CN115062873A

CN115062873A - Traffic travel mode prediction method and device, storage medium and electronic device

Info

Publication number: CN115062873A
Application number: CN202210965668.4A
Authority: CN
Inventors: 赵鹏军; 吴秀琛; 陈霄依; 李兆祥; 万婕; 万丹
Original assignee: Peking University
Current assignee: Peking University
Priority date: 2022-08-12
Filing date: 2022-08-12
Publication date: 2022-09-16
Anticipated expiration: 2042-08-12
Also published as: CN115062873B

Abstract

The invention discloses a method and a device for predicting a trip mode, a storage medium and electronic equipment. Wherein, the method comprises the following steps: acquiring resident travel survey data of a target city in a target time period from a travel database to acquire the traffic congestion rate, the group of residents, the travel starting point and the travel ending point, the traffic mode and the travel mode of the target city; acquiring road network topological data of a target city; integrating the resident trip survey data and the path impedance, inputting the integrated data into an MNL model to obtain the proportion of each traffic trip mode, and acquiring the traffic volume of each group in a target city from a departure area i to an arrival area j in different trip modes based on mobile phone signaling data; and adjusting the traffic volume of each group of the target city under different travel modes to obtain the target traffic volume of each group in the target city under different travel modes. The method and the device solve the technical problem that the urban traffic travel mode cannot be accurately predicted in the related technology.

Description

Travel mode prediction method and device, storage medium and electronic device

Technical Field

The invention relates to the technical field of travel mode prediction, in particular to a travel mode prediction method and device, a storage medium and electronic equipment.

Background

The mode of travel is of great importance to the sustainable development of cities in China, and the mode of travel of residents in a city is an important prerequisite factor influencing traffic jam, traffic energy consumption and pollution emission, traffic safety, traffic facility construction and the like of the city. The accurate simulation and prediction of the resident travel mode are key contents of urban traffic planning and management.

However, the traffic travel mode division simulation technology in the related art has the following problems:

firstly, in the related art, it is assumed that a traveler only adopts a single transportation mode for traveling, adopts single-step transportation mode division, does not consider transfer behaviors possibly existing in traveling, and does not accord with the actual situation of the traveling behaviors. For example, the 'P + R' parking transfer mode and the transfer of public transportation and non-motor transportation cannot occur in the model construction, and only the midway transfer points can be designated in a segmented manner for travel mode division.

Secondly, the sharing rate of the travel mode is not hard-constrained by indexes such as the holding capacity of a motor vehicle or a private car, and the sharing rate of the travel mode is not redistributed, so that the abnormal travel mode division prediction result cannot be corrected when appearing, the robustness of model prediction is influenced, and the follow-up flow distribution and policy evaluation are influenced.

Thirdly, most of the existing models are divided into trip modes based on resident trip survey data. In consideration of cost saving, the total amount, the space coverage rate, the time granularity and the like of samples in questionnaire data are limited, the accuracy and the application range of model prediction are restricted, dynamic real-time simulation on the travel modes of residents cannot be performed, and the refined business requirements of intelligent traffic planning management cannot be met.

Disclosure of Invention

The embodiment of the invention provides a method and a device for predicting a travel mode, a storage medium and electronic equipment, which at least solve the technical problem that the urban travel mode cannot be accurately predicted in the related technology.

According to an aspect of an embodiment of the present invention, there is provided a method for predicting a travel mode, including: acquiring resident trip survey data, basic geographic information data and mobile phone signaling data of a target city in a target time period from a trip database; acquiring the traffic congestion rate of the target city, the group to which the residents belong, the transportation mode and the travel mode to which the transportation mode belongs on the basis of the resident travel survey data; acquiring road network topology data of the target city based on the basic geographic data, wherein the road network topology data is used for calculating the path impedance of a preset path; integrating the resident trip survey data and the path impedance, inputting the integrated data into an MNL (mobile network layer) model to obtain the proportion of each trip mode, and acquiring the traffic volume of each group in the target city from a departure area i to an arrival area j under different trip modes based on the mobile phone signaling data; and adjusting the traffic volume of each group of the target city under different travel modes according to the motor vehicle holding capacity of the target city in the target time period to obtain the target traffic volume of each group in the target city under different travel modes.

According to another aspect of the embodiments of the present invention, there is also provided a travel mode prediction apparatus, including: the first acquisition unit is used for acquiring resident trip survey data, basic geographic information data and mobile phone signaling data of a target city in a target time period from a trip database;

a second obtaining unit, configured to obtain, based on the resident travel survey data, a traffic congestion rate of the target city, a group to which a resident belongs, a transportation mode, and a travel mode to which the transportation mode belongs; a third obtaining unit, configured to obtain road network topology data of the target city based on the basic geographic data, where the road network topology data is used to calculate a path impedance of a preset path; an integration determining unit, configured to perform data integration on the resident trip survey data and the path impedance, input the integrated data into an MNL model to obtain a proportion of each trip mode, and obtain, based on the mobile phone signaling data, traffic volumes of each group in the target city under different trip modes from a departure area i to an arrival area j; and the adjusting unit is used for adjusting the traffic volume of each group of the target city in different travel modes according to the motor vehicle holding capacity of the target city in the target time period to obtain the target traffic volume of each group in the target city in different travel modes.

According to still another aspect of the embodiments of the present invention, there is also provided an electronic device, including a memory and a processor, where the memory stores a computer program, and the processor is configured to execute the travel mode prediction method through the computer program.

According to still another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the above-mentioned travel mode prediction method when running.

In the embodiment of the invention, resident travel survey data, basic geographic information data and mobile phone signaling data of a target city in a target time period are acquired from a travel database; acquiring the traffic congestion rate of the target city, the group to which the residents belong, the transportation mode and the travel mode to which the transportation mode belongs on the basis of the resident travel survey data; acquiring road network topology data of the target city based on the basic geographic data, wherein the road network topology data is used for calculating the path impedance of a preset path; integrating the resident trip survey data and the path impedance, inputting the integrated data into an MNL (mobile network layer) model to obtain the proportion of each trip mode, and acquiring the traffic volume of each group in the target city from a departure area i to an arrival area j in different trip modes based on the mobile phone signaling data; according to the motor vehicle inventory of the target city in the target time period, the traffic volume of each group of the target city under different travel modes is adjusted to obtain the target traffic volume of each group in the target city under different travel modes.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:

fig. 1 is a schematic diagram of an application environment of an alternative method for predicting a travel pattern according to an embodiment of the present invention;

fig. 2 is a schematic diagram of an application environment of another alternative method for predicting a travel pattern according to an embodiment of the present invention;

fig. 3 is a flow chart illustrating an alternative method for predicting a travel mode according to an embodiment of the present invention;

fig. 4 is a schematic flow chart of another alternative method for predicting a travel pattern according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of an alternative trip prediction scheme according to an embodiment of the present invention;

fig. 6 is a schematic diagram of a transportation travel mode of an alternative target city according to an embodiment of the present invention;

fig. 7 is a schematic diagram of a transportation travel mode of another alternative target city according to the embodiment of the invention;

fig. 8 is a schematic structural diagram of an alternative travel pattern prediction apparatus according to an embodiment of the present invention;

fig. 9 is a schematic structural diagram of an alternative electronic device according to an embodiment of the invention.

Detailed Description

In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

According to an aspect of the embodiments of the present invention, a method for predicting a travel mode is provided, and as an alternative implementation, the method for predicting a travel mode may be applied to, but not limited to, an application environment as shown in fig. 1. The terminal equipment 102, the network 104 and the server 106 are used for human-computer interaction with the user. The user 108 and the terminal device 102 may perform human-computer interaction, and a traffic travel mode prediction application client is operated in the terminal device 102. The terminal device 102 includes a human-machine interaction screen 1022, a processor 1024, and a memory 1026. The man-machine interaction screen 1022 is used for presenting an interface of a travel mode of the target city; the processor 1024 is used for acquiring travel big data. The memory 1026 is used to store row big data.

In addition, the server 106 includes a database 1062 and a processing engine 1064, and the database 1062 is used for storing trip big data of the target city. Processing engine 1064 is configured to: acquiring resident trip survey data, basic geographic information data and mobile phone signaling data of a target city in a target time period from a trip database; acquiring the traffic congestion rate of the target city, the group to which the residents belong, the transportation mode and the travel mode to which the transportation mode belongs on the basis of the resident travel survey data; acquiring road network topology data of the target city based on the basic geographic data, wherein the road network topology data is used for calculating the path impedance of a preset path; integrating the resident trip survey data and the path impedance, inputting the integrated data into an MNL (mobile network layer) model to obtain the proportion of each trip mode, and acquiring the traffic volume of each group in the target city from a departure area i to an arrival area j in different trip modes based on the mobile phone signaling data; and adjusting the traffic volume of each group of the target city under different travel modes according to the motor vehicle holding capacity of the target city in the target time period to obtain the target traffic volume of each group in the target city under different travel modes.

As another alternative, the above-described methods of knowledge graph construction described herein may be applied to FIG. 2. As shown in fig. 2, a human-computer interaction may be performed between a user 202 and a user device 204. The user equipment 204 includes a memory 206 and a processor 208. The user device 204 in this embodiment may refer to, but is not limited to, performing the above operations performed by the terminal device 102, and determine the target traffic volume of each group in the target city in different travel modes.

Alternatively, the terminal device 102 and the user device 204 may be, but not limited to, a mobile phone, a tablet computer, a notebook computer, a PC, and the like, and the network 104 may include, but is not limited to, a wireless network or a wired network. Wherein, this wireless network includes: WIFI and other networks that enable wireless communication. Such wired networks may include, but are not limited to: wide area networks, metropolitan area networks, and local area networks. The server 106 may include, but is not limited to, any hardware device capable of performing computations. The server may be a single server, a server cluster composed of a plurality of servers, or a cloud server. The above is merely an example, and this is not limited in this embodiment.

In order to solve the above technical problem, as an optional implementation manner, as shown in fig. 3, an embodiment of the present invention provides a method for predicting a travel mode, including the following steps:

and S302, resident travel survey data, basic geographic information data and mobile phone signaling data of the target city in the target time period are obtained from the travel database.

In embodiments of the present invention, the target time period includes, but is not limited to, a time period in months or years; the mobile phone signaling is communication record data between the mobile phone and the communication base station. When the mobile phone is connected to the mobile communication network, a series of control commands are generated, and the data fields of the commands comprise various information such as time, position, number and the like. Here, the mobile phone signaling data is mainly used to obtain track start and end point (OD) data between different streets in a single city, that is, the start point and the end point of a trip, and the specific fields include a start point street, an end point street, a date, a gender group, an age group, and a sample expansion population number, where the sample expansion population number may be obtained by an operator through a yearbook and is adjusted accordingly.

S304, obtaining the traffic congestion rate, the group to which the residents belong, the transportation mode and the travel mode to which the transportation mode belongs of the target city based on the resident travel survey data.

Based on the original data of the resident trip survey, the vehicle congestion rate of each traffic unit, namely the vehicle congestion rate of each street is calculated, the crowd is divided according to the age and the gender, and the method corresponds to a common trip mode and the trip mode of the embodiment of the invention. The population is divided into six groups by the combination of underage (under 18 years), labor (19-59 years), aged (over 60 years) third-class age categories and gender of male and female. And for each traffic unit, namely a street in the resident travel survey data, counting the proportion of private cars owned by families in each group. The private car travel in the resident travel survey is classified into a first type private transportation travel mode, the motorcycle travel is classified into a second type private transportation travel mode, and the rest transportation modes are classified into public transportation travel modes.

S306, acquiring road network topology data of the target city based on the basic geographic data, wherein the road network topology data is used for calculating the path impedance of a preset path;

according to the embodiment of the invention, based on the years of the resident trip survey data, the road network topology of the corresponding years is constructed by using basic geographic information data such as OSM (open service management), map software data and national geographic data information center vector map data. The basic road network database can be divided into five parts of road sections, road section nodes, bus stations, subway stations and subway lines, and parts such as a speedy port can be added to truly reflect the organization condition of the actual comprehensive traffic network according to the fineness requirement of the road network topology. And all parts of the road network database are connected and superposed through the serial numbers of the road section nodes, so that the construction of the topological network is completed.

Based on a preset road network impedance calculation method, calculating the impedance of the shortest paths of various traffic modes between specific administrative divisions or traffic trip division units in the current city, and classifying the traffic modes into various traffic trip modes. For example, in the case of the XX city, there are seven transportation modes, namely, walking, bicycle, motorcycle, taxi, private car, bus, and subway, and there is a corresponding shortest path impedance for each starting point and end point pair. The path impedance includes the impedance of each section constituting the path and the transfer impedance between sections, and if the resident transfers from one bus line to another, the transfer impedance due to the extra waiting time must be calculated.

And S308, performing data integration on the resident trip survey data and the path impedance, inputting the integrated data into an MNL (mobile network layer) model to obtain the proportion of each traffic trip mode, and acquiring the traffic volume of each group in the target city from a departure area i to an arrival area j in different trip modes based on the mobile phone signaling data.

According to a pre-constructed MNL equation, parameters are obtained by utilizing resident travel and road network impedance data obtained through the processing of the steps, a parameter supplementing model is applied to the traffic volume between units obtained through mobile phone signaling big data capturing processing, and the traffic volume mode division corresponding to the big data year is obtained. For example, in the XX city example, after corresponding data is obtained by processing the 2015 resident trip survey data, the specific parameters of the MNL model are obtained by using the maximum likelihood method, and the specific parameters are applied to the 2019 urban traffic volume extracted by analyzing the mobile phone signaling big data, so that the inter-street trip pattern traffic volume in the current year is obtained. And then limiting the trip mode sharing rate according to the reserved quantity of the motor vehicles to obtain the final output after redistribution.

S310, adjusting the traffic volume of each group of the target city in different travel modes according to the motor vehicle holding capacity of the target city in the target time period, and obtaining the target traffic volume of each group in the target city in different travel modes.

Due to the practical limitations of private travel (i.e., the holdback level of the respective vehicle), embodiments of the present invention impose hard constraint limits on the assignment of travel patterns and reassign procedures. For example, the total amount of private car travel patterns in a city throughout the day cannot exceed the total private car inventory in the city. If the trip volume obtained through the preliminary calculation in the previous step exceeds a theoretical value, the redundant trip volume is distributed to the other two traffic trip modes according to the IIA assumption of the MNL model and the principle that the total trip volume of the specific OD is not changed, so that the model result is more practical, more flexible and more operable. Since a private traffic pattern may result in a traffic volume reallocation due to another private traffic pattern, the traffic volume reallocation value itself also exceeds the theoretical value. The redistribution process is set to two rounds, the overflow of the second round is totally distributed to the public transportation travel mode, and the final result is output

In one or more embodiments, the data integration of the resident travel survey data and the path impedance, and the input of the integrated data into the MNL model to obtain the proportion of each travel mode includes:

obtaining the path travel Cost of each group in each travel mode _ij,a,M Wherein a is a group category, and M is a travel mode;

according to the Car congestion rate Car _ Ownership _a,i Processing the mobile phone signaling data to obtain OD data T of each group between streets of the target city _ij,a Traffic volume T divided into vehicle sub-groups _ij,a,car And traffic volume T of non-vehicle group _{ij,a,no_car} ；

Computing the T from the MNL model _ij,a,car And said T _{ij,a,no_car} The ratio of (A) to (B);

the obtaining of the traffic volume of each group in the target city from the departure area i to the arrival area j in different travel modes based on the mobile phone signaling data includes:

according to the T _ij,a,car And said T _{ij,a,no_car} Determining the traffic volume of each group from the departure zone i to the arrival zone j under different travel modes.

At one or moreIn an embodiment, the obtaining of the travel Cost of the path of each group in each travel mode _ij,a,M The method comprises the following steps:

impedance Cost of a path from a departure zone i to an arrival zone j _m Carrying out weighted calculation, carrying out multi-round distribution on the traffic volume, and obtaining the travel Cost _ij,a,M (ii) a Wherein when T does not appear in the distribution process _ij,a,m Then, the Cost is obtained by adopting the formula (1) _ij,a,M Otherwise, obtaining the Cost by adopting a formula (2) _ij,a,M ：

(first round, M = M) ₁ ，M ₂ ，M ₃ ）（1）

(n-th wheel, n ≠ 1, M = M) ₁ ，M ₂ ，M ₃ ）（2）

Wherein, T _ij,a,m For the weight of the a-population under path M, M ₁ ，M ₂ ，M ₃ Respectively different travel modes.

In one or more embodiments, the computing the T from the MNL model _ij,a,car And said T _{ij,a,no_car} Comprises the following steps:

obtaining the T based on formulas (3), (4), (5), (6) _ij,a,car And said T _{ij,a,no_car} The proportion of (A):

（3）

（4）

（5）

（6）

wherein,

the traffic volume of a group a is not divided into a travel mode, Car _ Ownership _a,i As the traffic congestion rate of the target city,

selecting a parameter, p, for a logic mode derived from said resident survey data calibration _ij,a,car,M Is said T _ij,a,car Ratio of (a) p _{ij,a,no_car,M} Is said T _{ij,a,no_car} The ratio of (a);

according to the T _ij,a,car And said T _{ij,a,no_car} Determining the traffic volume of each group from the departure area i to the arrival area j under different travel modes

Said

Obtained by equation (7):

（7）。

in one or more embodiments, the impedance Cost _m Obtained by equation (8):

（8）

wherein l is road section, k is traffic mode, Time _ cost _l,k Fee _ cost as a time travel cost _l,k For monetary cost, Trans_cost _l,k To trade-off costs.

In one or more embodiments, the method further comprises: the residents of the target city are classified into the following six group categories according to age stage and gender:

a first population comprising underage 18 adult males;

a second population comprising minor females under 18 years of age;

a third group comprising males between age 19 and 59;

a fourth population comprising females between the ages of 19 and 59;

a fifth population comprising older males aged 60 or older;

a sixth population comprising older females aged 60 years old or older.

Based on the above embodiment, in an application embodiment, as shown in fig. 4, the method for predicting a travel mode includes the following steps: (1) firstly, based on the years of used resident trip survey data, constructing a road network topology corresponding to the years by using basic geographic information data such as an OSM (open service management), a map software database and national geographic data information center vector map data. The basic road network database can be divided into five parts of road sections, road section nodes, bus stations, subway stations and subway lines, and parts such as high-speed ports are added according to the fineness requirement of road network topology, so that the organization condition of the actual comprehensive traffic network can be reflected more truly. And all parts of the database are linked and superposed through the serial numbers of the road section nodes to complete the construction of the topology network.

(2) Based on a preset road network impedance calculation method, calculating the impedance of the shortest path of various traffic modes between specific administrative divisions or traffic trip division units in cities, and classifying the traffic modes into various traffic trip modes. For example, in the case of beijing, there are seven transportation methods, namely, walking, bicycle, motorcycle, taxi, private car, bus, and subway, and there is a corresponding shortest path impedance for each starting point and end point pair. The path impedance includes the impedance of each section constituting the path and the transfer impedance between sections, and if the resident transfers from one bus line to another, the transfer impedance due to the extra waiting time must be calculated.

(3) Based on the original data of the resident trip survey, the vehicle congestion rate of each traffic unit is calculated, the crowd is divided according to the age and the gender, and the common trip mode corresponds to the trip mode of the embodiment of the invention. The population is divided into six groups by the combination of underage (under 18 years), labor (19-59 years), aged (over 60 years) third-class age categories and gender of male and female. And for each traffic unit, namely a street in the resident travel survey data, counting the proportion of private cars owned by families in each group. And the private car travel in the resident travel survey is classified into a first type private transportation travel mode, the motorcycle travel is classified into a second type private transportation travel mode, and the rest transportation modes are classified into public transportation travel modes for subsequent MNL model solution.

(4) According to the constructed MNL equation, parameters are obtained by utilizing resident travel and road network impedance data obtained through the processing of the steps, a model with the parameters completed is applied to traffic volume between units obtained through mobile phone signaling big data capturing processing, and each traffic volume mode division corresponding to the big data year is obtained. For example, after corresponding data are obtained by processing 2015-year resident trip survey data in the target city example, the specific parameters of the MNL model are obtained by the maximum likelihood method, and the specific parameters are applied to 2019-year traffic volume in the city extracted by mobile phone signaling big data analysis, so that the street traffic volume in the current year in a trip mode is obtained. And then limiting the trip mode sharing rate according to the reserved quantity of the motor vehicles to obtain the redistributed final output.

(5) Due to practical limitations of private travel (i.e., the holdover levels of the respective vehicles), embodiments of the present invention propose hard constraint limits and reassignment procedures for travel mode assignment. For example, the total amount of private car travel in a city throughout the day cannot exceed the private car inventory throughout the city. If the trip volume obtained through the preliminary calculation in the previous step exceeds a theoretical value, the redundant trip volume is distributed to the other two traffic trip modes according to the IIA assumption of the MNL model and the principle that the total trip volume of the specific OD is not changed, so that the model result is more practical, more flexible and more operable. Since a private traffic pattern may result in a traffic volume reallocation due to another private traffic pattern, the traffic volume reallocation value itself also exceeds the theoretical value. The redistribution process is set to two rounds, the overflow of the second round is totally distributed to the public transportation travel mode, and the final result is output.

The construction process of the travel mode prediction model comprises the following steps:

the embodiment of the invention defines three types of traffic travel modes, namely a private traffic travel mode (a private car type and a motorcycle type) and a public traffic travel mode (the symbol is set as M) ₁ ，M ₂ ，M ₃ ). The technical general flow can be summarized into traffic travel volume T of a non-divided travel mode obtained by processing mobile phone signaling data _ij,a, Conversion to T _ij,a,m I.e. the amount of traffic in the group a using the travel mode M from the departure zone i to the arrival zone j within the target time period.

For a group, the average generalized travel Cost of the path of the M mode _ij,a,M Impedance Cost through the path it contains _m Obtained by weighted calculation with weight of T _ij,a,m . In the flow of traffic flow overall distribution, multiple iterations are needed to reach balance. Generally, there is no T for the first iteration round _ij,a,m Values, simple averages may be used. N represents the number of paths selected by each OD for the group a belonging to the M mode, and the paths are selected by a specific algorithm such as a greedy algorithm according to the principle that the impedance is from small to large.

(first round, M = M) ₁ ，M ₂ ，M ₃ ）

(n-th wheel, n ≠ 1, M = M) ₁ ，M ₂ ，M ₃ ）

Path impedance, i.e. pathGeneralized traffic Cost of (1), generalized travel Cost of (combination of physical road section and transportation mode) pairs of its components (l, k) _l，k (Inclusion of inter-link transfer Cost based on condition judgment _ε ) And (4) adding to obtain the final product. Overall, the size of the transfer Cost in the path affects Cost _l，k Thereby affecting the path impedance Cost _m Whether the path is selected as one of N paths is determined, which affects Cost _ij,a,M The distribution proportion of each travel mode is changed through the calculation of (1), which is the embodiment of the embedded transfer behavior algorithm hidden in the technology.

Transfer costs generally occur between different transportation means of adjacent road segments, but sometimes occur between the same transportation means depending on the topology of the road network and the fineness of the impedance algorithm. For the links included in the route selected from i to j, the travel cost according to each traffic mode k is as follows (distance unit default km, speed unit default km/h):

Cost _l,k =Time_cost _l,k +Fee_cost _l,k +Trans_cost _l,k ；

wherein, the Time _ cost _l,k The cost of time traveling is the actual time spent by residents on traveling. Fee _ cost _l,k The cost is converted into the cost of the same unit as the time trip cost by setting the unit time value VOT. Trans _ cost _l,k For the transfer cost, in addition to the time cost of transfer, the additional money cost of transfer caused by transfer of, for example, subway, public transport, taxi, etc. is also added up as the transfer cost item by the unit time value VOT conversion unit and the transfer time cost. For subways and buses, two situations exist in the generation of transfer cost of the subways and the buses: one is traffic mode change, namely, passengers change subways or buses from other traffic modes; the other is the carrier or line change of the same transportation mode, and is switched from one subway/bus line to another subway/bus line.

In transfer restriction, every kind of trafficThe mode can only be mutually transferred with the traffic mode belonging to the same class of traffic travel modes. Public transport travel mode M ₃ Including walking, bicycles, rail transit, buses, taxis. Two private traffic travel modes M ₁ ，M ₂ Respectively comprises two private traveling modes of private car and motorcycle, and can be used for riding M ₃ The traffic mode of (1). In particular, M ₁ ，M ₂ The private car or the motorcycle is forbidden to be transferred with the taxi, and the private car or the motorcycle can be used only at the beginning, the last travel section or the whole travel in the whole travel route and can be used only once.

And calculating the proportion of each travel mode according to the MNL model. According to the family traffic congestion rate Car _ Ownership of the street _a,i OD data between streets of each group obtained by processing mobile phone signaling data

Flow data T divided into two subclasses of vehicle and non-vehicle _ij,a,car And T _{ij,a,no_car} 。

And selecting parameters for the logit mode, and calibrating by resident survey data.

；

；

；

；

Obtaining the traffic volume T _ raw distributed with the traffic travel mode without constraint _ij,a,M 。

Secondly, the flow of the private traffic travel mode is restricted:

after the traffic is primarily distributed to the travel mode, the holding capacity LimitM of regional private cars and motorcycles ₁ And LimitM ₂ The upper limit of the theoretical traffic volume which can be taken as travel by using the two modes is taken as the upper limit of the theoretical traffic volume which can be taken as travel by using the two modes, and the upper limit of the theoretical traffic volume is taken as the upper limit of the traffic volume which can be taken as travel by using the two modes and M ₁ 、M ₂ The traffic volumes assigned to the routes of the travel pattern are compared.

(1) Without limitation: (

，d = 1，2）

Belong to M _d （dActual traffic flow of = 1, 2)

Does not exceed the theoretical upper limit of traffic flow LimitM _d Then the formula is satisfied: t is _ij,a,M =T_raw _ij,a,M 。

(2) Traverse from 1 to 2 when constraint is requireddIf is _ij,a T_raw _ij,a,Md >LimitM _d Then the traffic flow is limited in equal proportion to the theoretical upper limit of the traffic flow (in this casedWith a single value of 1 or 2, i.e. operating on only a single travel mode), for each OD pair, i.e. with determined i and j as the calculation scale:

then the paths of the other two travel modes in each OD pair are according to p _ij,a,M The proportion of (A) is distributed:

，M=M _e ( e≠d )；

at this time, T _ temp _ij,a,M For temporary traffic flow variables, the second round of traffic volume check and redistribution are also needed to prevent the actual flow of another private traffic mode from exceeding the upper limit, and finally the formal traffic flow T is determined _ij,a,m 。

If the actual traffic volume (sigma) is exceeded at this moment _ij,a T_temp _ij,a,Mp >LimitM _p ，p =1，2 ；p≠d) Then all assigned to the travel mode attribute as M ₃ Path m (calculated for each OD pair):

；p = 1，2 &p≠d

∆T _ij,a =T_temp _ij,a,Mp -T _ij,a,Mp ；

；

if the actual traffic volume (sigma) is not exceeded _ij,a T_temp _ij,a,Mp ≤LimitM _p ，p = 1，2 ；p≠d）：

；

f= 1，2，3 ；f≠d；

Through the technical scheme, the embodiment of the invention can simulate and predict the travel mode of residents with high precision, and has good parameter significance.

When the travel mode classification is initially selected, the accommodation degree of transfer behavior and the antagonism of the significance of the obtained parameters need to be considered. Only when the obtained parameters are significant under a given significance level can the reasonable architecture of the travel mode classification be explained. The MNL model parameter values and the significance obtained by utilizing the travel survey of residents and the basic geographic information data in 2015A are shown in table 1, 18 parameters in 24 parameters are significant under the significance level of 0.05, the significance of the parameters is good, and the model is reasonable.

The population categories in table 1 are from 1 to 6, and represent minor males, labor males, elderly males, minor females, labor females, and elderly females, respectively. The labor age range is defined as 18-60 years old. In order to verify the travel mode classification rationality of the embodiment of the invention, the number of the traffic travel mode classification categories is changed, and the hausmann independence test is carried out on each group. If the travel modes are divided into 4 classes, M4 non-motorized traffic (NMT, including walking and bicycle) is extracted, and the Haeman test of the old group shows that M3 and M4 are not independent; if a taxi is further extracted as a fifth travel mode M5, M3 is not independent of M4 and M5, respectively. The three-class traffic travel mode division method provided by the embodiment of the invention is most reasonable, and can accurately predict three travel modes of resident travel.

TABLE 1

Group of people category	M2 beta1	Significance of	M3 beta1	Significance of	M2 beta2	Significance of	M3 beta2	Significance of
									1	-2.597	0.549	0.65	0.000	-3.762	0.000	-3.681	0.000
2	-0.424	0.058	0.334	0.000	-4.146	0.000	-3.797	0.000
									3	-0.931	0.000	-0.106	0.052	-2.864	0.000	-3.476	0.000
4	-1.749	0.401	0.685	0.000	-3.962	0.003	-4.484	0.000
									5	-1.96	0.038	0.324	0.000	-24.526	0.000	-3.967	0.000
6	-0.676	0.505	-0.131	0.094	-21.896	0.000	-2.515	0.000

The embodiment of the invention has reasonable structure of selecting the nested model and simulating the technology based on the MNL model, the prediction result is more practical, and the invention can provide a preposed theoretical and practical basis for traffic flow distribution based on transfer behavior. In the method, in the process of establishing a travel mode prediction model, a road network space data set frame in 2015 and 2019 is constructed by using vector map data of a national geographic data information center, the road network data in the 2014 and 2017 are present, and the road network space data set frame is calibrated and supplemented by a traffic planning map, historical Gagde and OSM road network data in the target city overall planning. In order to reasonably reduce the path impedance and the calculation amount of path selection, roads of county level and higher levels than secondary main roads are reserved to form a road network topology. And for each road section, assigning basic impedance according to the length and the road grade, and calculating the shortest path impedance of various traffic modes between streets.

And processing the original data of the resident trip survey to obtain MNL equation parameters. After the original resident travel survey data are processed, attributes such as departure streets, arrival streets, traffic modes, family car-holding conditions and the like are reserved, and after the traffic modes correspond to the traffic travel mode of the technology, the MNL equation parameters of each group are calculated through the shortest path impedance of various traffic modes among the streets and the family car-holding ratio of each departure street.

Processing target annual mobile phone signaling data, identifying the residential streets and departure and arrival streets of each complete journey of the residents according to the flow characteristics and the living characteristics of the residents, and processing the residential streets into the same group classification as the steps according to the ages and the sexes. And applying the MNL equation to the traffic volume between the units obtained by capturing and processing the mobile phone signaling data to obtain the traffic volume between the streets in the current year according to the traffic mode division proportion. And finally, limiting and redistributing two wheels according to the remaining quantity of the motor vehicles to the trip mode sharing rate to obtain final output.

In the prediction result, as shown in fig. 5, the travel pattern is more accurately simulated from the perspective of the total travel pattern allocation rate. Travel mode M in FIG. 5 ₁ The private car is used as a private trip mode. Mode of travel M ₂ The motorcycle is taken as a private trip mode. Mode of travel M ₃ The mode of travel of other non-private vehicles is formed. According to traffic mode outgoing amount data (data source: XX traffic development research institute) of 2019 in XX city central city working days at different times in '2020 XX city traffic development annual report', the daily average outgoing amount is 3957 ten thousand, wherein the car outgoing amount accounts for 894 ten thousand, and accounts for 22.6%. Considering sample expansion deviation of mobile phone signaling big data to travelThe influence of the data sample distribution, overall 18.4% private car private travel pattern prediction is acceptable. The predicted value of the travel mode proportion of each group has an obvious rule, the larger the age is, the higher the ratio of the private car travel mode is, and the male tends to the private car travel mode.

FIGS. 6 and 7 show the travel patterns M of the XX city residents from and to each street predicted by the model ₁ The proportion occupied case. Public transport travel mode M ₃ The smaller the ratio. As shown in FIG. 6, the value of each street in FIG. 6 represents M for the total travel from each street to the other streets ₁ Is of a ratio, showing a dispersed agglomerate aggregation distribution. As shown in FIG. 7, the value of each street represents M for the total amount of travel to that street from the other streets ₁ The ratio of (a) to (b). Compared with the trip mode sharing rate of the departure place, the trip mode sharing rate of the arrival place represents a single-centered spatial pattern, and the private car trip modes from the central urban area to the suburban area present a decreasing circle-layer structure.

The embodiment of the invention also has the following beneficial technical effects:

firstly, a transfer behavior algorithm is embedded, mobile phone signaling big data analysis is integrated, the existing MNL model-based resident trip mode division simulation technology is optimized, a new more accurate resident trip mode simulation prediction technology is established, and simulation results are closer to the real urban situation. The technology brings the common nature of resident travel modes into a basic travel mode by analyzing the common nature of the resident travel modes, effectively extracts two private transport travel modes and a public transport travel mode, and realizes the embedding of a transfer behavior algorithm in the calculation process.

Secondly, the trip mode sharing rate is limited through indexes such as the motor vehicle holding capacity, and the adaptability and the robustness of model prediction are improved. The traffic of the private traffic travel mode is redistributed according to the reserved quantity of motor vehicles or private cars in the city or each region, so that the occurrence of abnormal mode division results is avoided, and the follow-up traffic distribution and policy evaluation are more reliable.

Thirdly, large data analysis of mobile phone signaling is integrated into the model, the space-time application range of the traffic trip mode division model is widened, dynamic real-time simulation of resident trip modes can be realized, and the refined business requirements of intelligent traffic planning management can be further met.

Fourthly, the method is economical and practical, wide in space-time big data range, low in cost and easy to collect. Other data used by the technology are universal data and are easy to collect. The MNL model parameter estimation process has universality, can be applied to different types of cities, is easy to operate and has high popularization degree.

According to another aspect of the embodiment of the invention, a travel mode prediction device for implementing the travel mode prediction method is further provided. As shown in fig. 8, the apparatus includes:

a first obtaining unit 802, configured to obtain, from a trip database, resident trip survey data, basic geographic information data, and mobile phone signaling data of a target city within a target time period;

a second obtaining unit 804, configured to obtain, based on the resident travel survey data, a traffic congestion rate of the target city, a group to which a resident belongs, a transportation mode, and a travel mode to which the transportation mode belongs;

a third obtaining unit 806, configured to obtain road network topology data of the target city based on the basic geographic data, where the road network topology data is used to calculate a path impedance of a preset path;

an integration determining unit 808, configured to perform data integration on the resident trip survey data and the path impedance, input the integrated data into an MNL model to obtain a proportion of each trip mode, and obtain, based on the mobile phone signaling data, traffic volumes of each group in the target city under different trip modes from a departure area i to an arrival area j;

an adjusting unit 810, configured to adjust traffic volumes of each group of the target city in different travel modes according to the vehicle occupancy of the target city in the target time period, so as to obtain target traffic volumes of each group in the target city in different travel modes.

In one or more embodiments, the integration determination unit 808 includes:

an obtaining module, configured to obtain a path travel Cost of each group in each travel mode _ij,a,M Wherein a is a group category, and M is a travel mode;

a dividing module for dividing according to the Car-congestion rate Car _ Ownership _a,i Processing the mobile phone signaling data to obtain OD data of each group between streets of the target city

Traffic volume T divided into vehicle sub-groups _ij,a,car And traffic volume T of non-vehicle group _{ij,a,no_car} ；

A calculation module for calculating the T according to the MNL model _ij,a,car And said T _{ij,a,no_car} The ratio of (A) to (B);

a determination module for determining T _ij,a,car And said T _{ij,a,no_car} Determining the traffic volume of each group from the departure zone i to the arrival zone j under different travel modes.

In one or more embodiments, the first obtaining module includes:

a calculation and allocation subunit for allocating the impedance Cost of the path from the departure zone i to the arrival zone j _m Carrying out weighted calculation, carrying out multi-round distribution on the traffic volume, and obtaining the travel Cost _ij,a,M (ii) a Wherein when T does not appear in the distribution process _ij,a,m Then, the Cost is obtained by adopting the formula (1) _ij,a,M Otherwise, obtaining the Cost by adopting a formula (2) _ij,a,M ：

(first round, M = M) ₁ ，M ₂ ，M ₃ ）（1）

(n-th wheel, n ≠ 1, M = M) ₁ ，M ₂ ，M ₃ ）（2）

In one or more embodiments, the determining module includes:

an acquisition subunit for acquiring the T based on the formulas (3), (4), (5), (6) _ij,a,car And said T _{ij,a,no_car} The proportion of (A):

（3）

（4）

（5）

（6）

wherein,

the traffic volume of a group without dividing the travel mode, Car _ Ownership _a,i As the traffic congestion rate of the target city,

selecting a parameter, p, for a logic mode derived from said resident survey data calibration _ij,a,car,M Is said T _ij,a,car Ratio of (a) p _{ij,a,no_car,M} Is said T _{ij,a,no_car} The ratio of (A) to (B);

Said

Obtained by equation (7):

（7）。

in one or more embodiments, the impedance Cost _m Obtained by equation (8):

（8）

wherein l is road section, k is traffic mode, Time _ cost _l,k Fee _ cost as a time travel cost _l,k For monetary cost, Trans _ cost _l,k To trade-off costs.

In one or more embodiments, the travel pattern prediction apparatus further includes a dividing unit configured to divide residents of the target city into the following six group categories according to age and gender:

a first population comprising underage 18 adult males;

a second population comprising minor females under 18 years of age;

a third population comprising males between age 19 and 59;

a fourth population comprising females between 19 and 59 years old;

a fifth population comprising older men over 60 years old;

a sixth population comprising older females aged 60 years old or older.

According to another aspect of the embodiment of the present invention, there is further provided an electronic device for implementing the method for predicting a transportation mode, where the electronic device may be a client or a server as shown in fig. 1. The present embodiment takes the electronic device as a server as an example for explanation. As shown in fig. 9, the electronic device comprises a memory 902 and a processor 904, the memory 902 having stored therein a computer program, the processor 904 being arranged to perform the steps of any of the above-described method embodiments by means of the computer program.

Optionally, in this embodiment, the electronic device may be located in at least one network device of a plurality of network devices of a computer network.

Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:

s1, acquiring resident trip survey data, basic geographic information data and mobile phone signaling data of a target city in a target time period from a trip database;

s2, acquiring the traffic congestion rate, the group to which the residents belong, the transportation mode and the travel mode to which the transportation mode belongs of the target city based on the resident travel survey data;

s3, acquiring road network topology data of the target city based on the basic geographic data, wherein the road network topology data are used for calculating the path impedance of a preset path;

s4, integrating the resident trip survey data and the path impedance, inputting the integrated data into an MNL model to obtain the proportion of each trip mode, and acquiring the traffic volume of each group in the target city from a departure area i to an arrival area j in different trip modes based on the mobile phone signaling data;

s5, adjusting the traffic volume of each group of the target city in different travel modes according to the motor vehicle holding capacity of the target city in the target time period, and obtaining the target traffic volume of each group in the target city in different travel modes.

Alternatively, it can be understood by those skilled in the art that the structure shown in fig. 9 is only an illustration, and the electronic device may also be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palm computer, a Mobile Internet Device (MID), a PAD, etc. with a network security detection function. Fig. 9 is a diagram illustrating a structure of the electronic device. For example, the electronics may also include more or fewer components (e.g., network interfaces, etc.) than shown in FIG. 9, or have a different configuration than shown in FIG. 9.

The memory 902 may be configured to store software programs and modules, such as program instructions/modules corresponding to the travel mode prediction method and apparatus in the embodiments of the present invention, and the processor 904 executes various functional applications and data processing by running the software programs and modules stored in the memory 902, that is, implements the travel mode prediction method. The memory 902 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 902 may further include memory located remotely from the processor 904, which may be connected to the terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof. The memory 902 may be, but is not limited to, specifically used for storing information such as multi-source spatiotemporal big data. As an example, as shown in fig. 9, the memory 902 may include, but is not limited to, a first obtaining unit 802, a second obtaining unit 804, a third obtaining unit 806, an integration determining unit 808, and an adjusting unit 810 in the travel mode prediction apparatus; in addition, the device may further include, but is not limited to, other module units in the travel mode prediction apparatus, which is not described in detail in this example.

Optionally, the transmitting device 906 is used for receiving or sending data via a network. Examples of the network may include a wired network and a wireless network. In one example, the transmission device 906 includes a Network adapter (NIC) that can be connected to a router via a Network cable and other Network devices to communicate with the internet or a local area Network. In one example, the transmission device 906 is a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.

In addition, the electronic device further includes: a display 908 for displaying the set state value of the protocol analysis plug-in; and a connection bus 910 for connecting the respective module components in the electronic apparatus.

In other embodiments, the electronic device may be a node in a distributed system, wherein the distributed system may be a blockchain system, and the blockchain system may be a distributed system formed by connecting a plurality of nodes through a network communication. Nodes can form a Peer-To-Peer (P2P, Peer To Peer) network, and any type of computing device, such as a server, a terminal, and other electronic devices, can become a node in the blockchain system by joining the Peer-To-Peer network.

According to an aspect of the application, a computer program product or computer program is provided, comprising computer instructions, the computer instructions being stored in a computer readable storage medium. A processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to execute the method for predicting a travel pattern, wherein the computer program is configured to execute the steps in any of the method embodiments described above.

Alternatively, in the present embodiment, the above-mentioned computer-readable storage medium may be configured to store a computer program for executing the steps of:

Alternatively, in this embodiment, a person skilled in the art may understand that all or part of the steps in the methods of the foregoing embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.

The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.

The integrated unit in the above embodiments, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in the above computer-readable storage medium. Based on such understanding, the technical solution of the present invention may be essentially or partially contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes several instructions for causing one or more computer devices (which may be personal computers, servers, network devices, and the like) to execute all or part of the steps of the methods of the embodiments of the present invention.

In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the several embodiments provided in the present application, it should be understood that the disclosed client may be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit is merely a division of a logic function, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.

Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The foregoing is only a preferred embodiment of the present invention, and it should be noted that those skilled in the art of the embodiment of the present invention can make several improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be construed as the protection scope of the present invention.

Claims

1. A method for predicting a travel mode is characterized by comprising the following steps:

acquiring resident trip survey data, basic geographic information data and mobile phone signaling data of a target city in a target time period from a trip database;

acquiring the traffic congestion rate of the target city, the group to which the residents belong, the transportation mode and the travel mode to which the transportation mode belongs on the basis of the resident travel survey data;

acquiring road network topology data of the target city based on the basic geographic data, wherein the road network topology data is used for calculating the path impedance of a preset path;

integrating the resident trip survey data and the path impedance, inputting the integrated data into an MNL (mobile network layer) model to obtain the proportion of each trip mode, and acquiring the traffic volume of each group in the target city from a departure area i to an arrival area j in different trip modes based on the mobile phone signaling data;

and adjusting the traffic volume of each group of the target city under different travel modes according to the motor vehicle holding capacity of the target city in the target time period to obtain the target traffic volume of each group in the target city under different travel modes.

2. The method according to claim 1, wherein the step of integrating the resident travel survey data and the path impedance data and inputting the integrated data into a MNL model to obtain the proportion of each travel mode comprises the following steps:

3. The method according to claim 2, wherein the obtaining of the travel Cost of the path of each group in each travel mode _ij,a,M The method comprises the following steps:

impedance Cost of a path from a departure zone i to an arrival zone j _m Carrying out weighted calculation, carrying out multi-round distribution on the traffic volume, and obtaining the travel Cost _ij,a,M (ii) a Wherein when T does not appear in the distribution process _ij,a,m Then, the Cost is obtained by adopting the formula (1) _ij,a,M Otherwise, acquiring the Cost by adopting a formula (2) _ij,a,M ：

（1）

（2）

Wherein, T _ij,a,m The weight of a group under a path M, M being M ₁ 、M ₂ And M ₃ An arbitrary value of (1), M ₁ ，M ₂ ，M ₃ Respectively different travel modes.

4. The method of claim 3, wherein said calculating said T from said MNL model _ij,a,car And said T _{ij,a,no_car} Comprises the following steps:

（3）

（4）

（5）

（6）

wherein,

intersection without dividing travel modes for a groupFlux, Car _ Ownership _a,i As the traffic congestion rate of the target city,

Said

Obtained by equation (7):

（7）。

5. the method of claim 3, wherein the impedance Cost _m Obtained by equation (8):

（8）

wherein l is road section, k is traffic mode, Time _ cost _l,k Fee _ cost as a time travel cost _l,k For monetary cost, Trans _ cost _l,k To the transfer cost.

6. The method of claim 1, further comprising: the residents of the target city are classified into the following six group categories according to age stage and gender:

a first population comprising underage 18 adult males;

a second population comprising minor females under 18 years of age;

a third population comprising males between age 19 and 59;

a fourth population comprising females between 19 and 59 years old;

a fifth population comprising older men over 60 years old;

a sixth population comprising older females older than 60 years old.

7. A travel pattern prediction apparatus comprising:

the first acquisition unit is used for acquiring resident trip survey data, basic geographic information data and mobile phone signaling data of a target city in a target time period from a trip database;

a second obtaining unit, configured to obtain, based on the resident travel survey data, a vehicle congestion rate of the target city, a group to which a resident belongs, a transportation mode, and a travel mode to which the transportation mode belongs;

a third obtaining unit, configured to obtain road network topology data of the target city based on the basic geographic data, where the road network topology data is used to calculate a path impedance of a preset path;

an integration determining unit, configured to perform data integration on the resident trip survey data and the path impedance, input the integrated data into an MNL model to obtain a proportion of each trip mode, and obtain, based on the mobile phone signaling data, traffic volumes of each group in the target city under different trip modes from a departure area i to an arrival area j;

and the adjusting unit is used for adjusting the traffic volume of each group of the target city in different travel modes according to the motor vehicle holding capacity of the target city in the target time period to obtain the target traffic volume of each group in the target city in different travel modes.

8. The apparatus of claim 7, wherein the integration determination unit comprises:

a dividing module used for dividing according to the Car congestion rate Car _ Ownership _a,i Processing the mobile phone signaling data to obtain OD data of each group between streets of the target city

9. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to execute the method of any of claims 1 to 6 by means of the computer program.

10. A computer-readable storage medium, comprising a stored program, wherein the program when executed performs the method of any of claims 1 to 6.