, the employment position layout suggestion display screen can also display the corresponding employment position layout suggestions in the form of a chart or the like (such as those shown in fig. 5 and 6).
The input display unit 24 is used for displaying the above-mentioned screens, so that the analyst can complete the corresponding human-computer interaction through the screens.
In this embodiment, the above-mentioned scheme is implemented by a computer having an input display device, the components of the optimal employment position layout addressing decision system 100 are components of the computer, such as the screen storage portion 23 and the input display portion are input display devices of the computer, and the rest of the components are stored in the memory of the computer as computer programs and complete the operation according to preset conditions.
Fig. 7 is a flow chart of a commute simulation process in an embodiment of the present invention.
As shown in fig. 7, after the analyst inputs the commute simulation range through the input display unit 24, the following procedure is started:
in step S1, the simulation range acquisition unit 18 acquires the commute simulation range input by the analyst, and then proceeds to step S2;
step S2, the simulation space search obtaining part 19 searches the city data storage part 12 according to the commuting simulation range obtained in step S1 and obtains the corresponding city space unit as the simulation space unit according to the corresponding unit information, and then proceeds to step S3;
step S3, the commute cost calculation unit 20 is configured to calculate the unit commute data stored in the city data storage unit 12 and the post simulation value obtained in advance according to the residual commute model (or the unit commute model), so as to obtain the commute cost of each simulated space unit before and after the post simulation value is added, and then go to step S4;
step S4, the commute value calculation unit 21 calculates the commute evaluation value before and after the post analog value is added for each analog space unit based on all the commute costs corresponding to each analog space unit calculated in step S3, and then proceeds to step S5;
in step S5, the plan advice generation/output unit 22 generates a short-term (or long-term) employment position layout advice based on the commuting evaluation value calculated in step S4, outputs the same to the input display unit 24, displays the same on the employment position layout advice display screen for the analyst to view, and enters the end state.
Examples effects and effects
According to the optimal employment post layout and site selection decision system based on the commuting model, the construction of the unit-divided model is realized through the mobile phone signaling data, the commuting cost of each urban space unit corresponding to other urban space units is calculated through the increased post simulation value, and the commuting evaluation values of each urban space unit before and after the post simulation value is increased are further obtained according to the commuting cost, so that the commuting effect generated by increasing the post number of each urban space unit is obtained through the change of the commuting evaluation values, an analyst is helped to complete the layout and site selection decision of the employment posts in the urban planning, and the employment post layout and site with the maximum utility is easier to select. Meanwhile, the adopted model is a residual commuting model, so that the fitting goodness is higher, and the accuracy and the reliability in short-term planning are higher.
In addition, the embodiment can also adopt a unit commute model, although the fitting degree of the model is lower relative to the residual commute model, the model can provide more stable commute data calculation in the long-term planning and also has a certain accuracy, so the invention can also realize the long-term planning of the urban planning layout based on the unit commute model.
The above-mentioned embodiments are merely illustrative of specific embodiments of the present invention, and the present invention is not limited to the description of the above-mentioned embodiments.
Claims (7)
1. An optimal employment post placement siting decision system based on a commuting model for generating employment post placement recommendations to assist analysts in completing recent employment post placement siting decisions in city planning, comprising:
a commute model storage unit for storing residual commute models corresponding to the city space units in the city;
a city data storage unit for storing unit information of each city space unit and corresponding unit commuting data;
a simulation range acquisition unit for acquiring a commute simulation range input by an analyst;
a simulated space retrieval acquisition part, which is used for retrieving the city data storage part according to the commuting simulation range and acquiring the corresponding city space unit as a simulated space unit according to the corresponding unit information;
a commute cost calculation unit configured to calculate the unit commute data and a post simulation value obtained in advance according to the residual commute model, so as to obtain commute costs of the other simulation space units corresponding to each of the simulation space units before and after the post simulation value is added;
a commute value calculation unit for calculating commute evaluation values of each of the simulation space units before and after the post simulation value is added according to all the commute costs corresponding to each of the simulation space units in sequence; and
and the planning suggestion generation output part is used for generating a corresponding recent employment position layout suggestion according to the commute evaluation value and outputting the suggestion to the analyst.
2. The commute model based optimal employment post placement decision system as claimed in claim 1, wherein:
wherein the formula of the residual commute model is:
ln Tij＝κi+αiln Nj+βiln dij+∑kαkD_SEk+εij
in the formula, TijI represents the ith departure place unit, j represents the jth employment place unit, N is the commute amount between the city space unitsjNumber of employment posts for jth employment unit, dijFor the commute cost between the ith origin unit and the jth employment unit, αiEmployment position influence coefficient for the ith departure location unit, βiIs the cost decay factor, κ, of the ith origin unitiIs a constant term of the ith origin unit, epsilonijIs the residual error of the commute amount between the ith departure place unit and the jth employment place unit, D _ SEkIs a residual virtual variable corresponding to the k-th class of cluster type, k is taken to be [0,1,2, 3]]，D_SEkIs taken as value of [0,1]，αkAre the corresponding residual coefficients.
3. An optimal employment post placement siting decision system based on a commuting model for generating employment post placement suggestions to assist analysts in completing a distant employment post placement siting decision in city planning, comprising:
a commute model storage unit for storing unit commute models corresponding to the city space units in the city;
a city data storage unit for storing unit information of each city space unit and corresponding unit commuting data;
a simulation range acquisition unit for acquiring a commute simulation range input by an analyst;
a simulated space retrieval acquisition part, which is used for retrieving the city data storage part according to the commuting simulation range and acquiring the corresponding city space unit as a simulated space unit according to the unit information;
a commute cost calculation unit configured to calculate the unit commute data and a post simulation value acquired in advance according to the unit commute model, so as to obtain commute costs of the other simulation space units corresponding to each of the simulation space units before and after the post simulation value is added;
a commute value calculation unit for calculating commute evaluation values of each of the simulation space units before and after the post simulation value is added according to all the commute costs corresponding to each of the simulation space units in sequence; and
and the planning suggestion generation output part is used for generating a corresponding remote employment position layout suggestion according to the commuting evaluation value and outputting the layout suggestion to the analyst.
4. The commute model based optimal employment post layout siting decision system as claimed in claim 3, wherein:
wherein the formula of the unit commute model is:
ln Tij＝κi+αiln Nj+βiln dij+εij
in the formula, TijI represents the ith departure place unit, j represents the jth employment place unit, N is the commute amount between the city space unitsjNumber of employment posts for jth employment unit, dijFor the commute cost between the ith origin unit and the jth employment unit, αiEmployment position influence coefficient for the ith departure location unit, βiIs the cost decay factor, κ, of the ith origin unitiIs a constant term of the ith origin unit, epsilonijIs the residual of the commute volume between the ith origin unit and the jth employment unit.
5. The commute model based optimal employment post layout siting decision system according to claim 1 or 3, further comprising:
a signaling data storage unit, an assignment weight storage unit, a commuting data analysis acquisition unit, and a commuting data assignment unit,
wherein the signaling data storage part stores the pre-acquired mobile phone signaling data of each mobile phone base station in the city,
the distribution weight storage unit stores distribution weights corresponding to a predetermined number of the city space units nearest to each of the cell phone base stations,
the commute data analysis and acquisition part analyzes the mobile phone signaling data to acquire resident commute data including a departure place base station and a employment place base station of a resident,
the commute data allocation section sequentially allocates the departure place base station and the employment foundation station in the resident commute data to the surrounding city space units according to the allocation weight, thereby obtaining unit commute data including the departure place unit and the employment place unit of the resident,
and the city data storage department stores the unit commuting data respectively and correspondingly with the unit information.
6. The commute model based optimal employment post layout siting decision system according to claim 1 or 3, wherein:
wherein the commute cost is a commute distance or a commute time,
the commute evaluation value is a mean value, a standard deviation, a quantile, a kurtosis or a skewness calculated according to the commute cost.
7. The commute model based optimal employment post layout siting decision system according to claim 1 or 3, wherein:
wherein the city space unit is a living committee unit composed of living committee ranges divided in the city.
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