CN113868830B - Method for constructing same-city intercity passenger flow generation model based on traffic accessibility - Google Patents

Method for constructing same-city intercity passenger flow generation model based on traffic accessibility Download PDF

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CN113868830B
CN113868830B CN202110953865.XA CN202110953865A CN113868830B CN 113868830 B CN113868830 B CN 113868830B CN 202110953865 A CN202110953865 A CN 202110953865A CN 113868830 B CN113868830 B CN 113868830B
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罗典
李汉飞
陆虎
朱墨
何继红
温卫华
卢火平
李健民
王琢玉
张海雷
阎泳楠
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Foshan Urban Planning and Design Research Institute Co.,Ltd.
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Abstract

The invention relates to a method for constructing a same-city intercity passenger flow generation model based on traffic reachability, which comprises six steps of basic data acquisition, average intercity passenger flow generation rate calibration, initial intercity passenger flow generation amount calculation, trip mode utility function calibration, reachability index construction and intercity passenger flow generation amount correction. The method is suitable for the refined prediction of the passenger flow generation amount among cities in the same urbanization process, and the model is subjected to parameter calibration by using multi-source big data; research results show that the same-city intercity passenger flow generation model based on traffic accessibility has good feasibility and effectiveness, provides a new thought and method for same-city intercity passenger flow prediction, and has important guiding significance on refined intercity passenger flow prediction.

Description

Method for constructing same-city intercity passenger flow generation model based on traffic accessibility
Technical Field
The invention belongs to the technical field of traffic model research, and particularly relates to a method for constructing a same-city intercity passenger flow generation model based on traffic reachability.
Background
The urban circle and the urban group become development strategies of novel urbanization in China, and the same-urbanization development is considered as an important means for improving the competitiveness of each large urban group. The city integration refers to the mutual integration and development of one city and another or a plurality of adjacent cities in the aspects of economy, society, natural resources and the like. With the further progress of the same-city development, larger-scale passenger flow connection is formed among cities. The intercity passenger flow characteristics under the same-city development reflect the process of the same-city, and are also important bases for determining the construction scale, layout and trend of the inter-city traffic infrastructure.
The intercity passenger flow prediction method has many methods, such as point-point growth rate prediction based on a potential energy model, intercity rail and bus passenger flow prediction based on a neural network, a grey theory and the like, and the traditional four-stage method is most widely applied. The generation prediction is used as the first stage of the four-stage model, and the prediction precision of the generation prediction directly influences the final passenger flow result. The intercity passenger flow generation amount prediction method generally adopts generation amount prediction methods in city models, such as a growth rate method, a trip rate method, a regression analysis method, a cross classification analysis method and the like, but because the intercity passenger flow generation model usually relates to more than two cities, the acquisition of resident survey data is difficult to break through the administrative boundary barrier, and the feasibility of the method is low. With the development of big data technology, students build and calibrate an inter-city traffic generation model by using multi-source dynamic traffic data such as mobile phone signaling data, internet positioning data and the like, and a foundation is laid for building a large-range regional traffic model. In addition, city and county level administrative units are mostly used as research objects for inter-city passenger flow generation, and a point-point growth rate prediction method is adopted, so that no inter-city passenger flow generation research using a small-range traffic cell as a unit exists. For intercity passenger flow research in the same urbanization process, due to the integration of the inter-city industry and space and the co-construction and sharing of traffic infrastructure, more refined intercity passenger flow analysis can be performed by referring to a small-granularity traffic partitioning method in the intercity passenger flow research. However, the mechanism of intercity passenger flow generation is different from city passenger flow. The intercity passenger flow generation intensity is directly influenced by the intercity traffic accessibility level, and is particularly obvious in cities developing in the same city. For example, the production amount in the adjacent areas of two cities is obviously higher than that in the areas far away from the adjacent cities; or the distance between the urban area and the adjacent city is the same, and the passenger flow generation intensity of the cell covered by the cross-city rail transit is higher than that of the cell covered by the non-rail transit. Meanwhile, the connection strength of built-up areas between cities is higher than that of non-built-up areas. The current intercity passenger flow generation prediction method can basically consider the difference of locations in different areas, the influence of traffic infrastructure and traffic accessibility level on intercity passenger flow generation is not considered, the related data coverage is large, the problems of low efficiency and accuracy rate of data calibration and the problem of insufficient early warning of traffic data are not researched.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method for constructing a same-urbanization intercity passenger flow generation model based on traffic reachability, or a method for improving the efficiency of calibrating the same-urbanization intercity passenger flow data of a traffic model, or a method for improving the accuracy of the same-urbanization intercity passenger flow data of the traffic model, or a method for improving the early warning of the same-urbanization intercity passenger flow data of the traffic model, which comprises the steps of basic data acquisition, average intercity passenger flow generation rate calibration, initial intercity passenger flow generation quantity calculation, trip mode utility function calibration, reachability index construction and intercity passenger flow generation quantity correction. The method is suitable for the refined prediction of the passenger flow generation amount among cities in the same urbanization process, and the model is subjected to parameter calibration by using multi-source big data; research results show that the city-sharing intercity passenger flow generation model based on traffic accessibility has good feasibility and effectiveness, a new thought and method are provided for predicting the intercity passenger flow, the method for improving the calibration efficiency of the city-sharing intercity passenger flow data of the traffic model has high accuracy and effectiveness, the prediction efficiency and accuracy are improved for predicting the intercity passenger flow, and the method has important guiding significance on refined intercity passenger flow prediction.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: a method for constructing a same-city intercity passenger flow generation model based on traffic reachability comprises the following steps:
firstly, basic data acquisition: population POP for acquiring and researching traffic cells k of city based on big data technologykAnd employment data EMPkPOP of population in different circle levels i (such as core area, main city area and peripheral area)iAnd number of employment EMPi(ii) a Current passenger flow Q of different circle layers i and j between citiesijAnd Qji；
Passenger flow Q based on current situationijAnd QjiObtaining the current intercity passenger flow generation amount G of different circle layersiThe current situation of intercity passenger flow attraction Ai；
Gi＝Qij (1)
Ai＝Qji (2)
Secondly, calibrating the average intercity passenger flow rate (including generation rate and attraction rate) of each circle of layers: calibrating the average intercity passenger flow trip rate of each circle by utilizing the intercity passenger flow generation amount, the attraction amount, the population and employment data of different circles among cities acquired in the first step
Figure BDA0003219619190000021
An intercity passenger flow generation model is constructed based on a generation rate method as follows:
Figure BDA0003219619190000022
Figure BDA0003219619190000023
in the formula (3), the reaction mixture is,
Figure BDA0003219619190000024
average intercity passenger flow generation rate of residential population and employment posts of circle layer i;
in the formula (4), the reaction mixture is,
Figure BDA0003219619190000031
the average intercity passenger flow attraction rate of the residential population and employment posts of the circle layer i;
thirdly, calculating the initial intercity passenger flow generation amount of each traffic cell: calculating the initial intercity passenger flow generation amount of the cell k belonging to different circle layers by using the average intercity passenger flow trip rate of each circle layer obtained in the second step
Figure BDA0003219619190000032
Initial inter-city passenger flow attraction
Figure BDA0003219619190000033
Figure BDA0003219619190000034
Figure BDA0003219619190000035
Fourthly, calibrating the travel utility functions of ground traffic (including cars, taxis and passenger buses) and rail traffic (including intercity railways and subways);
fifthly, constructing a reachability index: method for calculating accessibility according to opportunity accumulation and constructing accessibility index Rk；
And sixthly, correcting the initial inter-city passenger flow generation amount of the traffic cell: according to the formula (7) and the formula (8) respectivelyCalculating the corrected traffic cell intercity passenger flow generation GkIntercity passenger flow attraction Ak；
Figure BDA0003219619190000036
Figure BDA0003219619190000037
In the formulas (7) and (8),
Figure BDA0003219619190000038
the average reachability is the average value of all cell reachability in each circle of layers.
Further, the specific steps of acquiring the demographic employment data of each cell (or each circle of layer) in the first step are as follows: the residential population and the working population of a unit grid unit are obtained according to the Internet positioning data, and then the grid population is associated with the boundaries of the traffic cells (or all circle layers), so that the residential population and the employment post number of each traffic cell (or all circle layers) can be obtained.
Further, the specific steps of acquiring the current passenger flows of different circles among cities in the first step are as follows: the method comprises the steps of firstly establishing a matching relation between an urban circle layer and a user stop point by using internet positioning data, and then acquiring intercity travel passenger flow by identifying the population flow direction.
Further, the specific steps of calibrating the trip utility function in the fourth step are as follows:
s41, acquiring the proportion of the current situation that the local city cell k reaches the adjacent city cell g by adopting two travel modes of ground traffic (including cars, taxis and passenger buses) and rail traffic (including intercity railways and subways)
Figure BDA0003219619190000039
The rail transit passenger flow can be obtained by using railway ticket business or bus card swiping data, and the ground transit passenger flow can be obtained by subtracting the rail transit passenger flow from the current passenger flow.
S42: and (5) calibrating the travel utility of the travel of the local city cell k to the selection mode m of the adjacent city cell g according to the formula (9) by combining the proportions of the various modes of travel obtained in the S41
Figure BDA0003219619190000041
Sum trip utility function
Figure BDA0003219619190000042
Wherein, when m takes 1, the ground traffic is represented, and when m takes 2, the track traffic is represented:
Figure BDA0003219619190000043
in the formula (9), the reaction mixture is,
Figure BDA0003219619190000044
the time of the travel in the vehicle is,
Figure BDA0003219619190000045
is the travel time outside the automobile,
Figure BDA0003219619190000046
α1、α2is a calibrated parameter; the time variables in this step are current values;
s43: the parameters obtained in S42
Figure BDA0003219619190000047
α1、α2And planning the travel time of the year in the vehicle
Figure BDA0003219619190000048
And travel time outside the vehicle
Figure BDA0003219619190000049
Substituting the formula (9) to obtain the proportion of travel of the planning year from the local city cell k to the adjacent city cell g in the selection mode m;
further, in the fifth step, the reachability index RkThe specific calculation steps are as follows:
s51, calculating the chance number O of the neighbor cell g obtained from the cell k of the local city within the time threshold T according to the formula (10)k(T), which is a weighted sum of the number of acquisition opportunities in different ways;
Figure BDA00032196191900000410
in the formula (10), when m is 1, the ground traffic is represented, when m is 2, the track traffic is represented,
Figure BDA00032196191900000411
the table type local city cell k adopts the adjacent city opportunity number obtained by the mode m, and the opportunity can be referred to by population and/or employment post; the time threshold T is obtained by the quantile of the current intercity travel time; time of flight tkgCalculated by traffic network and equal to travel time in vehicle
Figure BDA00032196191900000412
External travel time of car
Figure BDA00032196191900000413
The quantile value can be 95%;
s52, calculating the accessibility index Rk: method for calculating reachability from chance accumulation, RkDefined as the ratio of the number of available neighbor opportunities in cell k to the total number of available neighbor opportunities within time threshold T:
Figure BDA00032196191900000414
in formula (11), O (T)max) Indicating that the number of all opportunities that can be reached for a sufficiently long time is equal to the number of opportunities for all cells in the neighborhood.
Compared with the prior art, the invention has the advantages that:
1) the traffic reachability index is creatively introduced, a new intercity passenger flow generation model is constructed, and the influence of factors such as location, distance, traffic infrastructure and the like on intercity passenger flow generation amount can be comprehensively considered;
2) the method utilizes the modern internet big data to construct the intercity passenger flow generation model, overcomes the shortage of sample size of the traditional manual sampling inquiry survey data, and gets rid of the limitation of single city administrative division.
3) The method for generating the same-urbanization intercity passenger flow model is suitable for the traffic cell partition with small granularity, and provides a new thought and a new method for refined intercity passenger flow prediction.
Drawings
FIG. 1 is a flow chart of examples 1-3 of the present invention;
FIG. 2 is a schematic diagram of a reachability profile based on cumulative opportunities;
FIG. 3 is a flow chart of embodiment 4 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 is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1:
a method for constructing a same-city intercity passenger flow generation model based on traffic reachability is shown in the attached figure 1 and comprises the following steps:
firstly, acquiring basic data: population POP for researching each traffic district k of citykAnd employment data EMPkPOP of population of different circle levels i (core area, main city area, peripheral area)iAnd number of employment EMPi(ii) a Current passenger flow Q of different circle layers i and j between citiesijAnd Qji(ii) a The data are generally obtained by traditional resident trip investigation or statistical departments of relevant cities in the urban traffic model, but the acquisition of the resident trip investigation data is difficult to break through the administrative boundary barrier and cannot provide support for the regional traffic model, and the big data technology provides possibility for acquiring data of intercity outgoing amount, population and employment posts.
The specific steps for acquiring the population employment data of each cell (or each circle layer) are as follows: the method comprises the steps of firstly obtaining residential population and working population of a unit grid unit (for example, 200 x 200 meters) according to Internet positioning data, and then associating the grid population with the boundaries of traffic cells (or all circle layers) to obtain the residential population and employment post number of each traffic cell (or all circle layers).
The specific steps for acquiring the current passenger flows of different circles among cities are as follows: firstly, establishing a matching relation between an urban circle layer and a user stop point by using internet positioning data, and then acquiring intercity travel passenger flow by identifying the flow direction of population;
intercity passenger flow Q based on current situationijAnd QjiCollecting and obtaining the current intercity passenger flow generation amount G of different circle layersiThe current situation of intercity passenger flow attraction AiWhere i, j ∈ { core region, primary metropolitan region, peripheral region };
Gi＝Qij (1)
Ai＝Qji (2)
secondly, calibrating the average intercity passenger flow rate (including generation rate and attraction rate) of each circle of layers: and calibrating the average intercity passenger flow trip rate of each circle layer by using the intercity passenger flow generation amount, the attraction amount, the population and employment data of different circle layers obtained in the first step. An intercity passenger flow generation model is constructed based on a generation rate method as follows:
Figure BDA0003219619190000061
Figure BDA0003219619190000062
in the formula (3), the reaction mixture is,
Figure BDA0003219619190000063
the average intercity passenger flow generation rate of the residential population and employment posts of the circle layer i is shown;
in the formula (4), the reaction mixture is,
Figure BDA0003219619190000064
the average intercity passenger flow attraction rate of the residential population and employment posts of the circle layer i;
thirdly, calculating the initial inter-city passenger flow generation amount of each traffic cell: calculating the initial intercity passenger flow generation amount of the cell k belonging to different circle layers by using the average intercity passenger flow trip rate of each circle layer obtained in the second step
Figure BDA0003219619190000065
Initial intercity passenger flow attraction
Figure BDA0003219619190000066
Figure BDA0003219619190000067
Figure BDA0003219619190000068
Fourthly, calibrating utility functions of ground traffic (including cars, taxis and passenger buses) and rail traffic (including intercity railways and subways);
s41: obtaining the proportion of the current situation that the local city cell k reaches the adjacent city cell g by adopting two travel modes of ground traffic (including cars, taxis and passenger buses) and rail traffic (including intercity railways and subways)
Figure BDA0003219619190000069
The rail transit passenger flow can be obtained by using railway ticket business or bus card swiping data, and the ground transit passenger flow can be obtained by subtracting the rail transit passenger flow from the current passenger flow.
S42: and (5) calibrating the utility function of the travel of the local city cell k to the selection mode m of the adjacent city cell g according to the formula (7) by combining the proportions of the travel of the modes obtained in the S41
Figure BDA00032196191900000610
Wherein m is 1 represents a ground intersectionOn, when m takes 2, the rail transit is represented:
Figure BDA0003219619190000071
in the formula (7), the reaction mixture is,
Figure BDA0003219619190000072
representing the traveling utility of ground traffic and rail traffic;
Figure BDA0003219619190000073
the time of the travel in the vehicle is,
Figure BDA0003219619190000074
is the travel time outside the automobile,
Figure BDA0003219619190000075
α1、α2is a calibrated parameter; the time variables in this step are current values;
s43: parameters obtained in S42
Figure BDA0003219619190000076
α1、α2And planning the travel time of the year in the vehicle
Figure BDA0003219619190000077
And travel time outside the vehicle
Figure BDA0003219619190000078
Substituting the formula (9) into the formula (9), and obtaining the proportion of travel of the planning year from the local city cell k to the adjacent city cell g in the selection mode m;
fifthly, constructing a reachability index: method for calculating accessibility according to opportunity accumulation and constructing accessibility index Rk；
Traffic accessibility reflects the inherent link between transportation systems and land use and is a primary goal of traffic planning. Reachability is often used to assess the convenience of public transportation systems, or the ease with which a group of people can reach a facility in a public place (e.g., hospital, park, school), or to investigate the vulnerability of a network with changes in reachability. At present, no research is available for introducing traffic accessibility indexes into an intercity passenger flow generation model.
The invention adopts the concept of cumulative-opportunity (cumulative-opportunity) to construct the reachability index suitable for predicting the generation amount of intercity passenger flow. The accumulated opportunity describes the number of opportunities (such as working posts) which can be developed by a traveler in contact with the traveler in a certain travel time range by using a certain transportation mode from a certain place. As shown in fig. 2, it is possible for a traveler to obtain all the opportunities for development as long as a given travel time is sufficiently long.
S51: calculating the chance number O of the neighbor cell g obtained from the cell k in the local city within the time threshold T according to the formula (8)k(T) obtaining the weighted sum of the opportunity numbers by adopting different trip modes;
Figure BDA0003219619190000079
in the formula (8), the reaction mixture is,
Figure BDA00032196191900000710
the table type local city cell k adopts the adjacent city opportunity number obtained by the mode m, and the opportunity can be referred to as population and employment post; the time threshold T is obtained by the quantile of the current intercity travel time; time of flight tkgCalculated by traffic network and equal to travel time in vehicle
Figure BDA0003219619190000081
External travel time of car
Figure BDA0003219619190000082
The quantile value may be 95%.
S52: calculating a reachability index Rk: according to the concept of cumulative opportunity, RkDefined as the ratio of the number of available neighbor opportunities in cell k to the total number of available neighbor opportunities within time threshold T:
Figure BDA0003219619190000083
in formula (9), O (T)max) Indicating that the number of all opportunities that can be reached for a sufficiently long time is equal to the number of opportunities for all cells in the neighborhood.
And sixthly, correcting the initial inter-city passenger flow generation amount of the traffic cell: respectively calculating and obtaining the corrected traffic cell intercity passenger flow generation amount G according to a formula (10) and a formula (11)kInter-city passenger flow attraction amount Ak；
Figure BDA0003219619190000084
Figure BDA0003219619190000085
In the formulas (10) and (11), n is the number of traffic cells,
Figure BDA0003219619190000086
the average reachability is the average value of all cell reachability in each circle of layers.
The method for generating the same-urbanization intercity passenger flow model constructed in the embodiment is suitable for the traffic cell partition with small granularity, and provides a new thought and a new method for refined intercity passenger flow prediction.
Example 2:
a method for improving the efficiency of calibrating the data of the city-sharing intercity passenger flow of a traffic model is characterized in that a construction model is generated based on the city-sharing intercity passenger flow of traffic reachability, as shown in the attached figure 1, the method comprises the following steps:
firstly, acquiring basic data: population POP for researching each traffic district k of citykAnd employment data EMPkPOP of population of different circle levels i (core area, main city area, peripheral area)iAnd employment quantity EMPi(ii) a Current passenger flow Q of different circle layers i and j between citiesijAnd Qji(ii) a The above data are inThe urban traffic model is generally obtained by a traditional resident trip survey or a statistical department of a related city, but the acquisition of resident trip survey data is difficult to break through the administrative boundary barrier and cannot provide support for the regional traffic model, and the big data technology provides possibility for acquiring data of intercity travel volume, population and employment post.
The specific steps for acquiring the population employment data of each cell (or each circle layer) are as follows: the method comprises the steps of firstly obtaining residential population and working population of a unit grid unit (for example, 200 x 200 meters) according to Internet positioning data, and then associating the grid population with the boundaries of traffic cells (or all circle layers) to obtain the residential population and employment post number of each traffic cell (or all circle layers).
When the internet positioning data is acquired in real time, based on real-time statistics and prediction of historical big data, according to different passenger flow distribution base numbers of research cities, peak time periods, unit grid unit coordinates and threshold value ranges [ a, b ] of residential population and working population are preset. And when the residential population and the working population of the unit grid cell are lower than the lowest threshold value a, automatically defaulting the unit grid data as a statistical average value according to the statistical average value of the current historical same time period. The lower the population density is, the smaller the working population number is, the average value of the statistical values is automatically defaulted to the unit grid data according to the statistical values of the current history in the same period, so that the data acquisition complexity is effectively reduced, the data processing amount is reduced, the data acquisition amount is reduced, and the data real-time acquisition efficiency is improved.
The specific steps for acquiring the current passenger flows of different circles among cities are as follows: firstly, establishing a matching relation between an urban circle layer and a user stop point by using internet positioning data, and then acquiring intercity travel passenger flow by identifying the flow direction of population;
intercity passenger flow Q based on current situationijAnd QjiAnd collecting and obtaining the current intercity passenger flow generation amount G of different circlesiThe current situation of intercity passenger flow attraction AiWhere i, j ∈ { core region, primary metropolitan region, peripheral region };
Gi＝Qij (1)
Ai＝Qji (2)
secondly, calibrating the average intercity passenger flow trip rate (including the generation rate and the attraction rate) of each circle: and calibrating the average intercity passenger flow trip rate of each circle layer by using the intercity passenger flow generation amount, the attraction amount, the population and employment data of different circle layers obtained in the first step. An intercity passenger flow generation model is constructed based on a generation rate method as follows:
Figure BDA0003219619190000091
Figure BDA0003219619190000092
in the formula (3), the reaction mixture is,
Figure BDA0003219619190000093
the average intercity passenger flow generation rate of the residential population and employment posts of the circle layer i is shown;
in the formula (4), the reaction mixture is,
Figure BDA0003219619190000094
the average intercity passenger flow attraction rate of the residential population and employment posts in the circle layer i;
thirdly, calculating the initial inter-city passenger flow generation amount of each traffic cell: calculating the initial intercity passenger flow generation amount of the cell k belonging to different circle layers by using the average intercity passenger flow trip rate of each circle layer obtained in the second step
Figure BDA0003219619190000095
Initial inter-city passenger flow attraction
Figure BDA0003219619190000096
Figure BDA0003219619190000097
Figure BDA0003219619190000098
Fourthly, calibrating utility functions of ground traffic (including cars, taxis and passenger buses) and rail traffic (including intercity railways and subways);
s41: obtaining the proportion of the current situation that the local city cell k reaches the adjacent city cell g by adopting two travel modes of ground traffic (including cars, taxis and passenger buses) and rail traffic (including intercity railways and subways)
Figure BDA0003219619190000101
The rail transit passenger flow can be obtained by using railway ticket business or bus card swiping data, and the ground transit passenger flow can be obtained by subtracting the rail transit passenger flow from the current passenger flow.
S42: and (5) calibrating the utility function of the travel of the local city cell k to the selection mode m of the adjacent city cell g according to the formula (7) by combining the proportions of the travel of the modes obtained in the S41
Figure BDA0003219619190000102
Wherein, m represents ground traffic when taking 1, and represents rail traffic when taking 2:
Figure BDA0003219619190000103
in the formula (7), the reaction mixture is,
Figure BDA0003219619190000104
representing the traveling utility of ground traffic and rail traffic;
Figure BDA0003219619190000105
the time of the travel in the vehicle is,
Figure BDA0003219619190000106
is the travel time outside the automobile,
Figure BDA0003219619190000107
α1，α2is a calibrated parameter; this step is carried outThe time variables in the step are current values;
s43: parameters obtained in S42
Figure BDA0003219619190000108
α1，α2And planning the travel time of the year in the vehicle
Figure BDA0003219619190000109
And travel time outside the vehicle
Figure BDA00032196191900001010
Substituting the formula (9) into the formula (9), and obtaining the proportion of travel of the planning year from the local city cell k to the adjacent city cell g in the selection mode m;
fifthly, constructing a reachability index: method for calculating accessibility according to opportunity accumulation and constructing accessibility index Rk；
Traffic accessibility reflects the inherent link between transportation systems and land use and is a primary goal of traffic planning. Reachability is often used to assess the convenience of public transportation systems, or the ease with which a group of people can reach a facility in a public place (e.g., hospital, park, school), or to investigate the vulnerability of a network with changes in reachability. At present, no research is available for introducing traffic accessibility indexes into an intercity passenger flow generation model.
The method adopts the concept of cumulative-opportunity (cumulative-opportunity) to construct the reachability index suitable for the inter-city passenger flow generation amount prediction. The accumulated opportunity describes the number of opportunities (such as working posts) which can be developed by a traveler in contact with the traveler in a certain travel time range by using a certain transportation mode from a certain place. As shown in fig. 2, it is possible for a traveler to obtain all the opportunities for development as long as a given travel time is sufficiently long.
S51: calculating the chance number O of the neighbor cell g obtained from the cell k in the local city within the time threshold T according to the formula (8)k(T) obtaining the weighted sum of the opportunity numbers by adopting different trip modes;
Figure BDA0003219619190000111
in the formula (8), the reaction mixture is,
Figure BDA0003219619190000112
the table type local city cell k adopts the adjacent city opportunity number obtained by the mode m, and the opportunity can be referred to as population and employment post; the time threshold T is obtained by the quantile of the current intercity travel time; time of flight tkgCalculated by traffic network and equal to travel time in vehicle
Figure BDA0003219619190000113
External travel time of car
Figure BDA0003219619190000114
The quantile value may be 95%.
S52: calculating a reachability index Rk: according to the concept of cumulative chance, RkDefined as the ratio of the number of available neighbor opportunities in cell k to the total number of available neighbor opportunities within time threshold T:
Figure BDA0003219619190000115
in formula (9), O (T)max) Indicating that the number of all opportunities that can be reached for a sufficiently long time is equal to the number of opportunities for all cells in the neighborhood.
And sixthly, correcting the initial inter-city passenger flow generation amount of the traffic cell: respectively calculating and obtaining the corrected traffic cell intercity passenger flow generation amount G according to a formula (10) and a formula (11)kInter-city passenger flow attraction amount Ak；
Figure BDA0003219619190000116
Figure BDA0003219619190000117
In the formulas (10) and (11), n is the number of traffic cells,
Figure BDA0003219619190000118
the average reachability is the average value of all cell reachability in each circle of layers.
The embodiment is based on the same-urbanization inter-city passenger flow generation model, can be suitable for the traffic district partitions with small granularity, can simplify a data acquisition mode by combining large historical population passenger flow data, can efficiently acquire real-time data, and provides efficiency guarantee for refined inter-city passenger flow prediction.
Example 3:
a method for improving accuracy of intercity passenger flow data of a traffic model with a city, as shown in fig. 1, comprising the following steps:
firstly, acquiring basic data: population POP for researching each traffic district k of citykAnd employment data EMPkPopulation POP of different circle layers i (core area, main city area and peripheral area)iAnd number of employment EMPi(ii) a Current passenger flow Q of different circle layers i and j between citiesijAnd Qji(ii) a The data are generally obtained by traditional resident trip survey or statistical departments of related cities in the urban traffic model, but the acquisition of the resident trip survey data is difficult to break through the administrative boundary barrier and cannot provide support for the regional traffic model, and the big data technology provides possibility for acquiring data of inter-city traffic volume, population and employment posts.
The specific steps for acquiring the population employment data of each cell (or each circle layer) are as follows: the method comprises the steps of firstly obtaining residential population and working population of a unit grid unit (for example, 200 x 200 meters) according to Internet positioning data, and then associating the grid population with the boundaries of traffic cells (or all circle layers) to obtain the residential population and employment post number of each traffic cell (or all circle layers).
When the internet positioning data is acquired in real time, based on real-time statistics and prediction of historical big data, according to different passenger flow distribution base numbers of research cities, the peak time period, unit grid unit coordinates and threshold value ranges [ a, b ] of resident population and working population are set. When the resident population and the working population in the unit grid cell coordinate are higher than the highest threshold b, the unit grid cell can be provided with sub-grid cells, and the value range of the sub-grid cells can be adjusted in real time or periodically according to the resident population density, the working population number, emergency events, peak periods and the like of each cell, for example, the value range can be dynamically adjusted from 50 meters by 50 meters to 100 meters by 100 meters. When the population density is higher, the working population number is larger, and/or in a peak period, the unit grid unit sets the sub-grid more accurately, so that the positioning data acquisition accuracy is higher.
The specific steps for acquiring the current passenger flows of different circles among cities are as follows: firstly, establishing a matching relation between an urban circle layer and a user stop point by using internet positioning data, and then acquiring intercity travel passenger flow by identifying the flow direction of population;
intercity passenger flow Q based on current situationijAnd QjiCollecting and obtaining the current intercity passenger flow generation amount G of different circle layersiThe current situation of intercity passenger flow attraction AiWhere i, j ∈ { core region, primary metropolitan region, peripheral region };
Gi＝Qij (1)
Ai＝Qji (2)
secondly, calibrating the average intercity passenger flow rate (including generation rate and attraction rate) of each circle of layers: and calibrating the average intercity passenger flow trip rate of each circle layer by using the intercity passenger flow generation amount, the attraction amount, the population and employment data of different circle layers obtained in the first step. The intercity passenger flow generation model is constructed based on a generation rate method as follows:
Figure BDA0003219619190000121
Figure BDA0003219619190000122
in the formula (3), the reaction mixture is,
Figure BDA0003219619190000131
the average intercity passenger flow generation rate of the residential population and employment posts of the circle layer i is shown;
in the formula (4), the reaction mixture is,
Figure BDA0003219619190000132
the average intercity passenger flow attraction rate of the residential population and employment posts of the circle layer i;
thirdly, calculating the initial inter-city passenger flow generation amount of each traffic cell: calculating the initial intercity passenger flow generation amount of the cell k belonging to different circle layers by using the average intercity passenger flow trip rate of each circle layer obtained in the second step
Figure BDA0003219619190000133
Initial intercity passenger flow attraction
Figure BDA0003219619190000134
Figure BDA0003219619190000135
Figure BDA0003219619190000136
Fourthly, calibrating utility functions of ground traffic (including cars, taxis and passenger buses) and rail traffic (including intercity railways and subways);
s41: obtaining the proportion of the current situation that the local city cell k reaches the adjacent city cell g by adopting two travel modes of ground traffic (including cars, taxis and passenger buses) and rail traffic (including intercity railways and subways)
Figure BDA0003219619190000137
The rail transit passenger flow can be obtained by using railway ticket business or bus card swiping data, and the ground transit passenger flow can be obtained by subtracting the rail transit passenger flow from the current passenger flow.
S42: combining the proportions of all the modes of travel obtained in S41, and marking according to the formula (7)Determining utility function of travel of g selection mode m for local city cell k to reach neighboring city cell
Figure BDA0003219619190000138
Wherein, when m takes 1, the ground traffic is represented, and when m takes 2, the track traffic is represented:
Figure BDA0003219619190000139
in the formula (7), the reaction mixture is,
Figure BDA00032196191900001310
representing the traveling utility of ground traffic and rail traffic;
Figure BDA00032196191900001311
the time of the travel in the vehicle is,
Figure BDA00032196191900001312
is the travel time outside the automobile,
Figure BDA00032196191900001313
α1、α2is a calibrated parameter; the time variables in this step are all current values;
s43: the parameters obtained in S42
Figure BDA00032196191900001314
α1、α2And planning the travel time of the year in the vehicle
Figure BDA00032196191900001315
And travel time outside the vehicle
Figure BDA00032196191900001316
Substituting the formula (9) into the formula (9), and obtaining the proportion of travel of the planning year from the local city cell k to the adjacent city cell g in the selection mode m;
fifthly, constructing a reachability index: method for calculating accessibility according to opportunity accumulation and constructing accessibility index Rk；
Traffic accessibility reflects the inherent link between transportation systems and land use and is a primary goal of traffic planning. Reachability is often used to assess the convenience of public transportation systems, or the ease with which a group of people can reach a facility in a public place (e.g., hospital, park, school), or to investigate the vulnerability of a network with changes in reachability. At present, no research is available for introducing traffic accessibility indexes into an intercity passenger flow generation model.
The invention adopts the concept of cumulative-opportunity (cumulative-opportunity) to construct the reachability index suitable for predicting the generation amount of intercity passenger flow. The accumulated opportunity describes the number of opportunities (such as working posts) which can be developed by a traveler in contact with the traveler in a certain travel time range by using a certain transportation mode from a certain place. As shown in fig. 2, it is possible for a traveler to obtain all the opportunities for development as long as a given travel time is sufficiently long.
S51: calculating the opportunity number O of the neighbor cell g obtained from the cell k of the local city within the time threshold T according to the formula (8)k(T) obtaining the weighted sum of the opportunity numbers by adopting different trip modes;
Figure BDA0003219619190000141
in the formula (8), the reaction mixture is,
Figure BDA0003219619190000142
the table type local city cell k adopts the adjacent city opportunity number obtained by the mode m, and the opportunity can be referred to as population and employment post; the time threshold T is obtained by the quantile of the current intercity travel time; time of flight tkgCalculated by traffic network and equal to travel time in vehicle
Figure BDA0003219619190000143
External travel time of car
Figure BDA0003219619190000144
The quantile value may be 95%.
S52: calculating a reachability index Rk: according to the concept of cumulative opportunity, RkDefined as the ratio of the number of available neighbor opportunities in cell k to the total number of available neighbor opportunities within time threshold T:
Figure BDA0003219619190000145
in formula (9), O (T)max) Indicating that the number of all opportunities that can be reached for a sufficiently long time is equal to the number of opportunities for all cells in the neighborhood.
And sixthly, correcting the initial inter-city passenger flow generation amount of the traffic cell: respectively calculating and obtaining the corrected traffic cell intercity passenger flow generation amount G according to a formula (10) and a formula (11)kInter-city passenger flow attraction amount Ak；
Figure BDA0003219619190000146
Figure BDA0003219619190000151
In the formulas (10) and (11), n is the number of traffic zones,
Figure BDA0003219619190000152
the average reachability is the average value of all cell reachability in each circle of layers.
The method for improving the accuracy of the intercity passenger flow data of the traffic model and the city is suitable for the traffic district partition with small granularity, and can efficiently and accurately acquire real-time data by combining the large historical population passenger flow data, thereby providing accuracy guarantee for the refined intercity passenger flow prediction.
Example 4:
a method for improving the inter-city passenger flow data early warning of a traffic model with a city as shown in the attached figure 3 comprises the following steps:
firstly, acquiring basic data: study of traffic in citiesPopulation POP of cell kkAnd employment data EMPkPopulation POP of different circle layers i (core area, main city area and peripheral area)iAnd number of employment EMPi(ii) a Current passenger flow Q of different circle layers i and j between citiesijAnd Qji(ii) a The data are generally obtained by traditional resident trip investigation or statistical departments of relevant cities in the urban traffic model, but the acquisition of the resident trip investigation data is difficult to break through the administrative boundary barrier and cannot provide support for the regional traffic model, and the big data technology provides possibility for acquiring data of intercity outgoing amount, population and employment posts.
The specific steps for acquiring the population employment data of each cell (or each circle layer) are as follows: the method comprises the steps of firstly obtaining the resident population and the working population of a unit grid unit (for example, 200 x 200 meters) according to Internet positioning data, and then associating the grid population with the boundaries of traffic cells (or all circles of layers) to obtain the resident population and the employment position number of each traffic cell (or all circles of layers).
When the internet positioning data is acquired in real time, based on real-time statistics and prediction of historical big data, according to different passenger flow distribution base numbers of research cities, the peak time period, unit grid unit coordinates and threshold value ranges [ a, b ] of resident population and working population are set. When the resident population and the working population in the unit grid cell coordinate are higher than the highest threshold b, the unit grid cell can be provided with sub-grid cells, and the value range of the sub-grid cells can be adjusted in real time or periodically according to the resident population density, the working population number, the emergency events, the peak time period and the like of each cell, for example, the value range can be dynamically adjusted from 50 × 50 meters to 100 × 100 meters. When the population density is higher, the working population number is larger, and/or in a peak period, the unit grid unit sets the sub-grid more accurately, so that the positioning data acquisition accuracy is higher.
The specific steps for acquiring the current passenger flows of different circles among cities are as follows: firstly, establishing a matching relation between an urban circle layer and a user stop point by using internet positioning data, and then acquiring intercity travel passenger flow by identifying the flow direction of population;
intercity passenger flow Q based on current situationijAnd QjiCollecting and obtaining the current intercity passenger flow generation amount G of different circle layersiThe current situation of intercity passenger flow attraction AiWhere i, j ∈ { core region, primary metropolitan region, peripheral region };
Gi＝Qij (1)
Ai＝Qji (2)
secondly, calibrating the average intercity passenger flow rate (including generation rate and attraction rate) of each circle of layers: and calibrating the average intercity passenger flow trip rate of each circle layer by using the intercity passenger flow generation amount, the attraction amount, the population and employment data of different circle layers obtained in the first step. An intercity passenger flow generation model is constructed based on a generation rate method as follows:
Figure BDA0003219619190000161
Figure BDA0003219619190000162
in the formula (3), the reaction mixture is,
Figure BDA0003219619190000163
the average intercity passenger flow generation rate of the residential population and employment posts of the circle layer i is shown;
in the formula (4), the reaction mixture is,
Figure BDA0003219619190000164
the average intercity passenger flow attraction rate of the residential population and employment posts of the circle layer i;
thirdly, calculating the initial inter-city passenger flow generation amount of each traffic cell: calculating the initial intercity passenger flow generation amount of the cell k belonging to different circle layers by using the average intercity passenger flow trip rate of each circle layer obtained in the second step
Figure BDA0003219619190000165
Initial intercity passenger flow attraction
Figure BDA0003219619190000166
Figure BDA0003219619190000167
Figure BDA0003219619190000168
Fourthly, calibrating utility functions of ground traffic (including cars, taxis and passenger buses) and rail traffic (including intercity railways and subways);
s41: obtaining the proportion of the current situation that the local city cell k reaches the adjacent city cell g by adopting two travel modes of ground traffic (including cars, taxis and passenger buses) and rail traffic (including intercity railways and subways)
Figure BDA0003219619190000169
The rail transit passenger flow can be obtained by using railway ticket business or bus card swiping data, and the ground transit passenger flow can be obtained by subtracting the rail transit passenger flow from the current passenger flow.
S42: and (5) calibrating the utility function of the travel of the local city cell k to the selection mode m of the adjacent city cell g according to the formula (7) by combining the proportions of the travel of the modes obtained in the S41
Figure BDA00032196191900001610
Wherein, when m takes 1, the ground traffic is represented, and when m takes 2, the track traffic is represented:
Figure BDA00032196191900001611
in the formula (7), the reaction mixture is,
Figure BDA0003219619190000171
representing the traveling utility of ground traffic and rail traffic;
Figure BDA0003219619190000172
the time of the travel in the vehicle is,
Figure BDA0003219619190000173
is the travel time outside the automobile,
Figure BDA0003219619190000174
α1、α2is a calibrated parameter; the time variables in this step are current values;
s43: the parameters obtained in S42
Figure BDA0003219619190000175
α1、α2And planning the travel time of the year in the vehicle
Figure BDA0003219619190000176
And travel time outside vehicle
Figure BDA0003219619190000177
Substituting the formula (9) into the formula (9), and obtaining the proportion of travel of the planning year from the local city cell k to the adjacent city cell g in the selection mode m;
fifthly, constructing accessibility indexes: method for calculating accessibility according to opportunity accumulation and constructing accessibility index Rk；
Traffic reachability reflects the inherent link between the transportation system and land use and is a primary goal of traffic planning. Reachability is often used to assess the convenience of public transportation systems, or the ease with which a group of people can reach a common-place facility (e.g., hospital, park, school), or to study the vulnerability of a network with changes in reachability. At present, no research is available for introducing traffic accessibility indexes into an intercity passenger flow generation model.
The invention adopts the concept of cumulative-opportunity (cumulative-opportunity) to construct the reachability index suitable for predicting the generation amount of intercity passenger flow. The accumulated opportunity describes the number of opportunities (such as working posts) which can be developed by a traveler in contact with the traveler in a certain travel time range by using a certain transportation mode from a certain place. As shown in fig. 2, it is possible for a traveler to obtain all the opportunities for development as long as a given travel time is sufficiently long.
S51: calculating the chance number O of the neighbor cell g obtained from the cell k in the local city within the time threshold T according to the formula (8)k(T) obtaining the weighted sum of the opportunity numbers by adopting different trip modes;
Figure BDA0003219619190000178
in the formula (8), the reaction mixture is,
Figure BDA0003219619190000179
the table type local city cell k adopts the adjacent city opportunity number obtained by the mode m, and the opportunity can be referred to as population and employment post; the time threshold T is obtained by the quantile of the current intercity travel time; time of flight tkgCalculated by traffic network and equal to travel time in vehicle
Figure BDA00032196191900001710
External travel time of car
Figure BDA00032196191900001711
The quantile value may be 95%.
S52: calculating a reachability index Rk: according to the concept of cumulative opportunity, RkDefined as the ratio of the number of available neighbor opportunities in cell k to the total number of available neighbor opportunities within time threshold T:
Figure BDA0003219619190000181
in formula (9), O (T)max) Indicating that the number of all opportunities that may be reached for a sufficiently long time is equal to the number of opportunities for all cells in the neighborhood.
And sixthly, correcting the initial inter-city passenger flow generation amount of the traffic cell: respectively calculating the corrected generation amount G of the intercity passenger flow of the traffic community according to a formula (10) and a formula (11)kInter-city passenger flow attraction amount Ak；
Figure BDA0003219619190000182
Figure BDA0003219619190000183
In the formulas (10) and (11), n is the number of traffic cells,
Figure BDA0003219619190000184
the average reachability is the average value of all cell reachability in each circle of layers.
And seventhly, performing short-term, medium-term and long-term early warning according to preset passenger flow generation amount and attraction amount. Generate the intercity passenger flow GkInter-city passenger flow attraction amount AkAnd comparing the average value with the historical period of the current day (for example, every week, every month and every year), correcting the value of a warning line by referring to the predicted conditions of holidays, important activities and the like, generating a passenger flow trend graph for early warning when the value exceeds the warning line, and reminding the short-term planning of public transport capacity, unreasonable traffic planning, emergency situations, long-term planning of city and population development and the like.
The embodiment is based on the same-urbanization intercity passenger flow generation model, is suitable for the traffic district partition with small granularity, can efficiently and accurately acquire real-time data by combining the large data of the historical population passenger flow, and provides accuracy guarantee for refined intercity passenger flow prediction.
The invention has been described in an illustrative manner, and it is to be understood that the invention is not limited to the specific embodiments described above, but is intended to cover various modifications, which may be made by the methods and technical solutions of the invention, or may be applied to other applications without modification.

Claims (6)

1. A method for constructing a same-city intercity passenger flow generation model based on traffic reachability is characterized by comprising the following steps:
firstly, acquiring basic data: big data technology-based acquisition research cityPOP for population of each traffic cell kkAnd employment data EMPkPOP of population of different circle levels iiAnd number of employment EMPi(ii) a The ring layer comprises a core area, a main urban area and a peripheral area; current passenger flow Q of different circle layers i and j between citiesijAnd Qji(ii) a Obtaining the current intercity passenger flow generation amount G of different circles based on the current passenger flowiThe current situation of intercity passenger flow attraction AiWherein:
Gi＝Qij (1)
Ai＝Qji (2)
secondly, calibrating the average intercity passenger flow trip rate of each circle of layers, wherein the trip rate comprises a generation rate and an attraction rate; the intercity passenger flow generation amount G of different circle layers between cities obtained in the first stepiSuction amount AiPOP (POP for people of population)kAnd employment data EMPiAnd calibrating the average intercity passenger flow trip rate of each circle of layers
Figure FDA0003648136530000011
θi 2，
Figure FDA0003648136530000012
The inter-city passenger flow generation model is constructed as follows:
Figure FDA0003648136530000013
Figure FDA0003648136530000014
in the formula (3), the reaction mixture is,
Figure FDA0003648136530000015
θi 2average intercity passenger flow generation rate of residential population and employment posts of circle layer i;
in the formula (4), the reaction mixture is,
Figure FDA0003648136530000016
the average intercity passenger flow attraction rate of the residential population and employment posts in the circle layer i;
thirdly, calculating the initial intercity passenger flow generation amount of each traffic cell k: calculating the initial intercity passenger flow generation amount of the cell k belonging to different circle layers by using the average intercity passenger flow trip rate of each circle layer obtained in the second step
Figure FDA0003648136530000017
Initial inter-city passenger flow attraction
Figure FDA0003648136530000018
Figure FDA0003648136530000019
Figure FDA00036481365300000110
Fourthly, calibrating a travel utility function comprising ground traffic and rail traffic; the ground traffic comprises cars, taxis and passenger buses, and the rail traffic comprises intercity railways and subways;
fifthly, constructing a reachability index: method for calculating accessibility according to opportunity accumulation and constructing accessibility index Rk；
And sixthly, correcting the initial intercity passenger flow generation amount and the initial intercity passenger flow attraction amount of the traffic community: respectively calculating and obtaining the corrected traffic cell intercity passenger flow generation amount G according to a formula (7) and a formula (8)kInter-city passenger flow attraction amount Ak；
Figure FDA0003648136530000021
Figure FDA0003648136530000022
In the formulas (7) and (8), n is the number of the traffic cells,
Figure FDA0003648136530000023
the average reachability index is the average value of all cell reachability indexes in each circle of layers.
2. The construction method according to claim 1, wherein in the first step, the specific step of acquiring the population employment data of each cell or each circle layer is: the method comprises the steps of firstly obtaining residential population and working population of unit grid units according to Internet positioning data, and then associating the grid population with a traffic cell or a boundary of each circle of layers to obtain the residential population and employment post number of each traffic cell or each circle of layers.
3. The construction method according to claim 1, wherein in the first step, the specific step of obtaining the current passenger flows of different circles among cities is as follows: firstly, a matching relation between an urban circle layer and a user stop point is established according to internet positioning data, and then the current situation passenger flow is obtained by identifying the population flow direction.
4. The construction method according to claim 1, wherein in the fourth step, the specific steps of calibrating the travel utility function are as follows:
s41: obtaining the travel proportion of the current situation that the local city cell k reaches the adjacent city cell g by adopting two travel modes of ground traffic and rail traffic
Figure FDA0003648136530000024
The rail transit passenger flow is obtained by using railway ticket business or bus card swiping data, and the ground transit passenger flow is obtained by subtracting the rail transit passenger flow from the current passenger flow;
s42: and (5) calibrating the local city cell k to the local city cell k according to the formula (9) by combining the travel proportion of each travel mode obtained in the S41Trip utility of g selection mode m trip in adjacent city district
Figure FDA0003648136530000025
Sum trip utility function
Figure FDA0003648136530000026
Wherein, when m takes 1, the ground traffic is represented, and when m takes 2, the track traffic is represented:
Figure FDA0003648136530000027
in the formula (9), the reaction mixture is,
Figure FDA0003648136530000031
the time of the travel in the vehicle is,
Figure FDA0003648136530000032
is the travel time outside the automobile,
Figure FDA0003648136530000033
α1、α2is a calibrated parameter; the time variables in this step are all current values;
s43: the parameters obtained in S42
Figure FDA0003648136530000034
α1、α2And travel time in a vehicle
Figure FDA0003648136530000035
And travel time outside the vehicle
Figure FDA0003648136530000036
And substituting the formula (9) to obtain the proportion of the travel of the selection mode m when the local city cell k reaches the adjacent city cell g.
5. The transaction-based system of claim 1The method for constructing the same-city intercity passenger flow generation model with accessibility is characterized in that in the fifth step, the accessibility index RkThe specific calculation steps are as follows:
s51: calculating the opportunity number O of the neighbor cell g which is obtained within the time threshold T from the cell k of the local city according to the formula (10)k(T) is the weighted summation of the acquired opportunity numbers in different travel modes m;
Figure FDA0003648136530000037
in the formula (10), when m is 1, the ground traffic is represented, when m is 2, the track traffic is represented,
Figure FDA0003648136530000038
the table type local city cell k adopts the adjacent city opportunity number obtained by the mode m, and the opportunity is referred to by population and/or employment post; the time threshold T is obtained by the quantile of the current intercity travel time; time of flight tkgCalculated by traffic network and equal to travel time in vehicle
Figure FDA0003648136530000039
External travel time of car
Figure FDA00036481365300000310
S52: calculating a reachability index Rk: said method of calculating reachability from chance accumulation, RkDefined as the ratio of the number of available neighbor opportunities to the total number of available neighbor opportunities for cell k within time threshold T:
Figure FDA00036481365300000311
in formula (11), O (T)max) Indicating that the number of all opportunities that can be reached for a sufficiently long time is equal to the number of opportunities for all cells in the neighborhood.
6. The construction method according to claim 5, wherein the quantile value is 95%.
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