CN117745108A - Passenger flow demand prediction method and system for advanced air traffic - Google Patents
Passenger flow demand prediction method and system for advanced air traffic Download PDFInfo
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Abstract
The invention relates to the technical field of traffic control systems, and provides a passenger flow demand prediction method and system for advanced air traffic, comprising the following steps: acquiring current situation basic data, and predicting background traffic generation quantity under the condition that the prior air traffic is not operated, wherein the background traffic generation quantity comprises background traffic generation quantity and background traffic attraction quantity; based on the predicted background traffic occurrence amount and the background traffic attraction amount, predicting background traffic distribution amount under the condition that advanced air traffic is not operated, and predicting attraction air traffic demand amount generated before and after advanced air traffic operation; analyzing influence factors and effectiveness of the travel modes selected by passengers, establishing a traffic mode selection model, and predicting traffic mode division of advanced air traffic; and distributing the passenger flow of the advanced air traffic in each traffic cell to the navigation network, so as to distribute traffic. The passenger flow demand prediction method improves passenger flow demand prediction precision, and the prediction result is more fit with reality.
Description
Technical Field
The invention relates to the technical field of traffic control systems, in particular to a passenger flow demand prediction method and system for advanced air traffic.
Background
In recent years, with the high-speed development of economy in China and the continuous promotion of urban process, the problems of urban traffic congestion, environmental deterioration and the like are increasingly prominent. Although the dense urban rail transit of the wire mesh relieves the pressure brought by the ground road traffic, the urban rail transit network also looks at the front of the fly and turns over in the ultra-large cities with increasingly high population aggregation. With the recent development and use of electric vertical take-off and landing aircraft (electric vertical take-off and landing, eVTOL), urban planning and traffic management personnel have turned their line of sight into the air in an effort to build a new traffic network for cities using low-altitude airspace. The innovative application concept of the unmanned aerial vehicle system is continuously appeared and developed, the current unmanned aerial vehicle system is rapidly gathered to the urban air transportation mode, the low-altitude structural design becomes a current airspace management research hot spot, the low-altitude resources and the road resources belong to the same kind of traffic resources, and the low-altitude route network taking the city as the center in the future is not a simple airspace planning problem any more, but a city public facility construction problem.
Advanced air traffic can be categorized into passenger transportation, including regional air traffic (Regional Air Mobility, RAM) and urban air traffic (Urban Air Mobility, UAM), and cargo transportation, including unmanned aerial vehicle logistics (Unmanned Aerial Vehicle, UAV). Unmanned aerial vehicle logistics has been widely developed for delivery airlines, but many challenges and problems still exist today for future advanced air traffic passenger transportation that is full of opportunities to break through and solve. Currently, the industry is consistent with the understanding that the most potential application scenario for advanced air traffic is on-demand services (ODS). The accurate passenger flow demand prediction is the basic work of advanced air traffic station site selection, network planning, operation management and transportation organization, and whether the result is accurate or not directly influences the investment and benefit of the advanced air traffic, and has very important significance for constructing a safe and efficient urban air traffic operation environment, promoting the advanced air traffic to be integrated into urban traffic development planning, optimizing the capacity resource allocation of the urban air traffic system and the like. Therefore, passenger demand analysis research on advanced air traffic is imperative. However, there are more qualitative studies on demand prediction of advanced air traffic, less quantitative studies, and no system.
The traditional research related to the demand prediction of the advanced air traffic system can be divided into quantitative and qualitative research. Qualitative research is mainly developed from the aspects of application scenes and application mode division of urban air traffic systems, influence factors of traffic demands and the like, the adopted technical means mainly comprise concept feature carding and user preference questionnaire investigation, whether various factors influence passenger demand or not and influence degree are judged, and the factors influencing demand prediction mainly comprise: time, cost, distance, ground traffic congestion, security and assurance, privacy and noise. Quantitative research can be divided into prediction of total demand and prediction of spatial distribution, wherein the prediction of total demand is usually based on the market marketing angle, the user travel preference and demand influence factors are considered, market share is estimated based on taxi travel demand and data and eVTOL carrier characteristics, the types of traffic generation predictions are not fully considered, advanced air traffic demand and traffic demand from outside cities are assumed, and the induced traffic demand and traffic demand from outside cities after a new generation traffic mode is put into use are not considered; the prediction of spatial distribution, the research content is usually only aimed at a single scene (such as airport connection, tour sightseeing and the like) and a specific area, and as different areas have respective traffic design standard standards, resident trip characteristics and traffic mode selection ranges, the advanced air traffic passenger flow demand prediction aiming at specific projects is carried out, the research range is a research area with obvious heterogeneous characteristics, the demand prediction results have obvious uncertainty and difference, the research results cannot be compared in a consistent mode, and the universality and expansibility are lacked; in the research method, in the prior art, a traffic mode selection model is used as a theoretical basis for research, a specific traffic network (such as a spoke type and a point-to-point type) is selected for traffic flow distribution, and demand prediction is not accurate enough.
Disclosure of Invention
The invention aims to provide a passenger flow demand prediction method and system for advanced air traffic, and provides a passenger flow demand calculation method for induced increase after advanced air traffic operation, so that passenger flow demand prediction precision is improved, and a prediction result is more fit and practical.
In order to achieve the above object, the embodiment of the present invention provides the following technical solutions:
a passenger flow demand prediction method of advanced air traffic comprises the following steps:
step 1, traffic generation prediction: acquiring current situation basic data, and predicting background traffic generation quantity under the condition that the prior air traffic is not operated, wherein the background traffic generation quantity comprises background traffic generation quantity and background traffic attraction quantity;
step 2, traffic distribution prediction: based on the predicted background traffic occurrence amount and the background traffic attraction amount, predicting background traffic distribution amount under the condition that advanced air traffic is not operated, and predicting attraction air traffic demand amount generated before and after advanced air traffic operation;
step 3, traffic mode division prediction: analyzing influence factors and effectiveness of the travel modes selected by passengers, establishing a traffic mode selection model, and predicting traffic mode division of advanced air traffic;
step 4, traffic distribution: and distributing the passenger flow of the advanced air traffic in each traffic cell to the navigation network, so as to distribute traffic.
Compared with the prior art, the invention has the beneficial effects that:
the invention divides the advanced air traffic demand into three types of regional air traffic demand, induced air traffic demand and urban air traffic demand, and simultaneously provides an induced passenger flow demand calculation method after advanced air traffic operation, thereby improving passenger flow demand prediction precision and ensuring that the prediction result is more fit with reality. The prediction model comprises all flows of a four-stage method, has comprehensive system and expandability, and has popularization and demonstration significance for the prediction of the advanced air traffic passenger flow demands of different cities.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Also, in the description of the present invention, the terms "first," "second," and the like are used merely to distinguish one from another, and are not to be construed as indicating or implying a relative importance or implying any actual such relationship or order between such entities or operations.
In the traffic planning, a four-stage method is adopted for the prediction of traffic demand, namely, the traffic demand prediction is divided into four steps of traffic generation prediction, traffic distribution prediction, traffic mode division prediction and traffic distribution. The urban traffic planning principle is migrated to the civil aviation field, four complete steps of traffic demand prediction four-stage method are taken as a frame, and advanced air traffic passenger flow demand prediction is divided into three components: the three prediction results are subjected to superposition analysis, and an advanced air traffic passenger flow demand prediction method which is comprehensive in demand composition system, universal in prediction model and easy to transfer is proposed from a macroscopic level, so that development and practice of advanced air traffic are promoted.
The invention is realized by the following technical scheme, as shown in figure 1, a passenger flow demand prediction method of advanced air traffic, comprising the following steps:
step 1, traffic generation prediction: and acquiring current situation basic data, and predicting background traffic generation quantity under the condition that the prior air traffic is not operated, wherein the background traffic generation quantity comprises background traffic generation quantity and background traffic attraction quantity.
The investigation of the current basic data is the basis of advanced air traffic demand prediction, and the time, labor and economic cost required by large-area investigation are considered to be higher, so the current basic data of the scheme is that the current road section traffic flow of a research area is firstly obtained, the current OD distribution matrix is obtained and corrected by adopting an OD back-pushing method according to the traffic distribution theory, and the OD back-pushing refers to a mathematical model for calculating travel distribution (namely an OD table) from road traffic flow.
The traffic generation prediction mainly aims at the prediction of the background traffic generation amount, and the prediction of the background traffic generation amount is that the urban residents generate passenger traffic demand under the existing urban form under the condition that the advanced air traffic is not constructed and operated, and specifically comprises the background traffic generation amount and the background traffic attraction amount.
And step 1-1, classifying the attributes of passengers generating passenger demands, obtaining the travel production of passengers with various attributes and different travel purposes, and predicting the background traffic occurrence.
For the prediction of the background traffic occurrence, a cross classification method using families as travel topics is adopted, individuals producing passenger demands are classified and subdivided according to certain attributes such as age, income, academic and the like, the travel occurrence of different travel purposes of passengers with various attributes is obtained through investigation, and then the background traffic occurrence is calculated, wherein the specific calculation formula is as follows:
wherein Y is i Representing the background traffic occurrence quantity of the traffic cell i; r is (r) c Representing the average travel production quantity of passengers with the attribute of class c; x is X ci Representing the general population of traffic cell i with attribute c; c is the attribute class number of the passenger.
And step 1-2, classifying land utilization properties, and predicting background traffic attraction by adopting a linear regression model.
Regarding the prediction of the background traffic attraction, considering that the background traffic attraction has extremely strong linear correlation with the land utilization property of the destination traffic cell, and the traffic attractions of different land utilization properties are different, the background traffic attraction is classified by the land utilization properties and is predicted by adopting a linear regression model, and the specific calculation formula is as follows:
wherein A is j A background traffic attraction representing traffic cell j; a, a v (j) Representing the traffic attraction rate with the land utilization property being attribute v; area v (j) Representing the building area with the land utilization property of attribute v in traffic cell j; v is the number of attribute categories of land use properties.
Step 2, traffic distribution prediction: based on the predicted background traffic occurrence amount and the background traffic attraction amount, the background traffic distribution amount under the condition that the advanced air traffic is not operated is predicted, and the attraction air traffic demand amount generated before and after the advanced air traffic operation is predicted.
Traffic distribution predictions include predictions of background traffic distribution and predictions of induced air traffic demand.
And 2-1, predicting the background traffic distribution amount based on the predicted background traffic occurrence amount and the background traffic attraction amount.
The predicted background traffic distribution quantity is the travel distribution quantity of each traffic cell of urban residents in the existing urban form under the condition that the advanced air traffic is not constructed and operated. The method comprises the steps of predicting background traffic distribution by using a gravity model, wherein the gravity model is based on Newton's law of universal gravitation, the traffic background distribution among traffic cells is in a direct proportion relation with the background traffic occurrence amount and the background traffic attraction amount of each traffic cell, and in an inverse proportion relation with the traffic impedance between the two traffic cells, the background traffic distribution is predicted by adopting a double-constraint gravity model, and a specific calculation formula is as follows:
wherein T is ij Representing the background traffic distribution quantity from traffic cell i to traffic cell j; y is Y i Representing the background traffic occurrence quantity of the traffic cell i; a is that j A background traffic attraction representing traffic cell j; r is R ij Representing the traffic impedance from the traffic cell i to the traffic cell j, taking two factors of transportation time and transportation cost into consideration, and forming generalized travel cost after standardized processing and dimensional influence elimination;、/>、/>representing model parameters, which can be calibrated by regression analysis; k (K) i 、K j Representing a constraint function, wherein the OD matrix obtained by predicting the background traffic distribution quantity is used for ensuring that the constraint conditions of the background traffic occurrence quantity and the background traffic attraction quantity are met, namely:
where K represents the number of traffic cells, i=1, 2,..k, j=1, 2,..k.
And 2-2, predicting induced air traffic demand according to the background traffic distribution before and after advanced air traffic operation.
The advanced air traffic is an important supplement to the existing urban transportation system, and newly-built advanced air traffic is likely to form induced passenger flow. The demand of induced air traffic can be classified into a near-term demand-released induced passenger flow and a medium-and-long-term economic induced passenger flow according to the generation mechanism of induced passenger flow. According to the scheme, the rail transit induced passenger flow prediction theory is combined, and the induced air traffic demand is predicted by using an improved gravity model and a growth curve.
(1) There is a recent need for a release type induced increase in passenger flow.
After the advanced air traffic is built, the original traffic condition is improved, the transportation service level is continuously improved, and the travel of passengers limited by urban road congestion is released. The passenger flow is in the early stage of advanced air traffic operation, and comprises travel which does not exist originally and travel times which are increased along with the improvement of conditions, and the passenger flow is a new passenger demand which is induced on the basis of the existing passenger demand. With the appearance of advanced air traffic, the passenger demand is increased by adopting a double-constraint gravity model according to the principle of 'whether the passenger demand is compared or not', namely, before and after the advanced air traffic operation, and according to the change of travel cost, the specific calculation formula is as follows:
wherein,indicating a recently demanded released induced passenger flow; />Representing the background traffic distribution quantity before advanced air traffic operation; />Representing the background traffic distribution quantity after advanced air traffic operation; />Representing traffic impedance before advanced air traffic operation; />Representing the traffic impedance after advanced air traffic operation.
It can be seen that the key to find the advanced air traffic induced passenger flow is the traffic impedance before and after operation、/>Background traffic distribution after operation +.>. The traffic impedance between the traffic cells i and j is related to the time, cost, route, equipment, transportation service, transportation means and the like of each traffic mode among the traffic cells, so that the traffic impedance under different traffic modes is different, and all relevant data between each traffic mode and each OD point pair cannot be obtained by investigation in the actual operation process. Therefore, the scheme only considers two factors of travel time and traffic cost, and the traffic impedance data of the existing traffic mode is obtained through the digital map. For the traffic impedance after advanced air traffic operation, the travel time is obtained by dividing the linear distance of the centroid of the traffic cell by the eVTOL average speed, and the traffic cost is obtained by multiplying the linear distance of the centroid by the average cost.
(2) Middle and long-term economic induction type induced passenger flow.
In the middle and later stages of the advanced air traffic operation, as the accessibility among traffic cells covered by the advanced air traffic network is enhanced, the traveling of passengers is more convenient and quicker, and the passenger transport requirements are further enhanced. The increase of passenger traffic demands directly affects the low-altitude industry of the coverage area of the advanced air traffic network, enriches urban states, drives industry clusters to continuously create new and synergistic effects, creates an economic innovation development demonstration area, creates more working posts and generates new passenger flow attraction points. The medium-and-long-term economic induced passenger flow comprises two induced passenger flows, namely an 'increased population' traveling quantity which is attractive by economic development after the advanced air traffic is put into operation and an 'original population' traveling demand which is increased in the influence range of the advanced air traffic. At present, the prediction of induced traffic is mostly aimed at expressway projects, and the change rule of medium-and-long-term economic induced passenger flow can be referred to, namely, the medium-and-long-term economic induced passenger flow has three stages of incubation, rapid growth and gradual stabilization, and a growth curve model is as follows:
wherein,representing medium-and-long-term economic induced passenger flow; h represents an upper limit value of induced passenger flow; t represents a time variable (year); a. b represents model parameters.
It can be seen that the prediction is performed from a macroscopic angle, the H value reflects the limitation of long-term economic induction induced passenger flow in an advanced air traffic network, and the values of H in different areas are different.
Step 3, traffic mode division prediction: and according to the influence factors and the utility of the travel mode selected by the passengers, and establishing a traffic mode selection model, predicting the traffic mode division of the advanced air traffic.
The transportation demand of the passengers is finally completed by a specific transportation mode, and the transportation mode division prediction is to divide the travel amount of the passengers (namely the result of the traffic distribution in the last stage) of each transportation district in the planning year into various transportation modes according to a certain transportation mode selection behavior standard. When dividing the traffic mode, the influence factors and the effectiveness of the travel mode selected by the passengers are considered, and a traffic mode selection model is established for prediction. Traffic pattern division prediction for advanced air traffic includes two parts: regional air traffic demand and urban air traffic demand.
And 3-1, analyzing influence factors and effectiveness of the passenger in selecting the traffic mode, establishing a traffic mode selection model, and predicting the air demand of regional traffic.
The important point of predicting the regional traffic air demand is the selection of various transportation modes sharing rate, and the consideration of the fact that the transportation distance of the existing general aviation carrier among cities is not too long is that the current general aviation carrier is not suitable, so that the establishment of a sharing rate model mainly considers three transportation modes of regional air traffic, inter-city rail traffic and highway traffic.
And (one) determining influence factors of the passenger selection traffic mode and analyzing the effect of the influence factors.
Based on the traditional passenger type division, passengers are divided into five categories: fare sensitivity, speed sensitivity, comfort sensitivity, convenience sensitivity and security sensitivity. Considering that the safety factor is the first key of transportation, if the technical level can not pass through and strict airworthiness examination, no passenger can choose to take the novel intelligent aircraft for traveling, the safety test verification requirement of the civil aviation bureau on the novel intelligent aircraft under the prior art condition is strict, the safety factor is very high, and the safety factor can analyze the safety accident rate without historical data for the emerging transportation mode, and can not be quantitatively expressed by time and cost, therefore, the scheme only selects four factors of comfort, rapidness, convenience and economy as the influence factors of the selection of the passenger transportation mode.
Meanwhile, the utility of each influencing factor is quantified by introducing the concept of time economic value, wherein the time economic value refers to the total time spent in the travel process by a passenger for producing the obtained benefits, and then the time economic value is calculated by using a production method:
wherein VOT represents time economic value; NP (NP) i Annual income of people in the city i is represented; NP (NP) j Annual income of people in city j is represented; t represents the holiday and legal holiday every year, taken 115 days.
(1) Comfort level: the comfort is quantitatively analyzed by referring to the travel fatigue recovery time, and the fatigue degree of a passenger in the travel process is in direct proportion to the travel time, so that the comfort effect of the traffic mode n is as follows:
wherein G is n Representing the comfort utility of traffic pattern n; g n (t) represents travel fatigue recovery time; m represents an ultimate fatigue recovery time; a, a n Representing dimensionless parameters; when the t is set to be 0, the process is carried out,representing a minimum fatigue recovery time for selecting traffic pattern n; b n The fatigue recovery time strength coefficient of unit time is expressed as h -1 The larger the value is, the longer the fatigue recovery time is; t represents travel time.
(2) Rapidity: travel timeCalculating or directly querying the related vehicle schedule for travel distance and speed in traffic pattern n, the rapidity utility can be expressed as:
wherein T is n Representing the rapid utility of traffic pattern n;indicating travel time.
(3) Convenience: the convenience of the traffic mode n is divided into 3 measurement indexes of arrival station time, waiting time and departure station time, and then the convenience utility can be expressed as:
wherein Con n Representing the convenience utility of the traffic mode n; t is t Starting from Indicating the arrival time of the station, the time from the departure place to the traffic mode station; t is t Waiting for Representing waiting time, including actual waiting time of passengers and up-down vehicle time; t is t Reach to Indicating the arrival time at the terminal, and the time at which the passenger arrives at the destination from the transit terminal.
(4) Economy: taking the fare as a quantitative index of economy, the fare can be obtained through a ticket selling website and the like, and then the economy utility can be expressed as:
wherein P is n Representing the economic utility of traffic pattern n;the m-th travel product proportion in the traffic mode n is represented, for example, the inter-city railway can be divided into a business seat, a first seat and a second seat, the highway traffic can be divided into a long-distance bus, a private car and a network bus, and the regional air traffic belongs to the pre-operation demand analysis stage at present, so that product subdivision is not considered at all>；The price of the mth travel product in the traffic pattern n is represented.
And (II) establishing a traffic mode selection model.
Based on the utility maximization theory, when a passenger selects a traffic mode, the passenger always selects the traffic mode which can maximize the self utility in cognition, and by combining the calculation of the traffic influence factors and the utility thereof, the utility function of the passenger selecting the traffic mode n can be expressed as:
wherein U is n A utility function representing the passenger's selected mode of transportation n;a comfort parameter representing a traffic pattern n;a rapidity parameter representing a traffic pattern n; />A convenience parameter representing a traffic pattern n; />And the economic parameter representing the traffic pattern n. />、/>、/>、/>Representing the preference degree of passengers on various influencing factors of the traffic pattern n, parameter +.>、、/>、/>The determination of the air sharing rate model of regional traffic determines the accuracy degree, and the preference degree of passengers to different service characteristics of various travel modes is influenced by factors such as gender, age, income, motor vehicle holding quantity and the like, so the passenger is prevented from being influenced by>、、/>、/>Can be performed by actual questionnaires in combination with regression analysis.
Because the Logit model can cause the expansion of the difference of the prediction results due to the exponential increase when calculating the travel sharing rate of the passengers, the scheme improves the probability of selecting the traffic mode n by the travel decision of the passengers, and is specifically expressed as follows:
wherein PR is PR n Representing a probability of selecting a traffic pattern n; n represents the number of all traffic patterns;representing the average utility of all selectable traffic patterns.
And 3-2, predicting urban air traffic demands.
For urban air traffic demand mode division, more urban internal traffic travel modes are considered under the prior art condition, so that the establishment of the sharing rate model mainly considers urban rail traffic, conventional buses, private cars, urban air traffic, walking and other six transportation modes, and the utility function and the sharing rate model of each transportation mode are the same as those of the step 3-1, and are not repeated.
It can be seen that the division of the traffic pattern in this step not only considers the urban air traffic demand within the scope of the investigation region, but also considers the regional air traffic demand outside the city or between cities.
Step 4, traffic distribution: and distributing the passenger flow of the advanced air traffic in each traffic cell to the navigation network, so as to distribute traffic.
As the final stage of the four-stage process of traffic demand prediction, the purpose of traffic distribution is to distribute the passenger flow of advanced air traffic among various traffic cells to a routing network to calculate the flow and service level on various air traffic segments. The user average allocation model is adopted this time, and the model assumes that all passengers can independently select the route with the minimum travel time for the individual. In the final flow distribution result, the passing time of each path between the same OD point pair is equal, the user balance state is achieved, and the specific model is as follows:
wherein:
wherein Z (X) represents an objective function of the user average allocation model;representing the independent variable of the integral function, wherein the value ranges from 0 to x a ；x a Representing traffic volume for leg a; t is t a Representing traffic impedance of the navigation segment a, mainly considering traffic time; t is t a (x a ) The impedance function of the navigation segment a taking the flow as an independent variable is represented, and generally, the larger the flow is, the larger the impedance isLarge; />Representing the traffic on the kth path with cell r as the origin and cell s as the destination; />Representing the traffic impedance on the kth path with cell r as the origin and cell s as the destination; />A variable from 0 to 1, 1 if leg a is on the kth path with cell r as the starting point and cell s as the destination, or 0 if leg a is on the kth path with cell r as the starting point and cell s as the destination; a represents an a-th leg, and L represents a leg set in a route network; r represents an R-th cell serving as a starting point, and R represents a starting point set in the airway network; s represents the S-th cell as the destination, S represents the destination set in the airway network; k represents the kth path, W rs Representing all path sets with cell r as a starting point and cell s as an end point; q rs The traffic volume with cell r as the origin and cell s as the destination is indicated.
In summary, the invention divides the advanced air traffic demand into three categories of induced air traffic demand, regional air traffic demand and urban air traffic demand, and simultaneously provides an induced passenger flow demand calculation method after advanced air traffic operation, thereby improving passenger flow demand prediction precision, and the prediction result is more fit and practical. The prediction model comprises all flows of a four-stage method, has comprehensive system and expandability, and has popularization and demonstration significance for the prediction of the advanced air traffic passenger flow demands of different cities.
Based on the method, the invention also provides a passenger flow demand prediction system of advanced air traffic, which comprises the following steps:
and the traffic generation prediction module is used for acquiring the current situation basic data, and predicting the background traffic generation amount under the condition that the prior air traffic is not operated, wherein the background traffic generation amount comprises the background traffic generation amount and the background traffic attraction amount.
The traffic distribution prediction module is used for predicting the background traffic distribution amount under the condition that the advanced air traffic is not operated based on the predicted background traffic occurrence amount and the background traffic attraction amount, and predicting the attraction air traffic demand amount generated before and after the advanced air traffic operation.
The traffic mode division prediction module is used for analyzing influence factors and the effectiveness of the travel mode selected by the passengers, establishing a traffic mode selection model and predicting the traffic mode division of the advanced air traffic.
And the traffic distribution module is used for distributing the passenger flow of the advanced air traffic in each traffic cell to the navigation network so as to distribute traffic.
Further, the traffic generation prediction module comprises a background traffic occurrence amount prediction unit and a background traffic attraction amount prediction unit; the background traffic occurrence amount prediction unit is used for classifying the attributes of passengers generating passenger traffic demands, obtaining the travel occurrence amounts of different travel purposes of the passengers with various attributes, and predicting the background traffic occurrence amount; the background traffic attraction prediction unit is used for classifying land utilization properties and predicting background traffic attraction by adopting a linear regression model.
The traffic distribution prediction module comprises a background traffic distribution prediction unit and an induced air traffic demand prediction unit; the background traffic distribution quantity prediction unit is used for predicting the background traffic distribution quantity based on the predicted background traffic occurrence quantity and the background traffic attraction quantity; the induced air traffic demand prediction unit is used for predicting the induced air traffic demand according to the background traffic distribution before and after advanced air traffic operation.
The traffic mode division prediction module comprises an area air traffic demand prediction unit and an urban air traffic demand prediction unit; the regional air traffic demand prediction unit is used for analyzing influence factors and effectiveness of the selection of the passenger traffic mode, establishing a traffic mode selection model and predicting regional traffic air demand; the urban air traffic demand prediction unit is used for predicting urban air traffic demand.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A passenger flow demand prediction method of advanced air traffic is characterized in that: the method comprises the following steps:
step 1, traffic generation prediction: acquiring current situation basic data, and predicting background traffic generation quantity under the condition that the prior air traffic is not operated, wherein the background traffic generation quantity comprises background traffic generation quantity and background traffic attraction quantity;
step 2, traffic distribution prediction: based on the predicted background traffic occurrence amount and the background traffic attraction amount, predicting background traffic distribution amount under the condition that advanced air traffic is not operated, and predicting attraction air traffic demand amount generated before and after advanced air traffic operation;
step 3, traffic mode division prediction: analyzing influence factors and effectiveness of the travel modes selected by passengers, establishing a traffic mode selection model, and predicting traffic mode division of advanced air traffic;
step 4, traffic distribution: and distributing the passenger flow of the advanced air traffic in each traffic cell to the navigation network, so as to distribute traffic.
2. The method for predicting passenger flow demand of advanced air traffic as recited in claim 1, wherein: in the step 1, the step of predicting the occurrence amount of the background traffic includes:
classifying the attributes of passengers generating passenger traffic demands, acquiring travel production amounts of different travel purposes of the passengers with various attributes, and predicting background traffic occurrence amounts:
wherein Y is i Background traffic light representing traffic cell iBiomass; r is (r) c Representing the average travel production quantity of passengers with the attribute of class c; x is X ci Representing the general population of traffic cell i with attribute c; c is the attribute class number of the passenger.
3. The method for predicting passenger flow demand of advanced air traffic as recited in claim 2, wherein: in the step 1, the step of predicting the background traffic attraction includes:
classifying land utilization properties, and predicting background traffic attraction by adopting a linear regression model:
wherein A is j A background traffic attraction representing traffic cell j; a, a v (j) Representing the traffic attraction rate with the land utilization property being attribute v; area v (j) Representing the building area with the land utilization property of attribute v in traffic cell j; v is the number of attribute categories of land use properties.
4. A passenger flow demand prediction method for advanced air traffic as claimed in claim 3, wherein: in the step 2, the step of predicting the background traffic distribution amount under the condition that the advanced air traffic is not operated includes:
before advanced air traffic operation, a double-constraint gravity model is adopted to predict the background traffic distribution quantity, and a specific calculation formula is as follows:
wherein T is ij Representing the background traffic distribution quantity from traffic cell i to traffic cell j; y is Y i Representing the background traffic occurrence quantity of the traffic cell i; a is that j A background traffic attraction representing traffic cell j; r is R ij Representing the traffic impedance of traffic cell i to traffic cell j;、/>、/>representing model parameters; k (K) i 、K j Representing a constraint function.
5. The method for predicting passenger flow demand of advanced air traffic as recited in claim 4, wherein: in the step 2, the step of predicting the induced air traffic demand generated before and after the advanced air traffic operation includes:
after advanced air traffic operation, predicting recent demand release type induced passenger flow:
wherein,indicating a recently demanded released induced passenger flow; />Representing the background traffic distribution quantity before advanced air traffic operation; />Representing the background traffic distribution quantity after advanced air traffic operation; />Representing traffic impedance before advanced air traffic operation; />Representing traffic impedance after advanced air traffic operation;
after advanced air traffic operation, predicting medium-and-long-term economic induction induced passenger flow:
wherein,representing medium-and-long-term economic induced passenger flow; h represents an upper limit value of induced passenger flow; t represents a time variable; a. b represents model parameters.
6. The method for predicting passenger flow demand of advanced air traffic as recited in claim 1, wherein: the step 3 specifically comprises the following steps:
step 3-1, analyzing influence factors and effectiveness of the passenger in selecting a traffic mode, establishing a traffic mode selection model, and predicting regional traffic air requirements;
the concept of introducing time economic value quantifies the utility of each influencing factor:
wherein VOT represents time economic value; NP (NP) i Annual income of people in the city i is represented; NP (NP) j Annual income of people in city j is represented; t represents a double holiday and legal holidays each year;
the influence factors of the passenger in selecting the traffic mode include comfort, rapidness, convenience and economy, and the utility of each influence factor is as follows:
(1) Comfort utility:
wherein G is n Representing the comfort utility of traffic pattern n; g n (t) represents travel fatigue recovery time; m represents an ultimate fatigue recovery time; a, a n Representing dimensionless parameters; when the t is set to be 0, the process is carried out,representing a minimum fatigue recovery time for selecting traffic pattern n; b n The fatigue recovery time intensity coefficient representing the unit time is longer as the value is larger; t represents travel time;
(2) Rapid utility:
wherein T is n Representing the rapid utility of traffic pattern n;representing travel time;
(3) Convenience utility:
wherein Con n Representing the convenience utility of the traffic mode n; t is t Starting from Indicating the arrival time of the station, the time from the departure place to the traffic mode station; t is t Waiting for Representing waiting time, including actual waiting time of passengers and up-down vehicle time; t is t Reach to Indicating the arrival time at the terminal, the time of arrival of the passenger at the destination from the transit terminal;
(4) Economic utility:
wherein P is n Representing the economic utility of traffic pattern n;representing the proportion of the mth travel product in the traffic pattern n, < >>；/>Representing the price of the mth travel product in the traffic mode n;
the utility function of passenger selection traffic pattern n is expressed as:
wherein U is n A utility function representing the passenger's selected mode of transportation n;a comfort parameter representing a traffic pattern n; />A rapidity parameter representing a traffic pattern n; />A convenience parameter representing a traffic pattern n; />An economy parameter representing a traffic pattern n;
and 3-2, predicting urban air traffic demands.
7. The method for predicting passenger flow demand of advanced air traffic as recited in claim 1, wherein: the step 4 specifically comprises the following steps:
constructing a user average allocation model:
wherein:
wherein Z (X) represents an objective function of the user average allocation model;representing the independent variable of the integral function, wherein the value ranges from 0 to x a ；x a Representing traffic volume for leg a; t is t a Representing the traffic impedance of leg a; t is t a (x a ) Representing the impedance function of the navigation segment a taking the flow as an independent variable; />Representing the traffic on the kth path with cell r as the origin and cell s as the destination; />Representing the traffic impedance on the kth path with cell r as the origin and cell s as the destination; />A variable of 0 to 1, if leg a is starting from cell r,The k path with the cell s as the destination is 1, otherwise, the k path with the cell s as the destination is 0; a represents an a-th leg, and L represents a leg set in a route network; r represents an R-th cell serving as a starting point, and R represents a starting point set in the airway network; s represents the S-th cell as the destination, S represents the destination set in the airway network; k represents the kth path, W rs Representing all path sets with cell r as a starting point and cell s as an end point; q rs The traffic volume with cell r as the origin and cell s as the destination is indicated.
8. A passenger flow demand prediction system for advanced air traffic, for implementing the passenger flow demand prediction method for advanced air traffic according to any one of claims 1-7, characterized in that: comprising the following steps:
the traffic generation prediction module is used for acquiring current situation basic data, and predicting background traffic generation amount under the condition that the prior air traffic is not operated, wherein the background traffic generation amount comprises background traffic generation amount and background traffic attraction amount;
the traffic distribution prediction module is used for predicting the background traffic distribution quantity under the condition that the advanced air traffic is not operated based on the predicted background traffic occurrence quantity and the background traffic attraction quantity, and predicting the attraction air traffic demand quantity generated before and after the advanced air traffic operation;
the traffic mode division prediction module is used for analyzing influence factors and the effectiveness of the travel mode selected by the passengers, establishing a traffic mode selection model and predicting the traffic mode division of the advanced air traffic;
and the traffic distribution module is used for distributing the passenger flow of the advanced air traffic in each traffic cell to the navigation network so as to distribute traffic.
9. The advanced air traffic passenger flow demand prediction system according to claim 8, wherein: the traffic generation prediction module comprises a background traffic occurrence prediction unit and a background traffic attraction prediction unit;
the background traffic occurrence amount prediction unit is used for classifying the attributes of passengers generating passenger traffic demands, obtaining the travel occurrence amounts of different travel purposes of the passengers with various attributes, and predicting the background traffic occurrence amount;
the background traffic attraction prediction unit is used for classifying land utilization properties and predicting background traffic attraction by adopting a linear regression model.
10. The advanced air traffic passenger flow demand prediction system according to claim 8, wherein: the traffic distribution prediction module comprises a background traffic distribution prediction unit and an induced air traffic demand prediction unit;
the background traffic distribution quantity prediction unit is used for predicting the background traffic distribution quantity based on the predicted background traffic occurrence quantity and the background traffic attraction quantity;
the induced air traffic demand prediction unit is used for predicting the induced air traffic demand according to the background traffic distribution before and after advanced air traffic operation.
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