CN110956305A - Urban space prediction model establishing method and urban space prediction system - Google Patents

Urban space prediction model establishing method and urban space prediction system Download PDF

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CN110956305A
CN110956305A CN201911007370.7A CN201911007370A CN110956305A CN 110956305 A CN110956305 A CN 110956305A CN 201911007370 A CN201911007370 A CN 201911007370A CN 110956305 A CN110956305 A CN 110956305A
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牛方曲
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Abstract

The invention discloses an establishing method of an urban space prediction model and an urban space prediction system. The method comprises the following steps: acquiring a location cost evaluation value and a traffic reachability evaluation value corresponding to the location cost; the location cost evaluation value is an urban family location cost evaluation value or an enterprise location cost evaluation value; calculating a utility evaluation value of the urban location based on the traffic accessibility evaluation value and the location cost evaluation value; and establishing an urban space prediction model based on the urban location utility evaluation value. The embodiment of the invention provides a method for establishing an urban space prediction model according to the complexity of urban space development change, constructs the urban space prediction model, simulates the urban space change process according to the urban land utilization-traffic interaction rule, predicts the development trend, can perform urban land utilization and traffic policy experiments, and assists in urban sustainable development decision making.

Description

Urban space prediction model establishing method and urban space prediction system
Technical Field
The invention relates to the field of geographic science, in particular to an urban space prediction model establishing method and an urban space prediction system.
Background
China is currently strongly promoting people-oriented novel urbanization work, more and more people enter cities, and a series of changes of urban space structures can also occur. In order to ensure healthy and orderly development of urban space, high requirements are put forward on the scientificity of urban decision. What will real estate development affect urban spaces? What impact is brought to the urban activity distribution by newly building a highway? Wait for the question to be answered. In order to ensure the sustainability of the urban space policy, the urban space evolution process is simulated, and the policy experiment is developed, so that the decision assistance is very important.
The research of urban spatial evolution has undergone a long history, and since the 20 th century 60 th era of the geographic metering revolution, the foreign countries have begun to apply a model simulation method to research the problems of urban spatial evolution, such as the Lowry model. In the 80 s, the Volterra-Lotka equation reflecting the predation relationship was introduced into the urban system by Dentrinios and Mullaly, and a dynamic analysis model was constructed. In the 90 s, Wegener integrated models of subsystems in cities, and established a Dortmund model. However, the application fields of the model are dispersed, the integrated simulation research of all the fields is weak, and the comprehensive integration of urban indexes such as population distribution and enterprise distribution prediction and the excavation of the internal driving force of urban evolution are lacked. Therefore, it is necessary to enhance the urban space development trend and aid decision making under the constraints of comprehensive integrated simulation research and dynamic prediction policy.
Disclosure of Invention
Objects of the invention
The invention aims to provide a method for establishing an urban space prediction model and an urban space prediction system, wherein the urban space prediction model is constructed according to the complexity of urban space development change, the model simulates the urban space change process according to the urban land utilization-traffic interaction rule, the development trend is predicted, the urban land utilization and traffic policy experiments can be carried out, and the urban sustainable development decision can be assisted.
(II) technical scheme
In order to solve the above problems, an aspect of the present invention provides a method for building an urban space prediction model, including: acquiring a location cost evaluation value and a traffic reachability evaluation value corresponding to the location cost; the location cost evaluation value is an urban family location cost evaluation value or an enterprise location cost evaluation value; calculating a utility evaluation value of the urban location based on the traffic accessibility evaluation value and the location cost evaluation value; and establishing an urban space prediction model based on the urban location utility evaluation value.
According to another aspect of the present invention, there is provided a city spatial distribution prediction method, including: establishing an urban spatial prediction model according to the method of the first aspect; obtaining urban spatial distribution according to an urban spatial prediction model, and predicting population change or enterprise quantity change in each block; and traversing all the blocks to predict the population distribution or enterprise distribution of the city. Preferably, the method further comprises the following steps: acquiring the amount of the tenancy according to the predicted urban population distribution or enterprise distribution; repeatedly executing the steps of the method based on the obtained amount of the tenants, and predicting the population distribution or enterprise distribution of the city again; and if the obtained population distribution of the predicted city and the population of the city before repeated execution are smaller than the threshold value, outputting the obtained population distribution of the city again.
According to another aspect of the present invention, there is provided an urban space prediction system, including an information acquisition module that acquires a location cost evaluation value, a location traffic reachability evaluation value, a number of families in a next time period in each block of a city to be predicted, or a number of enterprises in a next time period in each block of the city to be predicted; the location cost evaluation value is an urban family location cost evaluation value or an enterprise location cost evaluation value; the calculation module is used for calculating an urban location utility evaluation value based on the location cost evaluation value and the traffic reachability evaluation value and constructing an urban space prediction model; the prediction module is used for inputting the regional utility evaluation value and the number of families in each block of the city to be predicted in a first time period into the city space prediction model, outputting the total number of families in each block of a second time period of the city, and gradually predicting the family distribution in each time period in the future; wherein the second period belongs to a period after the first period; or inputting the location cost evaluation value, the enterprise traffic accessibility evaluation value and the enterprise number of the first time period in each block of the city to be predicted into the city space prediction model to obtain the number of enterprises migrated into each block, calculating the total enterprise number of each block, and further predicting the enterprise distribution of each time period in the future.
(III) advantageous effects
The technical scheme of the invention has the following beneficial technical effects:
(1) the embodiment of the invention provides a method for establishing a prediction model according to the complexity of urban space development change, constructs the urban space prediction model, simulates the urban space change process according to the urban land utilization-traffic interaction rule, predicts the development trend, can perform urban land utilization and traffic policy experiments, and assists in urban sustainable development decision making.
(2) The urban space prediction model established in the embodiment is a comprehensive integrated model for urban traffic and land utilization, can predict spatial variation of population and enterprise distribution year by year, and can well assist urban space decision for checking influence of urban real estate development on population and employment spatial distribution. The model simulates the infinite development and change process of urban space by using a computational cycle recursive algorithm. Is the first domestic research for successfully establishing and realizing the model. The spatial distribution of population and enterprise activities can be output by inputting data such as urban property, population, enterprise distribution, traffic network and the like into the model.
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FIG. 1 is a schematic flow diagram of a method according to a first embodiment of the present invention;
FIG. 2 is a schematic flow diagram of a method according to a second embodiment of the present invention;
FIG. 3(a) is a graphical illustration of a predicted population density distribution provided in accordance with a third embodiment of the present invention;
FIG. 3(b) is a schematic illustration of a forecasted business distribution provided in accordance with a third embodiment of the present invention;
fig. 4 is a schematic system configuration diagram according to a fourth embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
Fig. 1 is a schematic flow diagram of a method according to a first embodiment of the invention.
As shown in fig. 1, the method includes steps S101 to S103:
step S101, a location cost evaluation value and a traffic accessibility evaluation value are obtained. The location cost evaluation value is an urban family location cost evaluation value or an enterprise location cost evaluation value.
It should be noted that a city may be divided into a plurality of blocks.
In one embodiment, the traffic reachability of a block is evaluated through the spatial distribution of urban road networks and business volumes. Social activities include family activities and business activities. Thus, traffic reachability can be used to characterize the ease of travel from the block.
The traffic accessibility of the block is defined in the embodiment as the convenience of reaching various social activities from the block. For the home residents, the traffic reachability of a block reflects the convenience of the working trip of the resident who lives in the block (home traffic reachability). For businesses (economic activities), the traffic reachability of a block reflects the degree of convenience (business traffic reachability) that the block is reached by residents as an end point. Traffic accessibility is determined by the spatial distribution of conditions and opportunities, for homes, traffic accessibility by the surrounding spatial distribution of work opportunities, and for businesses, traffic accessibility by the surrounding residents. Traffic reachability evaluations are based on activities and blocks, i.e., the same block has different traffic reachability evaluations for different activities.
In a specific embodiment, if the location cost evaluation value is an urban home location cost evaluation value, the traffic reachability corresponding to the urban home location cost evaluation value is a home traffic reachability. At this time, if the urban space prediction model is used to predict urban household population distribution, the step of acquiring the home traffic reachability evaluation value includes:
the city to be predicted is divided into a plurality of blocks, and the blocks can be a street (villages and towns) or a certain area of land, such as a block.
And calculating the commute time between any two blocks according to the traffic network. In this step, a point may be used to represent the block, for example, the center of the block represents the block, and the commute time between two blocks is calculated as the commute time of the center of two blocks.
And calculating the product of the commuting time of other blocks and the block to be evaluated and the number of the working positions of other blocks, adding the products and taking the logarithm to obtain the family reachability evaluation value of the block to be evaluated.
It should be noted that, in the present application, the traffic reachability represents the convenience of the blocks, and each block has two values, one is home traffic reachability, which may be referred to as home reachability, and the other is enterprise traffic reachability, which may be referred to as enterprise reachability; family reachability is the ease of reaching all businesses throughout the city, starting from the block to which the family belongs. Enterprise reachability is the ease of reaching all enterprises in the city-wide starting from the block to which the enterprise belongs.
Specifically, the above steps may be expressed by the following formula:
Figure BDA0002243171670000051
wherein,
Figure BDA0002243171670000052
is the home traffic reachability evaluation value for block i; for home traffic accessibility, Wj HIs the number of work posts of block j; gc of gcijIs the minimum transit time between blocks i and j. gc of gcijThe calculation result is a matrix of M × M steps (M is the number of blocks), that is, the shortest transit time between every two blocks. λ is distance sensitivity, and if the family distribution is sensitive to its distance, the value becomes larger, otherwise, the value becomes smaller.
It should be noted that the traffic reachability evaluation value calculation formula is a logarithm summation constructed based on a logit model, and the traffic reachability aiAnd the traffic cost gcijWith the same dimensions. Therefore, the larger the traffic reachability evaluation value means the higher the time, the worse the traffic reachability. According to the calculation formula of the traffic accessibility evaluation value, the distance sensitivity lambda is assigned with a negative value, and the traffic cost gc is related toijThe value of the exponential function term gradually decreases and the weight of block j is further reduced. Therefore, if the commute time gcijIf the probability distribution is large, it means that the block j is far from the block i, which is hard to reach, and the probability distribution of the block j has little meaning to the block i. Opportunity here refers to employment opportunity to this block, characterizing the number of businesses within this block.
Specifically, if a certain block a is too far away from a certain block B, the traffic is not convenient for the block B, and even if the employment posts in the block a are more and more busy, the people who go to the block a from the block B become less because of the too far distance, and the time spent on the road is too long due to the inconvenient traffic, so that people are not willing to go. The same is true for removing block a from block B. In the above formula, the distance sensitivity λ may be given a negative value if the commute time gcijThe larger the weight WjThe more discounted, the less traffic accessibility.
In one embodiment, the location cost for a city home for the block is the sum of a first predetermined power of weight of the average dwelling area of the home and a second predetermined power of weight of the average spending of the home on other commodity services; the first preset weight is the percentage of the house rents of the family in income, and the sum of the second preset weight and the first preset weight is 1.
Generally, when a family dominates the income of the family, the proportion of the family used for housing and other consumption is adjusted to seek for maximum utility, so that the application classifies the family expenses into two types, the first type is housing consumption (renting), the second type is other consumption (other foods or services), namely the family income is distributed to the housing occupation ratio βHAnd proportion β of family's allocation of revenue to other consumption ogsOThe sum is 1, and the values of the two can be obtained according to experience.
Further, the formula of the location cost is as follows:
Figure BDA0002243171670000061
wherein, UiIs the consumer utility of the household within tile i;
Figure BDA0002243171670000062
is the average area of use for the home;
Figure BDA0002243171670000063
average cost of family on service consumption logs βHProportion of housing allocated for household income βOAllocating revenue to households to the share of the service consumption ogs.
It should be noted that the above formula for calculating the location cost evaluation value does not take the traffic cost into account, because the traffic cost is already included in the evaluation of the location utility and appears in the traffic reachability.
And S102, calculating a city location utility evaluation value based on the city home location cost evaluation value and the home traffic reachability evaluation value.
In one embodiment, the step of calculating the utility rating of the urban location comprises:
and the sum of the product of the change value of the traffic accessibility in the next period of the block to be evaluated (the change value of the traffic accessibility in the t +1 period relative to the t period) and the third preset weight and the product of the change value of the urban home location cost in the next period of the block to be evaluated and the fourth preset weight.
Specifically, the formula for calculating the location utility evaluation value is as follows:
Figure BDA0002243171670000071
Figure BDA0002243171670000072
the variable quantity of the household location utility of the block i in the t +1 time period is the weight of the household traffic accessibility change and the location cost evaluation value change, and theta is a weight coefficient; thetaUA weighting factor representing a home location cost variation value; thetaAA weighting coefficient representing a home traffic reachability change value; u shapet+1,iIs the zone bit cost evaluation value of the block i in the t +1 year;
Figure BDA0002243171670000073
home traffic reachability for block i in t +1 year;
Figure BDA0002243171670000074
home traffic reachability for tile i in t years.
And S103, establishing an urban space prediction model based on the urban location utility evaluation value.
In one embodiment, the output of the urban space prediction model is the number of families migrating to the block to be evaluated in the next time period, and the family population distribution in each block in the city next time period is determined by calculating the number of families in each block. For example, 5 ten thousand families on the sea street, 10 ten thousand families on the middle customs street, and the like.
The step of establishing the urban space prediction model comprises the following steps:
and obtaining the product of the variation of the utility evaluation value of the urban area of the block to be evaluated, the total area of the household housing of the block to be evaluated and the number of households in the block to be evaluated to obtain the demand of the block to be evaluated.
And dividing the demand of the block to be evaluated by the sum of the demands of all blocks in the city to obtain the ratio of the demands of the block to be evaluated.
The proportion of the demand of the block to be evaluated is multiplied by the number of families to be moved in the city to be evaluated in the next time period to obtain the number of the families to be moved to the block to be evaluated in the next time period, and the number of the families to be moved to the block to be evaluated in the next time period is obtained.
Specifically, the urban spatial prediction model (SDA) is:
Figure BDA0002243171670000075
wherein,
Figure BDA0002243171670000076
is the number of households migrating to block i within the time period t + 1;
Figure BDA0002243171670000077
is the total amount of families needing to be moved in the city at the time period of t + 1;
Figure BDA0002243171670000078
is the number of original families of the i block in the t period;
Figure BDA0002243171670000079
is the total area of available housing in the i block during the t +1 period. "H" in the model is represented as each parameter corresponding to the urban home location cost assessment value.
In one embodiment, if the location cost evaluation value is an enterprise location cost evaluation value, the traffic reachability evaluation value corresponding to the enterprise location cost evaluation value is an enterprise traffic reachability evaluation value. The urban space prediction model is used for predicting the enterprise number distribution of the city, and the step of acquiring the enterprise traffic reachability evaluation value comprises the following steps:
dividing a city to be predicted into a plurality of zone bits, and calculating the commuting time between any two zone bits according to a traffic network; and calculating the product of the commute time of each block and the block to be evaluated and the number of residents of each block, and taking the logarithm of the sum of the products to obtain the enterprise reachability from a certain block to the block to be evaluated.
Specifically, the above steps may be formulated as follows:
Figure BDA0002243171670000081
wherein,
Figure BDA0002243171670000082
is the enterprise traffic reachability evaluation value of the block i;
Figure BDA0002243171670000083
the number of households that are block j; gc of gcijIs the minimum transit time between blocks i and j. gc of gcijThe calculation result is a matrix of M × M steps (M is the number of blocks), that is, the shortest transit time between every two blocks. Lambda is the distance sensitivity, if the enterprise distribution is sensitive to its distance, the value becomes larger, otherwise, the value becomes smaller.
Businesses generally seek to maximize profits. Thus, in one embodiment, the location cost of the business for the block to be evaluated is the tenancy of the business' room that the business leases.
It should be noted that, the activities in the city are performed in the enterprise, so in the present invention, the enterprise includes a company, a school, a research institution, a hospital, etc., that is, a place where employment opportunities can be provided is referred to as an enterprise.
In one embodiment, the step of calculating a utility rating for urban location based on the enterprise location cost rating and the enterprise traffic reachability comprises:
and the sum of the product of the change value of the traffic reachability and the seventh preset weight of the next time period and the current time period of the block to be evaluated and the product of the change value of the enterprise location cost and the eighth preset weight of the next time period and the current time period of the block to be evaluated.
Specifically, it is formulated as:
Figure BDA0002243171670000084
Figure BDA0002243171670000091
is the variable quantity of the enterprise zone bit utility of the block i in the time period of t +1, and is used for the enterprise traffic accessibility change and the zone bit cost evaluation value changeWeighting, theta is a weighting coefficient; thetaCA weighting factor representing the enterprise location cost; thetaAA weighting factor representing the reachability of the enterprise traffic; ct+1,iIs the zone bit cost evaluation value of the block i in the t +1 year, CtiIs the zone bit cost of the enterprise in t years for block i;
Figure BDA0002243171670000092
traffic reachability for block i in t +1 year;
Figure BDA0002243171670000093
enterprise traffic reachability for block i in t years. In the formula thetaCCorresponding to a seventh predetermined weight, θACorresponding to the eighth preset weight.
In one embodiment, the output of the urban spatial prediction model is the number of enterprises in the time period under the block to be evaluated. The spatial distribution of the number of the enterprises developing in the city is obtained. For example, the lake street employment (business) population is 15 million people, and the middle customs street employment (business) population is 20 million.
The method for establishing the urban space prediction model comprises the following steps:
obtaining a product of the city location utility evaluation value of the block to be evaluated, the total area of commercial houses used by enterprises of the block to be evaluated and the number of the enterprises in the block to be evaluated to obtain the demand of the block to be evaluated; dividing the demand of the block to be evaluated by the sum of the demands of all blocks in the city to obtain the ratio of the demand of the block to be evaluated; the proportion of the demand quantity of the block to be evaluated is multiplied by the number of enterprises which need to be moved in the city to be evaluated in the time period to obtain the number of enterprises which are moved to the block to be evaluated in the next time period, and the number of enterprises which are moved to the block to be evaluated in the next time period is obtained.
Specifically, the urban space prediction model is as follows:
Figure BDA0002243171670000094
wherein,
Figure BDA0002243171670000095
the number of enterprises migrating to the block i in the time period of t + 1;
Figure BDA0002243171670000096
is the total amount of enterprises needing to be moved in the city at the time period of t + 1;
Figure BDA0002243171670000097
is the number of original enterprises of the i block in the t period;
Figure BDA0002243171670000098
is the total area of available commercial housing in i block during t + 1.
It should be noted that "E" indicates that the model can be used to predict the number of businesses of each block, which means that when the model is used to predict the business distribution, the corresponding parameters for the business distribution need to be selected.
In one embodiment, use of the urban spatial prediction model (SDA) described above is also provided. The urban space prediction model (SDA) is used for predicting the house renting expansion trend of a city, the urban space development trend, checking the influence of urban traffic and land policies on urban space distribution, the feasibility of urban land utilization and assisting in urban sustainable development decision-making.
(1) The embodiment of the invention provides a method for establishing an urban space prediction model according to the complexity of urban space development change, constructs the urban space prediction model, simulates the urban space change process according to the urban land utilization-traffic interaction rule, predicts the development trend, can perform urban land utilization and traffic policy experiments, and assists in urban sustainable development decision making.
(2) The urban space prediction model established in the embodiment is a comprehensive integrated model for urban traffic and land utilization, can predict the spatial distribution change of population and enterprises year by year, and can well assist urban space decision for checking the influence of urban house property development on the spatial distribution of population and employment. The model simulates the infinite development and change process of urban space by using a computational cycle recursive algorithm. Is the first domestic research for successfully establishing and realizing the model. The spatial distribution of population and enterprise activities can be output by inputting data such as urban property, population, enterprise distribution, traffic network and the like into the model.
Fig. 2 is a schematic flow chart of a city space prediction method according to a second embodiment of the present invention.
As shown in fig. 2, the method includes steps S201 to S202:
step S201, the method provided by the first embodiment is used to establish an urban space prediction model.
Step S202, obtaining urban spatial distribution according to the urban spatial prediction model, and predicting population change or enterprise change in a certain block;
step S203, traversing all blocks, predicting population distribution or enterprise distribution of the city.
It should be noted that the urban space evolution process is an interaction process of land utilization and a traffic system, the traffic system influences the land utilization system (social and economic activity distribution) through traffic accessibility, and the land utilization system influences the traffic system in turn, and the two are in a circulating action and tend to be in a balanced state. Any change in factors will result in the new balance of the urban system. Therefore, the steps can be repeated to perform a loop iteration process in order to obtain the state at equilibrium.
In one embodiment, the method further comprises step S204 and step S205:
and step S204, acquiring the amount of the tenants according to the predicted urban population distribution or enterprise distribution.
Alternatively, the house rental amount is obtained by the following house rental model. Wherein, the input of the house renting model is house property distribution and family number distribution; the output is the tenants for the home housing for each tile, and the new tenants will be used for the next urban home location cost assessment value.
Figure BDA0002243171670000111
Wherein,
Figure BDA0002243171670000112
is the estimated tenancy for block i;
Figure BDA0002243171670000113
is the last live tenant (location model is the process of recursion); the variable a is the current home distribution density; h (L)piIs the number of homes migrated to block i;
Figure BDA0002243171670000114
is the total area of the residential housing currently available.
Of course, if the distribution quantity of the commercial rooms and the quantity of the migrated enterprises are input in the house renting model; the output is the tenants of the business rooms for the enterprise tenancy for each block, and the new tenants will be used for the next enterprise location cost assessment value. That is, the commercial tenant adjustment formula can be obtained by replacing the variables related to the family in the above formula with the economic activity variables, i.e., the number of enterprises, r, is replaced with h (l)HTrade as an Enterprise tenant, aHTrade and trade Enterprise Density, F (A)HAnd changing the number of commercial rooms.
Step S205, repeating the steps S201-S203 based on the obtained amount of the tenants, and predicting the population distribution or enterprise distribution of the city again; and if the obtained population distribution of the predicted city is not different from or slightly different from the population distribution of the city before repeated execution, outputting the obtained population distribution of the city again.
It should be noted that, when the spatial distribution pattern of the urban households and enterprises (the number of households and the number of enterprises in each block) is obtained last time, the density distribution of the urban social activities is changed, and further the tenants are changed, the step S201 and the step S202 are executed again by using the changed tenants, the above processes are repeated until the ending condition is met, the program is stopped, and the outputted distribution of the number of households in each block is the urban population distribution, that is, the urban spatial prediction result. Or when the number of enterprises in each block is input, the number of enterprises in each block, namely the enterprise distribution of the city, is output, and the activity distribution of the city can be reflected, and the prediction result can also be called as a city space prediction result. The cycle ending condition refers to that the results of the two cycles have no change or have little change, the model outputs a predicted value of the next time period, and the next time period can be the time period of the next year, the next half year and the like.
Optionally, the difference between the number of family people in each block of the cycle prediction and the number of family people in each block obtained by the previous cycle prediction is set to be less than 1%, and the predicted value of the cycle prediction can be output, wherein the predicted value is the number of family people in each block. If the difference is more than 1%, continuously circulating until the difference is less than 1%, and outputting a predicted value. Of course, in some embodiments, when the difference is less than 10%, the number of households in each block of the prediction may be output, which is not limited by the present invention.
The principle of the steps is as follows: the distribution pattern is the distribution quantity of each block of the home or business, for example, the display is the quantity (density is used in fig. 3a and 3 b). The number of families changes for the first cycle of the model, resulting in a change in tenancy. The others are unchanged. For an enterprise, the enterprise distribution changes every time the enterprise runs, and the tenancy changes inevitably.
Of course, the model prediction value can also be used, and the policy scenario setting (the policy scenario of the year t +2, namely the traffic and land utilization scenario) can further predict the situation of the year t + 2. It can be understood that any policy may affect the change of tenancy of a home tenant or tenancy of an enterprise tenant, for example, when a subway line is newly opened, the traffic accessibility between two blocks may change, and tenancy along the subway line may increase, and the increase of tenancy may affect the city home location cost evaluation value and the enterprise location cost evaluation value, and may also affect the location utility evaluation value corresponding thereto, and the city space prediction model may also change accordingly, and finally, the output population number distribution or enterprise number distribution may change, and so on, and the future city space condition may be predicted year by year.
FIG. 3(a) is a graphical illustration of a predicted population density distribution provided in accordance with a third embodiment of the present invention;
fig. 3(b) is a schematic diagram of a forecasted business distribution provided in accordance with a third embodiment of the present invention.
As shown in fig. 3(a) and 3(b), the third embodiment of the present invention combined with the scenario of the land use policy predicted the urban household and business distribution pattern of beijing in 2030. In fig. 3(a), numerals 24, 23, 78, 77 and the numbers of blocks indicated by 51 in the lower left corner. The block with the number in fig. 3(a) is a block around the city of beijing, which is a densely distributed block of households or businesses, and may be developed into a block in the center of the city subcenter. For example, 173 is also good. FIG. 3(b) also shows the same principle.
Specifically, in the third embodiment of the present invention, assuming a land use development mode extending for the past 5 years in the future, the method provided by the second embodiment of the present invention predicts that the future population distribution and enterprise employment distribution of beijing in 2030 years are obtained, and the third embodiment of the present invention can prove that the method of the second embodiment can be used for checking urban land use policies, and also can be used for checking traffic policies, such as the influence of developing a subway on urban space. Providing basis for decision making.
Fig. 4 is a schematic structural diagram of a city space prediction system according to a fourth embodiment of the present invention.
As shown in fig. 4, the urban space prediction system 100 includes: an information acquisition module 10, a calculation module 20 and a prediction module 30.
The information acquisition module 10 is used for acquiring a location cost evaluation value, a traffic accessibility evaluation value, the number of families in each block of the city to be predicted in a first time period or the number of enterprises in each block of the city to be predicted in a first time period; the location cost evaluation value is an urban family location cost evaluation value or an enterprise location cost evaluation value.
The calculation module 20 is used for calculating an urban location utility evaluation value based on the location cost evaluation value and the traffic reachability evaluation value corresponding to the location cost evaluation value, and constructing an urban space prediction model;
the prediction module 30 is used for inputting the location utility evaluation value, the traffic reachability evaluation value corresponding to the location cost evaluation value and the number of families in each block of the city to be predicted in the second time period into the city space prediction model and outputting the number of families in each block of the city transferred in the second time period; calculating the total family number of each block, and further predicting the family distribution of each time period in the future; wherein the second period belongs to a period after the first period; or inputting the regional utility evaluation value, the traffic reachability evaluation value corresponding to the regional cost evaluation value and the number of enterprises in the current time period in each block of the city to be predicted into the city space prediction model to obtain the number of enterprises migrated into each block. And calculating the total enterprise number of each block, and further predicting the enterprise distribution of each period in the future.
Optionally, the first time interval is a current time interval, and the second time interval is a future time interval.
In one embodiment, if the location cost evaluation value is an urban home location cost evaluation value, the step of acquiring, by the information acquisition module 10, a traffic reachability evaluation value includes:
inputting the traffic network, the enterprise number of each block and a traffic accessibility evaluation value calculation formula, and outputting the traffic accessibility evaluation value of each block; the traffic reachability evaluation value calculation formula is as follows:
Figure BDA0002243171670000141
Figure BDA0002243171670000142
is the home traffic reachability evaluation value for block i; wj HIs the number of work posts of block j; gc of gcijIs the minimum transit time between blocks i and j; and lambda is the sensitivity of the family to the travel distance, if the travel of the resident is sensitive to the distance, the value is increased, otherwise, the value is decreased.
Further, if the location cost evaluation value is an urban home location cost evaluation value, the method for the information obtaining module 10 to obtain the urban location cost includes:
inputting the house renting and family income of each block of a city into a formula of the zone bit cost evaluation value to obtain the zone bit cost evaluation value of each block; wherein, the position cost formula of city family is:
Figure BDA0002243171670000143
wherein, UiIs the consumer utility of the household within a certain tile i;
Figure BDA0002243171670000144
is the average area of use for the home;
Figure BDA0002243171670000145
average cost of family on-service consumption ogs βHProportion allocated to housing for household income, βOThe proportion of revenue allocated to the service consumption ogs for the household.
Further, if the location cost evaluation value is an urban enterprise location cost evaluation value, the calculating module 20 calculates an urban location utility evaluation value based on the location cost evaluation value and the traffic reachability evaluation value, and the step of constructing an urban space prediction model includes:
inputting the urban family location cost and the enterprise traffic accessibility into a location utility formula to obtain an urban location utility evaluation value; wherein the location utility evaluation value formula is as follows:
Figure BDA0002243171670000146
Figure BDA0002243171670000147
the variable quantity of the household location utility of the block i in the t +1 time period is the weight of the household accessibility and the location cost change, and theta is a weight coefficient; thetaUA weighting factor representing a home location cost variation value; thetaAA weighting coefficient representing a home traffic reachability change value; u shapet+1,iIs the location cost of block i in the t +1 year;
Figure BDA0002243171670000148
for home traffic reachability for block i in the t +1 year,
Figure BDA0002243171670000149
home traffic reachability for tile i in t years.
The urban space prediction model is as follows:
Figure BDA00022431716700001410
wherein,
Figure BDA00022431716700001411
is the number of households migrating to block i within the time period t + 1;
Figure BDA00022431716700001412
is the total amount of families needing to be moved in the city at the time of t + 1;
Figure BDA0002243171670000151
is the number of original families of the i block in the t period;
Figure BDA0002243171670000152
is the total area of available housing in the i block during the t +1 period.
In another embodiment, if the location cost evaluation value is an enterprise location cost evaluation value, the step of acquiring the traffic reachability evaluation value by the information acquisition module 10 includes:
inputting the number of the families of the traffic network and each block into a traffic accessibility evaluation value calculation formula, and outputting the traffic accessibility evaluation value of each block; the traffic reachability evaluation value calculation formula is as follows:
Figure BDA0002243171670000153
Figure BDA0002243171670000154
is the enterprise traffic reachability evaluation value of the block i; wj EIs the number of residents in block j; gc of gcijThe minimum passing time between blocks i and j is defined, and lambda is the sensitivity of the resident commute to the distance; .
Further, if the location cost evaluation value is an enterprise location cost evaluation value, the location cost of the enterprise of the block to be evaluated is the house renting of the enterprise.
Further, if the location cost evaluation value is an enterprise location cost evaluation value, the calculating module 20 calculates an urban location utility evaluation value based on the location cost evaluation value and the traffic reachability evaluation value, and the step of constructing the urban space prediction model includes:
inputting the urban family location cost and the traffic accessibility into a location utility formula to obtain an urban location utility evaluation value; wherein the location utility evaluation value formula is as follows:
Figure BDA0002243171670000155
Figure BDA0002243171670000156
the variable quantity of the enterprise zone bit utility of the block i in the t +1 time period is the weighting of the enterprise accessibility and the enterprise zone bit cost variation, and theta is a weighting coefficient; thetaCA weighting factor representing the enterprise location cost; thetaAA weighting factor representing the reachability of the enterprise traffic; ct+1,iIs the zone bit cost of the enterprise in the t +1 year for block i;
Figure BDA0002243171670000157
enterprise traffic reachability for block i in t +1 year;
Figure BDA0002243171670000158
enterprise traffic reachability for block i in t years;
the urban space prediction model is as follows:
Figure BDA0002243171670000159
wherein,
Figure BDA0002243171670000161
the number of enterprises migrating to the block i in the time period of t + 1;
Figure BDA0002243171670000162
the total amount of enterprises needing to be moved in the city at the time of t + 1;
Figure BDA0002243171670000163
is the number of original enterprises of the i block in the t period;
Figure BDA0002243171670000164
is the total area of the available enterprise rooms in the i block in the time period of t + 1.
The fifth embodiment of the present invention further provides a storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method for establishing a spatial prediction model of a city, which is provided by the first embodiment; or the program when executed by a processor implements the steps of the city space prediction method provided by the second embodiment.
The sixth embodiment of the present invention further provides an electronic device, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of the method for building a city space prediction model provided in the first embodiment are implemented. Or the program when executed by a processor implements the steps of the city space prediction method provided by the second embodiment.
The seventh embodiment of the invention also provides a database of the method and a file description stored in the database.
The database file of the above method describes an urban land use and transportation system. These file structures and observation data and evaluation data (e.g., traffic reachability) for the input base year, based on which data model predicted values will be output. The partial data includes all required basic data. The base year is a year for predicting the beginning of the future, and for example, when data prediction 2011 and years after 2010 and years before 2010 are used, 2010 is a base year.
All data will be stored in excel file format, extension xlsx. All data is stored in blocks (zones). The file needs to include information for all city activities. The following table gives a brief description of the database files. Wherein the filename can be modified in subsequent versions, the system automatically generates a predicted year filename given the filename of the base year (the year information is added to the filename).
TABLE 1 System documentation
Figure BDA0002243171670000165
Figure BDA0002243171670000171
(1) WGT-1000. xlsx document
And the weights of the starting point and the end point of the reachability evaluation for various travel purposes are stored, and the weights of different travel purposes are different. Form of file (sheet) WGT _1000. The traffic reachability evaluation module needs to read the file for calculating the starting point reachability, the key point reachability, of each block, that is, the variable W in the traffic reachability module.
Example (c):
Figure BDA0002243171670000172
wherein:
Figure BDA0002243171670000173
Figure BDA0002243171670000181
at present, the purpose of each trip is not further divided. The weight value depends on the reachability evaluation (see 2.1 for module AccByPurpose).
(2)SpAct2010.xlsx
And storing the relation between various activities and space, including location cost, density and the like. The file stores the location utility evaluation result, i.e. the location utility evaluation result is output to the file, and the location model needs to read the file, i.e. read the location utility evaluation value (UConsOrCost). In addition, the tenant adjustment module also needs to read the Density data (Density) of the file.
Example (c):
Figure BDA0002243171670000182
(3) floorspace10 file
The file stores various property data. This file is the input file to the location model for calculating the location for home and economic activities.
Example (c):
Figure BDA0002243171670000183
Figure BDA0002243171670000191
the application comprises the following steps:
spct: the type of property.
A Zone: and marking the blocks.
Quantity: total amount of real estate.
RentPerUnit: tenants (meta/square meter).
FlspVacant: the number of empty spaces.
Quanlite: quality of property (temporarily unused).
(4) GCTR2010.xlsx file
Currently, the document records the traffic total cost (table gc) between blocks, and the m x m matrix is recorded. Cell (i, j) and the traffic fare for blocks i to j. And the input file of the traffic reachability module is used for calculating the traffic reachability of each block.
Example (c):
13 14 15 16 17 ……
13 30 68 152 132 88
14 70 30 118 114 68
15 132 107 30 72 114
16 128 103 70 30 110
17 88 68 114 110 1.5
18 81 62 105 102 51
……
(5)activities10.xlsx
and storing various types of activity space distribution data. This file is the output file of the locational model, i.e., the prediction of the SDA model, the spatial distribution of family and economic activities.
Example (c):
Figure BDA0002243171670000192
Figure BDA0002243171670000201
the application comprises the following steps:
actv: the city event number.
A Zone: and (5) blocking.
Quantity: the activity number is calculated by the family number for the family; for economic activities such as companies, the number of job posts (job posts) of each department is adopted, and the number of employees in each department can be calculated due to the difficulty in acquiring data.
Children: the number of children in the family, which is meaningless for economic activities, is given 0.
Workers: the number of family workers, meaningless for economic activities, is given 0.
NonWorkers: the working age group in the family is a worker, and is meaningless for economic activities and is endowed with 0.
Retired: and (5) returning and repairing personnel in the family.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.

Claims (14)

1. A method for establishing an urban space prediction model is characterized by comprising the following steps:
acquiring a location cost evaluation value and a traffic reachability evaluation value corresponding to the location cost; the location cost evaluation value is an urban family location cost evaluation value or an enterprise location cost evaluation value;
calculating a utility evaluation value of the urban location based on the traffic accessibility evaluation value and the location cost evaluation value;
and establishing an urban space prediction model based on the urban location utility evaluation value.
2. The method of claim 1, wherein the location cost evaluation value is an urban home location cost evaluation value, the traffic reachability evaluation value corresponding to the location cost evaluation value is a home traffic reachability evaluation value, and the urban space prediction model is used for predicting urban home population distribution; the step of acquiring the home traffic reachability evaluation value includes:
dividing a city to be predicted into a plurality of blocks;
calculating the commuting time between any two blocks according to a traffic network;
and calculating the product of the commuting time of a certain block and each of other blocks to be evaluated and the number of working positions of each block, adding the products and taking the logarithm to obtain the home traffic reachability evaluation value of the certain block.
3. The method of claim 2, wherein calculating a city location utility rating based on the traffic reachability and the city home location cost rating comprises:
and the sum of the product of the change value of the traffic reachability and the third preset weight of the upper time period of the block to be evaluated and the current time period and the product of the change value of the cost of the urban family location and the fourth preset weight of the upper time period of the block to be evaluated and the current time period.
4. The method of claim 3, wherein the output of the urban spatial prediction model is the number of families migrating to the block to be evaluated in the following time period, and the step of establishing the urban spatial prediction model comprises:
obtaining a product of the city zone bit utility evaluation value of the block to be evaluated, the total area of the family housing of the block to be evaluated and the number of families in the block to be evaluated to obtain the demand of the block to be evaluated;
dividing the demand of the block to be evaluated by the sum of the demands of all blocks in the city to obtain the ratio of the demand of the block to be evaluated;
the proportion of the demand quantity of the block to be evaluated is multiplied by the number of families to be moved in the city to be evaluated in the next time period to obtain the number of the families to be moved to the block to be evaluated in the next time period, and the total number of the families to be moved in the last time period of the block to be evaluated is subtracted from the total number of the families in the last time period of the block to be evaluated and added with the number of the families to be moved to obtain the total number of the families in.
5. The method of claim 1, wherein the location cost evaluation value is an enterprise location cost evaluation value, the traffic reachability evaluation value corresponding to the enterprise location cost evaluation value is an enterprise traffic reachability evaluation value, and the urban space prediction model is used for predicting enterprise number distribution of a city; the step of acquiring the enterprise traffic reachability evaluation value comprises the following steps:
dividing a city to be predicted into a plurality of blocks;
calculating the commuting time between any two blocks according to a traffic network;
and calculating the number of family residents in each block, multiplying the number of the family residents by the commuting time of the family residents reaching the block to be evaluated, adding the products, and calculating the logarithm to obtain the enterprise traffic reachability evaluation value of the block to be evaluated.
6. The method as claimed in claim 5, wherein the location cost of the enterprise of the block to be evaluated is renting of the enterprise, and the step of calculating the utility evaluation value of the urban location based on the traffic reachability evaluation value of the enterprise and the location cost evaluation value of the enterprise comprises:
and the sum of the product of the change value of the enterprise location cost of the last time period of the block to be evaluated and the current time period and the seventh preset weight and the product of the change value of the traffic reachability of the last time period of the block to be evaluated and the current time period and the eighth preset weight.
7. The method according to claim 6, wherein the output of the urban space prediction model is the number of enterprises migrated in and out in the time period under the block to be evaluated, and the step of establishing the urban space prediction model comprises:
the urban area effectiveness evaluation value of the block to be evaluated, the total area of the commercial houses used by enterprises of the block to be evaluated and the product of the number of the enterprises in the block to be evaluated are obtained, and the demand of the block to be evaluated is obtained;
dividing the demand of the block to be evaluated by the sum of the demands of all blocks in the city to obtain the ratio of the demand of the block to be evaluated;
the proportion of the demand quantity of the block to be evaluated is multiplied by the number of enterprises needing to be moved in the city to be evaluated in the time period to obtain the number of enterprises moved to the block to be evaluated in the next time period, the number of enterprises to be moved in the block to be evaluated in the next time period is obtained, the number of enterprises needing to be moved in the last time period of the block to be evaluated is subtracted from the number of enterprises in the last time period, and the number of enterprises to be moved is added to obtain the total number of enterprises in the next time period.
8. A city space prediction method is characterized by comprising the following steps:
establishing an urban spatial prediction model according to the method of any one of claims 1-7;
obtaining urban spatial distribution according to the urban spatial prediction model, and predicting population change or enterprise quantity change in a certain block;
traversing all the blocks, and predicting population distribution or enterprise distribution of a city;
preferably, the method further comprises the following steps:
acquiring the amount of the tenancy according to the predicted urban population distribution or enterprise distribution;
repeatedly executing the steps of the method based on the obtained amount of the tenants, and predicting the population distribution or enterprise distribution of the city again;
and if the obtained population distribution of the predicted city and the population of the city before repeated execution are smaller than the threshold value, outputting the obtained population distribution of the city again.
9. A city space prediction system is characterized by comprising
The information acquisition module (10) is used for acquiring the zone bit cost evaluation value, the zone bit traffic accessibility evaluation value, the number of families and the number of enterprises of each block of the city to be predicted; the location cost evaluation value is an urban family location cost evaluation value or an enterprise location cost evaluation value;
the computing module (20) is used for computing an urban location utility evaluation value based on the location cost evaluation value and the traffic reachability evaluation value and constructing an urban space prediction model;
the prediction module (30) is used for inputting the regional utility evaluation value and the number of families in each block of the city to be predicted in a first time period into the city space prediction model, outputting the total number of families in each block of a second time period of the city, and gradually predicting the family distribution in each time period in the future; wherein the second period belongs to a period after the first period;
or inputting the location cost evaluation value, the enterprise traffic accessibility evaluation value and the enterprise number of the first time period in each block of the city to be predicted into the city space prediction model to obtain the number of enterprises migrated into each block, calculating the total enterprise number of each block, and further predicting the enterprise distribution of each time period in the future.
10. The urban space prediction system according to claim 9, wherein if the location cost evaluation value is an urban home location cost evaluation value, the information obtaining module (10) obtains the traffic reachability evaluation value by:
inputting the traffic network, the enterprise number of each block and a traffic reachability evaluation value calculation formula, and outputting a home traffic reachability evaluation value of each block; the traffic reachability evaluation value calculation formula is as follows:
Figure FDA0002243171660000041
AH iis the home traffic reachability evaluation value for block i;
Figure FDA0002243171660000042
is the number of work posts of block j; gc of gcijIs the minimum transit time between blocks i and j; λ is distance sensitivity, if the resident is sensitive to distance when going out, the value becomes large, otherwise, the value becomes small;
if the location cost evaluation value is an enterprise location cost evaluation value, the step of acquiring the traffic reachability evaluation value by the information acquisition module (10) includes:
inputting the traffic network and the number of residents in each block into a traffic accessibility evaluation value calculation formula, and outputting the traffic accessibility evaluation value of each block; the traffic reachability evaluation value calculation formula is as follows:
Figure FDA0002243171660000043
AE iis the enterprise traffic reachability evaluation value of the block i;
Figure FDA0002243171660000044
is the number of residents in block j; gc of gcijIs the minimum transit time between blocks i and j, and λ is the distance sensitivity if the business is divided intoCloth is sensitive to the distance, the value becomes larger, and conversely, the value becomes smaller.
11. The urban space prediction system according to claim 10, wherein if the location cost evaluation value is an urban home location cost evaluation value, the method for acquiring urban location cost by the information acquisition module (10) comprises:
inputting residential tenants and family income of each block of a city into a zone bit cost evaluation formula to obtain a zone bit cost evaluation value of each block; wherein, the position cost formula of city family is:
Figure FDA0002243171660000051
wherein, UiIs the consumer utility of the household within a certain tile i;
Figure FDA0002243171660000052
is the average area of use for the home;
Figure FDA0002243171660000053
average cost of family on-service consumption ogs βHProportion allocated to housing for household income, βOThe proportion of revenue allocated to the service consumption ogs for the household;
and if the zone bit cost evaluation value is the enterprise zone bit cost evaluation value, the zone bit cost of the enterprise of the block to be evaluated is the house renting of the enterprise, namely, the unit price is multiplied by the average renting area of the enterprise.
12. The urban space prediction system according to claim 11, wherein if the location cost assessment value is an urban home location cost assessment value;
the calculation module (20) calculates an urban location utility evaluation value based on the location cost evaluation value and the traffic reachability evaluation value, and the step of constructing an urban space prediction model comprises the following steps:
inputting the urban family location cost and the urban family traffic accessibility into a location utility formula to obtain an urban location utility evaluation value; wherein the location utility evaluation value formula is as follows:
Figure FDA0002243171660000054
ΔVH t+1,ithe variable quantity of the household location utility of the block i in the t +1 time period is the weight of the household accessibility and the location cost change, and theta is a weight coefficient; thetaUA weighting factor representing a home location cost variation value; thetaAA weighting coefficient representing a home traffic reachability change value; u shapet+1,iIs the location cost of block i in the t +1 year;
Figure FDA0002243171660000055
for home traffic reachability for block i in the t +1 year,
Figure FDA0002243171660000056
home traffic reachability for block i in t years;
the urban space prediction model is as follows:
Figure FDA0002243171660000057
wherein,
Figure FDA0002243171660000058
is the number of households migrating to the i block within the time period of t + 1;
Figure FDA0002243171660000059
is the total amount of families needing to be moved in the city at the time of t + 1;
Figure FDA00022431716600000510
is the number of original families of the i block in the t period;
Figure FDA00022431716600000511
is the total area of available dwellings in the i block during the t +1 time period;
if the location cost evaluation value is an enterprise location cost evaluation value, a calculation module (20) calculates an urban location utility evaluation value based on the location cost evaluation value and the traffic reachability evaluation value, and the step of constructing an urban space prediction model comprises the following steps:
inputting the urban enterprise location cost and the urban enterprise traffic accessibility into a location utility formula to obtain an urban enterprise location utility evaluation value; wherein the location utility evaluation value formula is as follows:
Figure FDA0002243171660000061
Figure FDA0002243171660000062
the variable quantity of the enterprise zone bit utility of the block i in the t +1 time period is the weighting of the enterprise accessibility and the enterprise zone bit cost variation, and theta is a weighting coefficient; thetaCA weighting factor representing the enterprise location cost; thetaAA weighting factor representing the reachability of the enterprise traffic; ct+1,iIs the zone bit cost of the enterprise in the t +1 year for block i;
Figure FDA0002243171660000063
enterprise traffic reachability for block i in t +1 year;
Figure FDA0002243171660000064
enterprise traffic reachability for block i in t years;
the urban space prediction model is as follows:
Figure FDA0002243171660000065
wherein,
Figure FDA0002243171660000066
the number of enterprises migrating to the block i in the time period of t + 1;
Figure FDA0002243171660000067
the total amount of enterprises needing to be moved in the city at the time of t + 1;
Figure FDA0002243171660000068
is the number of original enterprises of the i block in the t period;
Figure FDA0002243171660000069
is the total area of the commercial premises available in the i block during the t +1 period.
13. A storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method of establishing a spatial prediction model of a city according to any one of claims 1 to 7; or which program, when being executed by a processor, carries out the steps of the urban spatial prediction method according to claim 8.
14. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of building a spatial prediction model for a city according to any one of claims 1 to 7 when executing the program; or which program, when being executed by a processor, carries out the steps of the urban spatial prediction method according to claim 8.
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CN113988447A (en) * 2021-11-05 2022-01-28 武汉理工大学 District-level land utilization space amount prediction method based on comprehensive traffic
CN113988447B (en) * 2021-11-05 2024-08-20 武汉理工大学 District-level land utilization space amount prediction method based on comprehensive traffic
CN114724414A (en) * 2022-03-14 2022-07-08 中国科学院地理科学与资源研究所 Method, device, electronic equipment and medium for determining urban air traffic sharing rate
CN114724414B (en) * 2022-03-14 2023-06-09 中国科学院地理科学与资源研究所 Method and device for determining urban air traffic sharing rate, electronic equipment and medium
CN115762150A (en) * 2022-11-10 2023-03-07 上海合本字节数字科技有限公司 Urban traffic trip prediction method and device based on zone bit value and electronic equipment
CN115762150B (en) * 2022-11-10 2024-08-02 上海合本字节数字科技有限公司 Urban traffic travel prediction method and device based on zone bit value and electronic equipment
CN116739169A (en) * 2023-06-13 2023-09-12 中国科学院地理科学与资源研究所 People flow prediction method and device
CN116682262A (en) * 2023-06-14 2023-09-01 中国科学院地理科学与资源研究所 Multi-mode traffic cost evaluation method
CN116682262B (en) * 2023-06-14 2024-03-08 中国科学院地理科学与资源研究所 Multi-mode traffic cost evaluation method

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