CN114724414A - Method, device, electronic equipment and medium for determining urban air traffic sharing rate - Google Patents

Method, device, electronic equipment and medium for determining urban air traffic sharing rate Download PDF

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CN114724414A
CN114724414A CN202210248307.8A CN202210248307A CN114724414A CN 114724414 A CN114724414 A CN 114724414A CN 202210248307 A CN202210248307 A CN 202210248307A CN 114724414 A CN114724414 A CN 114724414A
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traffic
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traffic flow
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CN114724414B (en
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廖小罕
屈文秋
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Institute of Geographic Sciences and Natural Resources of CAS
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/0095—Aspects of air-traffic control not provided for in the other subgroups of this main group
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/0073—Surveillance aids
    • G08G5/0082—Surveillance aids for monitoring traffic from a ground station
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G5/00—Traffic control systems for aircraft, e.g. air-traffic control [ATC]
    • G08G5/06—Traffic control systems for aircraft, e.g. air-traffic control [ATC] for control when on the ground
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00—Adapting or protecting infrastructure or their operation
    • Y02A30/60—Planning or developing urban green infrastructure
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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  • Aviation & Aerospace Engineering (AREA)
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  • Radar, Positioning & Navigation (AREA)
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  • Traffic Control Systems (AREA)

Abstract

The invention relates to a method, a device, electronic equipment and a medium for determining urban air traffic sharing rate, wherein the method comprises the following steps: acquiring current total traffic flow corresponding to each traffic mode in a research area, and predicting first predicted total traffic flow corresponding to each traffic mode at a specified time according to the current total traffic flow; acquiring current share rate, current data and prediction data corresponding to specified time of each traffic mode in the research area; determining a model parameter value through a double-layer NL model according to the current sharing rate and the current data; and predicting the sharing rate of the urban air traffic in the research area at the specified time through the MNL model according to the model parameter value, the first predicted total traffic flow and the prediction data. By the method, the accuracy of the corresponding sharing rate of the urban air traffic in the research area at the designated time can be improved, the manpower, material resources and time are saved, and the influence of the IA characteristic of the Logit model is reduced.

Description

Method, device, electronic equipment and medium for determining urban air traffic sharing rate
Technical Field
The invention relates to the technical field of urban traffic, in particular to a method, a device, electronic equipment and a medium for determining urban air traffic sharing rate.
Background
At present, the method for predicting the sharing rate of a novel traffic mode entering an urban space mostly adopts three modes, and the first mode is to predict the sharing rate of a certain novel traffic mode in a certain year by using a related intelligent algorithm. Secondly, designing a questionnaire according to research emphasis, combining an RP (recommended preference) survey with an SP (State preference) survey, and calculating the sharing rate and the demand of various traffic modes in a city according to the questionnaire result, and thirdly, adopting a four-stage method, wherein the current sharing rate and the current situation of the various traffic modes in the city and the predicted factor data of each traffic mode in a year are required to be utilized when the current sharing rate is predicted by adopting the four-stage method: time, cost, accessibility, etc.
The above-mentioned three ways have the following disadvantages,
first, an intelligent algorithm for supervised classification is adopted: the selection of the training samples is strong in subjective factor, and the selected training samples do not necessarily represent the travel structure of the existing urban traffic system well; the selection and evaluation of the training samples need to consume more manpower and time; only the factors influencing the traffic mode selection for constructing the model can be identified, and if a certain class is not known by the trainer or the number of the classes is too small, the supervised classification cannot be identified. An intelligent algorithm using unsupervised classification: factors influencing traffic mode selection, which are generated by unsupervised classification, need a large amount of subsequent analysis and processing, and are matched with factors of a research area to obtain a final result; it is more difficult for the analyst to control the factors that influence the choice of transportation means. In conclusion, the intelligent algorithm has poor robustness and the accuracy of the prediction result is poor.
Second, questionnaire: the rationality of questionnaire design is difficult to assess; the questionnaire is distributed and the questionnaire results are collected, so that a sample which is large enough and representative enough is obtained, and the great expenditure of manpower, material resources and time is needed.
Thirdly, a new traffic method has no current share rate when the new traffic method does not enter the urban traffic system, which may result in poor accuracy of the prediction result, and in addition, the Logit model in the four-stage method has iia (independence of irrational) characteristics, which may affect the accuracy of the prediction. The IIA characteristic of the Logit model means that in an Urban area, the ratio of the quality to the quality of two transportation modes is fixed, that is, the probability ratio of a resident to select a car or a bus is equal regardless of whether the transportation mode of UAM (Urban Air Mobility) exists inside the city.
Disclosure of Invention
The invention aims to solve at least one technical problem by providing a method, a device, electronic equipment and a medium for determining the urban air traffic sharing rate.
In a first aspect, the technical solution for solving the above technical problem of the present invention is as follows: a method for determining urban air traffic sharing rate comprises the following steps:
acquiring current total traffic flow corresponding to each traffic mode in a research area, and predicting first predicted total traffic flow corresponding to each traffic mode at a specified time according to the current total traffic flow, wherein each traffic mode comprises cars, public traffic and urban air traffic;
acquiring current sharing rate, current data and predicted data corresponding to appointed time of each traffic mode in a research area, wherein the current data and the predicted data are the same parameters corresponding to different time, and the parameters comprise time, cost and accessibility;
determining a model parameter value through a double-layer NL model according to the current sharing rate and the current data, wherein the double-layer NL model is used for representing the association relation among the sharing rate corresponding to each traffic mode, the current data and the model parameter value;
and predicting the sharing rate of the urban air traffic corresponding to the research area at the specified time through an MNL (MNL) model according to the model parameter values, the first predicted total traffic flow and the prediction data, wherein the MNL model is used for representing the association relation among the sharing rate of the urban air traffic corresponding to the research area at the specified time, the first predicted total traffic flow, the prediction data and the model parameter values.
The invention has the beneficial effects that: the method comprises the steps of firstly predicting first predicted total traffic flow corresponding to each traffic mode at a designated time based on current total traffic flow, then determining model parameter values through a double-layer NL model according to current sharing rate and current data corresponding to each traffic mode, predicting sharing rate corresponding to a research area of urban air traffic at the designated time through the MNL model based on the determined model parameter values, the first predicted total traffic flow and the predicted data, and predicting the sharing rate corresponding to the urban air traffic at the designated time through various factors (time, cost and accessibility) influencing the sharing rate Material resources and time. In addition, the NL model and the MNL model are combined for use, so that the influence of IIA characteristics of the Logit model can be reduced, and the problem that UAM does not have current share rate data can be solved.
On the basis of the technical scheme, the invention can be further improved as follows.
Further, the research area includes a plurality of traffic cells, and the method further includes:
acquiring the distribution of each traffic cell corresponding to a research area;
determining a second predicted total traffic flow of each traffic cell corresponding to the designated time according to the current total traffic flow and the distribution of each traffic cell, wherein the first predicted total traffic flow comprises the second predicted total traffic flow of each traffic cell;
the step of predicting the sharing rate of the urban air traffic at the specified time through the MNL model according to the model parameter value, the first predicted total traffic flow and the prediction data comprises the following steps:
forecasting the corresponding share rate of the urban air traffic in each traffic cell at the appointed time through the MNL model according to the model parameter values, the forecast data and each second forecast total traffic flow;
the MNL model is used for representing the incidence relation among the corresponding share rate of the urban air traffic in each traffic cell, the total traffic flow of each second forecast, the forecast data and the model parameter value at the appointed time.
The method has the advantages that the sharing rate of the urban air traffic in each traffic cell at the designated time can be predicted through the MNL model based on the model parameter values, the prediction data and the second predicted total traffic flow when the sharing rate of the urban air traffic corresponding to the research area at the designated time is predicted according to the fact that the total traffic flow of each traffic cell is possibly different, and therefore the predicted sharing rate is more accurate.
Further, the method further comprises:
determining the current total traffic flow of each traffic cell according to the current total traffic flow and the distribution of each traffic cell;
the determining a second predicted total traffic flow of each traffic cell corresponding to the designated time according to the current total traffic flow and the distribution of each traffic cell includes:
acquiring the current population number of a research area and a predicted population number corresponding to a specified time;
and determining a second predicted total traffic flow of each traffic cell corresponding to the specified time according to the current population number, the predicted population number and the current total traffic flow of each traffic cell.
The method has the advantages that considering that the total traffic flow of each traffic cell is possibly different, and the influence of the current population number and the predicted population number on the total traffic flow of each traffic cell, the method can determine the current total traffic flow of each traffic cell, and then predict the second predicted total traffic flow of each traffic cell corresponding to the designated time according to the current total traffic flow, the current population number and the predicted population number of each traffic cell, so that the predicted second predicted total traffic flow is more accurate.
Further, the method further comprises:
and determining the traffic flow of the urban air traffic in each traffic cell according to the corresponding sharing rate of the urban air traffic in each traffic cell and the second predicted total traffic flow of each traffic cell.
The method has the advantages that after the sharing rate of the urban air traffic in each traffic cell is determined, the traffic flow of the urban air traffic in each traffic cell is determined according to the sharing rate and the second predicted total traffic flow of each traffic cell, so that the traffic distribution condition of the urban air traffic in each traffic cell is reflected through the traffic flow.
Further, the method further comprises:
and providing a travel route for each resident according to the traffic flow of the urban air traffic in each traffic cell.
The further scheme has the beneficial effect that considering the traffic flow corresponding to the urban air traffic in each traffic cell, a travel route can be provided for residents according to the traffic flow.
Further, the method further comprises:
acquiring the current initial total traffic flow, and determining equivalent traffic volume corresponding to each road in an area corresponding to the current initial total traffic flow according to the current initial total traffic flow;
acquiring the proportion of the current cargo vehicle in all vehicles;
determining the actual passenger carrying capacity corresponding to each road according to the equivalent traffic volume and the proportion corresponding to each road;
and determining the current total traffic flow corresponding to each traffic mode according to the actual passenger carrying flow corresponding to each road and the current sharing rate of the cars.
The method has the advantages that the current initial total traffic flow comprises the traffic flow of the cargo vehicles, the current total traffic flow refers to the total traffic flow corresponding to different traffic modes when residents go out, and the traffic flow of the cargo vehicles is not included, so that the traffic flow of the cargo vehicles can be removed from the current initial total traffic flow, and the accuracy of the forecast sharing rate is improved.
Further, the predicting a first total predicted traffic flow corresponding to each transportation mode at a specified time according to the current total traffic flow includes:
acquiring the current population number of a research area and the corresponding predicted population number at a specified time;
and predicting a first predicted total traffic flow corresponding to each traffic mode at a specified time according to the current population number, the predicted population number and the current total traffic flow.
The method has the advantages that the influence of the current population number and the predicted population number on the first predicted total traffic flow is considered, and in the scheme, the first predicted total traffic flow can be predicted according to the current total traffic flow, the current population number and the predicted population number, so that the predicted first predicted total traffic flow is more accurate.
In a second aspect, the present invention provides an apparatus for determining an air traffic distribution rate of an urban area, to solve the above technical problem, the apparatus comprising:
the traffic total flow prediction module is used for acquiring the current traffic total flow corresponding to each traffic mode in a research area, and predicting a first predicted traffic total flow corresponding to each traffic mode at a specified time according to the current traffic total flow, wherein each traffic mode comprises cars, public traffic and urban air traffic;
the parameter prediction module is used for acquiring the current share rate, the current data and the prediction data corresponding to the designated time of each traffic mode in the traffic modes in the research area, wherein the current data and the prediction data are the same parameters corresponding to different times, and the parameters comprise time, cost and accessibility;
the model parameter value determining module is used for determining a model parameter value through a double-layer NL model according to the current sharing rate and the current data, and the double-layer NL model is used for representing the association relation among the sharing rate corresponding to each traffic mode, the current data and the model parameter value;
and the sharing rate prediction module is used for predicting the sharing rate corresponding to the research area of the urban air traffic at the specified time through the MNL model according to the model parameter values, the first total predicted traffic flow and the prediction data, and the MNL model is used for representing the association relation among the sharing rate corresponding to the research area of the urban air traffic at the specified time, the first total predicted traffic flow, the prediction data and the model parameter values.
In a third aspect, the present invention provides an electronic device to solve the above technical problem, where the electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the electronic device implements the method for determining the urban air traffic sharing rate according to the present application.
In a fourth aspect, the present invention further provides a computer readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the method for determining the urban air traffic sharing rate of the present application.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly described below.
Fig. 1 is a schematic flow chart of a method for determining an air traffic sharing rate in an urban area according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a double-layer NL model according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of another method for determining an urban air traffic sharing rate according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a device for determining an urban air traffic sharing rate according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with examples which are set forth to illustrate, but are not to be construed to limit the scope of the invention.
The technical solution of the present invention and how to solve the above technical problems will be described in detail with specific embodiments below. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
The scheme provided by the embodiment of the invention can be applied to any application scene needing to predict the urban air traffic sharing rate. The scheme provided by the embodiment of the invention can be executed by any electronic equipment, for example, the scheme can be a terminal device of a user, the terminal device can be any terminal device which can be installed with application and can predict the urban air traffic sharing rate through the application, and the scheme comprises at least one of the following items: smart phones, tablet computers, notebook computers, desktop computers, smart audio boxes, smart watches, smart televisions, and smart car-mounted devices.
An embodiment of the present invention provides a possible implementation manner, and as shown in fig. 1, provides a flowchart of a method for determining an air traffic sharing rate in a city, where the scheme may be executed by any electronic device, for example, may be a terminal device, or may be executed by both the terminal device and a server (hereinafter, referred to as a file server). For convenience of description, the method provided by the embodiment of the present invention will be described below by taking a server as an execution subject, and as shown in a flowchart shown in fig. 1, the method may include the following steps:
step S110, obtaining the current total traffic flow of each traffic mode in the research area, and predicting a first predicted total traffic flow corresponding to each traffic mode at a specified time according to the current total traffic flow, wherein each traffic mode comprises cars, public traffic and urban air traffic;
step S120, acquiring the current distribution rate, the current data and the predicted data corresponding to the appointed time of each traffic mode in the research area, wherein the current data and the predicted data are the same parameters corresponding to different times, and the parameters comprise time, cost and accessibility;
step S130, determining a model parameter value through a double-layer NL model according to the current sharing rate and the current data, wherein the double-layer NL model is used for representing the association relation among the sharing rate corresponding to each traffic mode, the current data and the model parameter value;
step S140, according to the model parameter values, the first total predicted traffic flow and the prediction data, the sharing rate corresponding to the research area of the urban air traffic at the specified time is predicted through an MNL model, and the MNL model is used for representing the association relation among the sharing rate corresponding to the research area of the urban air traffic at the specified time, the first total predicted traffic flow, the prediction data and the model parameter values.
Through the method of the invention, the first predicted total traffic flow corresponding to each traffic mode in the designated time is predicted based on the current total traffic flow, then the model parameter value is determined through the double-layer NL model according to the current sharing rate and the current data corresponding to each traffic mode, the model parameter value in the double-layer NL model is the same as the model parameter value of the MNL model, then the sharing rate corresponding to the research area of the urban air traffic in the designated time is predicted through the MNL model based on the determined model parameter value, the first predicted total traffic flow and the predicted data, in the scheme of the invention, the sharing rate corresponding to the urban air traffic in the designated time is predicted through various factors (time, cost and accessibility) influencing the sharing rate is more accurate compared with the prior art, meanwhile, through the scheme of the invention, the data statistics in a manual mode is not needed, saving manpower, material resources and time. In addition, the NL model and the MNL model are combined for use, so that the influence of IIA characteristics of the Logit model can be reduced, and the problem that UAM does not have current share rate data can be solved.
The following further describes the scheme of the present invention with reference to the following specific embodiments, in which the method for determining the urban air traffic sharing rate may include the following steps:
step S110, obtaining the current total traffic flow of each traffic mode in the research area, and predicting a first predicted total traffic flow corresponding to each traffic mode at a specified time according to the current total traffic flow, wherein each traffic mode comprises cars, public traffic and urban air traffic.
The research area refers to an area of a city in which the sharing rate needs to be determined, the current total traffic flow refers to a total traffic flow corresponding to the three traffic modes, and the current total traffic flow may reflect a traffic condition corresponding to an area of a certain city, for example, an area B (research area) in the city a, and the current total traffic flow may be determined by analyzing data acquired by a road camera arranged in an area (for example, the area B) in which analysis needs to be performed. The public transportation can comprise public transportation travel modes such as buses, subways and light rails.
The specified time refers to a certain period of time in the future, such as may be a certain month in the future, or a certain year in the future, or a few weeks in the future. The designated time can be set according to the research requirement. As an example, the current total traffic flow is, for example, a total traffic flow in a zone B of city a, and the first predicted total traffic flow is a total traffic flow in the zone B of city a at a predetermined time.
Optionally, before step S110, the method further includes:
acquiring the current initial total traffic flow, and determining the equivalent traffic volume corresponding to each road in the area corresponding to the current initial total traffic flow according to the current initial total traffic flow;
acquiring the proportion of the current cargo vehicle to all the vehicles;
determining the actual passenger carrying capacity corresponding to each road according to the equivalent traffic volume and the proportion corresponding to each road;
and determining the current total traffic flow corresponding to each traffic mode according to the actual passenger carrying flow corresponding to each road and the current sharing rate of the cars.
The current initial total traffic flow comprises the traffic flow of the trucks, the current total traffic flow refers to the total traffic flow corresponding to different traffic modes when residents go out, and the traffic flow of the trucks is not included, so that the traffic flow of the trucks can be removed from the current initial total traffic flow, and the accuracy of the prediction sharing rate is improved.
The proportion of the current trucks to all the vehicles can be determined based on the analysis of data collected by the collecting devices arranged in the area corresponding to the current initial total traffic flow.
As an example, the area a includes m roads and n monitoring devices, the current initial total traffic flow of the m roads corresponding to the area a is obtained through the n monitoring devices, the current initial total traffic flow is converted into equivalent traffic flows of roads in different directions, and the equivalent traffic flow of each road is multiplied by the proportion of the current truck to all the cars, so that the actual passenger carrying flow corresponding to each road can be determined. Because the traffic volume on each road is only the traffic volume of cars, the current total traffic volume corresponding to each road, namely the total travel volume of residents, can be calculated by dividing the actual passenger carrying volume corresponding to each road by the sharing rate corresponding to cars.
And step S120, acquiring the current distribution rate, the current data and the predicted data corresponding to the appointed time of each traffic mode in the traffic modes in the research area, wherein the current data and the predicted data are the same parameters corresponding to different times, and the parameters comprise time, cost and accessibility.
The current data is the current data of each factor influencing traffic mode division, and each traffic mode corresponds to one current sharing rate based on the three traffic modes described above, namely, a car corresponds to one current sharing rate, public traffic corresponds to one current sharing rate, and urban air traffic corresponds to one current sharing rate. The current data includes a current corresponding time, cost, and reachability, and the predicted data includes a time, cost, and reachability corresponding to the specified time. The time refers to the travel time of the vehicle, such as the time spent on a car from point a to point B, and the time spent on a bus from point B to point C. The fee refers to the fee spent by the residents on the vehicles, such as the fee from the point A to the point B on the car and the fee from the point B to the point C on the bus. Reachability refers to the degree of convenience of using a vehicle to reach from one location to another. Optionally, the point a, the point B, and the point C may be traffic cells.
Alternatively, the prediction data may be determined based on the current data and population changes of the area to which the current data corresponds.
And step S130, determining a model parameter value through a double-layer NL model according to the current sharing rate and the current data, wherein the double-layer NL model is used for representing the association relation among the sharing rate corresponding to each traffic mode, the current data and the model parameter value.
The double-layer NL model is a pre-established model and is used for representing the sharing rate corresponding to each traffic mode and the incidence relation between the current data and the model parameter value, the model parameter value is a factor influencing the sharing rate, different current data correspond to different sharing rates, and the different sharing rates and the different model parameter values corresponding to the different current data are different.
Optionally, referring to fig. 2, the principle of constructing the double-layer NL model is to divide the current transportation modes in the city into a non-motorized type and a motorized type, where the non-motorized type includes bicycles and walking, the motorized type includes cars and public transportation, and the public transportation includes buses and subways. The above-described double-layer NL model can be built in the following manner: taking an area A and an area B of a city of a certain year as an example, the time, the cost and the accessibility corresponding to each traffic mode in each traffic mode and the sharing rate corresponding to each traffic mode are acquired.
The relation between the time, the cost, the reachability and the sharing rate corresponding to each traffic mode is represented by the following formulas (1) to (5):
Figure BDA0003545927350000101
pij car＝1-pij bus (2)
Figure BDA0003545927350000102
wherein ,pij bus、pij carRespectively representing the sharing rate of the car and the bus;
Figure BDA0003545927350000105
respectively representing the running time of a car and a bus;
Figure BDA0003545927350000106
respectively representing the cost of the car and the bus;
Figure BDA0003545927350000107
respectively, the reachability of a car and a bus;
Figure BDA0003545927350000108
represents the total traffic flow corresponding to the car,
Figure BDA0003545927350000109
the total traffic flow corresponding to the bus is represented, and the alpha, the beta, the gamma and the theta represent model parameter values.
Wherein the accessibility of the car can be determined by the following equation (4):
Figure BDA0003545927350000103
wherein ,
Figure BDA00035459273500001010
indicating the reachability from the traffic cell i within a prescribed period of time P,
Figure BDA00035459273500001011
the number of opportunities of the traffic cell j in the time period P is shown, namely, the number of opportunities which can be approached from the traffic cell j in the time period P is shown;
Figure BDA00035459273500001012
represents the shortest travel time from the traffic cell i to the traffic cell j in the time period P, T represents the study time interval, i.e. a preset time period,
Figure BDA00035459273500001013
when the determined reachability is within the study time interval, then it may be selected
Figure BDA00035459273500001014
The corresponding parameter determines the reachability, at which time,
Figure BDA00035459273500001015
in the same way, the method for preparing the composite material,
Figure BDA00035459273500001016
indicating that the determined reachability is outside the study time interval, then
Figure BDA00035459273500001017
The corresponding parameter is not selected for determining reachability, at which time,
Figure BDA00035459273500001018
n represents the number of traffic cells within the B area.
The reachability of public traffic as well as urban air traffic UAM can be determined by the following equation (5):
Figure BDA0003545927350000104
wherein ,AFThe reachability of public transportation or urban air traffic UAM is represented, and rho represents the passenger travel density in a radiation area (B area); s represents the area in the radiation area; t represents the average trip time of passengers in the radiation area; m denotes the number of stations included in the UAM of public or urban air traffic in the radiation area.
In an alternative aspect of the present invention, the predicting a first predicted total traffic flow corresponding to each traffic mode at a specified time according to the current total traffic flow includes:
acquiring the current population number of a research area and a predicted population number corresponding to a specified time;
and predicting a first predicted total traffic flow corresponding to each traffic mode at a specified time according to the current population number, the predicted population number and the current total traffic flow.
Considering the influence of the current population number and the predicted population number on the first predicted total traffic flow, in the scheme of the invention, an original unit of a resident population can be determined by an original unit method according to the current population number and the predicted population number, wherein the original unit of the resident population is equal to the ratio of the predicted population number to the current population number. Then, the original unit of the resident population is multiplied by the current total traffic flow (the total traffic flow corresponding to each traffic mode), and a first predicted total traffic flow corresponding to each traffic mode at a specified time can be obtained. According to the scheme, the first predicted total traffic flow is predicted according to the current total traffic flow, the current population number and the predicted population number, so that the predicted first predicted total traffic flow is more accurate.
It should be noted that, if the current sharing rate of the urban air traffic cannot be obtained, the model parameter value may be determined based on the current sharing rate corresponding to the other two traffic modes and the current data corresponding to the other two traffic modes, so that the problem of the current sharing rate without a new traffic mode may be solved.
Step S140, according to the model parameter values, the first total predicted traffic flow and the prediction data, the sharing rate corresponding to the research area of the urban air traffic at the specified time is predicted through an MNL model, and the MNL model is used for representing the association relation among the sharing rate corresponding to the research area of the urban air traffic at the specified time, the first total predicted traffic flow, the prediction data and the model parameter values.
The MNL model is a pre-established model and is used for representing the incidence relation among the sharing rate, the first total predicted traffic flow, the predicted data and the model parameter values of the urban air traffic at the specified time, the model parameter values of the MNL model comprise the model parameter values in the double-layer NL model, and the MNL model is used for predicting the sharing rate of the urban air traffic at the research area of the specified time according to the predicted data and the first total predicted traffic flow on the premise that the model parameter values are known. Because the prediction data comprises parameters of different transportation modes, and the first predicted total traffic flow comprises first predicted total traffic flow corresponding to different transportation modes, the MNL model can determine the sharing rate of air traffic in cities at the specified time, and can also predict the sharing rate of public traffic and cars at the specified time.
Optionally, when predicting the sharing rate corresponding to the research area of the urban air traffic at the specified time, the MNL model may be obtained by the following formula (6) and formula (7)
Figure BDA0003545927350000121
Figure RE-GDA0003640190370000132
wherein ,
Figure BDA0003545927350000123
indicating that the urban air traffic is in the designated time, the time corresponding to the traffic cell i to the traffic cell j,
Figure BDA0003545927350000124
represents the corresponding cost of the urban air traffic from the traffic cell i to the traffic cell j at the designated time,
Figure BDA0003545927350000125
the reachability of the urban air traffic from the traffic cell i to the traffic cell j at the designated time is represented;
Figure BDA0003545927350000126
and VUAMAll represent the first predicted total traffic flow V corresponding to the urban air traffic at the specified timeallRepresenting the first predicted total traffic flow, P, corresponding to the three traffic modesUAMThe sharing rate of the urban air traffic at the designated time is represented, and the alpha, the beta and the gamma represent model parameter values.
In an alternative embodiment of the present application, the research area includes a plurality of traffic cells, and considering that the total traffic flow of each traffic cell may be different, when predicting the share rate corresponding to the air traffic in the city at the specified time, the second predicted total traffic flow of each traffic cell at the specified time may be determined by the following method, specifically including:
acquiring the distribution of each traffic cell corresponding to a research area;
determining a second predicted total traffic flow of each traffic cell corresponding to the designated time according to the current total traffic flow and the distribution of each traffic cell, wherein the first predicted total traffic flow comprises the second predicted total traffic flow of each traffic cell;
predicting the corresponding share rate of the urban air traffic at the specified time through an MNL model according to the model parameter value, the first predicted total traffic flow and the predicted data, wherein the method comprises the following steps:
forecasting the corresponding share rate of the urban air traffic in each traffic cell at the appointed time through the MNL model according to the model parameter values, the forecast data and each second forecast total traffic flow;
the MNL model is used for representing the incidence relation among the corresponding share rate of the urban air traffic in each traffic cell, the total traffic flow of each second forecast, the forecast data and the model parameter value at the appointed time.
According to the scheme, the allocation rate of the urban air traffic in each traffic cell at the appointed time is predicted through the MNL model based on the model parameter values, the prediction data and the second predicted traffic total flow, and therefore the predicted allocation rate is more accurate.
The first predicted total traffic flow is equal to the sum of second predicted total traffic flows corresponding to all the traffic cells, and the second predicted total traffic flow refers to the total traffic flow corresponding to all the traffic cells in all the traffic modes. The sharing rate of the urban air traffic at the designated time is equal to the sum of the sharing rates of the urban air traffic at each traffic cell at the designated time.
Optionally, the method further includes:
determining the current total traffic flow of each traffic cell according to the current total traffic flow and the distribution of each traffic cell;
the determining a second predicted total traffic flow of each traffic cell corresponding to the designated time according to the current total traffic flow and the distribution of each traffic cell includes:
acquiring the current population number of a research area and a predicted population number corresponding to a specified time;
and determining a second predicted total traffic flow of each traffic cell corresponding to the specified time according to the current population number, the predicted population number and the current total traffic flow of each traffic cell.
Considering that the total traffic flow of each traffic cell may be different, and the influence of the current population number and the predicted population number on the total traffic flow of each traffic cell, in the scheme of the present invention, the current total traffic flow of each traffic cell may be determined first, and then the second predicted total traffic flow of each traffic cell corresponding to the designated time may be predicted according to the current total traffic flow, the current population number and the predicted population number of each traffic cell, so that the predicted second predicted total traffic flow is more accurate.
Alternatively, the current total traffic flow of each traffic cell may be determined by OD back-stepping according to the current total traffic flow and the distribution of each traffic cell. And then, determining a resident original unit by an original unit method according to the current population number and the predicted population number, wherein the resident original unit is equal to the ratio of the predicted population number to the current population number. And then multiplying the original resident population unit by the current total traffic flow of each traffic cell to obtain a second predicted total traffic flow corresponding to each traffic cell, wherein the second predicted total traffic flow corresponding to each traffic cell can also be called OD traffic flow. The second predicted total traffic flow for each traffic cell may also be referred to as a second predicted total traffic flow between two traffic cells.
In an alternative scheme of the invention, after the distribution rate of the urban air traffic in each traffic cell is determined, the traffic flow of the urban air traffic in each traffic cell is determined according to the distribution rate of the urban air traffic in each traffic cell and the second predicted total traffic flow of each traffic cell, so that the traffic distribution condition of the urban air traffic in each traffic cell is reflected through the traffic flow.
For each traffic cell, multiplying the sharing rate corresponding to the traffic cell by the second predicted total traffic flow of the traffic cell is equal to the traffic flow corresponding to the urban air traffic in the traffic cell, and the traffic distribution condition of the urban air in the traffic cell can be reflected through the traffic flow.
In an alternative aspect of the present invention, the method further comprises:
and providing a travel route for each resident according to the traffic flow of the urban air traffic in each traffic cell.
Considering the traffic flow of urban air traffic in each traffic cell, a travel route can be provided for residents according to the traffic flow.
The invention selects the principle of optimal balance of users, namely, everyone on the road selects the route with the minimum comprehensive cost (short time, low cost and high accessibility), and the comprehensive cost index selects the route (travel route) with the shortest time for the resident to reach the destination for the travel meeting. That is to say, based on the traffic flow corresponding to each traffic cell, a route with the minimum comprehensive cost can be selected for the user as the travel route, so as to provide better travel experience for the user, and at the same time, relieve the current traffic pressure.
For a better illustration and understanding of the principles of the method provided by the present invention, the solution of the invention is described below with reference to an alternative embodiment. It should be noted that the specific implementation manner of each step in this specific embodiment should not be construed as a limitation to the scheme of the present invention, and other implementation manners that can be conceived by those skilled in the art based on the principle of the scheme provided by the present invention should also be considered as within the protection scope of the present invention.
Referring to fig. 3, a schematic diagram of a method for determining an air traffic sharing rate in an urban area is shown, in this example, the method includes the following steps:
step 1, according to the demand of sharing rate prediction, obtaining the current initial total traffic flow of each traffic mode in each traffic cell.
In this example, the allocation rate prediction requirement includes a research area a, where the area a is a city B that is controlled to be within four loops (north segment) in the north direction, controlled to be within a city highway in the capital city in the east direction, controlled to be within a city highway in the capital city in the south direction, and controlled to be within four loops (west segment) in the west direction. The spatial boundaries are approximately aligned with national roads and have an area of approximately 625 square kilometers. Planning the year from 2020 to 2025, namely taking the total traffic flow in 2020 as the current initial total traffic flow, predicting the air traffic sharing rate in the city in 2025 according to the current initial total traffic flow in 2020, wherein the designated time is 2025. The current traffic total flow is determined according to traffic flow data acquired by monitoring equipment on four-ring internal sub-roads, and the traffic flow data comprises information such as acquisition time, acquisition position, number of lanes, number of different types of motor vehicles, number of motor vehicles in different lanes and the like.
And 2, the area A comprises m roads and n traffic districts, the current initial total traffic flow is converted into equivalent traffic flows of the roads in different directions, and the actual passenger carrying flow corresponding to each road can be determined by multiplying the equivalent traffic flow of each road by the proportion of the current truck to all the cars. Because the traffic volume on each road is only the traffic volume of the car, the current total traffic volume corresponding to each road, namely the total travel volume of residents, can be calculated by dividing the actual passenger carrying volume corresponding to each road by the sharing rate corresponding to the car.
And 3, determining the current total traffic flow of each traffic cell in an OD (origin-destination) back-stepping mode according to the current total traffic flow corresponding to each road and the distribution of each traffic cell, namely determining the current total traffic flow of each traffic cell in the n traffic cells, wherein each traffic cell corresponds to one current total traffic flow.
Step 4, acquiring the current population number (the population number in 2020) of the area A and the predicted population number corresponding to 2025; the resident population original unit is determined by the original unit method according to the current population number and the predicted population number, and is equal to the ratio of the predicted population number to the current population number, and the ratio is 1.08 in the example. Then, the original resident population unit 1.08 is multiplied by the current total traffic flow of each traffic cell, so that a second predicted total traffic flow corresponding to each traffic cell can be obtained, and the second predicted total traffic flow corresponding to each traffic cell can also be called as OD traffic flow. The second predicted total traffic flow for each traffic cell may also be referred to as a second predicted total traffic flow between two traffic cells. This step 4 corresponds to the occurrence and attraction of traffic shown in fig. 3, and the obtained second predicted total traffic flow corresponding to each traffic cell corresponds to the total traffic flow of city B, 2025, shown in fig. 3.
And 5, predicting the total traffic flow corresponding to each traffic cell in 2025 years by a double-constraint gravity model according to the second predicted total traffic flow corresponding to each traffic cell and the time, cost and accessibility data corresponding to each traffic mode in 2025 years. Corresponding to the 2025 year traffic distribution in fig. 3, and to the traffic distribution in fig. 3.
Before the prediction of the four-phase method is carried out, a research area needs to be divided into a certain number of traffic cells with certain sizes, and the traffic distribution is to research the traffic volume change among the traffic cells. How much traffic is generated (which may be referred to as generated traffic) and how much traffic is attracted (which may be referred to as attracted traffic) in each traffic cell in the study area constitutes the traffic flow space OD distribution in the study area. The spatial characteristics of resident trip in the city layout of different land types can be well reflected at the stage, namely, the land type and the position of the resident are more in the generated or attracted traffic volume. In traffic prediction, due to data processing errors, errors of raw data sets, errors in comprehensive calculation of traffic impedance of a plurality of factors influencing traffic mode selection, and the like, the problem of non-conservation of traffic occurrence amount and attraction amount is often caused. When the dual-constraint gravity model is used for traffic distribution prediction, the conservation of the occurrence amount and the attraction amount can be considered at the same time, the accuracy of a prediction result can be greatly improved, and the problem that the traffic attraction amount and the occurrence amount need to be corrected in the follow-up process can be solved.
Wherein the above-mentioned dual-constraint gravity model can be represented by the following formula (8):
Figure BDA0003545927350000161
wherein the text is in power exponent
Figure BDA0003545927350000162
Prediction of traffic distribution in a doubly-constrained gravity model as a function of traffic impedance, where f (C)ij) Indicates the OD traffic volume, C, corresponding to each traffic cellijRepresenting the cost, a, from traffic cell i to traffic cell jiRepresenting the original occurrence unit of the i traffic cell (the original occurrence unit is equal to the current total traffic flow of the i traffic cell divided by the current total number of the population of the i traffic cell), bjRepresents the original unit of attraction of the j traffic cell (the original unit of attraction is equal to the current total traffic flow of the i traffic cell divided by the current population number of the i traffic cell), qijRepresents the travel amount (first predicted total traffic flow), O, between the traffic cell and the j traffic celliRepresenting the occurrence traffic volume of the traffic cell i; djRepresenting the amount of attractive traffic for traffic cell j.
The above-mentioned dual-constraint gravity model can be obtained by training in the following way:
step a, making m equal to 0, wherein m is the number of times of calculation;
b, giving gamma (calculated by a least square method);
step c, let
Figure BDA0003545927350000163
Find out
Figure BDA0003545927350000164
Step d, finding
Figure BDA0003545927350000165
And
Figure BDA0003545927350000166
e, making m equal to m +1, and continuously obtaining
Figure BDA0003545927350000167
And
Figure BDA0003545927350000168
performing multiple loop iterations;
f, judging the convergence of the dual-constraint gravity model, and if the convergence meets the requirement ai and bjFinishing the calculation according to the corresponding precision; otherwise, returning to the step b for recalculation, wherein ai and bjThe corresponding accuracies are respectively expressed by the following formulas.
Figure BDA0003545927350000169
Wherein epsilon is a set value.
And 6, dividing the transportation modes into two parts, namely a first part, inputting the sharing rate corresponding to each transportation mode in 2020 each transportation mode and the time, cost and accessibility data corresponding to each transportation mode in 2020 into a double-layer NL model, and calculating to obtain model parameter values (corresponding to the time, cost and accessibility parameter values shown in FIG. 3). In this example, the model parameter values are: α: -0.5503; beta: -0.0022; gamma is 0.00015; θ: -0.6297. And a second part, inputting the model parameter values, the time, the cost and the accessibility data corresponding to each traffic mode in 2025 years and the second predicted total traffic flow corresponding to each traffic cell obtained in the step 4 into the MNL model, and obtaining the sharing rate of UAM in 2025 years in each traffic cell (corresponding to the UAM sharing rate in 2025 years shown in FIG. 3).
Step 7, according to the sharing rate of UAM in each traffic cell in 2025 and the second predicted total traffic flow corresponding to each traffic cell in 2025, the traffic flow of UAM in 2025 in each traffic cell can be determined, which corresponds to the OD flow in 2025 shown in fig. 3. The method specifically comprises the following steps: the flow of UAM in the inner part of the three rings is relatively small, and the flow of UAM in the outer part of the three rings is large. In the range between the three rings and the four rings, the flow of the UAM in the south-east direction of the metropolis is small, and the flow of the UAM in the north-west direction is large.
Step 8, according to the traffic flow of UAM in each traffic cell in 2025 years, combining with a UAM route network with population density distribution, and by using the optimal balance principle of users, at least one item of the following information can be determined: the relationship between the UAM cost and the sharing rate, the relationship between the population growth amount and the UAM sharing rate, the relationship between the distance and the UAM sharing rate, the UAM flow distribution mode (the traffic flow distribution condition of the UAM in each traffic cell) and the proposal of UAM route planning (providing a better travel route for the user). Step 8 corresponds to the traffic flow distribution shown in fig. 3.
The scheme of the invention predicts the sharing rate of the UAM of the urban air traffic and the size and distribution of traffic flow requirements, analyzes that the trip cost of passengers has great influence on the sharing rate of the UAM through the prediction result, when the sharing operation is completely carried out, the UAM has the potential to face the market, the UAM sharing rate is increased along with the increase of the distance, and when the distance exceeds 15km, the UAM has the transportation advantage. The change of population has little influence on the UAM sharing rate, but has great influence on the UAM traffic flow. OD traffic volume is distributed to a road network through traffic flow distribution, and the basis for planning the airway routes of future metropolis can be given according to the comprehensive consideration of the distribution of the traffic flow and the distribution of the sharing rate.
Based on the same principle as the method shown in fig. 1, the embodiment of the present invention further provides an apparatus 20 for determining urban air traffic sharing rate, as shown in fig. 4, the apparatus 20 for determining urban air traffic sharing rate may include a total traffic flow prediction module 210, a parameter prediction module 220, a city model parameter value determination module 230 and a sharing rate prediction module 240, where:
the total traffic flow prediction module 210 is configured to obtain a current total traffic flow corresponding to each traffic mode in a research area, and predict a first predicted total traffic flow corresponding to each traffic mode at a specified time according to the current total traffic flow, where each traffic mode includes a car, public traffic, and urban air traffic;
the parameter prediction module 220 is configured to obtain a current share rate, current data and prediction data corresponding to a specified time of each transportation mode in the transportation modes in the research area, where the current data and the prediction data are the same parameters corresponding to different times, and the parameters include time, cost and reachability;
a model parameter value determining module 230, configured to determine a model parameter value through a double-layer NL model according to the current sharing rate and the current data, where the double-layer NL model is used to represent an association relationship between the sharing rate corresponding to each transportation mode, the current data, and the model parameter value;
and the sharing rate prediction module 240 is configured to predict, according to the model parameter value, the first predicted total traffic flow and the prediction data, the sharing rate corresponding to the research area of the urban air traffic at the specified time through the MNL model, where the MNL model is used to represent an association relationship among the sharing rate corresponding to the research area of the urban air traffic at the specified time, the first predicted total traffic flow, the prediction data and the model parameter value.
Optionally, the research area includes a plurality of traffic cells, and the apparatus further includes:
the first prediction module is used for acquiring the distribution of each traffic cell corresponding to the research cell; determining a second predicted total traffic flow of each traffic cell corresponding to the designated time according to the current total traffic flow and the distribution of each traffic cell, wherein the first predicted total traffic flow comprises the second predicted total traffic flow of each traffic cell;
the sharing rate predicting module 240 is specifically configured to, when predicting the sharing rate corresponding to the urban air traffic at the specified time through the MNL model according to the model parameter value, the first predicted total traffic flow and the prediction data:
forecasting the corresponding share rate of the urban air traffic in each traffic cell at the appointed time through the MNL model according to the model parameter values, the forecast data and each second forecast total traffic flow;
the MNL model is used for representing the incidence relation among the corresponding share rate of the urban air traffic in each traffic cell, the total traffic flow of each second forecast, the forecast data and the model parameter value at the appointed time.
Optionally, the apparatus further comprises:
the traffic cell flow determining module is used for determining the current total traffic flow of each traffic cell according to the current total traffic flow and the distribution of each traffic cell;
the first prediction module is specifically configured to, when determining the second predicted total traffic flow of each traffic cell corresponding to the specified time according to the current total traffic flow and the distribution of each traffic cell:
acquiring the current population number of a research area and a predicted population number corresponding to a specified time;
and determining a second predicted total traffic flow of each traffic cell corresponding to the specified time according to the current population number, the predicted population number and the current total traffic flow of each traffic cell.
Optionally, the apparatus further comprises:
and the traffic flow determining module is used for determining the traffic flow of the urban air traffic in each traffic cell according to the corresponding sharing rate of the urban air traffic in each traffic cell and the second predicted total traffic flow of each traffic cell.
Optionally, the apparatus further comprises:
and the travel route determining module is used for providing a travel route for each resident according to the traffic flow corresponding to the urban air traffic in each traffic cell.
Optionally, the apparatus further comprises:
the preprocessing module is used for acquiring the current initial total traffic flow and determining the equivalent traffic volume corresponding to each road in the area corresponding to the current initial total traffic flow according to the current initial total traffic flow; acquiring the proportion of the current cargo vehicle in all vehicles; determining the actual passenger carrying capacity corresponding to each road according to the equivalent traffic volume and the proportion corresponding to each road; and determining the current total traffic flow corresponding to each traffic mode according to the actual passenger carrying flow corresponding to each road and the current sharing rate of the cars.
Optionally, the total traffic flow predicting module 210 predicts a first predicted total traffic flow corresponding to each traffic mode at a specified time according to the current total traffic flow, and is specifically configured to:
acquiring the current population number of a research area and a predicted population number corresponding to a specified time;
and predicting a first predicted total traffic flow corresponding to each traffic mode at a specified time according to the current population number, the predicted population number and the current total traffic flow.
The determining device of the urban air traffic sharing rate according to the embodiment of the present invention may execute the determining method of the urban air traffic sharing rate according to the embodiment of the present invention, and the implementation principles thereof are similar, the actions performed by each module and unit in the determining device of the urban air traffic sharing rate according to the embodiments of the present invention correspond to the steps in the determining method of the urban air traffic sharing rate according to the embodiments of the present invention, and the detailed functional description of each module of the determining device of the urban air traffic sharing rate may specifically refer to the description in the determining method of the urban air traffic sharing rate shown in the foregoing, and will not be described again here.
The determining device of the urban air traffic sharing rate may be a computer program (including program code) running in a computer device, for example, the determining device of the urban air traffic sharing rate is an application software; the apparatus may be used to perform the corresponding steps in the methods provided by the embodiments of the present invention.
In some embodiments, the determining Device of the urban air traffic share rate provided by the embodiments of the present invention may be implemented by a combination of hardware and software, and by way of example, the determining Device of the urban air traffic share rate provided by the embodiments of the present invention may be a processor in the form of a hardware decoding processor, which is programmed to execute the determining method of the urban air traffic share rate provided by the embodiments of the present invention, for example, the processor in the form of the hardware decoding processor may employ one or more Application Specific Integrated Circuits (ASICs), DSPs, Programmable Logic Devices (PLDs), Complex Programmable Logic Devices (CPLDs), Field Programmable Gate Arrays (FPGAs), or other electronic elements.
In other embodiments, the determining device for urban air traffic sharing rate provided by the embodiment of the present invention may be implemented by software, and fig. 4 illustrates the determining device for urban air traffic sharing rate stored in the memory, which may be software in the form of program and plug-in, and includes a series of modules, including a total traffic flow predicting module 210, a parameter predicting module 220, a city model parameter determining module 230, and a sharing rate predicting module 240, for implementing the determining method for urban air traffic sharing rate provided by the embodiment of the present invention.
The modules described in the embodiments of the present invention may be implemented by software or hardware. Wherein the name of a module in some cases does not constitute a limitation on the module itself.
Based on the same principle as the method shown in the embodiment of the present invention, an embodiment of the present invention also provides an electronic device, which may include but is not limited to: a processor and a memory; a memory for storing a computer program; a processor for executing the method according to any of the embodiments of the present invention by calling the computer program.
In an alternative embodiment, an electronic device is provided, as shown in fig. 5, the electronic device 4000 shown in fig. 5 comprising: a processor 4001 and a memory 4003. Processor 4001 is coupled to memory 4003, such as via bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, and the transceiver 4004 may be used for data interaction between the electronic device and other electronic devices, such as transmission of data and/or reception of data. In addition, the transceiver 4004 is not limited to one in practical applications, and the structure of the electronic device 4000 is not limited to the embodiment of the present invention.
The Processor 4001 may be a CPU (Central Processing Unit), a general-purpose Processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other Programmable logic device, a transistor logic device, a hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor 4001 may also be a combination that performs a computational function, including, for example, a combination of one or more microprocessors, a combination of a DSP and a microprocessor, or the like.
Bus 4002 may include a path that carries information between the aforementioned components. The bus 4002 may be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus 4002 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 5, but this is not intended to represent only one bus or type of bus.
The Memory 4003 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical Disc storage, optical Disc storage (including Compact Disc, laser Disc, optical Disc, digital versatile Disc, blu-ray Disc, etc.), a magnetic Disc storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.
The memory 4003 is used for storing application codes (computer programs) for executing the aspects of the present invention, and is controlled to be executed by the processor 4001. Processor 4001 is configured to execute application code stored in memory 4003 to implement what is shown in the foregoing method embodiments.
The electronic device may also be a terminal device, and the electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the application scope of the embodiment of the present invention.
An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program runs on a computer, the computer is enabled to execute the corresponding contents in the foregoing method embodiments.
According to another aspect of the invention, there is also provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The 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 perform the methods provided in the various embodiment implementations described above.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It should be understood that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The computer readable storage medium provided by the embodiments of the present invention may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer-readable storage medium carries one or more programs which, when executed by the electronic device, cause the electronic device to perform the methods shown in the above embodiments.
The foregoing description is only exemplary of the preferred embodiments of the invention and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents is encompassed without departing from the spirit of the disclosure. For example, the above features and (but not limited to) the features with similar functions disclosed in the present invention are mutually replaced to form the technical solution.

Claims (10)

1. A method for determining urban air traffic sharing rate is characterized by comprising the following steps:
acquiring current total traffic flow of each traffic mode in a research area, and predicting first predicted total traffic flow corresponding to the specified time of each traffic mode in the research area according to the current total traffic flow, wherein each traffic mode comprises cars, public traffic and urban air traffic;
acquiring current sharing rate, current data and predicted data corresponding to the appointed time of each traffic mode in the traffic modes, wherein the current data and the predicted data are the same data corresponding to different times, and the data comprise time, cost and accessibility;
determining a model parameter value through a double-layer NL model according to the current sharing rate and the current data, wherein the double-layer NL model is used for representing the association relation among the sharing rate, the current data and the model parameter value corresponding to each transportation mode;
and predicting the sharing rate of the urban air traffic in the research area at the specified time through an MNL model according to the model parameter values, the first predicted total traffic flow and the prediction data, wherein the MNL model is used for representing the association relation among the sharing rate of the urban air traffic in the research area at the specified time, the first predicted total traffic flow, the prediction data and the model parameter values.
2. The method of claim 1, wherein the study area comprises a plurality of traffic cells, further comprising:
acquiring the distribution of each traffic cell corresponding to the research area;
determining a second predicted total traffic flow of each traffic cell corresponding to the specified time according to the current total traffic flow and the distribution of each traffic cell, wherein the first predicted total traffic flow comprises the second predicted total traffic flow of each traffic cell;
predicting the sharing rate of the urban air traffic at the specified time through an MNL model according to the model parameter value, the first predicted total traffic flow and the prediction data, wherein the predicting comprises the following steps:
predicting the corresponding sharing rate of the urban air traffic in each traffic cell at the specified time through the MNL model according to the model parameter values, the prediction data and the second predicted total traffic flow;
the MNL model is used for representing the incidence relation among the corresponding sharing rate of urban air traffic in each traffic cell, the second total predicted traffic flow, the predicted data and the model parameter value at the appointed time.
3. The method of claim 2, further comprising:
determining the current total traffic flow of each traffic cell according to the current total traffic flow and the distribution of each traffic cell;
the determining a second predicted total traffic flow of each traffic cell corresponding to the specified time according to the current total traffic flow and the distribution of each traffic cell includes:
acquiring the current population number of the research area and the predicted population number corresponding to the specified time;
and determining a second predicted total traffic flow of each traffic cell corresponding to the specified time according to the current population number, the predicted population number and the current total traffic flow of each traffic cell.
4. The method of claim 2, further comprising:
and determining the traffic flow of the urban air traffic in each traffic cell according to the corresponding sharing rate of the urban air traffic in each traffic cell and the second predicted total traffic flow of each traffic cell.
5. The method of claim 4, further comprising:
and providing a travel route for each resident according to the traffic flow of the urban air traffic in each traffic cell.
6. The method of any one of claims 1 to 5, further comprising:
acquiring the current initial total traffic flow, and determining the equivalent traffic volume corresponding to each road in the area corresponding to the current initial total traffic flow according to the current initial total traffic flow;
acquiring the proportion of the current cargo vehicle in all vehicles;
determining the actual passenger carrying capacity corresponding to each road according to the equivalent traffic volume corresponding to each road and the proportion;
and determining the current total traffic flow corresponding to each traffic mode according to the actual passenger carrying flow corresponding to each road and the current sharing rate of cars.
7. The method according to any one of claims 1 to 5, wherein the predicting a first predicted total traffic flow corresponding to each of the transportation modes at a specified time according to the current total traffic flow comprises:
acquiring the current population number of the research area and the predicted population number corresponding to the specified time;
and predicting a first predicted total traffic flow corresponding to each traffic mode at a specified time according to the current population number, the predicted population number and the current total traffic flow.
8. A device for determining the air traffic sharing rate in a city is characterized by comprising:
the traffic total flow prediction module is used for acquiring the current traffic total flow corresponding to each traffic mode in a research area, and predicting a first predicted traffic total flow corresponding to each traffic mode at a specified time according to the current traffic total flow, wherein each traffic mode comprises cars, public traffic and urban air traffic;
the parameter prediction module is used for acquiring the current sharing rate, the current data and the prediction data corresponding to the designated time of each traffic mode in the traffic modes, wherein the current data and the prediction data are the same parameters corresponding to different times, and the parameters comprise time, cost and accessibility;
a model parameter value determining module, configured to determine a model parameter value through a double-layer NL model according to the current sharing rate and the current data, where the double-layer NL model is used to represent an association relationship between the sharing rate, the current data, and the model parameter value corresponding to each transportation mode;
and the sharing rate prediction module is used for predicting the sharing rate corresponding to the research area of the urban air traffic at the specified time through an MNL (Mobile network layer) model according to the model parameter value, the first predicted total traffic flow and the prediction data, and the MNL model is used for representing the association relation among the sharing rate corresponding to the research area of the urban air traffic at the specified time, the first predicted total traffic flow, the prediction data and the model parameter value.
9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the method of any one of claims 1-7 when executing the computer program.
10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method of any one of claims 1-7.
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