CN114724414B - Method and device for determining urban air traffic sharing rate, electronic equipment and medium - Google Patents

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

Info

Publication number
CN114724414B
CN114724414B CN202210248307.8A CN202210248307A CN114724414B CN 114724414 B CN114724414 B CN 114724414B CN 202210248307 A CN202210248307 A CN 202210248307A CN 114724414 B CN114724414 B CN 114724414B
Authority
CN
China
Prior art keywords
traffic
predicted
current
total flow
cell
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202210248307.8A
Other languages
Chinese (zh)
Other versions
CN114724414A (en
Inventor
廖小罕
屈文秋
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Institute of Geographic Sciences and Natural Resources of CAS
Original Assignee
Institute of Geographic Sciences and Natural Resources of CAS
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Institute of Geographic Sciences and Natural Resources of CAS filed Critical Institute of Geographic Sciences and Natural Resources of CAS
Priority to CN202210248307.8A priority Critical patent/CN114724414B/en
Publication of CN114724414A publication Critical patent/CN114724414A/en
Application granted granted Critical
Publication of CN114724414B publication Critical patent/CN114724414B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • 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 traffic total flow corresponding to each traffic mode in a research area, and predicting first predicted traffic total flow corresponding to each traffic mode in a designated time according to the current traffic total flow; acquiring current sharing rate, current data and predicted data corresponding to appointed time corresponding to each traffic mode in a 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 corresponding to the urban air traffic in the research area at the designated time through an MNL model according to the model parameter value, the first predicted traffic total flow and the predicted data. By the method, the accuracy of the sharing rate corresponding to the research area of urban air traffic at the designated time can be improved, manpower, material resources and time are saved, and the influence of the IA characteristics of the Logi t model is reduced.

Description

Method and device for determining urban air traffic sharing rate, electronic equipment and medium
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, most of the existing prediction methods of the sharing rate of a novel traffic mode entering the urban space adopt three modes, and firstly, the sharing rate of a novel traffic mode in a certain year is predicted by utilizing a related intelligent algorithm. Secondly, designing a questionnaire according to a study key point, combining RP (Revealed Preference) survey with SP (Stated Preference) survey, calculating the sharing rate and the demand of various traffic modes in the city through a questionnaire result, and thirdly, adopting a four-stage method, wherein the four-stage method needs to utilize the current sharing rate and the current situation of each traffic mode of the city and the predicted factor data of each traffic mode of a certain year among different traffic cells when predicting the sharing rate: time, cost, reachability, etc.
The three ways described above have the following drawbacks,
first, intelligent algorithms for supervised classification are employed: the training samples are selected to have stronger human subjective factors, and the selected training samples can not necessarily well represent the travel structure of the existing urban traffic system; the selection and evaluation of training samples requires more manpower and time; only the factors influencing the choice of traffic means used for constructing the model can be identified, if a category is not known by the trainee or is not defined by too few, the supervision classification cannot be identified. An intelligent algorithm of non-supervision classification is adopted: factors influencing traffic mode selection, which are generated by non-supervision classification, need a large number of subsequent analysis and processing, and the factors are matched with the factors of a research area to obtain a final result; the analyst has difficulty controlling the factors that affect the choice of traffic patterns. In conclusion, the intelligent algorithm has poor robustness and the prediction result has poor accuracy.
Second, questionnaire: rationality of questionnaire design is difficult to evaluate; the questionnaire is distributed, and the questionnaire results are collected, so that a sample which is large enough and representative is obtained, and great manpower, material resources and time are required.
Third, a new traffic system does not have current sharing rate when it does not enter the urban traffic system, which may result in poor accuracy of the prediction result, and the logic model in the four-stage method has IIA (Independence of Irrelevant Alternative) characteristics, which may affect the prediction accuracy. The IIA characteristic of the logic model means that in the urban area, the ratio of the merits of the two traffic modes is fixed, i.e. the probability ratio of the resident going out to select a car or a bus is equal regardless of whether or not there is UAM (Urban Air Mobility, urban air traffic) in 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 urban air traffic sharing rate.
In a first aspect, the present invention solves the above technical problems by providing the following technical solutions: a method for determining urban air traffic sharing rate, the method comprising:
Obtaining the current traffic total flow corresponding to each traffic mode in a research area, and predicting the first predicted traffic total flow corresponding to each traffic mode in a designated time according to the current traffic total 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 relationship among the sharing rate, the current data and the model parameter value corresponding to each traffic mode;
and predicting the sharing rate corresponding to the urban air traffic in the research area at the designated time through an MNL model according to the model parameter value, the first predicted traffic total flow and the predicted data, wherein the MNL model is used for representing the association relationship among the sharing rate corresponding to the urban air traffic in the research area at the designated time, the first predicted traffic total flow, the predicted data and the model parameter value.
The beneficial effects of the invention are as follows: according to the scheme, the sharing rate corresponding to the urban air traffic at the appointed time is predicted through the MNL model, compared with the prior art, the sharing rate corresponding to the urban air traffic at the appointed time is more accurate through factors (time, cost and accessibility) affecting the sharing rate, meanwhile, according to the scheme, manual mode statistics data is not needed, and manpower, material resources and time are saved. In addition, the NL model and the MNL model are combined, so that the influence of IIA characteristics of the Logit model can be reduced, and the problem that UAM has no current sharing rate data can be solved.
On the basis of the technical scheme, the invention can be improved as follows.
Further, the investigation region includes a plurality of traffic cells, and the method further includes:
acquiring the distribution of each traffic cell corresponding to the research area;
determining a second predicted traffic total flow of each traffic cell corresponding to the designated time according to the current traffic total flow and the distribution of each traffic cell, wherein the first predicted traffic total flow comprises the second predicted traffic total flow of each traffic cell;
according to the model parameter value, the first predicted traffic total flow and the predicted data, the sharing rate corresponding to the urban air traffic at the designated time is predicted by the MNL model, and the method comprises the following steps:
predicting the sharing rate of the urban air traffic in each traffic cell at the appointed time through an MNL model according to the model parameter value, the prediction data and the second prediction traffic total flow;
the MNL model is used for representing the association relation among the sharing rate of the urban air traffic corresponding to each traffic cell, the total traffic flow of each second prediction, the prediction data and the model parameter value at the appointed time.
The adoption of the further scheme has the advantages that considering that the total traffic flow of each traffic cell is possibly different, when the sharing rate of urban air traffic corresponding to the research area at the appointed time is predicted, the sharing rate of the urban air traffic corresponding to each traffic cell at the appointed time can be predicted through the MNL model based on the model parameter value, the prediction data and the second total traffic flow, so that the predicted sharing rate is more accurate.
Further, the method further comprises the steps of:
determining the current traffic total flow of each traffic cell according to the current traffic total flow and the distribution of each traffic cell;
the determining the second predicted traffic total flow of each traffic cell corresponding to the designated time according to the current traffic total flow and the distribution of each traffic cell comprises the following steps:
acquiring the current population number of a research area and the predicted population number corresponding to the appointed time;
and determining a second predicted traffic total flow of each traffic cell corresponding to the appointed time according to the current population number, the predicted population number and the current traffic total flow of each traffic cell.
The method has the advantages that the current traffic total flow of each traffic cell can be determined firstly in consideration of the fact that the traffic total flow of each traffic cell can be different and the influence of the current population quantity and the predicted population quantity on the traffic total flow of each traffic cell, and then the second predicted traffic total flow of each traffic cell corresponding to the appointed time is predicted according to the current traffic total flow, the current population quantity and the predicted population quantity of each traffic cell, so that the predicted second predicted traffic total flow is more accurate.
Further, the method further comprises the steps of:
and determining the traffic flow corresponding to the urban air traffic in each traffic cell according to the sharing rate corresponding to the urban air traffic in each traffic cell and the second predicted traffic total flow of each traffic cell.
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 can be 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 can be reflected through the traffic flow.
Further, the method further comprises the steps of:
and providing a travel route for each resident according to the traffic flow of the urban air traffic in each traffic cell.
The adoption of the further scheme has the beneficial effect that the traffic flow corresponding to the urban air traffic in each traffic cell is considered, and the travel route can be provided for residents according to the traffic flow.
Further, the method further comprises the steps of:
acquiring the current initial traffic total flow, and determining the equivalent traffic volume corresponding to each road in the area corresponding to the current initial traffic total flow according to the current initial traffic total flow;
Acquiring the proportion of the current truck 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 car.
The method has the advantages that the current initial traffic total flow comprises traffic flow of the truck, the current traffic total flow refers to traffic total flow corresponding to different traffic modes when residents go out, and the traffic flow of the truck is not included, so that the traffic flow of the truck can be removed from the current initial traffic total flow, and accuracy of prediction sharing rate is improved.
Further, predicting a first predicted traffic total flow corresponding to each traffic mode at a specified time according to the current traffic total flow includes:
acquiring the current population number of a research area and the predicted population number corresponding to the appointed time;
and predicting a first predicted traffic total flow corresponding to each traffic mode at the appointed time according to the current population number, the predicted population number and the current traffic total flow.
The method has the beneficial effects that the first predicted traffic total flow can be predicted according to the current traffic total flow, the current population number and the predicted population number in consideration of the influence of the current population number and the predicted population number on the first predicted traffic total flow, so that the predicted first predicted traffic total flow is more accurate.
In a second aspect, the present invention further provides a device for determining an urban air traffic sharing rate, for solving the above technical problem, where the device includes:
the traffic total flow prediction module is used for obtaining the current traffic total flow corresponding to each traffic mode in the research area, predicting the first predicted traffic total flow corresponding to each traffic mode in the appointed time according to the current traffic total flow, and each traffic mode comprises cars, public traffic and urban air traffic;
the parameter prediction module is used for acquiring current sharing rate, current data and predicted data corresponding to 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 time, and the parameters comprise time, cost and reachability;
The model parameter value determining module is used for determining a model parameter value according to the current sharing rate and the current data through a double-layer NL model, wherein the double-layer NL model is used for representing the association relationship among the sharing rate, the current data and the model parameter value corresponding to each traffic mode;
and the sharing rate prediction module is used for predicting the sharing rate corresponding to the urban air traffic in the research area at the designated time through an MNL model according to the model parameter value, the first predicted traffic total flow and the predicted data, and the MNL model is used for representing the association relationship among the sharing rate corresponding to the urban air traffic in the research area at the designated time, the first predicted traffic total flow, the predicted data and the model parameter value.
In a third aspect, the present invention further provides an electronic device for solving the above technical problem, where the electronic device includes a memory, a processor, and a computer program stored on the memory and capable of running on the processor, and when the processor executes the computer program, the determining method of the urban air traffic share rate of the present application is implemented.
In a fourth aspect, the present invention further provides a computer readable storage medium, where a computer program is stored, where the computer program is executed by a processor to implement the method for determining the urban air traffic share rate according to the present application.
Additional aspects and advantages of the 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 application.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings that are required to be used in the description of the embodiments of the present invention will be briefly described below.
Fig. 1 is a flow chart of a method for determining an urban air traffic sharing rate according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a dual layer NL model structure according to an embodiment of the present invention;
fig. 3 is a flow chart 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 an apparatus 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 the present invention are described below with examples given for the purpose of illustration only and are not intended to limit the scope of the invention.
The following describes the technical scheme of the present invention and how the technical scheme of the present invention solves the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail 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 urban air traffic sharing rate. The scheme provided by the embodiment of the invention can be executed by any electronic equipment, for example, the terminal equipment can be a user terminal equipment, the terminal equipment can be any terminal equipment which can be provided with an application and can predict urban air traffic sharing rate through the application, and the scheme comprises at least one of the following steps: smart phone, tablet computer, notebook computer, desktop computer, intelligent audio amplifier, intelligent wrist-watch, smart television, intelligent vehicle equipment.
The embodiment of the invention provides a possible implementation manner, as shown in fig. 1, a flowchart of a method for determining an urban air traffic sharing rate is provided, and the method can be executed by any electronic device, for example, can be a terminal device, or can be jointly executed by the terminal device and a server (hereinafter, can be referred to as a file server). For convenience of description, a method provided by an embodiment of the present invention will be described below by taking a server as an execution body, and the method may include the following steps as shown in a flowchart in fig. 1:
step S110, obtaining the current traffic total flow corresponding to each traffic mode in a research area, and predicting the first predicted traffic total flow corresponding to each traffic mode in a designated time according to the current traffic total flow, wherein each traffic mode comprises cars, public traffic and urban air traffic;
Step S120, obtaining 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;
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 relationship among the sharing rate, the current data and the model parameter value corresponding to each traffic mode;
and step 140, predicting the sharing rate corresponding to the research area of the urban air traffic at the appointed time through an MNL model according to the model parameter value, the first predicted traffic total flow and the predicted data, wherein the MNL model is used for representing the association relationship among the sharing rate corresponding to the research area of the urban air traffic at the appointed time, the first predicted traffic total flow, the predicted data and the model parameter value.
According to the method, based on the current traffic total flow, the first predicted traffic total flow corresponding to each traffic mode at the appointed time is predicted, 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 identical to the model parameter value of the MNL model, the sharing rate corresponding to the urban air traffic at the research area at the appointed time is predicted through the MNL model based on the determined model parameter value, the first predicted traffic total flow and the predicted data, in the scheme of the invention, the sharing rate corresponding to the urban air traffic at the appointed time is predicted through factors (time, cost and accessibility) influencing the sharing rate, which is more accurate than the prior art, meanwhile, according to the scheme of the invention, manual mode statistics data is not needed, and manpower, material resources and time are saved. In addition, the NL model and the MNL model are combined, so that the influence of IIA characteristics of the Logit model can be reduced, and the problem that UAM has no current sharing rate data can be solved.
The scheme of the present invention is further described below with reference to the following specific embodiments, in which the method for determining the urban air traffic share may include the following steps:
step S110, obtaining the current traffic total flow corresponding to each traffic mode in the research area, and predicting the first predicted traffic total flow corresponding to each traffic mode in the appointed time according to the current traffic total flow, wherein each traffic mode comprises cars, public transportation and urban air transportation.
The research area refers to an area of one city, where the sharing rate needs to be determined, the current total traffic flow refers to total traffic flow corresponding to the three traffic modes together, the current total traffic flow may reflect traffic conditions corresponding to an area of a certain city, for example, an area B (research area) of an a city, and the current total traffic flow may be determined through data analysis acquired by a road camera set in the area (for example, the area B) needing to be analyzed. The public transportation can comprise public transportation travel modes such as buses, subways, light rails and the like.
The specified time refers to a future time period, which may be, for example, months or years or weeks in the future. The designated time can be set according to the study requirements. As an example, for example, the current total traffic flow is the total traffic flow of the B area of the a city, and the first predicted total traffic flow refers to the total traffic flow of the B area of the a city at a specified time.
Optionally, before step S110, the method further includes:
acquiring the current initial traffic total flow, and determining the equivalent traffic volume corresponding to each road in the area corresponding to the current initial traffic total flow according to the current initial traffic total flow;
acquiring the proportion of the current truck 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 car.
Because the current initial traffic total flow comprises traffic flow of the truck, and the current traffic total flow refers to traffic total flow corresponding to different traffic modes when residents go out and does not comprise traffic flow of the truck, the traffic flow of the truck can be removed from the current initial traffic total flow, and accuracy of prediction sharing rate is improved.
The proportion of the current truck to all the vehicles can be determined based on analysis of data collected by the collection device arranged in the corresponding area of the current initial traffic total flow.
As an example, the area a includes m roads and n monitoring devices, the n monitoring devices acquire the current initial traffic total flow of the m roads corresponding to the area a, the current initial traffic total flow is converted into the equivalent traffic flow 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 vehicles. Because the traffic volume on each road is only the traffic volume of the car, the current traffic total flow corresponding to each road, namely the total travel amount of residents, can be calculated according to the actual passenger carrying flow corresponding to each road and divided by the sharing rate corresponding to the car.
Step S120, obtaining current sharing rate, current data and predicted data corresponding to appointed time of each traffic mode in the study 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.
The current data are current data of various factors influencing traffic mode division, and each traffic mode corresponds to one current sharing rate based on the three traffic modes, namely, the car corresponds to one current sharing rate, the public traffic corresponds to one current sharing rate, and the 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 taken to travel from point a to point B for a car and from point B to point C for a bus. The cost refers to the cost of a resident riding a vehicle, such as the cost of riding a car from point a to point B, and the cost of riding a bus from point B to point C. Reachability refers to the degree of convenience in using a vehicle to reach from one location to another. Optionally, the points a, B, and C may be traffic cells.
Alternatively, the prediction data may be determined based on the current data and population changes of the region to which the current data corresponds.
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 relationship among the sharing rate, the current data and the model parameter value corresponding to each traffic mode.
The double-layer NL model is a pre-established model and is used for representing the sharing rate corresponding to each traffic mode, the association relation between current data and model parameter values, the model parameter values are factors influencing the sharing rate, different current data correspond to different sharing rates, and different sharing rates and different model parameter values corresponding to different current data are different.
Alternatively, referring to fig. 2, the principle of the two-layer NL model construction is to divide the current traffic mode in the city into two types, namely a non-motorized type and a motorized type, wherein the non-motorized type comprises bicycles and pedestrians, the motorized type comprises buses and public transportation, and the public transportation is divided into buses and subways. The above described bilayer NL model can be built by: taking an area A and an area B of a certain year as an example, acquiring time, cost and accessibility corresponding to each traffic mode in each traffic mode and sharing rate corresponding to each traffic mode.
The relationship between time, cost, reachability, and sharing rate for each mode of transportation is characterized by the following formulas (1) through (5):
Figure GDA0003640190370000101
p ij car ＝1-p ij bus (2)
Figure GDA0003640190370000111
wherein ,pij bus 、p ij car Respectively representing the sharing rate of the car and the bus;
Figure GDA0003640190370000112
respectively representing the running time of the car and the bus; />
Figure GDA0003640190370000113
Representing the cost of the car and bus respectively; />
Figure GDA0003640190370000114
Respectively representing the reachability of the car and the bus; />
Figure GDA0003640190370000115
Representing the total traffic flow corresponding to the car, +.>
Figure GDA0003640190370000116
The traffic total flow corresponding to the bus is represented, and alpha, beta, gamma and theta represent model parameter values.
The accessibility of the car can be determined by the following formula (4):
Figure GDA0003640190370000117
wherein ,
Figure GDA0003640190370000118
indicating the reachability from traffic cell i, < >/within a defined period P>
Figure GDA0003640190370000119
The number of opportunities of the traffic cell j in the time period P is represented, namely, the number of opportunities which can be accessed from the traffic cell j in the time period P is represented; />
Figure GDA00036401903700001110
Representing the shortest travel time from traffic cell i to traffic cell j within time period P, T representing the study time interval, i.e. a time period set in advance, +.>
Figure GDA00036401903700001111
When it is indicated that the determined reachability is within the study time interval, then it is optional
Figure GDA00036401903700001112
The corresponding parameter determines the reachability, in which case +. >
Figure GDA00036401903700001113
Similarly, let go of>
Figure GDA00036401903700001114
Indicating that the determined reachability is outside the investigation time interval, then +.>
Figure GDA00036401903700001115
The corresponding parameter is not selected for determining reachability, in which case +.>
Figure GDA00036401903700001116
N represents the number of traffic cells within the B area.
The reachability of public transportation and urban air traffic UAM can be determined by the following formula (5):
Figure GDA00036401903700001117
/>
wherein ,AF Indicating the reachability of public transportation or urban air transportation UAM, and ρ indicates the passenger travel density in the radiation area (B area); s represents the area in the radiation area; t represents the average travel time of passengers in the radiation area; m represents the number of stations included in public or urban air traffic UAM within the radiating area.
In an alternative aspect of the present invention, predicting, according to the current total traffic flow, a first predicted total traffic flow corresponding to each traffic mode at a specified time includes:
acquiring the current population number of a research area and the predicted population number corresponding to the appointed time;
and predicting a first predicted traffic total flow corresponding to each traffic mode at the appointed time according to the current population number, the predicted population number and the current traffic total flow.
Considering the influence of the current population number and the predicted population number on the first predicted traffic total flow, in the scheme of the invention, the resident population primary unit can be determined by a primary unit method according to the current population number and the predicted population number, wherein the resident population primary 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 traffic total flow (traffic total flow corresponding to each traffic mode) to obtain a first predicted traffic total flow corresponding to each traffic mode at the appointed time. In the scheme of the invention, the first predicted traffic total flow is predicted according to the current traffic total flow, the current population number and the predicted population number, so that the predicted first predicted traffic total 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 rates corresponding to the other two traffic modes and the current data corresponding to the other two traffic modes, so that the problem that there is no current sharing rate of the new traffic mode may be solved.
And step 140, predicting the sharing rate corresponding to the research area of the urban air traffic at the appointed time through an MNL model according to the model parameter value, the first predicted traffic total flow and the predicted data, wherein the MNL model is used for representing the association relationship among the sharing rate corresponding to the research area of the urban air traffic at the appointed time, the first predicted traffic total flow, the predicted data and the model parameter value.
The MNL model is a pre-established model, and is used for representing a relationship among a sharing rate, a first predicted traffic total flow, predicted data and model parameter values corresponding to urban air traffic at a specified time, wherein the model parameter values of the MNL model comprise model parameter values in a double-layer NL model, and the MNL model is used for predicting the sharing rate corresponding to a research area of the urban air traffic at the specified time according to the predicted data and the first predicted traffic total flow on the premise that the model parameter values are known. Because the predicted data comprises parameters of different traffic modes, and the first predicted traffic total flow comprises first predicted traffic total flows corresponding to different traffic modes, the sharing rate of urban air traffic at the appointed time can be determined through the MNL model, and the sharing rates of public traffic and buses at the appointed time can be predicted respectively.
Optionally, when predicting the sharing rate of urban air traffic corresponding to the research area at the designated time, the MNL model may be implemented by the following formula (6) and formula (7):
Figure GDA0003640190370000131
Figure GDA0003640190370000132
wherein ,
Figure GDA0003640190370000133
indicating that the urban air traffic is at the designated time, the corresponding time from traffic cell i to traffic cell j,
Figure GDA0003640190370000134
representing the cost of urban air traffic corresponding to traffic cell i to traffic cell j at a specified time, +.>
Figure GDA0003640190370000135
The reachability of the urban air traffic corresponding to the traffic cell i to the traffic cell j at the appointed time is represented; />
Figure GDA0003640190370000136
and />
Figure GDA0003640190370000137
All represent the first predicted traffic total flow corresponding to the urban air traffic at the appointed time, V all Representing the first corresponding to the three traffic modesPredicting total traffic flow, P UAM And the sharing rate corresponding to the urban air traffic at the designated time is represented, and alpha, beta and gamma represent model parameter values.
In an alternative solution 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 sharing rate of the air traffic of the city corresponding to the specified time, the second predicted total traffic flow of each traffic cell at the specified time may be determined by:
acquiring the distribution of each traffic cell corresponding to the research area;
Determining a second predicted traffic total flow of each traffic cell corresponding to the designated time according to the current traffic total flow and the distribution of each traffic cell, wherein the first predicted traffic total flow comprises the second predicted traffic total flow of each traffic cell;
predicting, by the MNL model, a sharing rate corresponding to the urban air traffic at the specified time according to the model parameter value, the first predicted traffic total flow and the predicted data, where the method includes:
predicting the sharing rate of the urban air traffic in each traffic cell at the appointed time through an MNL model according to the model parameter value, the prediction data and the second prediction traffic total flow;
the MNL model is used for representing the association relation among the sharing rate of the urban air traffic corresponding to each traffic cell, the total traffic flow of each second prediction, the prediction data and the model parameter value at the appointed time.
In the scheme, the sharing rate of urban air traffic in each traffic cell at the appointed time is predicted through the MNL model based on the model parameter value, the prediction data and the second prediction traffic total flow, so that the predicted sharing rate is more accurate.
The first predicted traffic total flow is equal to the sum of second predicted traffic total flows corresponding to all traffic cells, and the second predicted traffic total flow refers to the traffic total flow corresponding to all traffic cells in all traffic modes. The sharing rate of the urban air traffic at the appointed time is equal to the sum of the sharing rates of the urban air traffic at the traffic cells at the appointed time.
Optionally, the method further comprises:
determining the current traffic total flow of each traffic cell according to the current traffic total flow and the distribution of each traffic cell;
the determining the second predicted traffic total flow of each traffic cell corresponding to the designated time according to the current traffic total flow and the distribution of each traffic cell comprises the following steps:
acquiring the current population number of a research area and the predicted population number corresponding to the appointed time;
and determining a second predicted traffic total flow of each traffic cell corresponding to the appointed time according to the current population number, the predicted population number and the current traffic total flow of each traffic cell.
In consideration of the fact that the traffic total flow of each traffic cell may be different and the influence of the current population quantity and the predicted population quantity on the traffic total flow of each traffic cell, in the scheme of the invention, the current traffic total flow of each traffic cell can be determined first, and then the second predicted traffic total flow of each traffic cell corresponding to the appointed time is predicted according to the current traffic total flow, the current population quantity and the predicted population quantity of each traffic cell, so that the predicted second predicted traffic total flow is more accurate.
Alternatively, the current traffic total flow of each traffic cell can be determined by OD back-pushing according to the current traffic total flow and the distribution of each traffic cell. Then, according to the current population quantity and the predicted population quantity, a resident population primitive unit is determined by a primitive unit method, wherein the resident population primitive unit is equal to the ratio of the predicted population quantity to the current population quantity. And then multiplying the original resident population unit by the current traffic total flow of each traffic cell to obtain a second predicted traffic total flow corresponding to each traffic cell, wherein the second predicted traffic total flow corresponding to each traffic cell can also be called OD traffic. The second predicted total traffic flow for each traffic cell may also be referred to as the second predicted total traffic flow between two traffic cells.
In the alternative scheme of the invention, 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 can be determined according to the sharing rate of the urban air traffic in each traffic cell and the second predicted total traffic flow of each traffic cell, so as to reflect the traffic distribution condition of the urban air traffic in each traffic cell through the traffic flow.
And for each traffic cell, multiplying the sharing rate corresponding to the traffic cell by the second predicted total traffic flow of the traffic cell, wherein the second predicted total traffic flow is equal to the traffic flow corresponding to the urban air traffic in the traffic cell, and the traffic distribution situation 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 includes:
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 corresponding to each traffic cell, a travel route can be provided for residents according to the traffic flow.
The invention selects the optimal balance principle 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 residents to reach the destination. That is, based on the traffic flow corresponding to each traffic cell, a route with the minimum comprehensive cost can be selected as a travel route for the user, so that better travel experience is provided for the user, and meanwhile, the current traffic pressure can be relieved.
For a better description and understanding of the principles of the method provided by the present invention, the following description of the present invention is provided in connection with an alternative embodiment. It should be noted that, the specific implementation manner of each step in this specific embodiment should not be construed as limiting the solution of the present invention, and other implementation manners that can be considered by those skilled in the art based on the principle of the solution provided by the present invention should also be considered as being within the protection scope of the present invention.
Referring to a schematic diagram of a method for determining an urban air traffic share shown in fig. 3, in this example, the method includes the following steps:
step 1, obtaining the current initial traffic total flow corresponding to each traffic mode in each traffic cell according to the sharing rate prediction requirement.
In this example, the demand for sharing rate prediction includes a research area a, where the area a is that the city B is controlled to be within four loops (north sections), the eastern city is controlled to be within a city-surrounding expressway, and the western city is controlled to be within four loops (west sections). The spatial boundary is approximately consistent with national trails, and the area within the range is about 625 square kilometers. The planning year is 2020 to 2025, namely the total traffic flow in 2020 is taken as the current initial traffic total flow, the sharing rate of the urban air traffic in 2025 is predicted according to the current initial traffic total flow in 2020, and the designated time is 2025. The current traffic total flow is determined according to the traffic flow data collected by the monitoring equipment on the part of the road in the four-ring, and the traffic flow data comprises information such as collection time, collection position, number of lanes, number of different types of motor vehicles, number of motor vehicles in different lanes and the like.
And 2, converting the total current initial traffic flow into equivalent traffic flows of the roads in different directions, multiplying the equivalent traffic flow of each road by the proportion of the current truck to all the vehicles, and determining the actual passenger carrying flow corresponding to each road. Because the traffic volume on each road is only the traffic volume of the car, the current traffic total flow corresponding to each road, namely the total travel amount of residents, can be calculated according to the actual passenger carrying flow corresponding to each road and divided by the sharing rate corresponding to the car.
And 3, determining the current traffic total flow of each traffic cell, namely one current traffic total flow of each traffic cell in the n traffic cells according to the current traffic total flow of each road and the distribution of each traffic cell in an OD back-pushing mode.
Step 4, obtaining the current population number (the population number in 2020) of the area A and the predicted population number corresponding to 2025; based on the current population and the predicted population, a resident population primitive is determined by a primitive unit method, the resident population primitive being equal to the ratio of the predicted population to the current population, which in this example is 1.08. And then multiplying the original resident population unit 1.08 by the current traffic total flow of each traffic cell to obtain a second predicted traffic total flow corresponding to each traffic cell, wherein the second predicted traffic total flow corresponding to each traffic cell can also be called OD traffic volume. The second predicted total traffic flow for each traffic cell may also be referred to as the 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 resulting second predicted total traffic flow for each traffic cell corresponds to the total 2025B market traffic flow shown in fig. 3.
And 5, predicting the first predicted traffic total flow corresponding to each traffic cell in 2025 by a double-constraint gravity model according to the second predicted traffic total flow corresponding to each traffic cell and the time, cost and accessibility data corresponding to each traffic mode in 2025. Corresponds to the 2025 traffic distribution in fig. 3, and corresponds to the traffic distribution in fig. 3.
Before the four-stage method prediction is performed, a research area needs to be divided into a certain number and size of traffic cells, and traffic distribution is to research the travel amount change among the traffic cells. How much traffic each traffic cell generates (may be referred to as generating traffic) and how much traffic is drawn (may be referred to as drawing traffic) in the investigation region constitutes the traffic flow space OD distribution in the investigation region. The method can well reflect the space characteristics of the travel of residents in the urban layout of different land types, namely the land types and the positions of the residents, and the generated or attracted traffic quantity is more. In traffic prediction, due to data processing errors, errors of original data sets, errors of 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 total amount and attraction total amount is often caused. When the double-constraint gravity model is used for traffic distribution prediction, conservation of the occurrence amount and the attraction amount can be considered at the same time, accuracy of a prediction result can be greatly improved, and the problem that the value of the traffic attraction amount and the occurrence amount needs to be corrected possibly in the follow-up process is avoided.
Wherein the above-mentioned dual-constraint gravity model can be represented by the following formula (8):
Figure GDA0003640190370000171
wherein, the text is exponentiated
Figure GDA0003640190370000172
Prediction of traffic distribution as a function of traffic impedance for use in a dual-constrained gravity model, where f (C ij ) Represents the corresponding OD traffic volume of each traffic cell, C ij Representing the cost, a, from traffic cell i to traffic cell j i Representing the occurrence unit of an i traffic cell (the occurrence unit is equal to the current traffic total flow of the i traffic cell divided by the current population total of the i traffic cell), b j Representing the attractive origin unit of the j traffic cell (the attractive origin unit is equal to the current traffic total flow of the i traffic cell divided by the current population total of the i traffic cell), q ij Representing the travel amount (first predicted traffic total flow) between the traffic cell and the j traffic cell, O i Representing the occurrence traffic volume of the traffic cell i; d (D) j Representing the amount of attraction traffic for traffic cell j.
The dual-constraint gravity model can be trained by the following modes:
step a, let m=0, m is the calculated times;
step b, giving γ (obtained by a least square method);
step c, order
Figure GDA0003640190370000181
Find->
Figure GDA0003640190370000182
Step d, obtaining
Figure GDA0003640190370000183
and />
Figure GDA0003640190370000184
Step e, let m=m+1, continue to calculate
Figure GDA0003640190370000185
and />
Figure GDA0003640190370000186
Performing multiple loop iteration;
f, determining convergence of the dual-constraint gravity model, if a is satisfied i and bj The corresponding precision is finished; otherwise, returning to the step b for recalculation, wherein a i and bj The corresponding accuracy is expressed by the following formulas, respectively.
Figure GDA0003640190370000187
Wherein epsilon is a set value.
And 6, the traffic mode division comprises two parts, wherein the first part inputs the sharing rate corresponding to each traffic mode in the traffic modes in 2020, the time, cost and accessibility data corresponding to each traffic mode in 2020 into the double-layer NL model, and calculates 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 respectively: alpha: -0.5503; beta: -0.0022; gamma is 0.00015; θ: -0.6297. And a second part, inputting the model parameter value, the time, cost and reachability data corresponding to each traffic mode in 2025 and the second predicted total traffic flow corresponding to each traffic cell obtained in the step 4 into the MNL model to obtain the sharing rate of UAM in 2025 in each traffic cell (corresponding to the UAM sharing rate in 2025 shown in fig. 3).
And 7, determining the traffic flow of the UAM in 2025 in each traffic cell according to the sharing rate of the UAM in 2025 in each traffic cell and the second predicted total traffic flow corresponding to each traffic cell in 2025, wherein the traffic flow corresponds to the OD flow in 2025 shown in FIG. 3. The method comprises the following steps: the UAM flow is relatively small in the three rings, and the flow out of the three rings is large. In the range between the three rings and the four rings, the flow of UAM in the southeast direction of Chengdu is smaller, and the flow in the northwest direction is larger.
Step 8, according to the traffic flow of UAM in each traffic cell in 2025, combining population density distribution UAM airlines, and determining at least one of the following information according to the optimal balance principle of users: the relationship between UAM cost and sharing rate, the relationship between population growth and UAM sharing rate, the relationship between distance and UAM sharing rate, UAM flow distribution mode (traffic flow distribution condition of UAM in each traffic cell) and suggestion of UAM route planning (providing a better travel route for users). Step 8 corresponds to the traffic flow allocation shown in fig. 3.
According to the scheme provided by the invention, the sharing rate of the UAM of urban air traffic and the traffic flow demand and distribution are predicted, the influence of the travel cost of passengers on the sharing rate of the UAM is greatly analyzed according to the prediction result, the UAM has potential to face the market when the sharing operation is completely carried out, the UAM sharing rate is increased along with the increase of the distance, and the UAM has more transportation advantages when the distance exceeds 15 km. The population change has little effect on UAM sharing rate, but has a great influence on UAM traffic flow. OD traffic is distributed to the road network through the distribution of traffic flow, and the basis for road and route planning in future cities can be given according to 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 a determining device 20 for urban air traffic sharing rate, as shown in fig. 4, the determining device 20 for urban air traffic sharing rate may include a traffic total 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 traffic total flow prediction module 210 is configured to obtain a current traffic total flow corresponding to each traffic mode in the research area, and predict a first predicted traffic total flow corresponding to each traffic mode at a specified time according to the current traffic total 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 sharing rate, current data, and predicted data corresponding to a specified time in a research area for each traffic mode, where the current data and the predicted data are the same parameters corresponding to different times, and the parameters include time, cost, and reachability;
the model parameter value determining module 230 is configured to determine a model parameter value according to the current sharing rate and the current data through a dual-layer NL model, where the dual-layer NL model is used to characterize an association relationship between the sharing rate, the current data, and the model parameter value corresponding to each traffic mode;
The sharing rate prediction module 240 is configured to predict, according to the model parameter value, the first predicted traffic total flow, and the predicted data, a sharing rate corresponding to a research area of urban air traffic at a specified time through an MNL model, where the MNL model is used to characterize an association relationship among the sharing rate corresponding to the research area of urban air traffic at the specified time, the first predicted traffic total flow, the predicted data, and the model parameter value.
Optionally, the study 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 traffic total flow of each traffic cell corresponding to the designated time according to the current traffic total flow and the distribution of each traffic cell, wherein the first predicted traffic total flow comprises the second predicted traffic total flow of each traffic cell;
the sharing rate prediction module 240 predicts, according to the model parameter value, the first predicted traffic total flow and the predicted data, the sharing rate of the urban air traffic corresponding to the designated time by using the MNL model, and is specifically configured to:
predicting the sharing rate of the urban air traffic in each traffic cell at the appointed time through an MNL model according to the model parameter value, the prediction data and the second prediction traffic total flow;
The MNL model is used for representing the association relation among the sharing rate of the urban air traffic corresponding to each traffic cell, the total traffic flow of each second prediction, the prediction data and the model parameter value at the appointed time.
Optionally, the apparatus further includes:
the traffic cell flow determining module is used for determining the current traffic total flow of each traffic cell according to the current traffic total flow and the distribution of each traffic cell;
the first prediction module is specifically configured to, when determining, according to the current total traffic flow and the distribution of each traffic cell, a second predicted total traffic flow of each traffic cell corresponding to the specified time:
acquiring the current population number of a research area and the predicted population number corresponding to the appointed time;
and determining a second predicted traffic total flow of each traffic cell corresponding to the appointed time according to the current population number, the predicted population number and the current traffic total flow of each traffic cell.
Optionally, the apparatus further includes:
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 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 includes:
and the travel route determining module is used for providing travel routes for each resident according to the traffic flow corresponding to the urban air traffic in each traffic cell.
Optionally, the apparatus further includes:
the preprocessing module is used for acquiring the current initial traffic total flow and determining the equivalent traffic volume corresponding to each road in the area corresponding to the current initial traffic total flow according to the current initial traffic total flow; acquiring the proportion of the current truck 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 car.
Optionally, the traffic total flow prediction module 210 predicts a first predicted traffic total flow corresponding to each traffic mode at a specified time according to the current traffic total flow, which is specifically configured to:
acquiring the current population number of a research area and the predicted population number corresponding to the appointed time;
and predicting a first predicted traffic total flow corresponding to each traffic mode at the appointed time according to the current population number, the predicted population number and the current traffic total flow.
The determining device for the urban air traffic sharing rate according to the embodiment of the present invention may execute the determining method for the urban air traffic sharing rate according to the embodiment of the present invention, and its implementation principle is similar, and actions executed by each module and unit in the determining device for the urban air traffic sharing rate according to the embodiments of the present invention correspond to steps in the determining method for the urban air traffic sharing rate according to the embodiments of the present invention, and detailed functional descriptions of each module of the determining device for the urban air traffic sharing rate may be referred to the descriptions in the corresponding determining method for the urban air traffic sharing rate shown in the foregoing, which are not repeated herein.
The above-mentioned determination device of the urban air traffic share rate may be a computer program (including program code) running in a computer device, for example, the determination device of the urban air traffic share rate is an application software; the device can be used for executing corresponding steps in the method provided by the embodiment of the invention.
In some embodiments, the determining device for the urban air traffic share provided by the embodiments of the present invention may be implemented by combining software and hardware, and as an example, the determining device for the urban air traffic share provided by the embodiments of the present invention may be a processor in the form of a hardware decoding processor, which is programmed to perform the determining method for the urban air traffic share provided by the embodiments of the present invention, for example, the processor in the form of a hardware decoding processor may use one or more application specific integrated circuits (ASIC, application Specific Integrated Circuit), DSP, programmable logic device (PLD, programmable Logic Device), complex programmable logic device (CPLD, complex Programmable Logic Device), field programmable gate array (FPGA, field-Programmable Gate Array), or other electronic components.
In other embodiments, the determining device for the urban air traffic share provided in the embodiments of the present invention may be implemented in a software manner, and fig. 4 shows the determining device for the urban air traffic share stored in the memory, which may be software in the form of a program, a plug-in unit, and the like, and includes a series of modules including a traffic total flow prediction module 210, a parameter prediction module 220, a city model parameter value determination module 230, and a share prediction module 240, for implementing the determining method for the urban air traffic share provided in the embodiments of the present invention.
The modules involved in the embodiments of the present invention may be implemented in software or in hardware. The name of a module does not in some cases define the module itself.
Based on the same principles as the methods shown in the embodiments of the present invention, there is also provided in the embodiments of the present invention 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 invention by invoking a computer program.
In an alternative embodiment, there is provided an electronic device, as shown in fig. 5, the electronic device 4000 shown in fig. 5 includes: a processor 4001 and a memory 4003. Wherein the processor 4001 is coupled to the memory 4003, such as via a bus 4002. Optionally, the electronic device 4000 may further comprise a transceiver 4004, 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, etc. It should be noted that, in practical applications, the transceiver 4004 is not limited to one, 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 ), general purpose processor, DSP (Digital Signal Processor, data signal processor), ASIC (Application Specific Integrated Circuit ), FPGA (Field Programmable Gate Array, field programmable gate array) or other programmable logic device, transistor logic device, hardware components, or any combination thereof. Which may implement or perform the various exemplary logic blocks, modules and circuits described in connection with this disclosure. The processor 4001 may also be a combination that implements computing functionality, e.g., comprising one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.
Bus 4002 may include a path to transfer information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus or an EISA (Extended Industry Standard Architecture ) bus, or the like. The bus 4002 can 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 not only one bus or one type of bus.
Memory 4003 may be, but is not limited to, ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, RAM (Random Access Memory ) or other type of dynamic storage device that can store information and instructions, EEPROM (Electrically Erasable Programmable Read Only Memory ), CD-ROM (Compact Disc Read Only Memory, compact disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, 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.
The memory 4003 is used for storing application program codes (computer programs) for executing the present invention and is controlled to be executed by the processor 4001. The processor 4001 is configured to execute application program codes stored in the memory 4003 to realize what is shown in the foregoing method embodiment.
The electronic device shown in fig. 5 is only an example, and should not impose any limitation on the functions and application scope of the embodiment of the present invention.
Embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which when run on a computer, causes the computer to perform the corresponding method embodiments described above.
According to another aspect of the present 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, so that the computer device performs the methods provided in the implementation of the various embodiments described above.
Computer program code for carrying out operations 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 ++ 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 kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
It should be appreciated that the flow charts 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 according to 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 a combination of any of the foregoing. 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 context of this document, 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-described embodiments.
The above description is only illustrative of the preferred embodiments of the present invention and of the principles of the technology employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in the present invention is not limited to the specific combinations of technical features described above, but also covers other technical features formed by any combination of the technical features described above or their equivalents without departing from the spirit of the disclosure. Such as the above-mentioned features and the technical features disclosed in the present invention (but not limited to) having similar functions are replaced with each other.

Claims (10)

1. The method for determining the urban air traffic sharing rate is characterized by comprising the following steps of:
obtaining current traffic total flow corresponding to each traffic mode in a research area, and predicting first predicted traffic total flow corresponding to each traffic mode in appointed time of the research area according to the current traffic total 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 parameters corresponding to different time, and the same 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 relationship among the sharing rate, the current data and the model parameter value corresponding to each traffic mode;
according to the model parameter value, the first predicted traffic total flow and the predicted data, predicting the sharing rate of the urban air traffic corresponding to the research area at the appointed time through an MNL model, wherein the MNL model is used for representing the association relationship among the sharing rate of the urban air traffic corresponding to the research area at the appointed time, the first predicted traffic total flow, the predicted data and the model parameter value;
The traffic modes corresponding to the double-layer NL model are divided into non-motorized classes and motorized classes, wherein the non-motorized classes comprise bicycles and walking, the motorized classes comprise buses and public transportation, and the public transportation is divided into buses and subways;
and determining a model parameter value through a double-layer NL model according to the current sharing rate and the current data, wherein the method comprises the following steps of:
determining the model parameter value through the double-layer NL model determined by the formulas (1) to (5) according to the relations among the time, the cost, the accessibility and the corresponding current sharing rate corresponding to the various traffic modes, wherein the formulas (1) to (5) are respectively as follows:
Figure FDA0004186711700000011
p ij car ＝1-p ij bus (2)
Figure FDA0004186711700000012
Figure FDA0004186711700000013
wherein ,pij bus 、p ij car Respectively representing the current sharing rates of the car and the bus;
Figure FDA0004186711700000014
respectively representing the running time of the car and the bus; />
Figure FDA0004186711700000021
Representing the cost of the car and bus respectively; />
Figure FDA0004186711700000022
Respectively representing the reachability of the car and the bus; />
Figure FDA0004186711700000023
Representing the total traffic flow corresponding to the car, +.>
Figure FDA0004186711700000024
Representing the total traffic flow corresponding to the bus, and alpha, beta, gamma and theta represent model parameter values;
wherein the accessibility of the car is determined by the following formula (4):
Figure FDA0004186711700000025
Figure FDA0004186711700000026
wherein ,
Figure FDA0004186711700000027
Indicating the reachability from traffic cell i, < >/within a defined period P>
Figure FDA0004186711700000028
Representing the number of opportunities for traffic cell j during time period P; />
Figure FDA0004186711700000029
Representing the shortest travel time of traffic cell i to traffic cell j within time period P, T representing the study time interval, +.>
Figure FDA00041867117000000210
When it is indicated that the determined accessibility is within the investigation time interval, at which time +.>
Figure FDA00041867117000000211
Figure FDA00041867117000000212
Indicating that the determined reachability is outside the investigation time interval, at this time, the +.>
Figure FDA00041867117000000213
N represents the number of traffic cells within the B area;
the reachability of public transportation and urban air transportation UAM is determined by the following formula (5):
Figure FDA00041867117000000214
wherein ,AF Indicating the reachability of public transportation or urban air transportation UAM, wherein ρ indicates the passenger travel density in the radiation area; s represents the area in the radiation area; t represents the average travel time of passengers in the radiation area; m represents the number of stations included in public traffic or urban air traffic UAM in the radiation area;
the predicting, according to the model parameter value, the first predicted traffic total flow and the predicted data, the sharing rate of the urban air traffic corresponding to the research area at the specified time through an MNL model includes:
and predicting the sharing rate of the urban air traffic corresponding to the research area at the appointed time according to the model parameter value, the first predicted traffic total flow and the predicted data through an MNL model determined by a formula (6) and a formula (7), wherein the formula (6) and the formula (7) are respectively as follows:
Figure FDA00041867117000000215
Figure FDA0004186711700000031
wherein ,
Figure FDA0004186711700000032
indicating that the urban air traffic is at the designated time, the corresponding time from traffic cell i to traffic cell j,
Figure FDA0004186711700000033
representing the cost of urban air traffic corresponding to traffic cell i to traffic cell j at a specified time, +.>
Figure FDA0004186711700000034
The reachability of the urban air traffic corresponding to the traffic cell i to the traffic cell j at the appointed time is represented; />
Figure FDA0004186711700000035
and VUAM All represent the first predicted traffic total flow corresponding to the urban air traffic at the appointed time, V all Representing a first predicted traffic total flow, P, corresponding to three traffic modes together UAM And alpha, beta and gamma represent model parameter values, wherein the three traffic modes comprise cars, buses and urban air traffic.
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 traffic total flow of each traffic cell corresponding to the specified time according to the current traffic total flow and the distribution of each traffic cell, wherein the first predicted traffic total flow comprises the second predicted traffic total flow of each traffic cell;
And predicting, by an MNL model, a sharing rate of the urban air traffic at the specified time according to the model parameter value, the first predicted traffic total flow, and the predicted data, where the method includes:
predicting the sharing rate of the urban air traffic in each traffic cell at the appointed time through the MNL model according to the model parameter value, the prediction data and the second prediction traffic total flow;
the MNL model is used for representing the association relation among the sharing rate of the urban air traffic corresponding to each traffic cell, the total traffic flow of each second prediction, the prediction data and the model parameter value at the appointed time.
3. The method as recited in claim 2, further comprising:
determining the current traffic total flow of each traffic cell according to the current traffic total flow and the distribution of each traffic cell;
the step of determining a second predicted traffic total flow of each traffic cell corresponding to the specified time according to the current traffic total flow and the distribution of each traffic cell, comprising:
acquiring the current population number of the research area and the predicted population number corresponding to the appointed time;
And determining a second predicted traffic total flow of each traffic cell corresponding to the appointed time according to the current population quantity, the predicted population quantity and the current traffic total flow of each traffic cell.
4. The method as recited in claim 2, further comprising:
and determining the traffic flow of the urban air traffic in each traffic cell according to the sharing rate of the urban air traffic in each traffic cell and the second predicted traffic total flow of each traffic cell.
5. The method as recited in 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 according to any one of claims 1 to 5, further comprising:
acquiring current initial traffic total flow, and determining equivalent traffic volume corresponding to each road in a region corresponding to the current initial traffic total flow according to the current initial traffic total flow;
acquiring the proportion of the current truck to all the vehicles;
determining the actual passenger carrying flow 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 car.
7. The method according to any one of claims 1 to 5, wherein predicting a first predicted total traffic flow for each of the traffic patterns at a specified time based on the current total traffic flow comprises:
acquiring the current population number of the research area and the predicted population number corresponding to the appointed time;
and predicting a first predicted traffic total flow corresponding to each traffic mode at the appointed time according to the current population number, the predicted population number and the current traffic total flow.
8. A device for determining urban air traffic sharing rate, comprising:
the traffic total flow prediction module is used for obtaining the current traffic total flow corresponding to each traffic mode in a research area, predicting the first predicted traffic total flow corresponding to each traffic mode in the appointed time of the research area according to the current traffic total flow, and each traffic mode comprises cars, public traffic and urban air traffic; the parameter prediction module is used for 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 parameters corresponding to different time, and the same parameters comprise time, cost and reachability;
The model parameter value determining module is used for determining a model parameter value according to the current sharing rate and the current data through a double-layer NL model, wherein the double-layer NL model is used for representing the association relationship among the sharing rate, the current data and the model parameter value corresponding to each traffic mode;
the sharing rate prediction module is configured to predict, according to the model parameter value, the first predicted traffic total flow, and the predicted data, a sharing rate corresponding to the research area of the urban air traffic at the specified time through an MNL model, where the MNL model is used to characterize an association relationship among the sharing rate corresponding to the research area of the urban air traffic at the specified time, the first predicted traffic total flow, the predicted data, and the model parameter value;
the traffic modes corresponding to the double-layer NL model are divided into non-motorized classes and motorized classes, wherein the non-motorized classes comprise bicycles and walking, the motorized classes comprise buses and public transportation, and the public transportation is divided into buses and subways;
the model parameter value determining module is specifically configured to:
determining the model parameter value through the double-layer NL model determined by the formulas (1) to (5) according to the relations among the time, the cost, the accessibility and the corresponding current sharing rate corresponding to the various traffic modes, wherein the formulas (1) to (5) are respectively as follows:
Figure FDA0004186711700000051
p ij car ＝1-p ij bus (2)
Figure FDA0004186711700000052
Figure FDA0004186711700000053
wherein ,pij bus 、p ij car Respectively representing the current sharing rates of the car and the bus;
Figure FDA0004186711700000054
respectively representing the running time of the car and the bus; />
Figure FDA0004186711700000055
Representing the cost of the car and bus respectively; />
Figure FDA0004186711700000061
Respectively representing the reachability of the car and the bus; />
Figure FDA0004186711700000062
Representing the total traffic flow corresponding to the car, +.>
Figure FDA0004186711700000063
Representing the total traffic flow corresponding to the bus, and alpha, beta, gamma and theta represent model parameter values;
wherein the accessibility of the car is determined by the following formula (4):
Figure FDA0004186711700000064
Figure FDA0004186711700000065
wherein ,
Figure FDA0004186711700000066
indicating the reachability from traffic cell i, < >/within a defined period P>
Figure FDA0004186711700000067
Representing the number of opportunities for traffic cell j during time period P; />
Figure FDA0004186711700000068
Representing the shortest travel time of traffic cell i to traffic cell j within time period P, T representing the study time interval, +.>
Figure FDA0004186711700000069
When it is indicated that the determined accessibility is within the investigation time interval, at which time +.>
Figure FDA00041867117000000610
Figure FDA00041867117000000611
Indicating that the determined reachability is outside the investigation time interval, at this time, the +.>
Figure FDA00041867117000000612
N represents the number of traffic cells within the B area;
the reachability of public transportation and urban air transportation UAM is determined by the following formula (5):
Figure FDA00041867117000000613
wherein ,AF Indicating the reachability of public transportation or urban air transportation UAM, wherein ρ indicates the passenger travel density in the radiation area; s represents the area in the radiation area; t represents the average travel time of passengers in the radiation area; m represents the number of stations included in public traffic or urban air traffic UAM in the radiation area;
The sharing rate prediction module is specifically configured to:
and predicting the sharing rate of the urban air traffic corresponding to the research area at the appointed time according to the model parameter value, the first predicted traffic total flow and the predicted data through an MNL model determined by a formula (6) and a formula (7), wherein the formula (6) and the formula (7) are respectively as follows:
Figure FDA00041867117000000614
Figure FDA00041867117000000615
wherein ,
Figure FDA00041867117000000616
indicating that the urban air traffic is at the designated time, the corresponding time from traffic cell i to traffic cell j,
Figure FDA00041867117000000617
representing the cost of urban air traffic corresponding to traffic cell i to traffic cell j at a specified time, +.>
Figure FDA00041867117000000618
The reachability of the urban air traffic corresponding to the traffic cell i to the traffic cell j at the appointed time is represented; />
Figure FDA0004186711700000071
and VUAM All represent the first predicted traffic total flow corresponding to the urban air traffic at the appointed time, V all Representing a first predicted traffic total flow, P, corresponding to three traffic modes together UAM And alpha, beta and gamma represent model parameter values, wherein the three traffic modes comprise cars, buses and urban air traffic.
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 the computer program is executed.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the method of any of claims 1-7.
CN202210248307.8A 2022-03-14 2022-03-14 Method and device for determining urban air traffic sharing rate, electronic equipment and medium Active CN114724414B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210248307.8A CN114724414B (en) 2022-03-14 2022-03-14 Method and device for determining urban air traffic sharing rate, electronic equipment and medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210248307.8A CN114724414B (en) 2022-03-14 2022-03-14 Method and device for determining urban air traffic sharing rate, electronic equipment and medium

Publications (2)

Publication Number Publication Date
CN114724414A CN114724414A (en) 2022-07-08
CN114724414B true CN114724414B (en) 2023-06-09

Family

ID=82237733

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210248307.8A Active CN114724414B (en) 2022-03-14 2022-03-14 Method and device for determining urban air traffic sharing rate, electronic equipment and medium

Country Status (1)

Country Link
CN (1) CN114724414B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117745108B (en) * 2024-02-20 2024-05-07 中国民用航空飞行学院 Passenger flow demand prediction method and system for advanced air traffic

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
AU3544600A (en) * 1999-04-08 2000-11-14 Air Services Australia Air traffic management system
CN103530704A (en) * 2013-10-16 2014-01-22 南京航空航天大学 Predicating system and method for air dynamic traffic volume in terminal airspace
WO2015170289A1 (en) * 2014-05-09 2015-11-12 Vodafone Omnitel B.V. Method and system for vehicular traffic prediction
CN106846214A (en) * 2016-11-24 2017-06-13 西安建筑科技大学 Method of the analysis transport hub accessibility to region public transportation mode competitive influence
CN106951999A (en) * 2017-03-29 2017-07-14 北京航空航天大学 The modeling of a kind of travel modal and the moment Combination selection that sets out and analysis method
CN109448366A (en) * 2018-10-18 2019-03-08 南京航空航天大学 A kind of space domain sector degree of crowding prediction technique based on random forest
CN110956305A (en) * 2019-10-22 2020-04-03 中国科学院地理科学与资源研究所 Urban space prediction model establishing method and urban space prediction system
CN112070259A (en) * 2019-06-10 2020-12-11 中国航天系统工程有限公司 Method for predicting number of unloaded taxies in city
EP3770883A1 (en) * 2019-07-22 2021-01-27 The Boeing Company Predictive flight diversion management
CN112735189A (en) * 2020-12-24 2021-04-30 朱上翔 Method and system for ground-air mode conversion and intelligent air management of flying vehicle
CN113222271A (en) * 2021-05-25 2021-08-06 中国民用航空飞行学院 Medium and small airport site selection layout method under comprehensive transportation system
CN113543052A (en) * 2021-07-20 2021-10-22 中国民航科学技术研究院 Mobile phone signaling data-based city group traffic contact strength measuring method

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2002099769A1 (en) * 2001-06-01 2002-12-12 The Boeing Company Air traffic management system and method
US8682709B2 (en) * 2006-01-06 2014-03-25 Gregory M. Coldren System and method for modeling consumer choice behavior
DE102007015945A1 (en) * 2006-11-24 2008-06-12 Fraport Ag Frankfurt Airport Services Worldwide Method and device for controlling air traffic handling at an airport
US9842506B2 (en) * 2008-08-04 2017-12-12 Aviation Communication & Surveillance Systems Llc Systems and methods for conflict detection using dynamic thresholds
US9076326B2 (en) * 2013-02-21 2015-07-07 Honeywell International Inc. Systems and methods for traffic prioritization
US20190266902A1 (en) * 2018-02-26 2019-08-29 Honeywell International Inc. Method and system for generating a grid map that shows air traffic intensity
CN113868830B (en) * 2021-08-19 2022-07-05 佛山市城市规划设计研究院 Method for constructing same-city intercity passenger flow generation model based on traffic accessibility
CN114066062B (en) * 2021-11-18 2023-04-07 中国民用航空飞行学院 Logistics demand prediction method and system for urban air traffic

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
AU3544600A (en) * 1999-04-08 2000-11-14 Air Services Australia Air traffic management system
CN103530704A (en) * 2013-10-16 2014-01-22 南京航空航天大学 Predicating system and method for air dynamic traffic volume in terminal airspace
WO2015170289A1 (en) * 2014-05-09 2015-11-12 Vodafone Omnitel B.V. Method and system for vehicular traffic prediction
CN106846214A (en) * 2016-11-24 2017-06-13 西安建筑科技大学 Method of the analysis transport hub accessibility to region public transportation mode competitive influence
CN106951999A (en) * 2017-03-29 2017-07-14 北京航空航天大学 The modeling of a kind of travel modal and the moment Combination selection that sets out and analysis method
CN109448366A (en) * 2018-10-18 2019-03-08 南京航空航天大学 A kind of space domain sector degree of crowding prediction technique based on random forest
CN112070259A (en) * 2019-06-10 2020-12-11 中国航天系统工程有限公司 Method for predicting number of unloaded taxies in city
EP3770883A1 (en) * 2019-07-22 2021-01-27 The Boeing Company Predictive flight diversion management
CN110956305A (en) * 2019-10-22 2020-04-03 中国科学院地理科学与资源研究所 Urban space prediction model establishing method and urban space prediction system
CN112735189A (en) * 2020-12-24 2021-04-30 朱上翔 Method and system for ground-air mode conversion and intelligent air management of flying vehicle
CN113222271A (en) * 2021-05-25 2021-08-06 中国民用航空飞行学院 Medium and small airport site selection layout method under comprehensive transportation system
CN113543052A (en) * 2021-07-20 2021-10-22 中国民航科学技术研究院 Mobile phone signaling data-based city group traffic contact strength measuring method

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
上海市运输通道内交通方式分担率研究与竞争格局分析;朱潇滢;;青海交通科技(第02期);全文 *
基于NL模型的昌九客运交通方式选择分析;梁安宁;黄娜娜;张兵;桑梓;温尚武;;华东交通大学学报(第01期);全文 *
空间迁移分析中类型数据的logit线性模型;F.布劳威尔;P.尼贾坎普;谢炳庚;卜照义;;地理科学进展(第01期);全文 *

Also Published As

Publication number Publication date
CN114724414A (en) 2022-07-08

Similar Documents

Publication Publication Date Title
US11892312B2 (en) Methods and systems for providing information for an on-demand service
CN109155104B (en) System and method for recommending estimated arrival time
CN108446470B (en) Medical facility accessibility analysis method based on vehicle trajectory data and population distribution
EP3258430A1 (en) Transport capacity scheduling method and system
Hussein et al. A bi-directional agent-based pedestrian microscopic model
Miles et al. IoT-based decision support system for monitoring and mitigating atmospheric pollution in smart cities
WO2021082464A1 (en) Method and device for predicting destination of vehicle
CN112418696A (en) Method and device for constructing urban traffic dynamic knowledge map
Visan et al. Towards intelligent public transport systems in Smart Cities; Collaborative decisions to be made
CN109859505B (en) Early warning processing method, device, server and medium for high-speed station
CN112069635B (en) Method and device for deploying battery changing cabinet, medium and electronic equipment
CN114360239A (en) Traffic prediction method and system for multilayer space-time traffic knowledge map reconstruction
Yedavalli et al. Microsimulation analysis for network traffic assignment (MANTA) at metropolitan-scale for agile transportation planning
Patnaik et al. Entry capacity modelling of signalized roundabouts under heterogeneous traffic conditions
CN114724414B (en) Method and device for determining urban air traffic sharing rate, electronic equipment and medium
Uddin et al. A framework to measure transit-oriented development around transit nodes: Case study of a mass rapid transit system in Dhaka, Bangladesh
Fontoura et al. A framework for evaluating the dynamic impacts of the Brazilian Urban Mobility Policy for transportation socioeconomic systems: A case study in Rio de Janeiro
Cosido et al. Automatic calculation of bicycle routes by combining meta-heuristics and GIS techniques within the framework of smart cities
Sheng et al. Deep spatial-temporal travel time prediction model based on trajectory feature
Anowar et al. Univariate or multivariate analysis for better prediction accuracy? A case study of heterogeneity in vehicle ownership
US20200386569A1 (en) Trajectory sampling using spatial familiarity
CN115994787A (en) Car pooling demand prediction matching method based on neural network
Huang et al. Analysing taxi customer-search behaviour using Copula-based joint model
Dehkordi et al. Including network level safety measures in eco-routing
Rahman et al. MDLpark: Available Parking Prediction for Smart Parking Through Mobile Deep Learning

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant